tangyx/MetaQA_3entities · Datasets at Hugging Face

data_source stringclasses 1 value	prompt stringlengths 1.1k 13.9k	ability stringclasses 1 value	reward_model dict	extra_info dict
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35798, 2010 37090, A LITTLE HELP 29713, A SOMEWHAT GENTLE MAN 11665, ABIGAIL CRUTTENDEN 19743, ALPHA AND OMEGA 5054, AN ALLIGATOR NAMED DAISY 29304, ANIMALS UNITED 9116, ARMLESS 12144, BAD BOYS 27714, BARNEY'S VERSION 22023, BARRY MUNDAY 28846, BEGINNERS 31957, BOY 8003, CALAMARI UNION 18996, CEMETERY JUNCTION 29794, CHARLOTTE GRAY 10091, CHERRY 30463, COMEDY 1439, COP OUT 6133, CRAZY ON THE OUTSIDE 16600, CYRUS 13575, DATE NIGHT 37059, DEATH AT A FUNERAL 11161, DESPICABLE ME 16564, DIARY OF A WIMPY KID 38915, DINNER FOR SCHMUCKS 3172, DIRTY GIRL 18136, DOCTOR AT LARGE 25258, DONALD SINDEN 36212, DRAMA 19072, DRONES 33512, DUE DATE 10417, EASY A 25445, EASY MONEY 1069, ELEKTRA LUXX 14240, EVERYTHING MUST GO 2969, FATHER OF INVENTION 6666, FINNISH 24880, FLIPPED 32830, FOUR LIONS 5287, FROZEN 7656, FURRY VENGEANCE 21888, GET HIM TO THE GREEK 24815, GOING THE DISTANCE 27638, GREENBERG 19912, GRIFF THE INVISIBLE 18974, GROWN UPS 17440, GULLIVER'S TRAVELS 28838, GUNLESS 32384, HAMLET GOES BUSINESS 20140, HAPPY, HAPPY 33701, HAPPYTHANKYOUMOREPLEASE 11783, HEARTBREAKER 19039, HESHER 37827, HIGH SCHOOL 5617, HOT TUB TIME MACHINE 4931, HOW DO YOU KNOW 3485, HOW TO MAKE LOVE TO A WOMAN 883, I LOVE YOU TOO 37419, IT'S A WONDERFUL AFTERLIFE 21788, IT'S KIND OF A FUNNY STORY 15800, JACK GOES BOATING 17668, JACKASS 3D 5271, JAMES ROBERTSON JUSTICE 35402, JUST WRIGHT 12359, KICK-ASS 26839, KILLERS 5227, KLOWN 31646, KNIGHT AND DAY 1261, KNUCKLEHEAD 27072, LAPLAND ODYSSEY 22957, LEAP YEAR 6624, LET THE BULLETS FLY 9452, LIFE AS WE KNOW IT 27960, LITTLE BIG SOLDIER 251, LITTLE FOCKERS 16814, LITTLE WHITE LIES 35654, LOOSE CANNONS 971, LOTTERY TICKET 38894, LOVE AND OTHER TROUBLES 17855, MACGRUBER 29319, MAN EXPOSED 22924, MEET MONICA VELOUR 15238, MEGAMIND 5180, MISSION LONDON 1954, MOGAMBO 16578, MOOMINS ON THE RIVIERA 16662, MORNING GLORY 19597, MY GIRLFRIEND'S BOYFRIEND 33865, NICE GUY JOHNNY 4003, NORWEGIAN NINJA 12152, NOTHING TO DECLARE 32901, ON TOUR 30781, OPEN SEASON 3 12743, OUR FAMILY WEDDING 11129, PEEP WORLD 21830, PIRANHA 3D 34247, PLEASE GIVE 31445, PONTEROSA 11213, POTICHE 35110, PYAAR IMPOSSIBLE! 23764, RIO SEX COMEDY 34137, RUBBER 30519, SCOTT PILGRIM VS. THE WORLD 9094, SEX AND THE CITY 2 37803, SHADOWS IN PARADISE 34093, SHE'S OUT OF MY LEAGUE 21512, SHERLOCK HOLMES 30155, SIMPLE SIMON 37133, SOMEWHERE 13437, SOUND OF NOISE 30733, SUBMARINE 5700, SUPER 12457, TAMARA DREWE 12755, TANGLED 35302, THE A-TEAM 9091, THE ADVENTURES OF PICASSO 37777, THE BACK-UP PLAN 32454, THE BIG PICTURE 871, THE BOUNTY HUNTER 32654, THE CHOSEN ONE 39773, THE CLINK OF ICE 11696, THE CUCKOO 16488, THE EXTRA MAN 36722, THE FOUR-FACED LIAR 32459, THE HAMMER 5080, THE HOUSE OF BRANCHING LOVE 9510, THE INFIDEL 2507, THE KIDS ARE ALL RIGHT 20669, THE LOSERS 2147, THE MAN WITHOUT A PAST 25623, THE MATRIARCH 27824, THE OTHER GUYS 38086, THE PERFECT HOST 13614, THE ROMANTICS 18537, THE SPY NEXT DOOR 20830, THE STORAGE 37431, THE SWITCH 26036, THE TEMPEST 20739, THE VIRGINITY HIT 1903, TINY FURNITURE 16818, TOOTH FAIRY 22777, TOY STORY 3 11987, TRUST 26275, TRUST ME 16636, UUNO TURHAPURO 31620, UUNO TURHAPURO ARMEIJAN LEIVISSÄ 30682, VALENTINE'S DAY 27019, VIOLET TENDENCIES 33258, WEST IS WEST 39802, WHEN IN ROME 29999, WHY DID I GET MARRIED TOO? 18499, WILD TARGET 367, WITH LOVE... FROM THE AGE OF REASON 30255, YOGI BEAR 1826, YOU AGAIN 6279, YOU WILL MEET A TALL DARK STRANGER src, edge_attr, dst 37090, has_genre, 30463 37090, release_year, 35798 29713, has_genre, 30463 29713, release_year, 35798 19743, has_genre, 30463 19743, release_year, 35798 5054, has_genre, 30463 5054, starred_actors, 25258 5054, starred_actors, 5271 29304, has_genre, 30463 29304, release_year, 35798 9116, has_genre, 30463 9116, release_year, 35798 12144, has_genre, 30463 12144, has_tags, 30463 12144, in_language, 6666 27714, has_genre, 30463 27714, release_year, 35798 22023, has_genre, 30463 22023, release_year, 35798 28846, has_genre, 30463 28846, release_year, 35798 31957, has_genre, 30463 31957, release_year, 35798 8003, has_genre, 30463 8003, has_tags, 30463 8003, in_language, 6666 18996, has_genre, 30463 18996, has_tags, 30463 18996, release_year, 35798 29794, has_genre, 36212 29794, starred_actors, 11665 10091, has_genre, 30463 10091, release_year, 35798 1439, has_genre, 30463 1439, release_year, 35798 6133, has_genre, 30463 6133, release_year, 35798 16600, has_genre, 30463 16600, release_year, 35798 13575, has_genre, 30463 13575, has_tags, 30463 13575, release_year, 35798 37059, has_genre, 30463 37059, has_tags, 30463 37059, release_year, 35798 11161, has_genre, 30463 11161, release_year, 35798 16564, has_genre, 30463 16564, release_year, 35798 38915, has_genre, 30463 38915, release_year, 35798 3172, has_genre, 30463 3172, release_year, 35798 18136, has_genre, 30463 18136, starred_actors, 25258 18136, starred_actors, 5271 19072, has_genre, 30463 19072, release_year, 35798 33512, has_genre, 30463 33512, has_tags, 30463 33512, release_year, 35798 10417, has_genre, 30463 10417, has_tags, 30463 10417, release_year, 35798 25445, has_genre, 30463 25445, release_year, 35798 1069, has_genre, 30463 1069, release_year, 35798 14240, has_genre, 30463 14240, release_year, 35798 2969, has_genre, 30463 2969, release_year, 35798 24880, has_genre, 30463 24880, release_year, 35798 32830, has_genre, 30463 32830, release_year, 35798 5287, has_genre, 30463 5287, release_year, 35798 7656, has_genre, 30463 7656, release_year, 35798 21888, has_genre, 30463 21888, has_tags, 30463 21888, release_year, 35798 24815, has_genre, 30463 24815, has_tags, 30463 24815, release_year, 35798 27638, has_genre, 30463 27638, release_year, 35798 19912, has_genre, 30463 19912, release_year, 35798 18974, has_genre, 30463 18974, has_tags, 30463 18974, release_year, 35798 17440, has_genre, 30463 17440, has_tags, 30463 17440, release_year, 35798 28838, has_genre, 30463 28838, release_year, 35798 32384, has_genre, 30463 32384, has_tags, 30463 32384, in_language, 6666 20140, has_genre, 30463 20140, release_year, 35798 33701, has_genre, 30463 33701, release_year, 35798 11783, has_genre, 30463 11783, release_year, 35798 19039, has_genre, 30463 19039, release_year, 35798 37827, has_genre, 30463 37827, release_year, 35798 5617, has_genre, 30463 5617, has_tags, 30463 5617, release_year, 35798 4931, has_genre, 30463 4931, release_year, 35798 3485, has_genre, 30463 3485, release_year, 35798 883, has_genre, 30463 883, release_year, 35798 37419, has_genre, 30463 37419, release_year, 35798 21788, has_genre, 30463 21788, release_year, 35798 15800, has_genre, 30463 15800, release_year, 35798 17668, has_genre, 30463 17668, release_year, 35798 35402, has_genre, 30463 35402, release_year, 35798 12359, has_genre, 30463 12359, release_year, 35798 26839, has_genre, 30463 26839, release_year, 35798 5227, has_genre, 30463 5227, has_tags, 30463 5227, release_year, 35798 31646, has_genre, 30463 31646, has_tags, 30463 31646, release_year, 35798 1261, has_genre, 30463 1261, release_year, 35798 27072, has_genre, 30463 27072, has_tags, 30463 27072, has_tags, 6666 27072, in_language, 6666 27072, release_year, 35798 22957, has_genre, 30463 22957, release_year, 35798 6624, has_genre, 30463 6624, release_year, 35798 9452, has_genre, 30463 9452, has_tags, 30463 9452, release_year, 35798 27960, has_genre, 30463 27960, release_year, 35798 251, has_genre, 30463 251, has_tags, 30463 251, release_year, 35798 16814, has_genre, 30463 16814, release_year, 35798 35654, has_genre, 30463 35654, release_year, 35798 971, has_genre, 30463 971, release_year, 35798 38894, has_genre, 30463 38894, in_language, 6666 17855, has_genre, 30463 17855, has_tags, 30463 17855, release_year, 35798 29319, has_genre, 30463 29319, in_language, 6666 22924, has_genre, 30463 22924, release_year, 35798 15238, has_genre, 30463 15238, has_tags, 30463 15238, release_year, 35798 5180, has_genre, 30463 5180, release_year, 35798 1954, has_genre, 36212 1954, starred_actors, 25258 16578, has_genre, 30463 16578, in_language, 6666 16662, has_genre, 30463 16662, release_year, 35798 19597, has_genre, 30463 19597, release_year, 35798 33865, has_genre, 30463 33865, release_year, 35798 4003, has_genre, 30463 4003, release_year, 35798 12152, has_genre, 30463 12152, has_tags, 30463 12152, release_year, 35798 32901, has_genre, 30463 32901, release_year, 35798 30781, has_genre, 30463 30781, has_tags, 30463 30781, release_year, 35798 12743, has_genre, 30463 12743, release_year, 35798 11129, has_genre, 30463 11129, release_year, 35798 21830, has_genre, 30463 21830, release_year, 35798 34247, has_genre, 30463 34247, release_year, 35798 31445, has_genre, 30463 31445, has_tags, 30463 31445, has_tags, 6666 31445, in_language, 6666 11213, has_genre, 30463 11213, release_year, 35798 35110, has_genre, 30463 35110, release_year, 35798 23764, has_genre, 30463 23764, release_year, 35798 34137, has_genre, 30463 34137, release_year, 35798 30519, has_genre, 30463 30519, has_tags, 30463 30519, release_year, 35798 9094, has_genre, 30463 9094, release_year, 35798 37803, has_genre, 30463 37803, in_language, 6666 34093, has_genre, 30463 34093, release_year, 35798 21512, has_tags, 30463 21512, release_year, 35798 30155, has_genre, 30463 30155, release_year, 35798 37133, has_genre, 30463 37133, release_year, 35798 13437, has_genre, 30463 13437, release_year, 35798 30733, has_genre, 30463 30733, release_year, 35798 5700, has_genre, 30463 5700, release_year, 35798 12457, has_genre, 30463 12457, release_year, 35798 12755, has_genre, 30463 12755, release_year, 35798 35302, has_genre, 30463 35302, release_year, 35798 9091, has_genre, 30463 9091, in_language, 6666 37777, has_genre, 30463 37777, release_year, 35798 32454, has_genre, 30463 32454, release_year, 35798 871, has_genre, 30463 871, has_tags, 30463 871, release_year, 35798 32654, has_genre, 30463 32654, release_year, 35798 39773, has_genre, 30463 39773, release_year, 35798 11696, has_genre, 30463 11696, has_tags, 6666 11696, in_language, 6666 16488, has_genre, 30463 16488, release_year, 35798 36722, has_genre, 30463 36722, release_year, 35798 32459, has_genre, 30463 32459, release_year, 35798 5080, has_genre, 30463 5080, in_language, 6666 9510, has_genre, 30463 9510, release_year, 35798 2507, has_genre, 30463 2507, has_tags, 30463 2507, release_year, 35798 20669, has_tags, 30463 20669, release_year, 35798 2147, has_genre, 30463 2147, has_tags, 6666 2147, in_language, 6666 25623, has_genre, 30463 25623, has_tags, 6666 25623, in_language, 6666 27824, has_genre, 30463 27824, has_tags, 30463 27824, release_year, 35798 38086, has_genre, 30463 38086, release_year, 35798 13614, has_genre, 30463 13614, release_year, 35798 18537, has_genre, 30463 18537, release_year, 35798 20830, has_genre, 30463 20830, in_language, 6666 37431, has_genre, 30463 37431, has_tags, 30463 37431, release_year, 35798 26036, has_genre, 30463 26036, release_year, 35798 20739, has_genre, 30463 20739, release_year, 35798 1903, has_genre, 30463 1903, release_year, 35798 16818, has_genre, 30463 16818, release_year, 35798 22777, has_genre, 30463 22777, has_tags, 30463 22777, release_year, 35798 11987, has_genre, 30463 11987, release_year, 35798 26275, has_genre, 30463 26275, release_year, 35798 16636, has_genre, 30463 16636, in_language, 6666 31620, has_genre, 30463 31620, in_language, 6666 30682, has_genre, 30463 30682, has_tags, 30463 30682, release_year, 35798 27019, has_genre, 30463 27019, release_year, 35798 33258, has_genre, 30463 33258, release_year, 35798 39802, has_genre, 30463 39802, release_year, 35798 29999, has_genre, 30463 29999, release_year, 35798 18499, has_genre, 30463 18499, has_tags, 30463 18499, release_year, 35798 367, has_genre, 30463 367, release_year, 35798 30255, has_genre, 30463 30255, release_year, 35798 1826, has_genre, 30463 1826, release_year, 35798 6279, has_genre, 30463 6279, has_tags, 30463 6279, release_year, 35798 Question: How are ABIGAIL CRUTTENDEN, DONALD SINDEN, and LAPLAND ODYSSEY related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ABIGAIL CRUTTENDEN", "DONALD SINDEN", "LAPLAND ODYSSEY" ], "valid_edges": [ [ "A LITTLE HELP", "has_genre", "COMEDY" ], [ "A LITTLE HELP", "release_year", "2010" ], [ "A SOMEWHAT GENTLE MAN", "has_genre", "COMEDY" ], [ "A SOMEWHAT GENTLE MAN", "release_year", "2010" ], [ "ALPHA AND OMEGA", "has_genre", "COMEDY" ], [ "ALPHA AND OMEGA", "release_year", "2010" ], [ "AN ALLIGATOR NAMED DAISY", "has_genre", "COMEDY" ], [ "AN ALLIGATOR NAMED DAISY", "starred_actors", "DONALD SINDEN" ], [ "AN ALLIGATOR NAMED DAISY", "starred_actors", "JAMES ROBERTSON JUSTICE" ], [ "ANIMALS UNITED", "has_genre", "COMEDY" ], [ "ANIMALS UNITED", "release_year", "2010" ], [ "ARMLESS", "has_genre", "COMEDY" ], [ "ARMLESS", "release_year", "2010" ], [ "BAD BOYS", "has_genre", "COMEDY" ], [ "BAD BOYS", "has_tags", "COMEDY" ], [ "BAD BOYS", "in_language", "FINNISH" ], [ "BARNEY'S VERSION", "has_genre", "COMEDY" ], [ "BARNEY'S VERSION", "release_year", "2010" ], [ "BARRY MUNDAY", "has_genre", "COMEDY" ], [ "BARRY MUNDAY", "release_year", "2010" ], [ "BEGINNERS", "has_genre", "COMEDY" ], [ "BEGINNERS", "release_year", "2010" ], [ "BOY", "has_genre", "COMEDY" ], [ "BOY", "release_year", "2010" ], [ "CALAMARI UNION", "has_genre", "COMEDY" ], [ "CALAMARI UNION", "has_tags", "COMEDY" ], [ "CALAMARI UNION", "in_language", "FINNISH" ], [ "CEMETERY JUNCTION", "has_genre", "COMEDY" ], [ "CEMETERY JUNCTION", "has_tags", "COMEDY" ], [ "CEMETERY JUNCTION", "release_year", "2010" ], [ "CHARLOTTE GRAY", "has_genre", "DRAMA" ], [ "CHARLOTTE GRAY", "starred_actors", "ABIGAIL CRUTTENDEN" ], [ "CHERRY", "has_genre", "COMEDY" ], [ "CHERRY", "release_year", "2010" ], [ "COP OUT", "has_genre", "COMEDY" ], [ "COP OUT", "release_year", "2010" ], [ "CRAZY ON THE OUTSIDE", "has_genre", "COMEDY" ], [ "CRAZY ON THE OUTSIDE", "release_year", "2010" ], [ "CYRUS", "has_genre", "COMEDY" ], [ "CYRUS", "release_year", "2010" ], [ "DATE NIGHT", "has_genre", "COMEDY" ], [ "DATE NIGHT", "has_tags", "COMEDY" ], [ "DATE NIGHT", "release_year", "2010" ], [ "DEATH AT A FUNERAL", "has_genre", "COMEDY" ], [ "DEATH AT A FUNERAL", "has_tags", "COMEDY" ], [ "DEATH AT A FUNERAL", "release_year", "2010" ], [ "DESPICABLE ME", "has_genre", "COMEDY" ], [ "DESPICABLE ME", "release_year", "2010" ], [ "DIARY OF A WIMPY KID", "has_genre", "COMEDY" ], [ "DIARY OF A WIMPY KID", "release_year", "2010" ], [ "DINNER FOR SCHMUCKS", "has_genre", "COMEDY" ], [ "DINNER FOR SCHMUCKS", "release_year", "2010" ], [ "DIRTY GIRL", "has_genre", "COMEDY" ], [ "DIRTY GIRL", "release_year", "2010" ], [ "DOCTOR AT LARGE", "has_genre", "COMEDY" ], [ "DOCTOR AT LARGE", "starred_actors", "DONALD SINDEN" ], [ "DOCTOR AT LARGE", "starred_actors", "JAMES ROBERTSON JUSTICE" ], [ "DRONES", "has_genre", "COMEDY" ], [ "DRONES", "release_year", "2010" ], [ "DUE DATE", "has_genre", "COMEDY" ], [ "DUE DATE", "has_tags", "COMEDY" ], [ "DUE DATE", "release_year", "2010" ], [ "EASY A", "has_genre", "COMEDY" ], [ "EASY A", "has_tags", "COMEDY" ], [ "EASY A", "release_year", "2010" ], [ "EASY MONEY", "has_genre", "COMEDY" ], [ "EASY MONEY", "release_year", "2010" ], [ "ELEKTRA LUXX", "has_genre", "COMEDY" ], [ "ELEKTRA LUXX", "release_year", "2010" ], [ "EVERYTHING MUST GO", "has_genre", "COMEDY" ], [ "EVERYTHING MUST GO", "release_year", "2010" ], [ "FATHER OF INVENTION", "has_genre", "COMEDY" ], [ "FATHER OF INVENTION", "release_year", "2010" ], [ "FLIPPED", "has_genre", "COMEDY" ], [ "FLIPPED", "release_year", "2010" ], [ "FOUR LIONS", "has_genre", "COMEDY" ], [ "FOUR LIONS", "release_year", "2010" ], [ "FROZEN", "has_genre", "COMEDY" ], [ "FROZEN", "release_year", "2010" ], [ "FURRY VENGEANCE", "has_genre", "COMEDY" ], [ "FURRY VENGEANCE", "release_year", "2010" ], [ "GET HIM TO THE GREEK", "has_genre", "COMEDY" ], [ "GET HIM TO THE GREEK", "has_tags", "COMEDY" ], [ "GET HIM TO THE GREEK", "release_year", "2010" ], [ "GOING THE DISTANCE", "has_genre", "COMEDY" ], [ "GOING THE DISTANCE", "has_tags", "COMEDY" ], [ "GOING THE DISTANCE", "release_year", "2010" ], [ "GREENBERG", "has_genre", "COMEDY" ], [ "GREENBERG", "release_year", "2010" ], [ "GRIFF THE INVISIBLE", "has_genre", "COMEDY" ], [ "GRIFF THE INVISIBLE", "release_year", "2010" ], [ "GROWN UPS", "has_genre", "COMEDY" ], [ "GROWN UPS", "has_tags", "COMEDY" ], [ "GROWN UPS", "release_year", "2010" ], [ "GULLIVER'S TRAVELS", "has_genre", "COMEDY" ], [ "GULLIVER'S TRAVELS", "has_tags", "COMEDY" ], [ "GULLIVER'S TRAVELS", "release_year", "2010" ], [ "GUNLESS", "has_genre", "COMEDY" ], [ "GUNLESS", "release_year", "2010" ], [ "HAMLET GOES BUSINESS", "has_genre", "COMEDY" ], [ "HAMLET GOES BUSINESS", "has_tags", "COMEDY" ], [ "HAMLET GOES BUSINESS", "in_language", "FINNISH" ], [ "HAPPY, HAPPY", "has_genre", "COMEDY" ], [ "HAPPY, HAPPY", "release_year", "2010" ], [ "HAPPYTHANKYOUMOREPLEASE", "has_genre", "COMEDY" ], [ "HAPPYTHANKYOUMOREPLEASE", "release_year", "2010" ], [ "HEARTBREAKER", "has_genre", "COMEDY" ], [ "HEARTBREAKER", "release_year", "2010" ], [ "HESHER", "has_genre", "COMEDY" ], [ "HESHER", "release_year", "2010" ], [ "HIGH SCHOOL", "has_genre", "COMEDY" ], [ "HIGH SCHOOL", "release_year", "2010" ], [ "HOT TUB TIME MACHINE", "has_genre", "COMEDY" ], [ "HOT TUB TIME MACHINE", "has_tags", "COMEDY" ], [ "HOT TUB TIME MACHINE", "release_year", "2010" ], [ "HOW DO YOU KNOW", "has_genre", "COMEDY" ], [ "HOW DO YOU KNOW", "release_year", "2010" ], [ "HOW TO MAKE LOVE TO A WOMAN", "has_genre", "COMEDY" ], [ "HOW TO MAKE LOVE TO A WOMAN", "release_year", "2010" ], [ "I LOVE YOU TOO", "has_genre", "COMEDY" ], [ "I LOVE YOU TOO", "release_year", "2010" ], [ "IT'S A WONDERFUL AFTERLIFE", "has_genre", "COMEDY" ], [ "IT'S A WONDERFUL AFTERLIFE", "release_year", "2010" ], [ "IT'S KIND OF A FUNNY STORY", "has_genre", "COMEDY" ], [ "IT'S KIND OF A FUNNY STORY", "release_year", "2010" ], [ "JACK GOES BOATING", "has_genre", "COMEDY" ], [ "JACK GOES BOATING", "release_year", "2010" ], [ "JACKASS 3D", "has_genre", "COMEDY" ], [ "JACKASS 3D", "release_year", "2010" ], [ "JUST WRIGHT", "has_genre", "COMEDY" ], [ "JUST WRIGHT", "release_year", "2010" ], [ "KICK-ASS", "has_genre", "COMEDY" ], [ "KICK-ASS", "release_year", "2010" ], [ "KILLERS", "has_genre", "COMEDY" ], [ "KILLERS", "release_year", "2010" ], [ "KLOWN", "has_genre", "COMEDY" ], [ "KLOWN", "has_tags", "COMEDY" ], [ "KLOWN", "release_year", "2010" ], [ "KNIGHT AND DAY", "has_genre", "COMEDY" ], [ "KNIGHT AND DAY", "has_tags", "COMEDY" ], [ "KNIGHT AND DAY", "release_year", "2010" ], [ "KNUCKLEHEAD", "has_genre", "COMEDY" ], [ "KNUCKLEHEAD", "release_year", "2010" ], [ "LAPLAND ODYSSEY", "has_genre", "COMEDY" ], [ "LAPLAND ODYSSEY", "has_tags", "COMEDY" ], [ "LAPLAND ODYSSEY", "has_tags", "FINNISH" ], [ "LAPLAND ODYSSEY", "in_language", "FINNISH" ], [ "LAPLAND ODYSSEY", "release_year", "2010" ], [ "LEAP YEAR", "has_genre", "COMEDY" ], [ "LEAP YEAR", "release_year", "2010" ], [ "LET THE BULLETS FLY", "has_genre", "COMEDY" ], [ "LET THE BULLETS FLY", "release_year", "2010" ], [ "LIFE AS WE KNOW IT", "has_genre", "COMEDY" ], [ "LIFE AS WE KNOW IT", "has_tags", "COMEDY" ], [ "LIFE AS WE KNOW IT", "release_year", "2010" ], [ "LITTLE BIG SOLDIER", "has_genre", "COMEDY" ], [ "LITTLE BIG SOLDIER", "release_year", "2010" ], [ "LITTLE FOCKERS", "has_genre", "COMEDY" ], [ "LITTLE FOCKERS", "has_tags", "COMEDY" ], [ "LITTLE FOCKERS", "release_year", "2010" ], [ "LITTLE WHITE LIES", "has_genre", "COMEDY" ], [ "LITTLE WHITE LIES", "release_year", "2010" ], [ "LOOSE CANNONS", "has_genre", "COMEDY" ], [ "LOOSE CANNONS", "release_year", "2010" ], [ "LOTTERY TICKET", "has_genre", "COMEDY" ], [ "LOTTERY TICKET", "release_year", "2010" ], [ "LOVE AND OTHER TROUBLES", "has_genre", "COMEDY" ], [ "LOVE AND OTHER TROUBLES", "in_language", "FINNISH" ], [ "MACGRUBER", "has_genre", "COMEDY" ], [ "MACGRUBER", "has_tags", "COMEDY" ], [ "MACGRUBER", "release_year", "2010" ], [ "MAN EXPOSED", "has_genre", "COMEDY" ], [ "MAN EXPOSED", "in_language", "FINNISH" ], [ "MEET MONICA VELOUR", "has_genre", "COMEDY" ], [ "MEET MONICA VELOUR", "release_year", "2010" ], [ "MEGAMIND", "has_genre", "COMEDY" ], [ "MEGAMIND", "has_tags", "COMEDY" ], [ "MEGAMIND", "release_year", "2010" ], [ "MISSION LONDON", "has_genre", "COMEDY" ], [ "MISSION LONDON", "release_year", "2010" ], [ "MOGAMBO", "has_genre", "DRAMA" ], [ "MOGAMBO", "starred_actors", "DONALD SINDEN" ], [ "MOOMINS ON THE RIVIERA", "has_genre", "COMEDY" ], [ "MOOMINS ON THE RIVIERA", "in_language", "FINNISH" ], [ "MORNING GLORY", "has_genre", "COMEDY" ], [ "MORNING GLORY", "release_year", "2010" ], [ "MY GIRLFRIEND'S BOYFRIEND", "has_genre", "COMEDY" ], [ "MY GIRLFRIEND'S BOYFRIEND", "release_year", "2010" ], [ "NICE GUY JOHNNY", "has_genre", "COMEDY" ], [ "NICE GUY JOHNNY", "release_year", "2010" ], [ "NORWEGIAN NINJA", "has_genre", "COMEDY" ], [ "NORWEGIAN NINJA", "release_year", "2010" ], [ "NOTHING TO DECLARE", "has_genre", "COMEDY" ], [ "NOTHING TO DECLARE", "has_tags", "COMEDY" ], [ "NOTHING TO DECLARE", "release_year", "2010" ], [ "ON TOUR", "has_genre", "COMEDY" ], [ "ON TOUR", "release_year", "2010" ], [ "OPEN SEASON 3", "has_genre", "COMEDY" ], [ "OPEN SEASON 3", "has_tags", "COMEDY" ], [ "OPEN SEASON 3", "release_year", "2010" ], [ "OUR FAMILY WEDDING", "has_genre", "COMEDY" ], [ "OUR FAMILY WEDDING", "release_year", "2010" ], [ "PEEP WORLD", "has_genre", "COMEDY" ], [ "PEEP WORLD", "release_year", "2010" ], [ "PIRANHA 3D", "has_genre", "COMEDY" ], [ "PIRANHA 3D", "release_year", "2010" ], [ "PLEASE GIVE", "has_genre", "COMEDY" ], [ "PLEASE GIVE", "release_year", "2010" ], [ "PONTEROSA", "has_genre", "COMEDY" ], [ "PONTEROSA", "has_tags", "COMEDY" ], [ "PONTEROSA", "has_tags", "FINNISH" ], [ "PONTEROSA", "in_language", "FINNISH" ], [ "POTICHE", "has_genre", "COMEDY" ], [ "POTICHE", "release_year", "2010" ], [ "PYAAR IMPOSSIBLE!", "has_genre", "COMEDY" ], [ "PYAAR IMPOSSIBLE!", "release_year", "2010" ], [ "RIO SEX COMEDY", "has_genre", "COMEDY" ], [ "RIO SEX COMEDY", "release_year", "2010" ], [ "RUBBER", "has_genre", "COMEDY" ], [ "RUBBER", "release_year", "2010" ], [ "SCOTT PILGRIM VS. THE WORLD", "has_genre", "COMEDY" ], [ "SCOTT PILGRIM VS. THE WORLD", "has_tags", "COMEDY" ], [ "SCOTT PILGRIM VS. THE WORLD", "release_year", "2010" ], [ "SEX AND THE CITY 2", "has_genre", "COMEDY" ], [ "SEX AND THE CITY 2", "release_year", "2010" ], [ "SHADOWS IN PARADISE", "has_genre", "COMEDY" ], [ "SHADOWS IN PARADISE", "in_language", "FINNISH" ], [ "SHE'S OUT OF MY LEAGUE", "has_genre", "COMEDY" ], [ "SHE'S OUT OF MY LEAGUE", "release_year", "2010" ], [ "SHERLOCK HOLMES", "has_tags", "COMEDY" ], [ "SHERLOCK HOLMES", "release_year", "2010" ], [ "SIMPLE SIMON", "has_genre", "COMEDY" ], [ "SIMPLE SIMON", "release_year", "2010" ], [ "SOMEWHERE", "has_genre", "COMEDY" ], [ "SOMEWHERE", "release_year", "2010" ], [ "SOUND OF NOISE", "has_genre", "COMEDY" ], [ "SOUND OF NOISE", "release_year", "2010" ], [ "SUBMARINE", "has_genre", "COMEDY" ], [ "SUBMARINE", "release_year", "2010" ], [ "SUPER", "has_genre", "COMEDY" ], [ "SUPER", "release_year", "2010" ], [ "TAMARA DREWE", "has_genre", "COMEDY" ], [ "TAMARA DREWE", "release_year", "2010" ], [ "TANGLED", "has_genre", "COMEDY" ], [ "TANGLED", "release_year", "2010" ], [ "THE A-TEAM", "has_genre", "COMEDY" ], [ "THE A-TEAM", "release_year", "2010" ], [ "THE ADVENTURES OF PICASSO", "has_genre", "COMEDY" ], [ "THE ADVENTURES OF PICASSO", "in_language", "FINNISH" ], [ "THE BACK-UP PLAN", "has_genre", "COMEDY" ], [ "THE BACK-UP PLAN", "release_year", "2010" ], [ "THE BIG PICTURE", "has_genre", "COMEDY" ], [ "THE BIG PICTURE", "release_year", "2010" ], [ "THE BOUNTY HUNTER", "has_genre", "COMEDY" ], [ "THE BOUNTY HUNTER", "has_tags", "COMEDY" ], [ "THE BOUNTY HUNTER", "release_year", "2010" ], [ "THE CHOSEN ONE", "has_genre", "COMEDY" ], [ "THE CHOSEN ONE", "release_year", "2010" ], [ "THE CLINK OF ICE", "has_genre", "COMEDY" ], [ "THE CLINK OF ICE", "release_year", "2010" ], [ "THE CUCKOO", "has_genre", "COMEDY" ], [ "THE CUCKOO", "has_tags", "FINNISH" ], [ "THE CUCKOO", "in_language", "FINNISH" ], [ "THE EXTRA MAN", "has_genre", "COMEDY" ], [ "THE EXTRA MAN", "release_year", "2010" ], [ "THE FOUR-FACED LIAR", "has_genre", "COMEDY" ], [ "THE FOUR-FACED LIAR", "release_year", "2010" ], [ "THE HAMMER", "has_genre", "COMEDY" ], [ "THE HAMMER", "release_year", "2010" ], [ "THE HOUSE OF BRANCHING LOVE", "has_genre", "COMEDY" ], [ "THE HOUSE OF BRANCHING LOVE", "in_language", "FINNISH" ], [ "THE INFIDEL", "has_genre", "COMEDY" ], [ "THE INFIDEL", "release_year", "2010" ], [ "THE KIDS ARE ALL RIGHT", "has_genre", "COMEDY" ], [ "THE KIDS ARE ALL RIGHT", "has_tags", "COMEDY" ], [ "THE KIDS ARE ALL RIGHT", "release_year", "2010" ], [ "THE LOSERS", "has_tags", "COMEDY" ], [ "THE LOSERS", "release_year", "2010" ], [ "THE MAN WITHOUT A PAST", "has_genre", "COMEDY" ], [ "THE MAN WITHOUT A PAST", "has_tags", "FINNISH" ], [ "THE MAN WITHOUT A PAST", "in_language", "FINNISH" ], [ "THE MATRIARCH", "has_genre", "COMEDY" ], [ "THE MATRIARCH", "has_tags", "FINNISH" ], [ "THE MATRIARCH", "in_language", "FINNISH" ], [ "THE OTHER GUYS", "has_genre", "COMEDY" ], [ "THE OTHER GUYS", "has_tags", "COMEDY" ], [ "THE OTHER GUYS", "release_year", "2010" ], [ "THE PERFECT HOST", "has_genre", "COMEDY" ], [ "THE PERFECT HOST", "release_year", "2010" ], [ "THE ROMANTICS", "has_genre", "COMEDY" ], [ "THE ROMANTICS", "release_year", "2010" ], [ "THE SPY NEXT DOOR", "has_genre", "COMEDY" ], [ "THE SPY NEXT DOOR", "release_year", "2010" ], [ "THE STORAGE", "has_genre", "COMEDY" ], [ "THE STORAGE", "in_language", "FINNISH" ], [ "THE SWITCH", "has_genre", "COMEDY" ], [ "THE SWITCH", "has_tags", "COMEDY" ], [ "THE SWITCH", "release_year", "2010" ], [ "THE TEMPEST", "has_genre", "COMEDY" ], [ "THE TEMPEST", "release_year", "2010" ], [ "THE VIRGINITY HIT", "has_genre", "COMEDY" ], [ "THE VIRGINITY HIT", "release_year", "2010" ], [ "TINY FURNITURE", "has_genre", "COMEDY" ], [ "TINY FURNITURE", "release_year", "2010" ], [ "TOOTH FAIRY", "has_genre", "COMEDY" ], [ "TOOTH FAIRY", "release_year", "2010" ], [ "TOY STORY 3", "has_genre", "COMEDY" ], [ "TOY STORY 3", "has_tags", "COMEDY" ], [ "TOY STORY 3", "release_year", "2010" ], [ "TRUST", "has_genre", "COMEDY" ], [ "TRUST", "release_year", "2010" ], [ "TRUST ME", "has_genre", "COMEDY" ], [ "TRUST ME", "release_year", "2010" ], [ "UUNO TURHAPURO", "has_genre", "COMEDY" ], [ "UUNO TURHAPURO", "in_language", "FINNISH" ], [ "UUNO TURHAPURO ARMEIJAN LEIVISSÄ", "has_genre", "COMEDY" ], [ "UUNO TURHAPURO ARMEIJAN LEIVISSÄ", "in_language", "FINNISH" ], [ "VALENTINE'S DAY", "has_genre", "COMEDY" ], [ "VALENTINE'S DAY", "has_tags", "COMEDY" ], [ "VALENTINE'S DAY", "release_year", "2010" ], [ "VIOLET TENDENCIES", "has_genre", "COMEDY" ], [ "VIOLET TENDENCIES", "release_year", "2010" ], [ "WEST IS WEST", "has_genre", "COMEDY" ], [ "WEST IS WEST", "release_year", "2010" ], [ "WHEN IN ROME", "has_genre", "COMEDY" ], [ "WHEN IN ROME", "release_year", "2010" ], [ "WHY DID I GET MARRIED TOO?", "has_genre", "COMEDY" ], [ "WHY DID I GET MARRIED TOO?", "release_year", "2010" ], [ "WILD TARGET", "has_genre", "COMEDY" ], [ "WILD TARGET", "has_tags", "COMEDY" ], [ "WILD TARGET", "release_year", "2010" ], [ "WITH LOVE... FROM THE AGE OF REASON", "has_genre", "COMEDY" ], [ "WITH LOVE... FROM THE AGE OF REASON", "release_year", "2010" ], [ "YOGI BEAR", "has_genre", "COMEDY" ], [ "YOGI BEAR", "release_year", "2010" ], [ "YOU AGAIN", "has_genre", "COMEDY" ], [ "YOU AGAIN", "release_year", "2010" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_genre", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_tags", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "release_year", "2010" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 24438, 1993 2133, 1998 319, A BUG'S LIFE 5076, EXTREME JUSTICE 483, FARAWAY, SO CLOSE! 35493, FIRESTORM 1313, JOHN LASSETER 19394, OTTO SANDER 9281, SCOTT GLENN src, edge_attr, dst 319, directed_by, 1313 319, has_tags, 1313 319, release_year, 2133 319, written_by, 1313 5076, release_year, 24438 5076, starred_actors, 9281 483, release_year, 24438 483, starred_actors, 19394 35493, release_year, 2133 35493, starred_actors, 9281 Question: How are JOHN LASSETER, OTTO SANDER, and SCOTT GLENN related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "JOHN LASSETER", "OTTO SANDER", "SCOTT GLENN" ], "valid_edges": [ [ "A BUG'S LIFE", "directed_by", "JOHN LASSETER" ], [ "A BUG'S LIFE", "has_tags", "JOHN LASSETER" ], [ "A BUG'S LIFE", "release_year", "1998" ], [ "A BUG'S LIFE", "written_by", "JOHN LASSETER" ], [ "EXTREME JUSTICE", "release_year", "1993" ], [ "EXTREME JUSTICE", "starred_actors", "SCOTT GLENN" ], [ "FARAWAY, SO CLOSE!", "release_year", "1993" ], [ "FARAWAY, SO CLOSE!", "starred_actors", "OTTO SANDER" ], [ "FIRESTORM", "release_year", "1998" ], [ "FIRESTORM", "starred_actors", "SCOTT GLENN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8636, 1930 6718, A FAREWELL TO ARMS 7463, ALL QUIET ON THE WESTERN FRONT 6966, FORBIDDEN GAMES 3306, FRANÇOIS BOYER 39145, FURY 29757, GARY COOPER 20693, GONE WITH THE WIND 1422, MOROCCO 37497, NATIONAL FILM REGISTRY 8379, ROMANCE 3719, SERGEANT YORK 35043, SYLVIA SIDNEY 38627, TEN TALL MEN 22214, WAR src, edge_attr, dst 6718, has_genre, 8379 6718, starred_actors, 29757 7463, has_tags, 37497 7463, release_year, 8636 6966, has_genre, 22214 6966, written_by, 3306 39145, has_genre, 22214 39145, has_tags, 22214 39145, starred_actors, 35043 20693, has_genre, 8379 20693, has_tags, 37497 20693, has_tags, 8379 1422, has_genre, 8379 1422, has_tags, 29757 1422, has_tags, 37497 1422, release_year, 8636 1422, starred_actors, 29757 3719, has_tags, 29757 3719, has_tags, 37497 3719, starred_actors, 29757 38627, has_genre, 22214 38627, has_tags, 1422 Question: For what reason are FRANÇOIS BOYER, MOROCCO, and SYLVIA SIDNEY associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "FRANÇOIS BOYER", "MOROCCO", "SYLVIA SIDNEY" ], "valid_edges": [ [ "A FAREWELL TO ARMS", "has_genre", "ROMANCE" ], [ "A FAREWELL TO ARMS", "starred_actors", "GARY COOPER" ], [ "ALL QUIET ON THE WESTERN FRONT", "has_tags", "NATIONAL FILM REGISTRY" ], [ "ALL QUIET ON THE WESTERN FRONT", "release_year", "1930" ], [ "FORBIDDEN GAMES", "has_genre", "WAR" ], [ "FORBIDDEN GAMES", "written_by", "FRANÇOIS BOYER" ], [ "FURY", "has_genre", "WAR" ], [ "FURY", "has_tags", "WAR" ], [ "FURY", "starred_actors", "SYLVIA SIDNEY" ], [ "GONE WITH THE WIND", "has_genre", "ROMANCE" ], [ "GONE WITH THE WIND", "has_tags", "NATIONAL FILM REGISTRY" ], [ "GONE WITH THE WIND", "has_tags", "ROMANCE" ], [ "MOROCCO", "has_genre", "ROMANCE" ], [ "MOROCCO", "has_tags", "GARY COOPER" ], [ "MOROCCO", "has_tags", "NATIONAL FILM REGISTRY" ], [ "MOROCCO", "release_year", "1930" ], [ "MOROCCO", "starred_actors", "GARY COOPER" ], [ "SERGEANT YORK", "has_tags", "GARY COOPER" ], [ "SERGEANT YORK", "has_tags", "NATIONAL FILM REGISTRY" ], [ "SERGEANT YORK", "starred_actors", "GARY COOPER" ], [ "TEN TALL MEN", "has_genre", "WAR" ], [ "TEN TALL MEN", "has_tags", "MOROCCO" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8486, 1999 17315, 2007 17159, A MIGHTY HEART 3197, ANGELINA JOLIE 30501, BEOWULF 34555, FLAWLESS 3697, FLIGHT OF FURY 22532, GIRL, INTERRUPTED 8068, MALEFICENT 35249, RING 2 26125, SUNSHINE 451, THE BONE COLLECTOR 25509, THE DEBT src, edge_attr, dst 17159, has_tags, 3197 17159, release_year, 17315 17159, starred_actors, 3197 30501, has_tags, 3197 30501, release_year, 8486 30501, release_year, 17315 34555, release_year, 8486 34555, release_year, 17315 3697, release_year, 17315 22532, has_tags, 3197 22532, release_year, 8486 22532, starred_actors, 3197 8068, has_tags, 3197 8068, starred_actors, 3197 35249, release_year, 8486 26125, release_year, 8486 26125, release_year, 17315 451, has_tags, 3197 451, release_year, 8486 451, starred_actors, 3197 25509, release_year, 8486 25509, release_year, 17315 Question: For what reason are FLIGHT OF FURY, MALEFICENT, and RING 2 associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "FLIGHT OF FURY", "MALEFICENT", "RING 2" ], "valid_edges": [ [ "A MIGHTY HEART", "has_tags", "ANGELINA JOLIE" ], [ "A MIGHTY HEART", "release_year", "2007" ], [ "A MIGHTY HEART", "starred_actors", "ANGELINA JOLIE" ], [ "BEOWULF", "has_tags", "ANGELINA JOLIE" ], [ "BEOWULF", "release_year", "1999" ], [ "BEOWULF", "release_year", "2007" ], [ "FLAWLESS", "release_year", "1999" ], [ "FLAWLESS", "release_year", "2007" ], [ "FLIGHT OF FURY", "release_year", "2007" ], [ "GIRL, INTERRUPTED", "has_tags", "ANGELINA JOLIE" ], [ "GIRL, INTERRUPTED", "release_year", "1999" ], [ "GIRL, INTERRUPTED", "starred_actors", "ANGELINA JOLIE" ], [ "MALEFICENT", "has_tags", "ANGELINA JOLIE" ], [ "MALEFICENT", "starred_actors", "ANGELINA JOLIE" ], [ "RING 2", "release_year", "1999" ], [ "SUNSHINE", "release_year", "1999" ], [ "SUNSHINE", "release_year", "2007" ], [ "THE BONE COLLECTOR", "has_tags", "ANGELINA JOLIE" ], [ "THE BONE COLLECTOR", "release_year", "1999" ], [ "THE BONE COLLECTOR", "starred_actors", "ANGELINA JOLIE" ], [ "THE DEBT", "release_year", "1999" ], [ "THE DEBT", "release_year", "2007" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 14004, 1955 12094, A LAWLESS STREET 2216, ALONG THE GREAT DIVIDE 20106, BATTLE CRY 30230, COLORADO TERRITORY 34347, COUNT THREE AND PRAY 10663, DARK COMMAND 28252, DISTANT DRUMS 12841, DOCUMENTARY 7649, MAN WITH THE GUN 12039, NIGHT AND FOG 10835, RAGE AT DAWN 33941, RAOUL WALSH 38516, REGENERATION 20243, RUN FOR COVER 31613, SAN ANTONIO 32417, SINS OF MY FATHER 7585, TEXAS 33304, THE FAR HORIZONS 34953, THE GUN THAT WON THE WEST 20896, THE LAST FRONTIER 15059, THE LAWLESS BREED 17394, THE MAN FROM LARAMIE 9409, THE TALL MEN 30942, THE VIOLENT MEN 36026, WESTERN 18860, WICHITA src, edge_attr, dst 12094, has_genre, 36026 12094, release_year, 14004 2216, directed_by, 33941 2216, has_genre, 36026 20106, directed_by, 33941 20106, release_year, 14004 30230, directed_by, 33941 30230, has_genre, 36026 30230, has_tags, 33941 34347, has_genre, 36026 34347, release_year, 14004 10663, directed_by, 33941 10663, has_genre, 36026 28252, directed_by, 33941 28252, has_genre, 36026 7649, has_genre, 36026 7649, release_year, 14004 12039, has_genre, 12841 12039, has_tags, 12841 12039, release_year, 14004 10835, has_genre, 36026 10835, release_year, 14004 38516, directed_by, 33941 38516, has_genre, 12841 38516, written_by, 33941 20243, has_genre, 36026 20243, release_year, 14004 31613, directed_by, 33941 31613, has_genre, 36026 32417, has_genre, 12841 7585, has_genre, 36026 33304, has_genre, 36026 33304, release_year, 14004 34953, has_genre, 36026 34953, release_year, 14004 20896, has_genre, 36026 20896, release_year, 14004 15059, directed_by, 33941 15059, has_genre, 36026 17394, has_genre, 36026 17394, release_year, 14004 9409, directed_by, 33941 9409, release_year, 14004 30942, has_genre, 36026 30942, release_year, 14004 18860, has_genre, 36026 18860, release_year, 14004 Question: How are SINS OF MY FATHER, TEXAS, and THE TALL MEN related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "SINS OF MY FATHER", "TEXAS", "THE TALL MEN" ], "valid_edges": [ [ "A LAWLESS STREET", "has_genre", "WESTERN" ], [ "A LAWLESS STREET", "release_year", "1955" ], [ "ALONG THE GREAT DIVIDE", "directed_by", "RAOUL WALSH" ], [ "ALONG THE GREAT DIVIDE", "has_genre", "WESTERN" ], [ "BATTLE CRY", "directed_by", "RAOUL WALSH" ], [ "BATTLE CRY", "release_year", "1955" ], [ "COLORADO TERRITORY", "directed_by", "RAOUL WALSH" ], [ "COLORADO TERRITORY", "has_genre", "WESTERN" ], [ "COLORADO TERRITORY", "has_tags", "RAOUL WALSH" ], [ "COUNT THREE AND PRAY", "has_genre", "WESTERN" ], [ "COUNT THREE AND PRAY", "release_year", "1955" ], [ "DARK COMMAND", "directed_by", "RAOUL WALSH" ], [ "DARK COMMAND", "has_genre", "WESTERN" ], [ "DISTANT DRUMS", "directed_by", "RAOUL WALSH" ], [ "DISTANT DRUMS", "has_genre", "WESTERN" ], [ "MAN WITH THE GUN", "has_genre", "WESTERN" ], [ "MAN WITH THE GUN", "release_year", "1955" ], [ "NIGHT AND FOG", "has_genre", "DOCUMENTARY" ], [ "NIGHT AND FOG", "has_tags", "DOCUMENTARY" ], [ "NIGHT AND FOG", "release_year", "1955" ], [ "RAGE AT DAWN", "has_genre", "WESTERN" ], [ "RAGE AT DAWN", "release_year", "1955" ], [ "REGENERATION", "directed_by", "RAOUL WALSH" ], [ "REGENERATION", "has_genre", "DOCUMENTARY" ], [ "REGENERATION", "written_by", "RAOUL WALSH" ], [ "RUN FOR COVER", "has_genre", "WESTERN" ], [ "RUN FOR COVER", "release_year", "1955" ], [ "SAN ANTONIO", "directed_by", "RAOUL WALSH" ], [ "SAN ANTONIO", "has_genre", "WESTERN" ], [ "SINS OF MY FATHER", "has_genre", "DOCUMENTARY" ], [ "TEXAS", "has_genre", "WESTERN" ], [ "THE FAR HORIZONS", "has_genre", "WESTERN" ], [ "THE FAR HORIZONS", "release_year", "1955" ], [ "THE GUN THAT WON THE WEST", "has_genre", "WESTERN" ], [ "THE GUN THAT WON THE WEST", "release_year", "1955" ], [ "THE LAST FRONTIER", "has_genre", "WESTERN" ], [ "THE LAST FRONTIER", "release_year", "1955" ], [ "THE LAWLESS BREED", "directed_by", "RAOUL WALSH" ], [ "THE LAWLESS BREED", "has_genre", "WESTERN" ], [ "THE MAN FROM LARAMIE", "has_genre", "WESTERN" ], [ "THE MAN FROM LARAMIE", "release_year", "1955" ], [ "THE TALL MEN", "directed_by", "RAOUL WALSH" ], [ "THE TALL MEN", "release_year", "1955" ], [ "THE VIOLENT MEN", "has_genre", "WESTERN" ], [ "THE VIOLENT MEN", "release_year", "1955" ], [ "WICHITA", "has_genre", "WESTERN" ], [ "WICHITA", "release_year", "1955" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15506, 1933 7421, APART FROM YOU 27426, AS I LAY DYING 26475, BABY FACE 4731, BARBARY COAST 34812, BECKY SHARP 16273, BOMBSHELL 18018, CAVALCADE 30463, COMEDY 15047, COUNSELLOR AT LAW 15791, DINNER AT EIGHT 36212, DRAMA 17986, GABRIEL OVER THE WHITE HOUSE 33241, GUILLERMO DÍAZ 3745, INTRUDER IN THE DUST 36639, LADIES THEY TALK ABOUT 13476, LADY KILLER 19475, LAURA SAN GIACOMO 26784, LITTLE WOMEN 31195, LOOKING FORWARD 31323, MIRIAM HOPKINS 16662, MORNING GLORY 20443, NIGHT FLIGHT 18256, OLD ACQUAINTANCE 40059, ONCE AROUND 28686, S.O.S. EISBERG 39856, SHE DONE HIM WRONG 18733, STONEWALL 39071, THE BITTER TEA OF GENERAL YEN 15207, THE EMPEROR JONES 30083, THE HOUSE ON 56TH STREET 4700, THE STORY OF TEMPLE DRAKE 29621, THE STRANGER'S RETURN 27022, THE TARNISHED ANGELS 4442, WHERE THE DAY TAKES YOU 33943, WILLIAM FAULKNER src, edge_attr, dst 7421, has_genre, 36212 7421, release_year, 15506 27426, has_genre, 36212 27426, has_tags, 33943 27426, written_by, 33943 26475, has_genre, 36212 26475, release_year, 15506 4731, has_genre, 36212 4731, starred_actors, 31323 34812, has_genre, 36212 34812, starred_actors, 31323 16273, has_genre, 36212 16273, release_year, 15506 18018, has_genre, 36212 18018, release_year, 15506 15047, has_genre, 36212 15047, release_year, 15506 15791, has_genre, 36212 15791, release_year, 15506 17986, has_genre, 36212 17986, release_year, 15506 3745, has_genre, 36212 3745, has_tags, 33943 3745, written_by, 33943 36639, has_genre, 36212 36639, release_year, 15506 13476, has_genre, 36212 13476, release_year, 15506 26784, has_genre, 36212 26784, has_tags, 36212 26784, release_year, 15506 31195, has_genre, 36212 31195, release_year, 15506 16662, has_genre, 36212 16662, release_year, 15506 20443, has_genre, 36212 20443, release_year, 15506 18256, has_genre, 36212 18256, starred_actors, 31323 40059, has_genre, 30463 40059, has_genre, 36212 40059, has_tags, 36212 40059, starred_actors, 19475 28686, has_genre, 36212 28686, release_year, 15506 39856, has_genre, 36212 39856, release_year, 15506 18733, has_genre, 30463 18733, has_genre, 36212 18733, starred_actors, 33241 39071, has_genre, 36212 39071, release_year, 15506 15207, has_genre, 36212 15207, release_year, 15506 30083, has_genre, 36212 30083, release_year, 15506 4700, has_genre, 36212 4700, has_tags, 33943 4700, release_year, 15506 4700, starred_actors, 31323 4700, written_by, 33943 29621, has_genre, 36212 29621, release_year, 15506 29621, starred_actors, 31323 27022, has_genre, 36212 27022, written_by, 33943 4442, has_genre, 36212 4442, starred_actors, 19475 Question: In what context are GUILLERMO DÍAZ, LAURA SAN GIACOMO, and THE STORY OF TEMPLE DRAKE connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "GUILLERMO DÍAZ", "LAURA SAN GIACOMO", "THE STORY OF TEMPLE DRAKE" ], "valid_edges": [ [ "APART FROM YOU", "has_genre", "DRAMA" ], [ "APART FROM YOU", "release_year", "1933" ], [ "AS I LAY DYING", "has_genre", "DRAMA" ], [ "AS I LAY DYING", "has_tags", "WILLIAM FAULKNER" ], [ "AS I LAY DYING", "written_by", "WILLIAM FAULKNER" ], [ "BABY FACE", "has_genre", "DRAMA" ], [ "BABY FACE", "release_year", "1933" ], [ "BARBARY COAST", "has_genre", "DRAMA" ], [ "BARBARY COAST", "starred_actors", "MIRIAM HOPKINS" ], [ "BECKY SHARP", "has_genre", "DRAMA" ], [ "BECKY SHARP", "starred_actors", "MIRIAM HOPKINS" ], [ "BOMBSHELL", "has_genre", "DRAMA" ], [ "BOMBSHELL", "release_year", "1933" ], [ "CAVALCADE", "has_genre", "DRAMA" ], [ "CAVALCADE", "release_year", "1933" ], [ "COUNSELLOR AT LAW", "has_genre", "DRAMA" ], [ "COUNSELLOR AT LAW", "release_year", "1933" ], [ "DINNER AT EIGHT", "has_genre", "DRAMA" ], [ "DINNER AT EIGHT", "release_year", "1933" ], [ "GABRIEL OVER THE WHITE HOUSE", "has_genre", "DRAMA" ], [ "GABRIEL OVER THE WHITE HOUSE", "release_year", "1933" ], [ "INTRUDER IN THE DUST", "has_genre", "DRAMA" ], [ "INTRUDER IN THE DUST", "has_tags", "WILLIAM FAULKNER" ], [ "INTRUDER IN THE DUST", "written_by", "WILLIAM FAULKNER" ], [ "LADIES THEY TALK ABOUT", "has_genre", "DRAMA" ], [ "LADIES THEY TALK ABOUT", "release_year", "1933" ], [ "LADY KILLER", "has_genre", "DRAMA" ], [ "LADY KILLER", "release_year", "1933" ], [ "LITTLE WOMEN", "has_genre", "DRAMA" ], [ "LITTLE WOMEN", "has_tags", "DRAMA" ], [ "LITTLE WOMEN", "release_year", "1933" ], [ "LOOKING FORWARD", "has_genre", "DRAMA" ], [ "LOOKING FORWARD", "release_year", "1933" ], [ "MORNING GLORY", "has_genre", "DRAMA" ], [ "MORNING GLORY", "release_year", "1933" ], [ "NIGHT FLIGHT", "has_genre", "DRAMA" ], [ "NIGHT FLIGHT", "release_year", "1933" ], [ "OLD ACQUAINTANCE", "has_genre", "DRAMA" ], [ "OLD ACQUAINTANCE", "starred_actors", "MIRIAM HOPKINS" ], [ "ONCE AROUND", "has_genre", "COMEDY" ], [ "ONCE AROUND", "has_genre", "DRAMA" ], [ "ONCE AROUND", "has_tags", "DRAMA" ], [ "ONCE AROUND", "starred_actors", "LAURA SAN GIACOMO" ], [ "S.O.S. EISBERG", "has_genre", "DRAMA" ], [ "S.O.S. EISBERG", "release_year", "1933" ], [ "SHE DONE HIM WRONG", "has_genre", "DRAMA" ], [ "SHE DONE HIM WRONG", "release_year", "1933" ], [ "STONEWALL", "has_genre", "COMEDY" ], [ "STONEWALL", "has_genre", "DRAMA" ], [ "STONEWALL", "starred_actors", "GUILLERMO DÍAZ" ], [ "THE BITTER TEA OF GENERAL YEN", "has_genre", "DRAMA" ], [ "THE BITTER TEA OF GENERAL YEN", "release_year", "1933" ], [ "THE EMPEROR JONES", "has_genre", "DRAMA" ], [ "THE EMPEROR JONES", "release_year", "1933" ], [ "THE HOUSE ON 56TH STREET", "has_genre", "DRAMA" ], [ "THE HOUSE ON 56TH STREET", "release_year", "1933" ], [ "THE STORY OF TEMPLE DRAKE", "has_genre", "DRAMA" ], [ "THE STORY OF TEMPLE DRAKE", "has_tags", "WILLIAM FAULKNER" ], [ "THE STORY OF TEMPLE DRAKE", "release_year", "1933" ], [ "THE STORY OF TEMPLE DRAKE", "starred_actors", "MIRIAM HOPKINS" ], [ "THE STORY OF TEMPLE DRAKE", "written_by", "WILLIAM FAULKNER" ], [ "THE STRANGER'S RETURN", "has_genre", "DRAMA" ], [ "THE STRANGER'S RETURN", "release_year", "1933" ], [ "THE STRANGER'S RETURN", "starred_actors", "MIRIAM HOPKINS" ], [ "THE TARNISHED ANGELS", "has_genre", "DRAMA" ], [ "THE TARNISHED ANGELS", "written_by", "WILLIAM FAULKNER" ], [ "WHERE THE DAY TAKES YOU", "has_genre", "DRAMA" ], [ "WHERE THE DAY TAKES YOU", "starred_actors", "LAURA SAN GIACOMO" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 11112, 1939 25717, 1953 21802, ABBOTT AND COSTELLO GO TO MARS 15922, ABBOTT AND COSTELLO MEET DR. JEKYLL AND MR. HYDE 21132, ANOTHER THIN MAN 5192, AT THE CIRCUS 38849, BACHELOR MOTHER 38657, BEAT THE DEVIL 29294, BREAD, LOVE AND DREAMS 24967, BROADWAY BILL 8430, CALL ME MADAM 30523, CHEAPER BY THE DOZEN 30463, COMEDY 27563, DOUBLE WEDDING 1480, DREAM WIFE 36249, GENEVIEVE 17440, GULLIVER'S TRAVELS 12557, HOW TO MARRY A MILLIONAIRE 12591, I LOVE YOU AGAIN 17344, I VITELLONI 38176, IDIOT'S DELIGHT 26280, IT'S A WONDERFUL WORLD 2563, LIBELED LADY 728, LOVE CRAZY 22032, LUCKY NIGHT 23421, MEET ME AT THE FAIR 38276, MIDNIGHT 7186, MR. BLANDINGS BUILDS HIS DREAM HOUSE 19619, MYRNA LOY 5338, NINOTCHKA 13096, ROMAN HOLIDAY 35507, SONG OF THE THIN MAN 22407, THE ACTRESS 23870, THE ANIMAL KINGDOM 8318, THE BACHELOR AND THE BOBBY-SOXER 32851, THE BAND WAGON 18855, THE CADDY 18055, THE CAPTAIN'S PARADISE 31239, THE CAT AND THE CANARY 31283, THE HOUND OF THE BASKERVILLES 17571, THE INTERNSHIP 33153, THE MIKADO 12672, THE MOON IS BLUE 9027, THE RAINS CAME 3848, THE RETURN OF DON CAMILLO 37461, THE RULES OF THE GAME 10991, THE SUN SHINES BRIGHT 15833, THE THIN MAN 7816, THE THREE MUSKETEERS 8289, THE TITFIELD THUNDERBOLT 15263, THE TWONKY 20210, THE WOMEN 584, THREE SMART GIRLS GROW UP 36409, WELCOME MR. MARSHALL! 14809, WIFE VS. SECRETARY src, edge_attr, dst 21802, has_genre, 30463 21802, release_year, 25717 15922, has_genre, 30463 15922, release_year, 25717 21132, has_tags, 19619 21132, release_year, 11112 21132, starred_actors, 19619 5192, has_genre, 30463 5192, release_year, 11112 38849, has_genre, 30463 38849, release_year, 11112 38657, has_genre, 30463 38657, release_year, 25717 29294, has_genre, 30463 29294, release_year, 25717 24967, has_genre, 30463 24967, starred_actors, 19619 8430, has_genre, 30463 8430, release_year, 25717 30523, has_genre, 30463 30523, starred_actors, 19619 27563, has_genre, 30463 27563, has_tags, 19619 27563, starred_actors, 19619 1480, has_genre, 30463 1480, release_year, 25717 36249, has_genre, 30463 36249, release_year, 25717 17440, has_genre, 30463 17440, has_tags, 30463 17440, release_year, 11112 12557, has_genre, 30463 12557, release_year, 25717 12591, has_genre, 30463 12591, has_tags, 19619 12591, starred_actors, 19619 17344, has_genre, 30463 17344, release_year, 25717 38176, has_genre, 30463 38176, release_year, 11112 26280, has_genre, 30463 26280, release_year, 11112 2563, has_genre, 30463 2563, starred_actors, 19619 728, has_genre, 30463 728, has_tags, 19619 728, starred_actors, 19619 22032, has_genre, 30463 22032, release_year, 11112 22032, starred_actors, 19619 23421, release_year, 25717 38276, has_genre, 30463 38276, release_year, 11112 7186, has_genre, 30463 7186, starred_actors, 19619 5338, has_genre, 30463 5338, release_year, 11112 13096, has_genre, 30463 13096, has_tags, 30463 13096, release_year, 25717 35507, has_genre, 30463 35507, starred_actors, 19619 22407, has_genre, 30463 22407, release_year, 25717 23870, has_genre, 30463 23870, starred_actors, 19619 8318, has_genre, 30463 8318, starred_actors, 19619 32851, has_genre, 30463 32851, release_year, 25717 18855, has_genre, 30463 18855, release_year, 25717 18055, has_genre, 30463 18055, release_year, 25717 31239, has_genre, 30463 31239, release_year, 11112 31283, has_genre, 30463 31283, release_year, 11112 17571, has_genre, 30463 33153, has_genre, 30463 33153, release_year, 11112 12672, has_genre, 30463 12672, release_year, 25717 9027, release_year, 11112 9027, starred_actors, 19619 3848, has_genre, 30463 3848, release_year, 25717 37461, has_genre, 30463 37461, has_tags, 30463 37461, release_year, 11112 10991, has_genre, 30463 10991, release_year, 25717 15833, has_genre, 30463 15833, has_tags, 30463 15833, has_tags, 19619 15833, starred_actors, 19619 7816, has_genre, 30463 7816, release_year, 11112 8289, has_genre, 30463 8289, release_year, 25717 15263, has_genre, 30463 15263, release_year, 25717 20210, has_genre, 30463 20210, release_year, 11112 584, has_genre, 30463 584, release_year, 11112 36409, has_genre, 30463 36409, release_year, 25717 14809, has_genre, 30463 14809, has_tags, 19619 14809, starred_actors, 19619 Question: For what reason are MEET ME AT THE FAIR, THE INTERNSHIP, and THE RAINS CAME associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "MEET ME AT THE FAIR", "THE INTERNSHIP", "THE RAINS CAME" ], "valid_edges": [ [ "ABBOTT AND COSTELLO GO TO MARS", "has_genre", "COMEDY" ], [ "ABBOTT AND COSTELLO GO TO MARS", "release_year", "1953" ], [ "ABBOTT AND COSTELLO MEET DR. JEKYLL AND MR. HYDE", "has_genre", "COMEDY" ], [ "ABBOTT AND COSTELLO MEET DR. JEKYLL AND MR. HYDE", "release_year", "1953" ], [ "ANOTHER THIN MAN", "has_tags", "MYRNA LOY" ], [ "ANOTHER THIN MAN", "release_year", "1939" ], [ "ANOTHER THIN MAN", "starred_actors", "MYRNA LOY" ], [ "AT THE CIRCUS", "has_genre", "COMEDY" ], [ "AT THE CIRCUS", "release_year", "1939" ], [ "BACHELOR MOTHER", "has_genre", "COMEDY" ], [ "BACHELOR MOTHER", "release_year", "1939" ], [ "BEAT THE DEVIL", "has_genre", "COMEDY" ], [ "BEAT THE DEVIL", "release_year", "1953" ], [ "BREAD, LOVE AND DREAMS", "has_genre", "COMEDY" ], [ "BREAD, LOVE AND DREAMS", "release_year", "1953" ], [ "BROADWAY BILL", "has_genre", "COMEDY" ], [ "BROADWAY BILL", "starred_actors", "MYRNA LOY" ], [ "CALL ME MADAM", "has_genre", "COMEDY" ], [ "CALL ME MADAM", "release_year", "1953" ], [ "CHEAPER BY THE DOZEN", "has_genre", "COMEDY" ], [ "CHEAPER BY THE DOZEN", "starred_actors", "MYRNA LOY" ], [ "DOUBLE WEDDING", "has_genre", "COMEDY" ], [ "DOUBLE WEDDING", "has_tags", "MYRNA LOY" ], [ "DOUBLE WEDDING", "starred_actors", "MYRNA LOY" ], [ "DREAM WIFE", "has_genre", "COMEDY" ], [ "DREAM WIFE", "release_year", "1953" ], [ "GENEVIEVE", "has_genre", "COMEDY" ], [ "GENEVIEVE", "release_year", "1953" ], [ "GULLIVER'S TRAVELS", "has_genre", "COMEDY" ], [ "GULLIVER'S TRAVELS", "has_tags", "COMEDY" ], [ "GULLIVER'S TRAVELS", "release_year", "1939" ], [ "HOW TO MARRY A MILLIONAIRE", "has_genre", "COMEDY" ], [ "HOW TO MARRY A MILLIONAIRE", "release_year", "1953" ], [ "I LOVE YOU AGAIN", "has_genre", "COMEDY" ], [ "I LOVE YOU AGAIN", "has_tags", "MYRNA LOY" ], [ "I LOVE YOU AGAIN", "starred_actors", "MYRNA LOY" ], [ "I VITELLONI", "has_genre", "COMEDY" ], [ "I VITELLONI", "release_year", "1953" ], [ "IDIOT'S DELIGHT", "has_genre", "COMEDY" ], [ "IDIOT'S DELIGHT", "release_year", "1939" ], [ "IT'S A WONDERFUL WORLD", "has_genre", "COMEDY" ], [ "IT'S A WONDERFUL WORLD", "release_year", "1939" ], [ "LIBELED LADY", "has_genre", "COMEDY" ], [ "LIBELED LADY", "starred_actors", "MYRNA LOY" ], [ "LOVE CRAZY", "has_genre", "COMEDY" ], [ "LOVE CRAZY", "has_tags", "MYRNA LOY" ], [ "LOVE CRAZY", "starred_actors", "MYRNA LOY" ], [ "LUCKY NIGHT", "has_genre", "COMEDY" ], [ "LUCKY NIGHT", "release_year", "1939" ], [ "LUCKY NIGHT", "starred_actors", "MYRNA LOY" ], [ "MEET ME AT THE FAIR", "release_year", "1953" ], [ "MIDNIGHT", "has_genre", "COMEDY" ], [ "MIDNIGHT", "release_year", "1939" ], [ "MR. BLANDINGS BUILDS HIS DREAM HOUSE", "has_genre", "COMEDY" ], [ "MR. BLANDINGS BUILDS HIS DREAM HOUSE", "starred_actors", "MYRNA LOY" ], [ "NINOTCHKA", "has_genre", "COMEDY" ], [ "NINOTCHKA", "release_year", "1939" ], [ "ROMAN HOLIDAY", "has_genre", "COMEDY" ], [ "ROMAN HOLIDAY", "has_tags", "COMEDY" ], [ "ROMAN HOLIDAY", "release_year", "1953" ], [ "SONG OF THE THIN MAN", "has_genre", "COMEDY" ], [ "SONG OF THE THIN MAN", "starred_actors", "MYRNA LOY" ], [ "THE ACTRESS", "has_genre", "COMEDY" ], [ "THE ACTRESS", "release_year", "1953" ], [ "THE ANIMAL KINGDOM", "has_genre", "COMEDY" ], [ "THE ANIMAL KINGDOM", "starred_actors", "MYRNA LOY" ], [ "THE BACHELOR AND THE BOBBY-SOXER", "has_genre", "COMEDY" ], [ "THE BACHELOR AND THE BOBBY-SOXER", "starred_actors", "MYRNA LOY" ], [ "THE BAND WAGON", "has_genre", "COMEDY" ], [ "THE BAND WAGON", "release_year", "1953" ], [ "THE CADDY", "has_genre", "COMEDY" ], [ "THE CADDY", "release_year", "1953" ], [ "THE CAPTAIN'S PARADISE", "has_genre", "COMEDY" ], [ "THE CAPTAIN'S PARADISE", "release_year", "1953" ], [ "THE CAT AND THE CANARY", "has_genre", "COMEDY" ], [ "THE CAT AND THE CANARY", "release_year", "1939" ], [ "THE HOUND OF THE BASKERVILLES", "has_genre", "COMEDY" ], [ "THE HOUND OF THE BASKERVILLES", "release_year", "1939" ], [ "THE INTERNSHIP", "has_genre", "COMEDY" ], [ "THE MIKADO", "has_genre", "COMEDY" ], [ "THE MIKADO", "release_year", "1939" ], [ "THE MOON IS BLUE", "has_genre", "COMEDY" ], [ "THE MOON IS BLUE", "release_year", "1953" ], [ "THE RAINS CAME", "release_year", "1939" ], [ "THE RAINS CAME", "starred_actors", "MYRNA LOY" ], [ "THE RETURN OF DON CAMILLO", "has_genre", "COMEDY" ], [ "THE RETURN OF DON CAMILLO", "release_year", "1953" ], [ "THE RULES OF THE GAME", "has_genre", "COMEDY" ], [ "THE RULES OF THE GAME", "has_tags", "COMEDY" ], [ "THE RULES OF THE GAME", "release_year", "1939" ], [ "THE SUN SHINES BRIGHT", "has_genre", "COMEDY" ], [ "THE SUN SHINES BRIGHT", "release_year", "1953" ], [ "THE THIN MAN", "has_genre", "COMEDY" ], [ "THE THIN MAN", "has_tags", "COMEDY" ], [ "THE THIN MAN", "has_tags", "MYRNA LOY" ], [ "THE THIN MAN", "starred_actors", "MYRNA LOY" ], [ "THE THREE MUSKETEERS", "has_genre", "COMEDY" ], [ "THE THREE MUSKETEERS", "release_year", "1939" ], [ "THE TITFIELD THUNDERBOLT", "has_genre", "COMEDY" ], [ "THE TITFIELD THUNDERBOLT", "release_year", "1953" ], [ "THE TWONKY", "has_genre", "COMEDY" ], [ "THE TWONKY", "release_year", "1953" ], [ "THE WOMEN", "has_genre", "COMEDY" ], [ "THE WOMEN", "release_year", "1939" ], [ "THREE SMART GIRLS GROW UP", "has_genre", "COMEDY" ], [ "THREE SMART GIRLS GROW UP", "release_year", "1939" ], [ "WELCOME MR. MARSHALL!", "has_genre", "COMEDY" ], [ "WELCOME MR. MARSHALL!", "release_year", "1953" ], [ "WIFE VS. SECRETARY", "has_genre", "COMEDY" ], [ "WIFE VS. SECRETARY", "has_tags", "MYRNA LOY" ], [ "WIFE VS. SECRETARY", "starred_actors", "MYRNA LOY" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 16681, ALAN PAO 9644, ANNE MEARA 904, ANOTHER HARVEST MOON 36212, DRAMA 10688, HENRY BEAN 16480, LOADED 33677, NOISE 267, THE BELIEVER src, edge_attr, dst 904, has_genre, 36212 904, starred_actors, 9644 16480, directed_by, 16681 16480, has_genre, 36212 16480, written_by, 16681 33677, directed_by, 10688 33677, has_genre, 36212 33677, written_by, 10688 267, directed_by, 10688 267, has_genre, 36212 267, written_by, 10688 Question: In what context are ALAN PAO, ANNE MEARA, and HENRY BEAN connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ALAN PAO", "ANNE MEARA", "HENRY BEAN" ], "valid_edges": [ [ "ANOTHER HARVEST MOON", "has_genre", "DRAMA" ], [ "ANOTHER HARVEST MOON", "starred_actors", "ANNE MEARA" ], [ "LOADED", "directed_by", "ALAN PAO" ], [ "LOADED", "has_genre", "DRAMA" ], [ "LOADED", "written_by", "ALAN PAO" ], [ "NOISE", "directed_by", "HENRY BEAN" ], [ "NOISE", "has_genre", "DRAMA" ], [ "NOISE", "written_by", "HENRY BEAN" ], [ "THE BELIEVER", "directed_by", "HENRY BEAN" ], [ "THE BELIEVER", "has_genre", "DRAMA" ], [ "THE BELIEVER", "written_by", "HENRY BEAN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 19293, ALL THE PRESIDENT'S MEN 6725, BLACK RAINBOW 30463, COMEDY 36212, DRAMA 25387, JOAN ALLEN 10985, NIXON 14096, NORMA RAE 14282, PLEASANTVILLE 15974, SEARCHING FOR BOBBY FISCHER 10041, SILKWOOD 15323, THE CONTENDER 5278, THE CRUCIBLE 15840, THE ICE STORM 7168, THE UPSIDE OF ANGER 24811, THRILLER 37678, TOBEY MAGUIRE 2078, UNION src, edge_attr, dst 19293, has_genre, 24811 19293, has_tags, 10985 19293, has_tags, 24811 6725, has_genre, 24811 10985, starred_actors, 25387 14096, has_genre, 36212 14096, has_tags, 2078 14282, has_genre, 30463 14282, has_genre, 36212 14282, has_tags, 25387 14282, has_tags, 37678 14282, starred_actors, 25387 14282, starred_actors, 37678 15974, has_genre, 36212 15974, starred_actors, 25387 10041, has_genre, 36212 10041, has_tags, 2078 15323, has_genre, 36212 15323, has_tags, 25387 15323, starred_actors, 25387 5278, has_genre, 36212 5278, starred_actors, 25387 15840, has_genre, 36212 15840, has_tags, 36212 15840, has_tags, 37678 15840, starred_actors, 25387 7168, has_genre, 30463 7168, has_genre, 36212 7168, starred_actors, 25387 Question: How are BLACK RAINBOW, JOAN ALLEN, and UNION related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BLACK RAINBOW", "JOAN ALLEN", "UNION" ], "valid_edges": [ [ "ALL THE PRESIDENT'S MEN", "has_genre", "THRILLER" ], [ "ALL THE PRESIDENT'S MEN", "has_tags", "NIXON" ], [ "ALL THE PRESIDENT'S MEN", "has_tags", "THRILLER" ], [ "BLACK RAINBOW", "has_genre", "THRILLER" ], [ "NIXON", "starred_actors", "JOAN ALLEN" ], [ "NORMA RAE", "has_genre", "DRAMA" ], [ "NORMA RAE", "has_tags", "UNION" ], [ "PLEASANTVILLE", "has_genre", "COMEDY" ], [ "PLEASANTVILLE", "has_genre", "DRAMA" ], [ "PLEASANTVILLE", "has_tags", "JOAN ALLEN" ], [ "PLEASANTVILLE", "has_tags", "TOBEY MAGUIRE" ], [ "PLEASANTVILLE", "starred_actors", "JOAN ALLEN" ], [ "PLEASANTVILLE", "starred_actors", "TOBEY MAGUIRE" ], [ "SEARCHING FOR BOBBY FISCHER", "has_genre", "DRAMA" ], [ "SEARCHING FOR BOBBY FISCHER", "starred_actors", "JOAN ALLEN" ], [ "SILKWOOD", "has_genre", "DRAMA" ], [ "SILKWOOD", "has_tags", "UNION" ], [ "THE CONTENDER", "has_genre", "DRAMA" ], [ "THE CONTENDER", "has_tags", "JOAN ALLEN" ], [ "THE CONTENDER", "starred_actors", "JOAN ALLEN" ], [ "THE CRUCIBLE", "has_genre", "DRAMA" ], [ "THE CRUCIBLE", "starred_actors", "JOAN ALLEN" ], [ "THE ICE STORM", "has_genre", "DRAMA" ], [ "THE ICE STORM", "has_tags", "DRAMA" ], [ "THE ICE STORM", "has_tags", "TOBEY MAGUIRE" ], [ "THE ICE STORM", "starred_actors", "JOAN ALLEN" ], [ "THE UPSIDE OF ANGER", "has_genre", "COMEDY" ], [ "THE UPSIDE OF ANGER", "has_genre", "DRAMA" ], [ "THE UPSIDE OF ANGER", "starred_actors", "JOAN ALLEN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 31486, 1970 10045, BD-R 10712, MICHAEL DORMAN 15262, NO WAY TO TREAT A LADY 34389, NORTON JUSTER 35865, ON THE WATERFRONT 5286, POOLHALL JUNKIES 14216, ROD STEIGER 18821, RUN OF THE ARROW 25850, THE PAWNBROKER 24512, THE PHANTOM TOLLBOOTH 24811, THRILLER 37099, TRIANGLE 558, WATERLOO src, edge_attr, dst 15262, has_genre, 24811 15262, starred_actors, 14216 35865, has_tags, 10045 35865, starred_actors, 14216 5286, has_genre, 24811 5286, starred_actors, 14216 18821, has_tags, 10045 18821, starred_actors, 14216 25850, has_tags, 10045 25850, starred_actors, 14216 24512, has_tags, 10045 24512, release_year, 31486 24512, written_by, 34389 37099, has_genre, 24811 37099, starred_actors, 10712 558, has_tags, 14216 558, release_year, 31486 558, starred_actors, 14216 Question: How are MICHAEL DORMAN, NORTON JUSTER, and ROD STEIGER related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "MICHAEL DORMAN", "NORTON JUSTER", "ROD STEIGER" ], "valid_edges": [ [ "NO WAY TO TREAT A LADY", "has_genre", "THRILLER" ], [ "NO WAY TO TREAT A LADY", "starred_actors", "ROD STEIGER" ], [ "ON THE WATERFRONT", "has_tags", "BD-R" ], [ "ON THE WATERFRONT", "starred_actors", "ROD STEIGER" ], [ "POOLHALL JUNKIES", "has_genre", "THRILLER" ], [ "POOLHALL JUNKIES", "starred_actors", "ROD STEIGER" ], [ "RUN OF THE ARROW", "has_tags", "BD-R" ], [ "RUN OF THE ARROW", "starred_actors", "ROD STEIGER" ], [ "THE PAWNBROKER", "has_tags", "BD-R" ], [ "THE PAWNBROKER", "starred_actors", "ROD STEIGER" ], [ "THE PHANTOM TOLLBOOTH", "has_tags", "BD-R" ], [ "THE PHANTOM TOLLBOOTH", "release_year", "1970" ], [ "THE PHANTOM TOLLBOOTH", "written_by", "NORTON JUSTER" ], [ "TRIANGLE", "has_genre", "THRILLER" ], [ "TRIANGLE", "starred_actors", "MICHAEL DORMAN" ], [ "WATERLOO", "has_tags", "ROD STEIGER" ], [ "WATERLOO", "release_year", "1970" ], [ "WATERLOO", "starred_actors", "ROD STEIGER" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 36163, 1967 26762, 2008 33217, 35 SHOTS OF RUM 9698, A CHRISTMAS TALE 17521, ACES 'N' EIGHTS 36359, AFTERWARDS 36494, ANYTHING FOR HER 4131, ASTERIX AT THE OLYMPIC GAMES 31322, ASTERIX THE GAUL 16426, BELLE DE JOUR 10081, BOTTLE SHOCK 18400, CASE DÉPART 2246, CRITERION 38134, DANTE 01 9709, DE L'AUTRE CÔTÉ DU LIT 30696, DIARY OF A NYMPHOMANIAC 21405, DRAGON HUNTERS 34422, FEMALE AGENTS 6012, FRENCH 14669, FRONTIER OF THE DAWN 3586, I'VE LOVED YOU SO LONG 8150, JACQUES TATI 24305, JCVD 23661, LE SAMOURAÏ 8807, LET'S TALK ABOUT THE RAIN 14659, LOVE ME NO MORE 24257, MARK OF AN ANGEL 24399, MARTYRS 31657, MON ONCLE 12100, MOUCHETTE 5937, ORPHEUS 31134, PARIS 8740, PARIS 36 5311, PLAYTIME 21835, POINT BLANK 8764, STELLA 32587, SUMMER HOURS 5467, SÉRAPHINE 30003, TAKEN 38592, THE BEACHES OF AGNÈS 21712, THE CLASS 37721, THE FIRST DAY OF THE REST OF YOUR LIFE 25529, THE ILLUSIONIST 34283, THE LAST DEADLY MISSION 22166, THE LOWER DEPTHS 11654, THE THIEF OF PARIS 30481, THE TWO OF US 34585, THE YOUNG GIRLS OF ROCHEFORT 7251, THOMAS N'GIJOL 17536, TRAFIC 33483, WEEKEND src, edge_attr, dst 33217, in_language, 6012 33217, release_year, 26762 9698, in_language, 6012 9698, release_year, 26762 17521, release_year, 26762 36359, in_language, 6012 36359, release_year, 26762 36494, in_language, 6012 36494, release_year, 26762 4131, has_tags, 6012 4131, in_language, 6012 4131, release_year, 26762 31322, in_language, 6012 31322, release_year, 36163 16426, in_language, 6012 16426, release_year, 36163 10081, in_language, 6012 10081, release_year, 26762 18400, directed_by, 7251 18400, has_tags, 6012 18400, in_language, 6012 18400, starred_actors, 7251 18400, written_by, 7251 38134, has_tags, 6012 38134, in_language, 6012 38134, release_year, 26762 9709, in_language, 6012 9709, release_year, 26762 30696, in_language, 6012 30696, release_year, 26762 21405, in_language, 6012 21405, release_year, 26762 34422, in_language, 6012 34422, release_year, 26762 14669, in_language, 6012 14669, release_year, 26762 3586, in_language, 6012 3586, release_year, 26762 24305, in_language, 6012 24305, release_year, 26762 23661, in_language, 6012 23661, release_year, 36163 8807, in_language, 6012 8807, release_year, 26762 14659, has_tags, 6012 14659, in_language, 6012 14659, release_year, 26762 24257, in_language, 6012 24257, release_year, 26762 24399, has_tags, 6012 24399, in_language, 6012 24399, release_year, 26762 31657, directed_by, 8150 31657, has_tags, 8150 31657, in_language, 6012 31657, written_by, 8150 12100, has_tags, 2246 12100, in_language, 6012 12100, release_year, 36163 5937, has_tags, 2246 5937, in_language, 6012 31134, in_language, 6012 31134, release_year, 26762 8740, in_language, 6012 8740, release_year, 26762 5311, directed_by, 8150 5311, has_tags, 2246 5311, has_tags, 8150 5311, in_language, 6012 5311, release_year, 36163 5311, starred_actors, 8150 5311, written_by, 8150 21835, in_language, 6012 21835, release_year, 36163 8764, in_language, 6012 8764, release_year, 26762 32587, has_tags, 2246 32587, in_language, 6012 32587, release_year, 26762 5467, in_language, 6012 5467, release_year, 26762 30003, in_language, 6012 30003, release_year, 26762 38592, in_language, 6012 38592, release_year, 26762 21712, has_tags, 6012 21712, in_language, 6012 21712, release_year, 26762 37721, in_language, 6012 37721, release_year, 26762 25529, in_language, 6012 25529, written_by, 8150 34283, in_language, 6012 34283, release_year, 26762 22166, has_tags, 2246 22166, in_language, 6012 11654, in_language, 6012 11654, release_year, 36163 30481, in_language, 6012 30481, release_year, 36163 34585, in_language, 6012 34585, release_year, 36163 17536, directed_by, 8150 17536, has_tags, 8150 17536, in_language, 6012 17536, starred_actors, 8150 17536, written_by, 8150 33483, has_tags, 6012 33483, in_language, 6012 33483, release_year, 36163 Question: For what reason are ACES 'N' EIGHTS, PLAYTIME, and THOMAS N'GIJOL associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ACES 'N' EIGHTS", "PLAYTIME", "THOMAS N'GIJOL" ], "valid_edges": [ [ "35 SHOTS OF RUM", "in_language", "FRENCH" ], [ "35 SHOTS OF RUM", "release_year", "2008" ], [ "A CHRISTMAS TALE", "in_language", "FRENCH" ], [ "A CHRISTMAS TALE", "release_year", "2008" ], [ "ACES 'N' EIGHTS", "release_year", "2008" ], [ "AFTERWARDS", "in_language", "FRENCH" ], [ "AFTERWARDS", "release_year", "2008" ], [ "ANYTHING FOR HER", "in_language", "FRENCH" ], [ "ANYTHING FOR HER", "release_year", "2008" ], [ "ASTERIX AT THE OLYMPIC GAMES", "has_tags", "FRENCH" ], [ "ASTERIX AT THE OLYMPIC GAMES", "in_language", "FRENCH" ], [ "ASTERIX AT THE OLYMPIC GAMES", "release_year", "2008" ], [ "ASTERIX THE GAUL", "in_language", "FRENCH" ], [ "ASTERIX THE GAUL", "release_year", "1967" ], [ "BELLE DE JOUR", "in_language", "FRENCH" ], [ "BELLE DE JOUR", "release_year", "1967" ], [ "BOTTLE SHOCK", "in_language", "FRENCH" ], [ "BOTTLE SHOCK", "release_year", "2008" ], [ "CASE DÉPART", "directed_by", "THOMAS N'GIJOL" ], [ "CASE DÉPART", "has_tags", "FRENCH" ], [ "CASE DÉPART", "in_language", "FRENCH" ], [ "CASE DÉPART", "starred_actors", "THOMAS N'GIJOL" ], [ "CASE DÉPART", "written_by", "THOMAS N'GIJOL" ], [ "DANTE 01", "has_tags", "FRENCH" ], [ "DANTE 01", "in_language", "FRENCH" ], [ "DANTE 01", "release_year", "2008" ], [ "DE L'AUTRE CÔTÉ DU LIT", "in_language", "FRENCH" ], [ "DE L'AUTRE CÔTÉ DU LIT", "release_year", "2008" ], [ "DIARY OF A NYMPHOMANIAC", "in_language", "FRENCH" ], [ "DIARY OF A NYMPHOMANIAC", "release_year", "2008" ], [ "DRAGON HUNTERS", "in_language", "FRENCH" ], [ "DRAGON HUNTERS", "release_year", "2008" ], [ "FEMALE AGENTS", "in_language", "FRENCH" ], [ "FEMALE AGENTS", "release_year", "2008" ], [ "FRONTIER OF THE DAWN", "in_language", "FRENCH" ], [ "FRONTIER OF THE DAWN", "release_year", "2008" ], [ "I'VE LOVED YOU SO LONG", "in_language", "FRENCH" ], [ "I'VE LOVED YOU SO LONG", "release_year", "2008" ], [ "JCVD", "in_language", "FRENCH" ], [ "JCVD", "release_year", "2008" ], [ "LE SAMOURAÏ", "in_language", "FRENCH" ], [ "LE SAMOURAÏ", "release_year", "1967" ], [ "LET'S TALK ABOUT THE RAIN", "in_language", "FRENCH" ], [ "LET'S TALK ABOUT THE RAIN", "release_year", "2008" ], [ "LOVE ME NO MORE", "has_tags", "FRENCH" ], [ "LOVE ME NO MORE", "in_language", "FRENCH" ], [ "LOVE ME NO MORE", "release_year", "2008" ], [ "MARK OF AN ANGEL", "in_language", "FRENCH" ], [ "MARK OF AN ANGEL", "release_year", "2008" ], [ "MARTYRS", "has_tags", "FRENCH" ], [ "MARTYRS", "in_language", "FRENCH" ], [ "MARTYRS", "release_year", "2008" ], [ "MON ONCLE", "directed_by", "JACQUES TATI" ], [ "MON ONCLE", "has_tags", "JACQUES TATI" ], [ "MON ONCLE", "in_language", "FRENCH" ], [ "MON ONCLE", "written_by", "JACQUES TATI" ], [ "MOUCHETTE", "has_tags", "CRITERION" ], [ "MOUCHETTE", "in_language", "FRENCH" ], [ "MOUCHETTE", "release_year", "1967" ], [ "ORPHEUS", "has_tags", "CRITERION" ], [ "ORPHEUS", "in_language", "FRENCH" ], [ "PARIS", "in_language", "FRENCH" ], [ "PARIS", "release_year", "2008" ], [ "PARIS 36", "in_language", "FRENCH" ], [ "PARIS 36", "release_year", "2008" ], [ "PLAYTIME", "directed_by", "JACQUES TATI" ], [ "PLAYTIME", "has_tags", "CRITERION" ], [ "PLAYTIME", "has_tags", "JACQUES TATI" ], [ "PLAYTIME", "in_language", "FRENCH" ], [ "PLAYTIME", "release_year", "1967" ], [ "PLAYTIME", "starred_actors", "JACQUES TATI" ], [ "PLAYTIME", "written_by", "JACQUES TATI" ], [ "POINT BLANK", "in_language", "FRENCH" ], [ "POINT BLANK", "release_year", "1967" ], [ "STELLA", "in_language", "FRENCH" ], [ "STELLA", "release_year", "2008" ], [ "SUMMER HOURS", "has_tags", "CRITERION" ], [ "SUMMER HOURS", "in_language", "FRENCH" ], [ "SUMMER HOURS", "release_year", "2008" ], [ "SÉRAPHINE", "in_language", "FRENCH" ], [ "SÉRAPHINE", "release_year", "2008" ], [ "TAKEN", "in_language", "FRENCH" ], [ "TAKEN", "release_year", "2008" ], [ "THE BEACHES OF AGNÈS", "in_language", "FRENCH" ], [ "THE BEACHES OF AGNÈS", "release_year", "2008" ], [ "THE CLASS", "has_tags", "FRENCH" ], [ "THE CLASS", "in_language", "FRENCH" ], [ "THE CLASS", "release_year", "2008" ], [ "THE FIRST DAY OF THE REST OF YOUR LIFE", "in_language", "FRENCH" ], [ "THE FIRST DAY OF THE REST OF YOUR LIFE", "release_year", "2008" ], [ "THE ILLUSIONIST", "in_language", "FRENCH" ], [ "THE ILLUSIONIST", "written_by", "JACQUES TATI" ], [ "THE LAST DEADLY MISSION", "in_language", "FRENCH" ], [ "THE LAST DEADLY MISSION", "release_year", "2008" ], [ "THE LOWER DEPTHS", "has_tags", "CRITERION" ], [ "THE LOWER DEPTHS", "in_language", "FRENCH" ], [ "THE THIEF OF PARIS", "in_language", "FRENCH" ], [ "THE THIEF OF PARIS", "release_year", "1967" ], [ "THE TWO OF US", "in_language", "FRENCH" ], [ "THE TWO OF US", "release_year", "1967" ], [ "THE YOUNG GIRLS OF ROCHEFORT", "in_language", "FRENCH" ], [ "THE YOUNG GIRLS OF ROCHEFORT", "release_year", "1967" ], [ "TRAFIC", "directed_by", "JACQUES TATI" ], [ "TRAFIC", "has_tags", "JACQUES TATI" ], [ "TRAFIC", "in_language", "FRENCH" ], [ "TRAFIC", "starred_actors", "JACQUES TATI" ], [ "TRAFIC", "written_by", "JACQUES TATI" ], [ "WEEKEND", "has_tags", "FRENCH" ], [ "WEEKEND", "in_language", "FRENCH" ], [ "WEEKEND", "release_year", "1967" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 24525, 1984 35935, 2002 19509, A BREED APART 30146, A CHRISTMAS CAROL 37797, A CRUEL ROMANCE 24319, A PASSAGE TO INDIA 35695, A SOLDIER'S STORY 38344, A STREETCAR NAMED DESIRE 27196, ALPHABET CITY 20033, ANGEL 6091, ANOTHER COUNTRY 37608, AUSTRALIA 4765, BEAT STREET 33154, BIRDY 8655, BOY MEETS GIRL 9935, CAMILA 33088, CARMEN 31906, CATCH A FIRE 207, CHOOSE ME 36212, DRAMA 25651, DUMMY 28612, ELECTRIC DREAMS 1055, FALLING IN LOVE 36477, FIRSTBORN 17958, FLASHPOINT 2670, FOOTLOOSE 13111, GIVE MY REGARDS TO BROAD STREET 19773, GRANDVIEW, U.S.A. 6004, HARD TO HOLD 2941, ILLEANA DOUGLAS 37251, IRRECONCILABLE DIFFERENCES 38694, MARIA'S LOVERS 39880, MICHAEL REDGRAVE 38118, MOSCOW ON THE HUDSON 31250, MRS. SOFFEL 3910, NEWSFRONT 18680, NO SMALL AFFAIR 17528, ONCE UPON A TIME IN AMERICA 38335, PARIS, TEXAS 19877, PHILLIP NOYCE 13961, PLACES IN THE HEART 4005, PURPLE RAIN 24073, RABBIT-PROOF FENCE 26830, RACING WITH THE MOON 38232, STORIES OF LOST SOULS 11759, STREETS OF FIRE 10952, TANK 34308, TEACHERS 2212, THE BOUNTY 18176, THE COTTON CLUB 24493, THE FUNERAL 34673, THE GREEN 959, THE HIT 38956, THE HOTEL NEW HAMPSHIRE 13329, THE KARATE KID 35027, THE KILLING FIELDS 1306, THE PUBLIC WOMAN 20850, THE QUIET AMERICAN 19283, THE RAZOR'S EDGE 18649, THE RIVER 14415, THE WILD LIFE 28482, THIEF OF HEARTS 13100, THREADS 16292, TRUE STORY 21396, UNTIL SEPTEMBER 31772, WHAT HAVE I DONE TO DESERVE THIS? src, edge_attr, dst 24525, starred_actors, 39880 19509, has_genre, 36212 19509, release_year, 24525 30146, has_genre, 36212 30146, release_year, 24525 37797, has_genre, 36212 37797, release_year, 24525 24319, has_genre, 36212 24319, release_year, 24525 35695, has_genre, 36212 35695, release_year, 24525 38344, has_genre, 36212 38344, release_year, 24525 27196, has_genre, 36212 27196, release_year, 24525 20033, has_genre, 36212 20033, release_year, 24525 6091, has_genre, 36212 6091, release_year, 24525 37608, has_genre, 36212 4765, has_genre, 36212 4765, release_year, 24525 33154, has_genre, 36212 33154, has_tags, 36212 33154, release_year, 24525 8655, has_genre, 36212 8655, release_year, 24525 9935, has_genre, 36212 9935, release_year, 24525 33088, has_genre, 36212 33088, release_year, 24525 31906, directed_by, 19877 31906, has_genre, 36212 207, has_genre, 36212 207, release_year, 24525 25651, has_genre, 36212 25651, starred_actors, 2941 28612, has_genre, 36212 28612, release_year, 24525 1055, has_genre, 36212 1055, release_year, 24525 36477, has_genre, 36212 36477, release_year, 24525 17958, has_genre, 36212 17958, release_year, 24525 2670, has_genre, 36212 2670, release_year, 24525 13111, has_genre, 36212 13111, release_year, 24525 19773, has_genre, 36212 19773, release_year, 24525 6004, has_genre, 36212 6004, release_year, 24525 37251, has_genre, 36212 37251, release_year, 24525 38694, has_genre, 36212 38694, release_year, 24525 38118, has_genre, 36212 38118, release_year, 24525 31250, has_genre, 36212 31250, release_year, 24525 3910, directed_by, 19877 3910, has_genre, 36212 3910, has_tags, 37608 3910, has_tags, 19877 3910, written_by, 19877 18680, has_genre, 36212 18680, release_year, 24525 17528, has_genre, 36212 17528, release_year, 24525 38335, has_genre, 36212 38335, release_year, 24525 13961, has_genre, 36212 13961, release_year, 24525 4005, has_genre, 36212 4005, release_year, 24525 24073, directed_by, 19877 24073, has_genre, 36212 24073, has_tags, 37608 24073, has_tags, 19877 24073, has_tags, 16292 24073, release_year, 35935 26830, has_genre, 36212 26830, release_year, 24525 38232, directed_by, 2941 38232, written_by, 2941 11759, has_genre, 36212 11759, release_year, 24525 10952, has_genre, 36212 10952, release_year, 24525 34308, has_genre, 36212 34308, release_year, 24525 2212, has_genre, 36212 2212, release_year, 24525 18176, has_genre, 36212 18176, release_year, 24525 24493, has_genre, 36212 24493, release_year, 24525 34673, has_genre, 36212 34673, starred_actors, 2941 959, has_genre, 36212 959, release_year, 24525 38956, has_genre, 36212 38956, release_year, 24525 13329, has_genre, 36212 13329, has_tags, 36212 13329, release_year, 24525 35027, has_genre, 36212 35027, release_year, 24525 1306, has_genre, 36212 1306, has_tags, 36212 1306, release_year, 24525 20850, directed_by, 19877 20850, has_tags, 19877 20850, release_year, 35935 20850, starred_actors, 39880 19283, has_genre, 36212 19283, release_year, 24525 18649, has_genre, 36212 18649, release_year, 24525 14415, has_genre, 36212 14415, release_year, 24525 28482, has_genre, 36212 28482, release_year, 24525 13100, has_genre, 36212 13100, release_year, 24525 16292, has_genre, 36212 16292, has_tags, 36212 21396, has_genre, 36212 21396, release_year, 24525 31772, has_genre, 36212 31772, release_year, 24525 Question: For what reason are PHILLIP NOYCE, STORIES OF LOST SOULS, and THIEF OF HEARTS associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "PHILLIP NOYCE", "STORIES OF LOST SOULS", "THIEF OF HEARTS" ], "valid_edges": [ [ "1984", "starred_actors", "MICHAEL REDGRAVE" ], [ "A BREED APART", "has_genre", "DRAMA" ], [ "A BREED APART", "release_year", "1984" ], [ "A CHRISTMAS CAROL", "has_genre", "DRAMA" ], [ "A CHRISTMAS CAROL", "release_year", "1984" ], [ "A CRUEL ROMANCE", "has_genre", "DRAMA" ], [ "A CRUEL ROMANCE", "release_year", "1984" ], [ "A PASSAGE TO INDIA", "has_genre", "DRAMA" ], [ "A PASSAGE TO INDIA", "release_year", "1984" ], [ "A SOLDIER'S STORY", "has_genre", "DRAMA" ], [ "A SOLDIER'S STORY", "release_year", "1984" ], [ "A STREETCAR NAMED DESIRE", "has_genre", "DRAMA" ], [ "A STREETCAR NAMED DESIRE", "release_year", "1984" ], [ "ALPHABET CITY", "has_genre", "DRAMA" ], [ "ALPHABET CITY", "release_year", "1984" ], [ "ANGEL", "has_genre", "DRAMA" ], [ "ANGEL", "release_year", "1984" ], [ "ANOTHER COUNTRY", "has_genre", "DRAMA" ], [ "ANOTHER COUNTRY", "release_year", "1984" ], [ "AUSTRALIA", "has_genre", "DRAMA" ], [ "BEAT STREET", "has_genre", "DRAMA" ], [ "BEAT STREET", "release_year", "1984" ], [ "BIRDY", "has_genre", "DRAMA" ], [ "BIRDY", "has_tags", "DRAMA" ], [ "BIRDY", "release_year", "1984" ], [ "BOY MEETS GIRL", "has_genre", "DRAMA" ], [ "BOY MEETS GIRL", "release_year", "1984" ], [ "CAMILA", "has_genre", "DRAMA" ], [ "CAMILA", "release_year", "1984" ], [ "CARMEN", "has_genre", "DRAMA" ], [ "CARMEN", "release_year", "1984" ], [ "CATCH A FIRE", "directed_by", "PHILLIP NOYCE" ], [ "CATCH A FIRE", "has_genre", "DRAMA" ], [ "CHOOSE ME", "has_genre", "DRAMA" ], [ "CHOOSE ME", "release_year", "1984" ], [ "DUMMY", "has_genre", "DRAMA" ], [ "DUMMY", "starred_actors", "ILLEANA DOUGLAS" ], [ "ELECTRIC DREAMS", "has_genre", "DRAMA" ], [ "ELECTRIC DREAMS", "release_year", "1984" ], [ "FALLING IN LOVE", "has_genre", "DRAMA" ], [ "FALLING IN LOVE", "release_year", "1984" ], [ "FIRSTBORN", "has_genre", "DRAMA" ], [ "FIRSTBORN", "release_year", "1984" ], [ "FLASHPOINT", "has_genre", "DRAMA" ], [ "FLASHPOINT", "release_year", "1984" ], [ "FOOTLOOSE", "has_genre", "DRAMA" ], [ "FOOTLOOSE", "release_year", "1984" ], [ "GIVE MY REGARDS TO BROAD STREET", "has_genre", "DRAMA" ], [ "GIVE MY REGARDS TO BROAD STREET", "release_year", "1984" ], [ "GRANDVIEW, U.S.A.", "has_genre", "DRAMA" ], [ "GRANDVIEW, U.S.A.", "release_year", "1984" ], [ "HARD TO HOLD", "has_genre", "DRAMA" ], [ "HARD TO HOLD", "release_year", "1984" ], [ "IRRECONCILABLE DIFFERENCES", "has_genre", "DRAMA" ], [ "IRRECONCILABLE DIFFERENCES", "release_year", "1984" ], [ "MARIA'S LOVERS", "has_genre", "DRAMA" ], [ "MARIA'S LOVERS", "release_year", "1984" ], [ "MOSCOW ON THE HUDSON", "has_genre", "DRAMA" ], [ "MOSCOW ON THE HUDSON", "release_year", "1984" ], [ "MRS. SOFFEL", "has_genre", "DRAMA" ], [ "MRS. SOFFEL", "release_year", "1984" ], [ "NEWSFRONT", "directed_by", "PHILLIP NOYCE" ], [ "NEWSFRONT", "has_genre", "DRAMA" ], [ "NEWSFRONT", "has_tags", "AUSTRALIA" ], [ "NEWSFRONT", "has_tags", "PHILLIP NOYCE" ], [ "NEWSFRONT", "written_by", "PHILLIP NOYCE" ], [ "NO SMALL AFFAIR", "has_genre", "DRAMA" ], [ "NO SMALL AFFAIR", "release_year", "1984" ], [ "ONCE UPON A TIME IN AMERICA", "has_genre", "DRAMA" ], [ "ONCE UPON A TIME IN AMERICA", "release_year", "1984" ], [ "PARIS, TEXAS", "has_genre", "DRAMA" ], [ "PARIS, TEXAS", "release_year", "1984" ], [ "PLACES IN THE HEART", "has_genre", "DRAMA" ], [ "PLACES IN THE HEART", "release_year", "1984" ], [ "PURPLE RAIN", "has_genre", "DRAMA" ], [ "PURPLE RAIN", "release_year", "1984" ], [ "RABBIT-PROOF FENCE", "directed_by", "PHILLIP NOYCE" ], [ "RABBIT-PROOF FENCE", "has_genre", "DRAMA" ], [ "RABBIT-PROOF FENCE", "has_tags", "AUSTRALIA" ], [ "RABBIT-PROOF FENCE", "has_tags", "PHILLIP NOYCE" ], [ "RABBIT-PROOF FENCE", "has_tags", "TRUE STORY" ], [ "RABBIT-PROOF FENCE", "release_year", "2002" ], [ "RACING WITH THE MOON", "has_genre", "DRAMA" ], [ "RACING WITH THE MOON", "release_year", "1984" ], [ "STORIES OF LOST SOULS", "directed_by", "ILLEANA DOUGLAS" ], [ "STORIES OF LOST SOULS", "written_by", "ILLEANA DOUGLAS" ], [ "STREETS OF FIRE", "has_genre", "DRAMA" ], [ "STREETS OF FIRE", "release_year", "1984" ], [ "TANK", "has_genre", "DRAMA" ], [ "TANK", "release_year", "1984" ], [ "TEACHERS", "has_genre", "DRAMA" ], [ "TEACHERS", "release_year", "1984" ], [ "THE BOUNTY", "has_genre", "DRAMA" ], [ "THE BOUNTY", "release_year", "1984" ], [ "THE COTTON CLUB", "has_genre", "DRAMA" ], [ "THE COTTON CLUB", "release_year", "1984" ], [ "THE FUNERAL", "has_genre", "DRAMA" ], [ "THE FUNERAL", "release_year", "1984" ], [ "THE GREEN", "has_genre", "DRAMA" ], [ "THE GREEN", "starred_actors", "ILLEANA DOUGLAS" ], [ "THE HIT", "has_genre", "DRAMA" ], [ "THE HIT", "release_year", "1984" ], [ "THE HOTEL NEW HAMPSHIRE", "has_genre", "DRAMA" ], [ "THE HOTEL NEW HAMPSHIRE", "release_year", "1984" ], [ "THE KARATE KID", "has_genre", "DRAMA" ], [ "THE KARATE KID", "has_tags", "DRAMA" ], [ "THE KARATE KID", "release_year", "1984" ], [ "THE KILLING FIELDS", "has_genre", "DRAMA" ], [ "THE KILLING FIELDS", "release_year", "1984" ], [ "THE PUBLIC WOMAN", "has_genre", "DRAMA" ], [ "THE PUBLIC WOMAN", "has_tags", "DRAMA" ], [ "THE PUBLIC WOMAN", "release_year", "1984" ], [ "THE QUIET AMERICAN", "directed_by", "PHILLIP NOYCE" ], [ "THE QUIET AMERICAN", "has_tags", "PHILLIP NOYCE" ], [ "THE QUIET AMERICAN", "release_year", "2002" ], [ "THE QUIET AMERICAN", "starred_actors", "MICHAEL REDGRAVE" ], [ "THE RAZOR'S EDGE", "has_genre", "DRAMA" ], [ "THE RAZOR'S EDGE", "release_year", "1984" ], [ "THE RIVER", "has_genre", "DRAMA" ], [ "THE RIVER", "release_year", "1984" ], [ "THE WILD LIFE", "has_genre", "DRAMA" ], [ "THE WILD LIFE", "release_year", "1984" ], [ "THIEF OF HEARTS", "has_genre", "DRAMA" ], [ "THIEF OF HEARTS", "release_year", "1984" ], [ "THREADS", "has_genre", "DRAMA" ], [ "THREADS", "release_year", "1984" ], [ "TRUE STORY", "has_genre", "DRAMA" ], [ "TRUE STORY", "has_tags", "DRAMA" ], [ "UNTIL SEPTEMBER", "has_genre", "DRAMA" ], [ "UNTIL SEPTEMBER", "release_year", "1984" ], [ "WHAT HAVE I DONE TO DESERVE THIS?", "has_genre", "DRAMA" ], [ "WHAT HAVE I DONE TO DESERVE THIS?", "release_year", "1984" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 24818, 1992 17588, AX 'EM 16654, BRITISH 25435, CANDYMAN 6743, CRITTERS 11807, CRITTERS 4 28873, DAVID J. SCHOW 37059, DEATH AT A FUNERAL 2228, DON KEITH OPPER 15064, EQUINOX 31630, EVIL TOONS 6215, FOUR WEDDINGS AND A FUNERAL 38663, FUNERAL 5870, HORROR 7315, HOUSE IV 38385, INNOCENT BLOOD 28729, REMAKE 37397, RICHARD JOHNSON 16247, SLEEPWALKERS 11787, THE HAUNTING 30271, THE HILLS RUN RED 10379, THE LAWNMOWER MAN src, edge_attr, dst 17588, has_genre, 5870 17588, release_year, 24818 25435, has_genre, 5870 25435, has_tags, 5870 25435, release_year, 24818 6743, has_genre, 5870 6743, written_by, 2228 11807, has_genre, 5870 11807, release_year, 24818 11807, starred_actors, 2228 11807, written_by, 28873 37059, has_tags, 16654 37059, has_tags, 38663 37059, has_tags, 28729 15064, has_genre, 5870 15064, release_year, 24818 31630, has_genre, 5870 31630, release_year, 24818 6215, has_tags, 16654 6215, has_tags, 38663 7315, has_genre, 5870 7315, release_year, 24818 38385, has_genre, 5870 38385, release_year, 24818 16247, has_genre, 5870 16247, has_tags, 5870 16247, release_year, 24818 11787, has_genre, 5870 11787, has_tags, 16654 11787, has_tags, 5870 11787, has_tags, 28729 11787, starred_actors, 37397 30271, has_genre, 5870 30271, written_by, 28873 10379, has_genre, 5870 10379, release_year, 24818 Question: For what reason are CRITTERS 4, FUNERAL, and RICHARD JOHNSON associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "CRITTERS 4", "FUNERAL", "RICHARD JOHNSON" ], "valid_edges": [ [ "AX 'EM", "has_genre", "HORROR" ], [ "AX 'EM", "release_year", "1992" ], [ "CANDYMAN", "has_genre", "HORROR" ], [ "CANDYMAN", "has_tags", "HORROR" ], [ "CANDYMAN", "release_year", "1992" ], [ "CRITTERS", "has_genre", "HORROR" ], [ "CRITTERS", "written_by", "DON KEITH OPPER" ], [ "CRITTERS 4", "has_genre", "HORROR" ], [ "CRITTERS 4", "release_year", "1992" ], [ "CRITTERS 4", "starred_actors", "DON KEITH OPPER" ], [ "CRITTERS 4", "written_by", "DAVID J. SCHOW" ], [ "DEATH AT A FUNERAL", "has_tags", "BRITISH" ], [ "DEATH AT A FUNERAL", "has_tags", "FUNERAL" ], [ "DEATH AT A FUNERAL", "has_tags", "REMAKE" ], [ "EQUINOX", "has_genre", "HORROR" ], [ "EQUINOX", "release_year", "1992" ], [ "EVIL TOONS", "has_genre", "HORROR" ], [ "EVIL TOONS", "release_year", "1992" ], [ "FOUR WEDDINGS AND A FUNERAL", "has_tags", "BRITISH" ], [ "FOUR WEDDINGS AND A FUNERAL", "has_tags", "FUNERAL" ], [ "HOUSE IV", "has_genre", "HORROR" ], [ "HOUSE IV", "release_year", "1992" ], [ "INNOCENT BLOOD", "has_genre", "HORROR" ], [ "INNOCENT BLOOD", "release_year", "1992" ], [ "SLEEPWALKERS", "has_genre", "HORROR" ], [ "SLEEPWALKERS", "has_tags", "HORROR" ], [ "SLEEPWALKERS", "release_year", "1992" ], [ "THE HAUNTING", "has_genre", "HORROR" ], [ "THE HAUNTING", "has_tags", "BRITISH" ], [ "THE HAUNTING", "has_tags", "HORROR" ], [ "THE HAUNTING", "has_tags", "REMAKE" ], [ "THE HAUNTING", "starred_actors", "RICHARD JOHNSON" ], [ "THE HILLS RUN RED", "has_genre", "HORROR" ], [ "THE HILLS RUN RED", "written_by", "DAVID J. SCHOW" ], [ "THE LAWNMOWER MAN", "has_genre", "HORROR" ], [ "THE LAWNMOWER MAN", "release_year", "1992" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 13192, ABBY MANN 10045, BD-R 24416, CEREBRAL PALSY 6480, GERMAN 22600, JUDGMENT AT NUREMBERG 30, KURT VONNEGUT 5127, MOTHER NIGHT 17669, MY LEFT FOOT 22214, WAR src, edge_attr, dst 22600, has_genre, 22214 22600, has_tags, 10045 22600, has_tags, 22214 22600, in_language, 6480 22600, written_by, 13192 5127, has_genre, 22214 5127, has_tags, 30 5127, in_language, 6480 17669, has_tags, 10045 17669, has_tags, 24416 Question: For what reason are ABBY MANN, CEREBRAL PALSY, and KURT VONNEGUT associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ABBY MANN", "CEREBRAL PALSY", "KURT VONNEGUT" ], "valid_edges": [ [ "JUDGMENT AT NUREMBERG", "has_genre", "WAR" ], [ "JUDGMENT AT NUREMBERG", "has_tags", "BD-R" ], [ "JUDGMENT AT NUREMBERG", "has_tags", "WAR" ], [ "JUDGMENT AT NUREMBERG", "in_language", "GERMAN" ], [ "JUDGMENT AT NUREMBERG", "written_by", "ABBY MANN" ], [ "MOTHER NIGHT", "has_genre", "WAR" ], [ "MOTHER NIGHT", "has_tags", "KURT VONNEGUT" ], [ "MOTHER NIGHT", "in_language", "GERMAN" ], [ "MY LEFT FOOT", "has_tags", "BD-R" ], [ "MY LEFT FOOT", "has_tags", "CEREBRAL PALSY" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6216, 1952 35935, 2002 4310, 24 HOUR PARTY PEOPLE 9408, 40 DAYS AND 40 NIGHTS 36647, 8 WOMEN 11957, 9 DEAD GAY GUYS 2847, ABBOTT AND COSTELLO MEET CAPTAIN KIDD 6008, ABOUT A BOY 37599, ABOUT SCHMIDT 34883, ADAM'S RIB 17111, ALI G INDAHOUSE 22569, ALL ABOUT THE BENJAMINS 16113, ANALYZE THAT 35314, AUSTIN POWERS IN GOLDMEMBER 29535, AVENGING ANGELO 38849, BACHELOR MOTHER 23899, BAD COMPANY 36819, BARBERSHOP 30771, BECAUSE YOU'RE MINE 15433, BEND IT LIKE BECKHAM 39619, BIG FAT LIAR 8732, BIG TROUBLE 27128, BOAT TRIP 37616, BORN YESTERDAY 235, BRINGING UP BABY 32620, BUBBA HO-TEP 14572, BUYING THE COW 27929, CABIN FEVER 23286, CARNAGE 20073, CHERISH 10349, CHICAGO 32644, CHINESE ODYSSEY 2002 30463, COMEDY 21391, CRACKERJACK 30019, CROSSROADS 36382, DANTE'S INFERNO 21730, DAY OF THE WACKO 19179, DEATH TO SMOOCHY 34250, DESK SET 15791, DINNER AT EIGHT 21079, DIRTY DEEDS 14249, DREAMBOAT 25651, DUMMY 26815, EIGHT CRAZY NIGHTS 38543, EIGHT LEGGED FREAKS 38250, FATHER OF THE BRIDE 8745, FATHER'S LITTLE DIVIDEND 27539, FRANK MCKLUSKY, C.I. 30712, FREEBIE AND THE BEAN 29241, FRIDAY AFTER NEXT 14466, GARSON KANIN 19932, GEORGE CUKOR 20995, GIRLS ABOUT TOWN 29563, GUESS WHO'S COMING TO DINNER 6388, HAROLD AND MAUDE 10990, HEARTLANDS 3829, HERO 7289, HOLIDAY 15629, HOME ALONE 4 22069, I SPY 27919, I'M WITH LUCY 16371, ICE AGE 17197, IGBY GOES DOWN 30470, IT SHOULD HAPPEN TO YOU 11295, IT'S A MAD, MAD, MAD, MAD WORLD 33001, JUST A KISS 2204, JUWANNA MANN 26838, KATHARINE HEPBURN 32010, KISS THE BRIDE 19966, LE PLAISIR 20863, LES GIRLS 10267, LET'S MAKE LOVE 2563, LIBELED LADY 12137, LIFE OR SOMETHING LIKE IT 28832, LIFE WITHOUT DICK 16155, LOVE LIZA 9812, MAID IN MANHATTAN 32481, MANNEQUIN 4270, MEN WITH BROOMS 36083, MIRANDA 13717, MONKEY BUSINESS 16662, MORNING GLORY 20803, MR. DEEDS 22064, MY BIG FAT GREEK WEDDING 32122, MY FAVORITE WIFE 4887, MY LEFT EYE SEES GHOSTS 35262, MY MOTHER LIKES WOMEN 25269, NINE LIVES 35794, NOVO 1493, NOW YOU KNOW 2721, OCCIDENT 4582, ONCE UPON A TIME IN THE MIDLANDS 24375, ONE HOUR WITH YOU 37178, ORANGE COUNTY 27117, PASSIONADA 36277, PAT AND MIKE 9976, PIPE DREAM 3688, PUMPKIN 34602, PUNCH-DRUNK LOVE 21890, R.S.V.P. 30422, RICHARD RUSH 3525, ROAD TO BALI 25769, ROGER DODGER 37522, ROOM FOR ONE MORE 10638, RUTH GORDON 12987, SCOOBY-DOO 19244, SERVING SARA 6603, SEX IS COMEDY 26261, SHOWTIME 28298, SINGIN' IN THE RAIN 15043, SLACKERS 25151, SNOW DOGS 16907, SON OF PALEFACE 24961, SORORITY BOYS 31563, SPENCER TRACY 35843, SPUN 5170, STEALING HARVARD 15100, SUPER SUCKER 9276, SWEET HOME ALABAMA 26790, SWEPT AWAY 11247, SYLVIA SCARLETT 22407, THE ACTRESS 36316, THE ADVENTURES OF PLUTO NASH 2316, THE ANARCHIST COOKBOOK 304, THE BANGER SISTERS 21330, THE CRIMSON PIRATE 11696, THE CUCKOO 3324, THE DANGEROUS LIVES OF ALTAR BOYS 914, THE GOOD GIRL 26531, THE GURU 24302, THE HOT CHICK 13534, THE IMPORTANCE OF BEING EARNEST 2147, THE MAN WITHOUT A PAST 6908, THE MASTER OF DISGUISE 32297, THE MERRY WIDOW 295, THE NEW GUY 1192, THE ONE AND ONLY 28172, THE PHILADELPHIA STORY 31851, THE PRISONER OF ZENDA 16199, THE QUIET MAN 31401, THE RULES OF ATTRACTION 7052, THE SWEETEST THING 34172, THE TUXEDO 34529, THE WHITE SHEIK 20210, THE WOMEN 488, TRAVELS WITH MY AUNT 10720, TRIGGERMEN 9612, TWO WEEKS NOTICE 13214, UNCONDITIONAL LOVE 16991, UNDERCOVER BROTHER 14438, UP THE RIVER 21576, WAKING UP IN RENO 31364, WE'RE NOT MARRIED! 37568, WELCOME TO COLLINWOOD 39802, WHEN IN ROME 28231, WHERE'S POPPA? 8249, WITHOUT LOVE 2659, WOMAN OF THE YEAR src, edge_attr, dst 4310, has_genre, 30463 4310, release_year, 35935 9408, has_genre, 30463 9408, has_tags, 30463 9408, release_year, 35935 36647, has_genre, 30463 36647, release_year, 35935 11957, has_genre, 30463 11957, release_year, 35935 2847, has_genre, 30463 2847, release_year, 6216 6008, has_genre, 30463 6008, has_tags, 30463 6008, release_year, 35935 37599, has_genre, 30463 37599, has_tags, 30463 37599, release_year, 35935 34883, directed_by, 19932 34883, has_genre, 30463 34883, has_tags, 19932 34883, has_tags, 26838 34883, has_tags, 31563 34883, starred_actors, 26838 34883, starred_actors, 31563 34883, written_by, 14466 34883, written_by, 10638 17111, has_genre, 30463 17111, release_year, 35935 22569, has_genre, 30463 22569, has_tags, 30463 22569, release_year, 35935 16113, has_genre, 30463 16113, has_tags, 30463 16113, release_year, 35935 35314, has_genre, 30463 35314, has_tags, 30463 35314, release_year, 35935 29535, has_genre, 30463 29535, release_year, 35935 38849, directed_by, 14466 38849, has_genre, 30463 23899, has_genre, 30463 23899, release_year, 35935 36819, has_genre, 30463 36819, release_year, 35935 30771, has_genre, 30463 30771, release_year, 6216 15433, has_genre, 30463 15433, release_year, 35935 39619, has_genre, 30463 39619, release_year, 35935 8732, has_genre, 30463 8732, release_year, 35935 27128, has_genre, 30463 27128, release_year, 35935 37616, directed_by, 19932 37616, has_genre, 30463 37616, has_tags, 19932 37616, written_by, 14466 235, has_genre, 30463 235, has_tags, 30463 235, has_tags, 26838 235, starred_actors, 26838 32620, has_genre, 30463 32620, has_tags, 30463 32620, release_year, 35935 14572, has_genre, 30463 14572, release_year, 35935 27929, has_genre, 30463 27929, release_year, 35935 23286, has_genre, 30463 23286, release_year, 35935 20073, has_genre, 30463 20073, release_year, 35935 10349, has_genre, 30463 10349, release_year, 35935 32644, has_genre, 30463 32644, release_year, 35935 21391, has_genre, 30463 21391, release_year, 35935 30019, has_genre, 30463 30019, release_year, 35935 36382, has_genre, 30463 36382, starred_actors, 31563 21730, has_genre, 30463 21730, release_year, 35935 19179, has_genre, 30463 19179, release_year, 35935 34250, has_genre, 30463 34250, has_tags, 26838 34250, starred_actors, 26838 34250, starred_actors, 31563 15791, directed_by, 19932 15791, has_genre, 30463 15791, has_tags, 19932 21079, has_genre, 30463 21079, has_tags, 30463 21079, release_year, 35935 14249, has_genre, 30463 14249, release_year, 6216 25651, has_genre, 30463 25651, release_year, 35935 26815, has_genre, 30463 26815, release_year, 35935 38543, has_genre, 30463 38543, release_year, 35935 38250, has_genre, 30463 38250, has_tags, 30463 38250, has_tags, 31563 38250, starred_actors, 31563 8745, has_genre, 30463 8745, starred_actors, 31563 27539, has_genre, 30463 27539, release_year, 35935 30712, directed_by, 30422 30712, has_genre, 30463 29241, has_genre, 30463 29241, release_year, 35935 20995, directed_by, 19932 20995, has_genre, 30463 20995, has_tags, 19932 29563, has_genre, 30463 29563, has_tags, 31563 29563, starred_actors, 26838 29563, starred_actors, 31563 6388, has_genre, 30463 6388, has_tags, 10638 6388, starred_actors, 10638 10990, has_genre, 30463 10990, release_year, 35935 3829, has_genre, 30463 3829, release_year, 35935 7289, directed_by, 19932 7289, has_genre, 30463 7289, has_tags, 30463 7289, has_tags, 19932 7289, has_tags, 26838 7289, starred_actors, 26838 15629, has_genre, 30463 15629, release_year, 35935 22069, has_genre, 30463 22069, has_tags, 30463 22069, release_year, 35935 27919, has_genre, 30463 27919, release_year, 35935 16371, has_genre, 30463 16371, has_tags, 30463 16371, release_year, 35935 17197, has_genre, 30463 17197, release_year, 35935 30470, directed_by, 19932 30470, has_genre, 30463 30470, has_tags, 19932 30470, written_by, 14466 11295, has_genre, 30463 11295, has_tags, 30463 11295, starred_actors, 31563 33001, has_genre, 30463 33001, release_year, 35935 2204, has_genre, 30463 2204, release_year, 35935 32010, has_genre, 30463 32010, release_year, 35935 19966, has_genre, 30463 19966, release_year, 6216 20863, directed_by, 19932 20863, has_genre, 30463 10267, directed_by, 19932 10267, has_genre, 30463 2563, has_genre, 30463 2563, starred_actors, 31563 12137, has_genre, 30463 12137, release_year, 35935 28832, has_genre, 30463 28832, release_year, 35935 16155, has_genre, 30463 16155, release_year, 35935 9812, has_genre, 30463 9812, release_year, 35935 32481, has_genre, 30463 32481, starred_actors, 31563 4270, has_genre, 30463 4270, release_year, 35935 36083, has_genre, 30463 36083, release_year, 35935 13717, has_genre, 30463 13717, has_tags, 30463 13717, release_year, 6216 16662, has_genre, 30463 16662, starred_actors, 26838 20803, has_genre, 30463 20803, release_year, 35935 22064, has_genre, 30463 22064, has_tags, 30463 22064, release_year, 35935 32122, directed_by, 14466 32122, has_genre, 30463 4887, has_genre, 30463 4887, release_year, 35935 35262, has_genre, 30463 35262, release_year, 35935 25269, release_year, 35935 35794, has_genre, 30463 35794, release_year, 35935 1493, has_genre, 30463 1493, release_year, 35935 2721, has_genre, 30463 2721, release_year, 35935 4582, has_genre, 30463 4582, release_year, 35935 24375, directed_by, 19932 24375, has_genre, 30463 37178, has_genre, 30463 37178, release_year, 35935 27117, has_genre, 30463 27117, release_year, 35935 36277, directed_by, 19932 36277, has_genre, 30463 36277, has_tags, 19932 36277, release_year, 6216 36277, starred_actors, 26838 36277, starred_actors, 31563 36277, written_by, 14466 36277, written_by, 10638 9976, has_genre, 30463 9976, release_year, 35935 3688, has_genre, 30463 3688, release_year, 35935 34602, has_genre, 30463 34602, has_tags, 30463 34602, release_year, 35935 21890, has_genre, 30463 21890, release_year, 35935 3525, has_genre, 30463 3525, release_year, 6216 25769, has_genre, 30463 25769, release_year, 35935 37522, has_genre, 30463 37522, release_year, 6216 12987, has_genre, 30463 12987, release_year, 35935 19244, has_genre, 30463 19244, release_year, 35935 6603, has_genre, 30463 6603, release_year, 35935 26261, has_genre, 30463 26261, release_year, 35935 28298, has_genre, 30463 28298, has_tags, 30463 28298, release_year, 6216 15043, has_genre, 30463 15043, release_year, 35935 25151, has_genre, 30463 25151, release_year, 35935 16907, has_genre, 30463 16907, release_year, 6216 24961, has_genre, 30463 24961, release_year, 35935 35843, has_genre, 30463 35843, release_year, 35935 5170, has_genre, 30463 5170, release_year, 35935 15100, has_genre, 30463 15100, release_year, 35935 9276, has_genre, 30463 9276, release_year, 35935 26790, has_genre, 30463 26790, release_year, 35935 11247, directed_by, 19932 11247, has_genre, 30463 11247, starred_actors, 26838 22407, directed_by, 19932 22407, has_genre, 30463 22407, starred_actors, 31563 22407, written_by, 10638 36316, has_genre, 30463 36316, release_year, 35935 2316, has_genre, 30463 2316, release_year, 35935 304, has_genre, 30463 304, release_year, 35935 21330, has_genre, 30463 21330, release_year, 6216 11696, has_genre, 30463 11696, release_year, 35935 3324, has_genre, 30463 3324, release_year, 35935 914, has_genre, 30463 914, release_year, 35935 26531, has_genre, 30463 26531, release_year, 35935 24302, has_genre, 30463 24302, has_tags, 30463 24302, release_year, 35935 13534, has_genre, 30463 13534, release_year, 35935 2147, has_genre, 30463 2147, release_year, 35935 6908, has_genre, 30463 6908, release_year, 35935 32297, has_genre, 30463 32297, release_year, 6216 295, has_genre, 30463 295, release_year, 35935 1192, has_genre, 30463 1192, release_year, 35935 28172, directed_by, 19932 28172, has_genre, 30463 28172, has_tags, 19932 28172, has_tags, 26838 28172, starred_actors, 26838 31851, has_genre, 30463 31851, release_year, 6216 16199, has_genre, 30463 16199, release_year, 6216 31401, has_genre, 30463 31401, release_year, 35935 7052, has_genre, 30463 7052, has_tags, 30463 7052, release_year, 35935 34172, has_genre, 30463 34172, has_tags, 30463 34172, release_year, 35935 34529, has_genre, 30463 34529, release_year, 6216 20210, directed_by, 19932 20210, has_genre, 30463 20210, has_tags, 19932 488, directed_by, 19932 488, has_genre, 30463 10720, has_genre, 30463 10720, release_year, 35935 9612, has_genre, 30463 9612, release_year, 35935 13214, has_genre, 30463 13214, release_year, 35935 16991, has_genre, 30463 16991, release_year, 35935 14438, has_genre, 30463 14438, starred_actors, 31563 21576, has_genre, 30463 21576, release_year, 35935 31364, has_genre, 30463 31364, release_year, 6216 37568, has_genre, 30463 37568, has_tags, 30463 37568, release_year, 35935 39802, has_genre, 30463 39802, release_year, 35935 28231, has_genre, 30463 28231, has_tags, 10638 28231, starred_actors, 10638 8249, has_genre, 30463 8249, has_tags, 26838 8249, has_tags, 31563 8249, starred_actors, 26838 8249, starred_actors, 31563 2659, has_genre, 30463 2659, starred_actors, 26838 2659, starred_actors, 31563 Question: In what context are NINE LIVES, PAT AND MIKE, and RICHARD RUSH connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "NINE LIVES", "PAT AND MIKE", "RICHARD RUSH" ], "valid_edges": [ [ "24 HOUR PARTY PEOPLE", "has_genre", "COMEDY" ], [ "24 HOUR PARTY PEOPLE", "release_year", "2002" ], [ "40 DAYS AND 40 NIGHTS", "has_genre", "COMEDY" ], [ "40 DAYS AND 40 NIGHTS", "has_tags", "COMEDY" ], [ "40 DAYS AND 40 NIGHTS", "release_year", "2002" ], [ "8 WOMEN", "has_genre", "COMEDY" ], [ "8 WOMEN", "release_year", "2002" ], [ "9 DEAD GAY GUYS", "has_genre", "COMEDY" ], [ "9 DEAD GAY GUYS", "release_year", "2002" ], [ "ABBOTT AND COSTELLO MEET CAPTAIN KIDD", "has_genre", "COMEDY" ], [ "ABBOTT AND COSTELLO MEET CAPTAIN KIDD", "release_year", "1952" ], [ "ABOUT A BOY", "has_genre", "COMEDY" ], [ "ABOUT A BOY", "has_tags", "COMEDY" ], [ "ABOUT A BOY", "release_year", "2002" ], [ "ABOUT SCHMIDT", "has_genre", "COMEDY" ], [ "ABOUT SCHMIDT", "has_tags", "COMEDY" ], [ "ABOUT SCHMIDT", "release_year", "2002" ], [ "ADAM'S RIB", "directed_by", "GEORGE CUKOR" ], [ "ADAM'S RIB", "has_genre", "COMEDY" ], [ "ADAM'S RIB", "has_tags", "GEORGE CUKOR" ], [ "ADAM'S RIB", "has_tags", "KATHARINE HEPBURN" ], [ "ADAM'S RIB", "has_tags", "SPENCER TRACY" ], [ "ADAM'S RIB", "starred_actors", "KATHARINE HEPBURN" ], [ "ADAM'S RIB", "starred_actors", "SPENCER TRACY" ], [ "ADAM'S RIB", "written_by", "GARSON KANIN" ], [ "ADAM'S RIB", "written_by", "RUTH GORDON" ], [ "ALI G INDAHOUSE", "has_genre", "COMEDY" ], [ "ALI G INDAHOUSE", "release_year", "2002" ], [ "ALL ABOUT THE BENJAMINS", "has_genre", "COMEDY" ], [ "ALL ABOUT THE BENJAMINS", "has_tags", "COMEDY" ], [ "ALL ABOUT THE BENJAMINS", "release_year", "2002" ], [ "ANALYZE THAT", "has_genre", "COMEDY" ], [ "ANALYZE THAT", "has_tags", "COMEDY" ], [ "ANALYZE THAT", "release_year", "2002" ], [ "AUSTIN POWERS IN GOLDMEMBER", "has_genre", "COMEDY" ], [ "AUSTIN POWERS IN GOLDMEMBER", "has_tags", "COMEDY" ], [ "AUSTIN POWERS IN GOLDMEMBER", "release_year", "2002" ], [ "AVENGING ANGELO", "has_genre", "COMEDY" ], [ "AVENGING ANGELO", "release_year", "2002" ], [ "BACHELOR MOTHER", "directed_by", "GARSON KANIN" ], [ "BACHELOR MOTHER", "has_genre", "COMEDY" ], [ "BAD COMPANY", "has_genre", "COMEDY" ], [ "BAD COMPANY", "release_year", "2002" ], [ "BARBERSHOP", "has_genre", "COMEDY" ], [ "BARBERSHOP", "release_year", "2002" ], [ "BECAUSE YOU'RE MINE", "has_genre", "COMEDY" ], [ "BECAUSE YOU'RE MINE", "release_year", "1952" ], [ "BEND IT LIKE BECKHAM", "has_genre", "COMEDY" ], [ "BEND IT LIKE BECKHAM", "release_year", "2002" ], [ "BIG FAT LIAR", "has_genre", "COMEDY" ], [ "BIG FAT LIAR", "release_year", "2002" ], [ "BIG TROUBLE", "has_genre", "COMEDY" ], [ "BIG TROUBLE", "release_year", "2002" ], [ "BOAT TRIP", "has_genre", "COMEDY" ], [ "BOAT TRIP", "release_year", "2002" ], [ "BORN YESTERDAY", "directed_by", "GEORGE CUKOR" ], [ "BORN YESTERDAY", "has_genre", "COMEDY" ], [ "BORN YESTERDAY", "has_tags", "GEORGE CUKOR" ], [ "BORN YESTERDAY", "written_by", "GARSON KANIN" ], [ "BRINGING UP BABY", "has_genre", "COMEDY" ], [ "BRINGING UP BABY", "has_tags", "COMEDY" ], [ "BRINGING UP BABY", "has_tags", "KATHARINE HEPBURN" ], [ "BRINGING UP BABY", "starred_actors", "KATHARINE HEPBURN" ], [ "BUBBA HO-TEP", "has_genre", "COMEDY" ], [ "BUBBA HO-TEP", "has_tags", "COMEDY" ], [ "BUBBA HO-TEP", "release_year", "2002" ], [ "BUYING THE COW", "has_genre", "COMEDY" ], [ "BUYING THE COW", "release_year", "2002" ], [ "CABIN FEVER", "has_genre", "COMEDY" ], [ "CABIN FEVER", "release_year", "2002" ], [ "CARNAGE", "has_genre", "COMEDY" ], [ "CARNAGE", "release_year", "2002" ], [ "CHERISH", "has_genre", "COMEDY" ], [ "CHERISH", "release_year", "2002" ], [ "CHICAGO", "has_genre", "COMEDY" ], [ "CHICAGO", "release_year", "2002" ], [ "CHINESE ODYSSEY 2002", "has_genre", "COMEDY" ], [ "CHINESE ODYSSEY 2002", "release_year", "2002" ], [ "CRACKERJACK", "has_genre", "COMEDY" ], [ "CRACKERJACK", "release_year", "2002" ], [ "CROSSROADS", "has_genre", "COMEDY" ], [ "CROSSROADS", "release_year", "2002" ], [ "DANTE'S INFERNO", "has_genre", "COMEDY" ], [ "DANTE'S INFERNO", "starred_actors", "SPENCER TRACY" ], [ "DAY OF THE WACKO", "has_genre", "COMEDY" ], [ "DAY OF THE WACKO", "release_year", "2002" ], [ "DEATH TO SMOOCHY", "has_genre", "COMEDY" ], [ "DEATH TO SMOOCHY", "release_year", "2002" ], [ "DESK SET", "has_genre", "COMEDY" ], [ "DESK SET", "has_tags", "KATHARINE HEPBURN" ], [ "DESK SET", "starred_actors", "KATHARINE HEPBURN" ], [ "DESK SET", "starred_actors", "SPENCER TRACY" ], [ "DINNER AT EIGHT", "directed_by", "GEORGE CUKOR" ], [ "DINNER AT EIGHT", "has_genre", "COMEDY" ], [ "DINNER AT EIGHT", "has_tags", "GEORGE CUKOR" ], [ "DIRTY DEEDS", "has_genre", "COMEDY" ], [ "DIRTY DEEDS", "has_tags", "COMEDY" ], [ "DIRTY DEEDS", "release_year", "2002" ], [ "DREAMBOAT", "has_genre", "COMEDY" ], [ "DREAMBOAT", "release_year", "1952" ], [ "DUMMY", "has_genre", "COMEDY" ], [ "DUMMY", "release_year", "2002" ], [ "EIGHT CRAZY NIGHTS", "has_genre", "COMEDY" ], [ "EIGHT CRAZY NIGHTS", "release_year", "2002" ], [ "EIGHT LEGGED FREAKS", "has_genre", "COMEDY" ], [ "EIGHT LEGGED FREAKS", "release_year", "2002" ], [ "FATHER OF THE BRIDE", "has_genre", "COMEDY" ], [ "FATHER OF THE BRIDE", "has_tags", "COMEDY" ], [ "FATHER OF THE BRIDE", "has_tags", "SPENCER TRACY" ], [ "FATHER OF THE BRIDE", "starred_actors", "SPENCER TRACY" ], [ "FATHER'S LITTLE DIVIDEND", "has_genre", "COMEDY" ], [ "FATHER'S LITTLE DIVIDEND", "starred_actors", "SPENCER TRACY" ], [ "FRANK MCKLUSKY, C.I.", "has_genre", "COMEDY" ], [ "FRANK MCKLUSKY, C.I.", "release_year", "2002" ], [ "FREEBIE AND THE BEAN", "directed_by", "RICHARD RUSH" ], [ "FREEBIE AND THE BEAN", "has_genre", "COMEDY" ], [ "FRIDAY AFTER NEXT", "has_genre", "COMEDY" ], [ "FRIDAY AFTER NEXT", "release_year", "2002" ], [ "GIRLS ABOUT TOWN", "directed_by", "GEORGE CUKOR" ], [ "GIRLS ABOUT TOWN", "has_genre", "COMEDY" ], [ "GIRLS ABOUT TOWN", "has_tags", "GEORGE CUKOR" ], [ "GUESS WHO'S COMING TO DINNER", "has_genre", "COMEDY" ], [ "GUESS WHO'S COMING TO DINNER", "has_tags", "SPENCER TRACY" ], [ "GUESS WHO'S COMING TO DINNER", "starred_actors", "KATHARINE HEPBURN" ], [ "GUESS WHO'S COMING TO DINNER", "starred_actors", "SPENCER TRACY" ], [ "HAROLD AND MAUDE", "has_genre", "COMEDY" ], [ "HAROLD AND MAUDE", "has_tags", "RUTH GORDON" ], [ "HAROLD AND MAUDE", "starred_actors", "RUTH GORDON" ], [ "HEARTLANDS", "has_genre", "COMEDY" ], [ "HEARTLANDS", "release_year", "2002" ], [ "HERO", "has_genre", "COMEDY" ], [ "HERO", "release_year", "2002" ], [ "HOLIDAY", "directed_by", "GEORGE CUKOR" ], [ "HOLIDAY", "has_genre", "COMEDY" ], [ "HOLIDAY", "has_tags", "COMEDY" ], [ "HOLIDAY", "has_tags", "GEORGE CUKOR" ], [ "HOLIDAY", "has_tags", "KATHARINE HEPBURN" ], [ "HOLIDAY", "starred_actors", "KATHARINE HEPBURN" ], [ "HOME ALONE 4", "has_genre", "COMEDY" ], [ "HOME ALONE 4", "release_year", "2002" ], [ "I SPY", "has_genre", "COMEDY" ], [ "I SPY", "has_tags", "COMEDY" ], [ "I SPY", "release_year", "2002" ], [ "I'M WITH LUCY", "has_genre", "COMEDY" ], [ "I'M WITH LUCY", "release_year", "2002" ], [ "ICE AGE", "has_genre", "COMEDY" ], [ "ICE AGE", "has_tags", "COMEDY" ], [ "ICE AGE", "release_year", "2002" ], [ "IGBY GOES DOWN", "has_genre", "COMEDY" ], [ "IGBY GOES DOWN", "release_year", "2002" ], [ "IT SHOULD HAPPEN TO YOU", "directed_by", "GEORGE CUKOR" ], [ "IT SHOULD HAPPEN TO YOU", "has_genre", "COMEDY" ], [ "IT SHOULD HAPPEN TO YOU", "has_tags", "GEORGE CUKOR" ], [ "IT SHOULD HAPPEN TO YOU", "written_by", "GARSON KANIN" ], [ "IT'S A MAD, MAD, MAD, MAD WORLD", "has_genre", "COMEDY" ], [ "IT'S A MAD, MAD, MAD, MAD WORLD", "has_tags", "COMEDY" ], [ "IT'S A MAD, MAD, MAD, MAD WORLD", "starred_actors", "SPENCER TRACY" ], [ "JUST A KISS", "has_genre", "COMEDY" ], [ "JUST A KISS", "release_year", "2002" ], [ "JUWANNA MANN", "has_genre", "COMEDY" ], [ "JUWANNA MANN", "release_year", "2002" ], [ "KISS THE BRIDE", "has_genre", "COMEDY" ], [ "KISS THE BRIDE", "release_year", "2002" ], [ "LE PLAISIR", "has_genre", "COMEDY" ], [ "LE PLAISIR", "release_year", "1952" ], [ "LES GIRLS", "directed_by", "GEORGE CUKOR" ], [ "LES GIRLS", "has_genre", "COMEDY" ], [ "LET'S MAKE LOVE", "directed_by", "GEORGE CUKOR" ], [ "LET'S MAKE LOVE", "has_genre", "COMEDY" ], [ "LIBELED LADY", "has_genre", "COMEDY" ], [ "LIBELED LADY", "starred_actors", "SPENCER TRACY" ], [ "LIFE OR SOMETHING LIKE IT", "has_genre", "COMEDY" ], [ "LIFE OR SOMETHING LIKE IT", "release_year", "2002" ], [ "LIFE WITHOUT DICK", "has_genre", "COMEDY" ], [ "LIFE WITHOUT DICK", "release_year", "2002" ], [ "LOVE LIZA", "has_genre", "COMEDY" ], [ "LOVE LIZA", "release_year", "2002" ], [ "MAID IN MANHATTAN", "has_genre", "COMEDY" ], [ "MAID IN MANHATTAN", "release_year", "2002" ], [ "MANNEQUIN", "has_genre", "COMEDY" ], [ "MANNEQUIN", "starred_actors", "SPENCER TRACY" ], [ "MEN WITH BROOMS", "has_genre", "COMEDY" ], [ "MEN WITH BROOMS", "release_year", "2002" ], [ "MIRANDA", "has_genre", "COMEDY" ], [ "MIRANDA", "release_year", "2002" ], [ "MONKEY BUSINESS", "has_genre", "COMEDY" ], [ "MONKEY BUSINESS", "has_tags", "COMEDY" ], [ "MONKEY BUSINESS", "release_year", "1952" ], [ "MORNING GLORY", "has_genre", "COMEDY" ], [ "MORNING GLORY", "starred_actors", "KATHARINE HEPBURN" ], [ "MR. DEEDS", "has_genre", "COMEDY" ], [ "MR. DEEDS", "release_year", "2002" ], [ "MY BIG FAT GREEK WEDDING", "has_genre", "COMEDY" ], [ "MY BIG FAT GREEK WEDDING", "has_tags", "COMEDY" ], [ "MY BIG FAT GREEK WEDDING", "release_year", "2002" ], [ "MY FAVORITE WIFE", "directed_by", "GARSON KANIN" ], [ "MY FAVORITE WIFE", "has_genre", "COMEDY" ], [ "MY LEFT EYE SEES GHOSTS", "has_genre", "COMEDY" ], [ "MY LEFT EYE SEES GHOSTS", "release_year", "2002" ], [ "MY MOTHER LIKES WOMEN", "has_genre", "COMEDY" ], [ "MY MOTHER LIKES WOMEN", "release_year", "2002" ], [ "NINE LIVES", "release_year", "2002" ], [ "NOVO", "has_genre", "COMEDY" ], [ "NOVO", "release_year", "2002" ], [ "NOW YOU KNOW", "has_genre", "COMEDY" ], [ "NOW YOU KNOW", "release_year", "2002" ], [ "OCCIDENT", "has_genre", "COMEDY" ], [ "OCCIDENT", "release_year", "2002" ], [ "ONCE UPON A TIME IN THE MIDLANDS", "has_genre", "COMEDY" ], [ "ONCE UPON A TIME IN THE MIDLANDS", "release_year", "2002" ], [ "ONE HOUR WITH YOU", "directed_by", "GEORGE CUKOR" ], [ "ONE HOUR WITH YOU", "has_genre", "COMEDY" ], [ "ORANGE COUNTY", "has_genre", "COMEDY" ], [ "ORANGE COUNTY", "release_year", "2002" ], [ "PASSIONADA", "has_genre", "COMEDY" ], [ "PASSIONADA", "release_year", "2002" ], [ "PAT AND MIKE", "directed_by", "GEORGE CUKOR" ], [ "PAT AND MIKE", "has_genre", "COMEDY" ], [ "PAT AND MIKE", "has_tags", "GEORGE CUKOR" ], [ "PAT AND MIKE", "release_year", "1952" ], [ "PAT AND MIKE", "starred_actors", "KATHARINE HEPBURN" ], [ "PAT AND MIKE", "starred_actors", "SPENCER TRACY" ], [ "PAT AND MIKE", "written_by", "GARSON KANIN" ], [ "PAT AND MIKE", "written_by", "RUTH GORDON" ], [ "PIPE DREAM", "has_genre", "COMEDY" ], [ "PIPE DREAM", "release_year", "2002" ], [ "PUMPKIN", "has_genre", "COMEDY" ], [ "PUMPKIN", "release_year", "2002" ], [ "PUNCH-DRUNK LOVE", "has_genre", "COMEDY" ], [ "PUNCH-DRUNK LOVE", "has_tags", "COMEDY" ], [ "PUNCH-DRUNK LOVE", "release_year", "2002" ], [ "R.S.V.P.", "has_genre", "COMEDY" ], [ "R.S.V.P.", "release_year", "2002" ], [ "ROAD TO BALI", "has_genre", "COMEDY" ], [ "ROAD TO BALI", "release_year", "1952" ], [ "ROGER DODGER", "has_genre", "COMEDY" ], [ "ROGER DODGER", "release_year", "2002" ], [ "ROOM FOR ONE MORE", "has_genre", "COMEDY" ], [ "ROOM FOR ONE MORE", "release_year", "1952" ], [ "SCOOBY-DOO", "has_genre", "COMEDY" ], [ "SCOOBY-DOO", "release_year", "2002" ], [ "SERVING SARA", "has_genre", "COMEDY" ], [ "SERVING SARA", "release_year", "2002" ], [ "SEX IS COMEDY", "has_genre", "COMEDY" ], [ "SEX IS COMEDY", "release_year", "2002" ], [ "SHOWTIME", "has_genre", "COMEDY" ], [ "SHOWTIME", "release_year", "2002" ], [ "SINGIN' IN THE RAIN", "has_genre", "COMEDY" ], [ "SINGIN' IN THE RAIN", "has_tags", "COMEDY" ], [ "SINGIN' IN THE RAIN", "release_year", "1952" ], [ "SLACKERS", "has_genre", "COMEDY" ], [ "SLACKERS", "release_year", "2002" ], [ "SNOW DOGS", "has_genre", "COMEDY" ], [ "SNOW DOGS", "release_year", "2002" ], [ "SON OF PALEFACE", "has_genre", "COMEDY" ], [ "SON OF PALEFACE", "release_year", "1952" ], [ "SORORITY BOYS", "has_genre", "COMEDY" ], [ "SORORITY BOYS", "release_year", "2002" ], [ "SPUN", "has_genre", "COMEDY" ], [ "SPUN", "release_year", "2002" ], [ "STEALING HARVARD", "has_genre", "COMEDY" ], [ "STEALING HARVARD", "release_year", "2002" ], [ "SUPER SUCKER", "has_genre", "COMEDY" ], [ "SUPER SUCKER", "release_year", "2002" ], [ "SWEET HOME ALABAMA", "has_genre", "COMEDY" ], [ "SWEET HOME ALABAMA", "release_year", "2002" ], [ "SWEPT AWAY", "has_genre", "COMEDY" ], [ "SWEPT AWAY", "release_year", "2002" ], [ "SYLVIA SCARLETT", "directed_by", "GEORGE CUKOR" ], [ "SYLVIA SCARLETT", "has_genre", "COMEDY" ], [ "SYLVIA SCARLETT", "starred_actors", "KATHARINE HEPBURN" ], [ "THE ACTRESS", "directed_by", "GEORGE CUKOR" ], [ "THE ACTRESS", "has_genre", "COMEDY" ], [ "THE ACTRESS", "starred_actors", "SPENCER TRACY" ], [ "THE ACTRESS", "written_by", "RUTH GORDON" ], [ "THE ADVENTURES OF PLUTO NASH", "has_genre", "COMEDY" ], [ "THE ADVENTURES OF PLUTO NASH", "release_year", "2002" ], [ "THE ANARCHIST COOKBOOK", "has_genre", "COMEDY" ], [ "THE ANARCHIST COOKBOOK", "release_year", "2002" ], [ "THE BANGER SISTERS", "has_genre", "COMEDY" ], [ "THE BANGER SISTERS", "release_year", "2002" ], [ "THE CRIMSON PIRATE", "has_genre", "COMEDY" ], [ "THE CRIMSON PIRATE", "release_year", "1952" ], [ "THE CUCKOO", "has_genre", "COMEDY" ], [ "THE CUCKOO", "release_year", "2002" ], [ "THE DANGEROUS LIVES OF ALTAR BOYS", "has_genre", "COMEDY" ], [ "THE DANGEROUS LIVES OF ALTAR BOYS", "release_year", "2002" ], [ "THE GOOD GIRL", "has_genre", "COMEDY" ], [ "THE GOOD GIRL", "release_year", "2002" ], [ "THE GURU", "has_genre", "COMEDY" ], [ "THE GURU", "release_year", "2002" ], [ "THE HOT CHICK", "has_genre", "COMEDY" ], [ "THE HOT CHICK", "has_tags", "COMEDY" ], [ "THE HOT CHICK", "release_year", "2002" ], [ "THE IMPORTANCE OF BEING EARNEST", "has_genre", "COMEDY" ], [ "THE IMPORTANCE OF BEING EARNEST", "release_year", "2002" ], [ "THE MAN WITHOUT A PAST", "has_genre", "COMEDY" ], [ "THE MAN WITHOUT A PAST", "release_year", "2002" ], [ "THE MASTER OF DISGUISE", "has_genre", "COMEDY" ], [ "THE MASTER OF DISGUISE", "release_year", "2002" ], [ "THE MERRY WIDOW", "has_genre", "COMEDY" ], [ "THE MERRY WIDOW", "release_year", "1952" ], [ "THE NEW GUY", "has_genre", "COMEDY" ], [ "THE NEW GUY", "release_year", "2002" ], [ "THE ONE AND ONLY", "has_genre", "COMEDY" ], [ "THE ONE AND ONLY", "release_year", "2002" ], [ "THE PHILADELPHIA STORY", "directed_by", "GEORGE CUKOR" ], [ "THE PHILADELPHIA STORY", "has_genre", "COMEDY" ], [ "THE PHILADELPHIA STORY", "has_tags", "GEORGE CUKOR" ], [ "THE PHILADELPHIA STORY", "has_tags", "KATHARINE HEPBURN" ], [ "THE PHILADELPHIA STORY", "starred_actors", "KATHARINE HEPBURN" ], [ "THE PRISONER OF ZENDA", "has_genre", "COMEDY" ], [ "THE PRISONER OF ZENDA", "release_year", "1952" ], [ "THE QUIET MAN", "has_genre", "COMEDY" ], [ "THE QUIET MAN", "release_year", "1952" ], [ "THE RULES OF ATTRACTION", "has_genre", "COMEDY" ], [ "THE RULES OF ATTRACTION", "release_year", "2002" ], [ "THE SWEETEST THING", "has_genre", "COMEDY" ], [ "THE SWEETEST THING", "has_tags", "COMEDY" ], [ "THE SWEETEST THING", "release_year", "2002" ], [ "THE TUXEDO", "has_genre", "COMEDY" ], [ "THE TUXEDO", "has_tags", "COMEDY" ], [ "THE TUXEDO", "release_year", "2002" ], [ "THE WHITE SHEIK", "has_genre", "COMEDY" ], [ "THE WHITE SHEIK", "release_year", "1952" ], [ "THE WOMEN", "directed_by", "GEORGE CUKOR" ], [ "THE WOMEN", "has_genre", "COMEDY" ], [ "THE WOMEN", "has_tags", "GEORGE CUKOR" ], [ "TRAVELS WITH MY AUNT", "directed_by", "GEORGE CUKOR" ], [ "TRAVELS WITH MY AUNT", "has_genre", "COMEDY" ], [ "TRIGGERMEN", "has_genre", "COMEDY" ], [ "TRIGGERMEN", "release_year", "2002" ], [ "TWO WEEKS NOTICE", "has_genre", "COMEDY" ], [ "TWO WEEKS NOTICE", "release_year", "2002" ], [ "UNCONDITIONAL LOVE", "has_genre", "COMEDY" ], [ "UNCONDITIONAL LOVE", "release_year", "2002" ], [ "UNDERCOVER BROTHER", "has_genre", "COMEDY" ], [ "UNDERCOVER BROTHER", "release_year", "2002" ], [ "UP THE RIVER", "has_genre", "COMEDY" ], [ "UP THE RIVER", "starred_actors", "SPENCER TRACY" ], [ "WAKING UP IN RENO", "has_genre", "COMEDY" ], [ "WAKING UP IN RENO", "release_year", "2002" ], [ "WE'RE NOT MARRIED!", "has_genre", "COMEDY" ], [ "WE'RE NOT MARRIED!", "release_year", "1952" ], [ "WELCOME TO COLLINWOOD", "has_genre", "COMEDY" ], [ "WELCOME TO COLLINWOOD", "has_tags", "COMEDY" ], [ "WELCOME TO COLLINWOOD", "release_year", "2002" ], [ "WHEN IN ROME", "has_genre", "COMEDY" ], [ "WHEN IN ROME", "release_year", "2002" ], [ "WHERE'S POPPA?", "has_genre", "COMEDY" ], [ "WHERE'S POPPA?", "has_tags", "RUTH GORDON" ], [ "WHERE'S POPPA?", "starred_actors", "RUTH GORDON" ], [ "WITHOUT LOVE", "has_genre", "COMEDY" ], [ "WITHOUT LOVE", "has_tags", "KATHARINE HEPBURN" ], [ "WITHOUT LOVE", "has_tags", "SPENCER TRACY" ], [ "WITHOUT LOVE", "starred_actors", "KATHARINE HEPBURN" ], [ "WITHOUT LOVE", "starred_actors", "SPENCER TRACY" ], [ "WOMAN OF THE YEAR", "has_genre", "COMEDY" ], [ "WOMAN OF THE YEAR", "starred_actors", "KATHARINE HEPBURN" ], [ "WOMAN OF THE YEAR", "starred_actors", "SPENCER TRACY" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 36163, 1967 17315, 2007 26123, 5 CENTIMETERS PER SECOND 14771, A CAT IN PARIS 10244, A COLT IS MY PASSPORT 13496, A SECRET 31672, A.K. 21128, ACTRICES 31322, ASTERIX THE GAUL 30795, ATLANTIC CITY 23893, BEACH RED 16426, BELLE DE JOUR 22690, BIG MAN JAPAN 23951, BIRDS 26908, BLACK MOON 33881, BOARDING GATE 12252, BRANDED TO KILL 38311, CHAOS 13627, CONVERSATIONS WITH MY GARDENER 14724, CRIME 38495, CRIME SPREE 26635, CROWS ZERO 18405, DAMAGE 22667, DAYS OF DARKNESS 24410, DEMONLOVER 962, DORORO 8333, ELEVATOR TO THE GALLOWS 6012, FRENCH 37699, FRENCH CONNECTION II 1677, GENEVIÈVE BUJOLD 28864, GLORY TO THE FILMMAKER! 3553, GODZILLA 31911, HEARTBEAT DETECTOR 9613, HUNTING AND GATHERING 24208, INSIDE 36874, JAPANESE 24305, JCVD 2685, JUDEX 14772, KING KONG ESCAPES 22352, LA VIE EN ROSE 24812, LACOMBE, LUCIEN 23661, LE SAMOURAÏ 14433, LOUIS MALLE 9792, LOVE CRIME 11735, LOVE SONGS 22804, LUST, CAUTION 10560, MAN BITES DOG 29660, MAY FOOLS 11170, MOLIÈRE 12100, MOUCHETTE 7745, MURMUR OF THE HEART 11056, PAULETTE 16348, PERSEPOLIS 5311, PLAYTIME 21835, POINT BLANK 29620, POLICE 9019, RATATOUILLE 15938, RIFIFI 18478, RUSH HOUR 3 25217, SAMURAI REBELLION 39915, SHALL WE KISS? 15772, SILK 10280, SON OF GODZILLA 17447, SON OF RAMBOW 8436, SPIRITS OF THE DEAD 36983, SUMMER DAYS WITH COO 35926, SÉRIE NOIRE 24351, TAXI 4 14557, THE ADVENTURES OF ARSÈNE LUPIN 21712, THE CLASS 26236, THE CRIME OF MONSIEUR LANGE 21280, THE DUCHESS OF LANGEAIS 38918, THE FAMILY 28928, THE FIRE WITHIN 29103, THE LAST MISTRESS 9799, THE LOST SON 17428, THE LOVERS 21696, THE MAN FROM LONDON 21821, THE MOURNING FOREST 27909, THE SECRET OF THE GRAIN 26678, THE SWINDLE 11654, THE THIEF OF PARIS 30481, THE TWO OF US 31589, THE WAR IS OVER 5537, THE WITNESSES 2363, THE X FROM OUTER SPACE 34585, THE YOUNG GIRLS OF ROCHEFORT 19606, TWO MEN IN MANHATTAN 19779, UNDER THE BOMBS 9811, VEXILLE 11659, VIVA MARIA! 29077, WASABI 15557, WATER LILIES 33483, WEEKEND 19257, WINGED MIGRATION 31658, YOU ONLY LIVE TWICE 18197, ZATOICHI THE OUTLAW src, edge_attr, dst 26123, has_tags, 36874 26123, in_language, 36874 26123, release_year, 17315 14771, has_genre, 14724 14771, in_language, 6012 10244, in_language, 36874 10244, release_year, 36163 13496, in_language, 6012 13496, release_year, 17315 31672, in_language, 6012 31672, in_language, 36874 21128, in_language, 6012 21128, release_year, 17315 31322, in_language, 6012 31322, release_year, 36163 30795, directed_by, 14433 30795, has_genre, 14724 30795, has_tags, 14433 30795, in_language, 6012 23893, in_language, 36874 23893, release_year, 36163 16426, in_language, 6012 16426, release_year, 36163 22690, in_language, 36874 22690, release_year, 17315 26908, directed_by, 14433 26908, has_tags, 14433 26908, in_language, 6012 26908, written_by, 14433 33881, in_language, 6012 33881, release_year, 17315 12252, in_language, 36874 12252, release_year, 36163 38311, in_language, 6012 38311, in_language, 36874 13627, in_language, 6012 13627, release_year, 17315 38495, has_genre, 14724 38495, in_language, 6012 26635, in_language, 36874 26635, release_year, 17315 18405, directed_by, 14433 18405, has_tags, 14433 18405, in_language, 6012 22667, in_language, 6012 22667, release_year, 17315 24410, in_language, 6012 24410, in_language, 36874 962, in_language, 36874 962, release_year, 17315 8333, directed_by, 14433 8333, has_genre, 14724 8333, has_tags, 14433 8333, in_language, 6012 8333, written_by, 14433 37699, has_genre, 14724 37699, in_language, 6012 28864, in_language, 36874 28864, release_year, 17315 3553, in_language, 6012 3553, in_language, 36874 31911, in_language, 6012 31911, release_year, 17315 9613, in_language, 6012 9613, release_year, 17315 24208, has_tags, 6012 24208, in_language, 6012 24208, release_year, 17315 24305, has_genre, 14724 24305, in_language, 6012 2685, has_genre, 14724 2685, in_language, 6012 14772, has_tags, 36874 14772, in_language, 36874 14772, release_year, 36163 22352, has_tags, 6012 22352, in_language, 6012 22352, release_year, 17315 24812, directed_by, 14433 24812, has_tags, 14433 24812, in_language, 6012 24812, written_by, 14433 23661, has_genre, 14724 23661, has_tags, 14724 23661, in_language, 6012 23661, release_year, 36163 9792, has_genre, 14724 9792, in_language, 6012 11735, in_language, 6012 11735, release_year, 17315 22804, in_language, 36874 22804, release_year, 17315 10560, has_genre, 14724 10560, in_language, 6012 29660, directed_by, 14433 29660, has_tags, 14433 29660, in_language, 6012 29660, written_by, 14433 11170, in_language, 6012 11170, release_year, 17315 12100, in_language, 6012 12100, release_year, 36163 7745, directed_by, 14433 7745, has_tags, 14433 7745, in_language, 6012 7745, written_by, 14433 11056, has_genre, 14724 11056, in_language, 6012 16348, has_tags, 6012 16348, in_language, 6012 16348, release_year, 17315 5311, in_language, 6012 5311, release_year, 36163 21835, has_genre, 14724 21835, in_language, 6012 21835, release_year, 36163 29620, has_genre, 14724 29620, in_language, 6012 9019, in_language, 6012 9019, release_year, 17315 15938, has_genre, 14724 15938, has_tags, 14724 15938, in_language, 6012 18478, in_language, 6012 18478, release_year, 17315 25217, in_language, 36874 25217, release_year, 36163 39915, in_language, 6012 39915, release_year, 17315 15772, in_language, 36874 15772, release_year, 17315 10280, in_language, 36874 10280, release_year, 36163 17447, in_language, 6012 17447, release_year, 17315 8436, directed_by, 14433 8436, in_language, 6012 8436, written_by, 14433 36983, in_language, 36874 36983, release_year, 17315 35926, has_genre, 14724 35926, in_language, 6012 24351, in_language, 6012 24351, release_year, 17315 14557, has_genre, 14724 14557, in_language, 6012 21712, has_tags, 6012 21712, in_language, 6012 21712, release_year, 17315 26236, has_genre, 14724 26236, in_language, 6012 21280, in_language, 6012 21280, release_year, 17315 38918, has_genre, 14724 38918, in_language, 6012 28928, directed_by, 14433 28928, has_tags, 14433 28928, in_language, 6012 29103, in_language, 6012 29103, release_year, 17315 9799, has_genre, 14724 9799, in_language, 6012 17428, directed_by, 14433 17428, has_tags, 14433 17428, in_language, 6012 21696, has_genre, 14724 21696, in_language, 6012 21696, release_year, 17315 21821, in_language, 36874 21821, release_year, 17315 27909, in_language, 6012 27909, release_year, 17315 26678, has_genre, 14724 26678, in_language, 6012 11654, directed_by, 14433 11654, has_genre, 14724 11654, in_language, 6012 11654, release_year, 36163 11654, starred_actors, 1677 11654, written_by, 14433 30481, in_language, 6012 30481, release_year, 36163 31589, in_language, 6012 31589, starred_actors, 1677 5537, in_language, 6012 5537, release_year, 17315 2363, in_language, 36874 2363, release_year, 36163 34585, in_language, 6012 34585, release_year, 36163 19606, has_genre, 14724 19606, in_language, 6012 19779, in_language, 6012 19779, release_year, 17315 9811, in_language, 36874 9811, release_year, 17315 11659, directed_by, 14433 11659, has_tags, 6012 11659, has_tags, 14433 11659, in_language, 6012 11659, written_by, 14433 29077, in_language, 6012 29077, in_language, 36874 15557, in_language, 6012 15557, release_year, 17315 33483, has_tags, 6012 33483, in_language, 6012 33483, release_year, 36163 19257, has_tags, 23951 19257, in_language, 6012 31658, in_language, 36874 31658, release_year, 36163 18197, in_language, 36874 18197, release_year, 36163 Question: For what reason are BIRDS, DORORO, and THE THIEF OF PARIS associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BIRDS", "DORORO", "THE THIEF OF PARIS" ], "valid_edges": [ [ "5 CENTIMETERS PER SECOND", "has_tags", "JAPANESE" ], [ "5 CENTIMETERS PER SECOND", "in_language", "JAPANESE" ], [ "5 CENTIMETERS PER SECOND", "release_year", "2007" ], [ "A CAT IN PARIS", "has_genre", "CRIME" ], [ "A CAT IN PARIS", "in_language", "FRENCH" ], [ "A COLT IS MY PASSPORT", "in_language", "JAPANESE" ], [ "A COLT IS MY PASSPORT", "release_year", "1967" ], [ "A SECRET", "in_language", "FRENCH" ], [ "A SECRET", "release_year", "2007" ], [ "A.K.", "in_language", "FRENCH" ], [ "A.K.", "in_language", "JAPANESE" ], [ "ACTRICES", "in_language", "FRENCH" ], [ "ACTRICES", "release_year", "2007" ], [ "ASTERIX THE GAUL", "in_language", "FRENCH" ], [ "ASTERIX THE GAUL", "release_year", "1967" ], [ "ATLANTIC CITY", "directed_by", "LOUIS MALLE" ], [ "ATLANTIC CITY", "has_genre", "CRIME" ], [ "ATLANTIC CITY", "has_tags", "LOUIS MALLE" ], [ "ATLANTIC CITY", "in_language", "FRENCH" ], [ "BEACH RED", "in_language", "JAPANESE" ], [ "BEACH RED", "release_year", "1967" ], [ "BELLE DE JOUR", "in_language", "FRENCH" ], [ "BELLE DE JOUR", "release_year", "1967" ], [ "BIG MAN JAPAN", "in_language", "JAPANESE" ], [ "BIG MAN JAPAN", "release_year", "2007" ], [ "BLACK MOON", "directed_by", "LOUIS MALLE" ], [ "BLACK MOON", "has_tags", "LOUIS MALLE" ], [ "BLACK MOON", "in_language", "FRENCH" ], [ "BLACK MOON", "written_by", "LOUIS MALLE" ], [ "BOARDING GATE", "in_language", "FRENCH" ], [ "BOARDING GATE", "release_year", "2007" ], [ "BRANDED TO KILL", "in_language", "JAPANESE" ], [ "BRANDED TO KILL", "release_year", "1967" ], [ "CHAOS", "in_language", "FRENCH" ], [ "CHAOS", "in_language", "JAPANESE" ], [ "CONVERSATIONS WITH MY GARDENER", "in_language", "FRENCH" ], [ "CONVERSATIONS WITH MY GARDENER", "release_year", "2007" ], [ "CRIME SPREE", "has_genre", "CRIME" ], [ "CRIME SPREE", "in_language", "FRENCH" ], [ "CROWS ZERO", "in_language", "JAPANESE" ], [ "CROWS ZERO", "release_year", "2007" ], [ "DAMAGE", "directed_by", "LOUIS MALLE" ], [ "DAMAGE", "has_tags", "LOUIS MALLE" ], [ "DAMAGE", "in_language", "FRENCH" ], [ "DAYS OF DARKNESS", "in_language", "FRENCH" ], [ "DAYS OF DARKNESS", "release_year", "2007" ], [ "DEMONLOVER", "in_language", "FRENCH" ], [ "DEMONLOVER", "in_language", "JAPANESE" ], [ "DORORO", "in_language", "JAPANESE" ], [ "DORORO", "release_year", "2007" ], [ "ELEVATOR TO THE GALLOWS", "directed_by", "LOUIS MALLE" ], [ "ELEVATOR TO THE GALLOWS", "has_genre", "CRIME" ], [ "ELEVATOR TO THE GALLOWS", "has_tags", "LOUIS MALLE" ], [ "ELEVATOR TO THE GALLOWS", "in_language", "FRENCH" ], [ "ELEVATOR TO THE GALLOWS", "written_by", "LOUIS MALLE" ], [ "FRENCH CONNECTION II", "has_genre", "CRIME" ], [ "FRENCH CONNECTION II", "in_language", "FRENCH" ], [ "GLORY TO THE FILMMAKER!", "in_language", "JAPANESE" ], [ "GLORY TO THE FILMMAKER!", "release_year", "2007" ], [ "GODZILLA", "in_language", "FRENCH" ], [ "GODZILLA", "in_language", "JAPANESE" ], [ "HEARTBEAT DETECTOR", "in_language", "FRENCH" ], [ "HEARTBEAT DETECTOR", "release_year", "2007" ], [ "HUNTING AND GATHERING", "in_language", "FRENCH" ], [ "HUNTING AND GATHERING", "release_year", "2007" ], [ "INSIDE", "has_tags", "FRENCH" ], [ "INSIDE", "in_language", "FRENCH" ], [ "INSIDE", "release_year", "2007" ], [ "JCVD", "has_genre", "CRIME" ], [ "JCVD", "in_language", "FRENCH" ], [ "JUDEX", "has_genre", "CRIME" ], [ "JUDEX", "in_language", "FRENCH" ], [ "KING KONG ESCAPES", "has_tags", "JAPANESE" ], [ "KING KONG ESCAPES", "in_language", "JAPANESE" ], [ "KING KONG ESCAPES", "release_year", "1967" ], [ "LA VIE EN ROSE", "has_tags", "FRENCH" ], [ "LA VIE EN ROSE", "in_language", "FRENCH" ], [ "LA VIE EN ROSE", "release_year", "2007" ], [ "LACOMBE, LUCIEN", "directed_by", "LOUIS MALLE" ], [ "LACOMBE, LUCIEN", "has_tags", "LOUIS MALLE" ], [ "LACOMBE, LUCIEN", "in_language", "FRENCH" ], [ "LACOMBE, LUCIEN", "written_by", "LOUIS MALLE" ], [ "LE SAMOURAÏ", "has_genre", "CRIME" ], [ "LE SAMOURAÏ", "has_tags", "CRIME" ], [ "LE SAMOURAÏ", "in_language", "FRENCH" ], [ "LE SAMOURAÏ", "release_year", "1967" ], [ "LOVE CRIME", "has_genre", "CRIME" ], [ "LOVE CRIME", "in_language", "FRENCH" ], [ "LOVE SONGS", "in_language", "FRENCH" ], [ "LOVE SONGS", "release_year", "2007" ], [ "LUST, CAUTION", "in_language", "JAPANESE" ], [ "LUST, CAUTION", "release_year", "2007" ], [ "MAN BITES DOG", "has_genre", "CRIME" ], [ "MAN BITES DOG", "in_language", "FRENCH" ], [ "MAY FOOLS", "directed_by", "LOUIS MALLE" ], [ "MAY FOOLS", "has_tags", "LOUIS MALLE" ], [ "MAY FOOLS", "in_language", "FRENCH" ], [ "MAY FOOLS", "written_by", "LOUIS MALLE" ], [ "MOLIÈRE", "in_language", "FRENCH" ], [ "MOLIÈRE", "release_year", "2007" ], [ "MOUCHETTE", "in_language", "FRENCH" ], [ "MOUCHETTE", "release_year", "1967" ], [ "MURMUR OF THE HEART", "directed_by", "LOUIS MALLE" ], [ "MURMUR OF THE HEART", "has_tags", "LOUIS MALLE" ], [ "MURMUR OF THE HEART", "in_language", "FRENCH" ], [ "MURMUR OF THE HEART", "written_by", "LOUIS MALLE" ], [ "PAULETTE", "has_genre", "CRIME" ], [ "PAULETTE", "in_language", "FRENCH" ], [ "PERSEPOLIS", "has_tags", "FRENCH" ], [ "PERSEPOLIS", "in_language", "FRENCH" ], [ "PERSEPOLIS", "release_year", "2007" ], [ "PLAYTIME", "in_language", "FRENCH" ], [ "PLAYTIME", "release_year", "1967" ], [ "POINT BLANK", "has_genre", "CRIME" ], [ "POINT BLANK", "in_language", "FRENCH" ], [ "POINT BLANK", "release_year", "1967" ], [ "POLICE", "has_genre", "CRIME" ], [ "POLICE", "in_language", "FRENCH" ], [ "RATATOUILLE", "in_language", "FRENCH" ], [ "RATATOUILLE", "release_year", "2007" ], [ "RIFIFI", "has_genre", "CRIME" ], [ "RIFIFI", "has_tags", "CRIME" ], [ "RIFIFI", "in_language", "FRENCH" ], [ "RUSH HOUR 3", "in_language", "FRENCH" ], [ "RUSH HOUR 3", "release_year", "2007" ], [ "SAMURAI REBELLION", "in_language", "JAPANESE" ], [ "SAMURAI REBELLION", "release_year", "1967" ], [ "SHALL WE KISS?", "in_language", "FRENCH" ], [ "SHALL WE KISS?", "release_year", "2007" ], [ "SILK", "in_language", "JAPANESE" ], [ "SILK", "release_year", "2007" ], [ "SON OF GODZILLA", "in_language", "JAPANESE" ], [ "SON OF GODZILLA", "release_year", "1967" ], [ "SON OF RAMBOW", "in_language", "FRENCH" ], [ "SON OF RAMBOW", "release_year", "2007" ], [ "SPIRITS OF THE DEAD", "directed_by", "LOUIS MALLE" ], [ "SPIRITS OF THE DEAD", "in_language", "FRENCH" ], [ "SPIRITS OF THE DEAD", "written_by", "LOUIS MALLE" ], [ "SUMMER DAYS WITH COO", "in_language", "JAPANESE" ], [ "SUMMER DAYS WITH COO", "release_year", "2007" ], [ "SÉRIE NOIRE", "has_genre", "CRIME" ], [ "SÉRIE NOIRE", "in_language", "FRENCH" ], [ "TAXI 4", "in_language", "FRENCH" ], [ "TAXI 4", "release_year", "2007" ], [ "THE ADVENTURES OF ARSÈNE LUPIN", "has_genre", "CRIME" ], [ "THE ADVENTURES OF ARSÈNE LUPIN", "in_language", "FRENCH" ], [ "THE CLASS", "has_tags", "FRENCH" ], [ "THE CLASS", "in_language", "FRENCH" ], [ "THE CLASS", "release_year", "2007" ], [ "THE CRIME OF MONSIEUR LANGE", "has_genre", "CRIME" ], [ "THE CRIME OF MONSIEUR LANGE", "in_language", "FRENCH" ], [ "THE DUCHESS OF LANGEAIS", "in_language", "FRENCH" ], [ "THE DUCHESS OF LANGEAIS", "release_year", "2007" ], [ "THE FAMILY", "has_genre", "CRIME" ], [ "THE FAMILY", "in_language", "FRENCH" ], [ "THE FIRE WITHIN", "directed_by", "LOUIS MALLE" ], [ "THE FIRE WITHIN", "has_tags", "LOUIS MALLE" ], [ "THE FIRE WITHIN", "in_language", "FRENCH" ], [ "THE LAST MISTRESS", "in_language", "FRENCH" ], [ "THE LAST MISTRESS", "release_year", "2007" ], [ "THE LOST SON", "has_genre", "CRIME" ], [ "THE LOST SON", "in_language", "FRENCH" ], [ "THE LOVERS", "directed_by", "LOUIS MALLE" ], [ "THE LOVERS", "has_tags", "LOUIS MALLE" ], [ "THE LOVERS", "in_language", "FRENCH" ], [ "THE MAN FROM LONDON", "has_genre", "CRIME" ], [ "THE MAN FROM LONDON", "in_language", "FRENCH" ], [ "THE MAN FROM LONDON", "release_year", "2007" ], [ "THE MOURNING FOREST", "in_language", "JAPANESE" ], [ "THE MOURNING FOREST", "release_year", "2007" ], [ "THE SECRET OF THE GRAIN", "in_language", "FRENCH" ], [ "THE SECRET OF THE GRAIN", "release_year", "2007" ], [ "THE SWINDLE", "has_genre", "CRIME" ], [ "THE SWINDLE", "in_language", "FRENCH" ], [ "THE THIEF OF PARIS", "directed_by", "LOUIS MALLE" ], [ "THE THIEF OF PARIS", "has_genre", "CRIME" ], [ "THE THIEF OF PARIS", "in_language", "FRENCH" ], [ "THE THIEF OF PARIS", "release_year", "1967" ], [ "THE THIEF OF PARIS", "starred_actors", "GENEVIÈVE BUJOLD" ], [ "THE THIEF OF PARIS", "written_by", "LOUIS MALLE" ], [ "THE TWO OF US", "in_language", "FRENCH" ], [ "THE TWO OF US", "release_year", "1967" ], [ "THE WAR IS OVER", "in_language", "FRENCH" ], [ "THE WAR IS OVER", "starred_actors", "GENEVIÈVE BUJOLD" ], [ "THE WITNESSES", "in_language", "FRENCH" ], [ "THE WITNESSES", "release_year", "2007" ], [ "THE X FROM OUTER SPACE", "in_language", "JAPANESE" ], [ "THE X FROM OUTER SPACE", "release_year", "1967" ], [ "THE YOUNG GIRLS OF ROCHEFORT", "in_language", "FRENCH" ], [ "THE YOUNG GIRLS OF ROCHEFORT", "release_year", "1967" ], [ "TWO MEN IN MANHATTAN", "has_genre", "CRIME" ], [ "TWO MEN IN MANHATTAN", "in_language", "FRENCH" ], [ "UNDER THE BOMBS", "in_language", "FRENCH" ], [ "UNDER THE BOMBS", "release_year", "2007" ], [ "VEXILLE", "in_language", "JAPANESE" ], [ "VEXILLE", "release_year", "2007" ], [ "VIVA MARIA!", "directed_by", "LOUIS MALLE" ], [ "VIVA MARIA!", "has_tags", "FRENCH" ], [ "VIVA MARIA!", "has_tags", "LOUIS MALLE" ], [ "VIVA MARIA!", "in_language", "FRENCH" ], [ "VIVA MARIA!", "written_by", "LOUIS MALLE" ], [ "WASABI", "in_language", "FRENCH" ], [ "WASABI", "in_language", "JAPANESE" ], [ "WATER LILIES", "in_language", "FRENCH" ], [ "WATER LILIES", "release_year", "2007" ], [ "WEEKEND", "has_tags", "FRENCH" ], [ "WEEKEND", "in_language", "FRENCH" ], [ "WEEKEND", "release_year", "1967" ], [ "WINGED MIGRATION", "has_tags", "BIRDS" ], [ "WINGED MIGRATION", "in_language", "FRENCH" ], [ "YOU ONLY LIVE TWICE", "in_language", "JAPANESE" ], [ "YOU ONLY LIVE TWICE", "release_year", "1967" ], [ "ZATOICHI THE OUTLAW", "in_language", "JAPANESE" ], [ "ZATOICHI THE OUTLAW", "release_year", "1967" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 31196, 1974 31365, COCKFIGHTER 4119, DYLAN MCDERMOTT 35711, HARRY DEAN STANTON 7083, SONNY 15352, THE QUESTOR TAPES 30585, TWISTER 16336, WHERE SLEEPING DOGS LIE src, edge_attr, dst 31365, release_year, 31196 31365, starred_actors, 35711 7083, starred_actors, 35711 15352, release_year, 31196 30585, starred_actors, 4119 30585, starred_actors, 35711 16336, starred_actors, 4119 Question: How are SONNY, THE QUESTOR TAPES, and WHERE SLEEPING DOGS LIE related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "SONNY", "THE QUESTOR TAPES", "WHERE SLEEPING DOGS LIE" ], "valid_edges": [ [ "COCKFIGHTER", "release_year", "1974" ], [ "COCKFIGHTER", "starred_actors", "HARRY DEAN STANTON" ], [ "SONNY", "starred_actors", "HARRY DEAN STANTON" ], [ "THE QUESTOR TAPES", "release_year", "1974" ], [ "TWISTER", "starred_actors", "DYLAN MCDERMOTT" ], [ "TWISTER", "starred_actors", "HARRY DEAN STANTON" ], [ "WHERE SLEEPING DOGS LIE", "starred_actors", "DYLAN MCDERMOTT" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 30407, ALIEN 31076, CHARLOTTE STEWART 38780, DETENTION 9611, ERASERHEAD 35711, HARRY DEAN STANTON 5870, HORROR 37497, NATIONAL FILM REGISTRY 34822, SHANLEY CASWELL src, edge_attr, dst 30407, has_genre, 5870 30407, has_tags, 30407 30407, has_tags, 5870 30407, has_tags, 37497 30407, starred_actors, 35711 38780, has_genre, 5870 38780, starred_actors, 34822 9611, has_genre, 5870 9611, has_tags, 37497 9611, starred_actors, 31076 Question: How are CHARLOTTE STEWART, HARRY DEAN STANTON, and SHANLEY CASWELL related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "CHARLOTTE STEWART", "HARRY DEAN STANTON", "SHANLEY CASWELL" ], "valid_edges": [ [ "ALIEN", "has_genre", "HORROR" ], [ "ALIEN", "has_tags", "ALIEN" ], [ "ALIEN", "has_tags", "HORROR" ], [ "ALIEN", "has_tags", "NATIONAL FILM REGISTRY" ], [ "ALIEN", "starred_actors", "HARRY DEAN STANTON" ], [ "DETENTION", "has_genre", "HORROR" ], [ "DETENTION", "starred_actors", "SHANLEY CASWELL" ], [ "ERASERHEAD", "has_genre", "HORROR" ], [ "ERASERHEAD", "has_tags", "NATIONAL FILM REGISTRY" ], [ "ERASERHEAD", "starred_actors", "CHARLOTTE STEWART" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35798, 2010 14531, A HORRIBLE WAY TO DIE 24480, A NIGHTMARE ON ELM STREET 25532, ALICE IN MURDERLAND 23960, ATROCIOUS 19826, BLACK DEATH 31909, BLACK SWAN 30352, BURNING BRIGHT 30430, CHERRY TREE LANE 20871, DEVIL 13272, DON'T BE AFRAID OF THE DARK 26523, EXORCISMUS 29544, FORGET ME NOT 3553, GODZILLA 3120, GODZILLA VS. DESTOROYAH 25799, GODZILLA VS. KING GHIDORAH 20909, GODZILLA VS. MECHAGODZILLA II 5451, GROWTH 5870, HORROR 31812, I AM LEGEND 15439, I SPIT ON YOUR GRAVE 16313, INSIDIOUS 36874, JAPANESE 19452, JULIA'S EYES 28584, KAIJU 29987, LET ME IN 29511, MEGA PIRANHA 1943, MEGUMI ODAKA 15489, MIRRORS 2 39644, MUTANTS 2831, MY SOUL TO TAKE 38244, NINE DEAD 39250, PARANORMAL ACTIVITY 2 21830, PIRANHA 3D 36052, PROWL 33753, PSYCHOSIS 11677, RAMMBOCK 6924, ROAD KILL 1722, SHARKTOPUS 25057, STREETDANCE 3D 10173, TAKAO OKAWARA 25463, THE CRAZIES 16105, THE DEAD 14270, THE FINAL 18609, THE LAST EXORCISM 1250, THE LAST MAN ON EARTH 28305, THE MAZE 12531, THE PRESENCE 22440, THE REEF 16147, THE SILENT HOUSE 12533, THE TORTURED 497, THE TRAVELER 8494, THE VIOLENT KIND 35250, THE WARD 23343, THE WOLFMAN 20520, TOHO 15490, WE ARE THE NIGHT 18049, WE ARE WHAT WE ARE src, edge_attr, dst 14531, has_genre, 5870 14531, release_year, 35798 24480, has_genre, 5870 24480, has_tags, 5870 24480, release_year, 35798 25532, has_genre, 5870 25532, release_year, 35798 23960, has_genre, 5870 23960, release_year, 35798 19826, has_genre, 5870 19826, release_year, 35798 31909, has_tags, 5870 31909, release_year, 35798 30352, has_genre, 5870 30352, release_year, 35798 30430, has_genre, 5870 30430, release_year, 35798 20871, has_genre, 5870 20871, has_tags, 5870 20871, release_year, 35798 13272, has_genre, 5870 13272, release_year, 35798 26523, has_genre, 5870 26523, release_year, 35798 29544, has_genre, 5870 29544, release_year, 35798 3553, has_genre, 5870 3120, directed_by, 10173 3120, has_tags, 3553 3120, has_tags, 28584 3120, has_tags, 20520 3120, in_language, 36874 3120, starred_actors, 1943 25799, has_tags, 3553 25799, has_tags, 28584 25799, has_tags, 20520 25799, in_language, 36874 25799, starred_actors, 1943 20909, directed_by, 10173 20909, has_tags, 3553 20909, has_tags, 28584 20909, has_tags, 20520 20909, in_language, 36874 20909, starred_actors, 1943 5451, has_genre, 5870 5451, release_year, 35798 31812, has_tags, 5870 31812, has_tags, 31812 31812, has_tags, 39644 15439, has_genre, 5870 15439, release_year, 35798 16313, has_genre, 5870 16313, has_tags, 5870 16313, release_year, 35798 19452, has_genre, 5870 19452, release_year, 35798 29987, has_genre, 5870 29987, has_tags, 5870 29987, release_year, 35798 29511, has_genre, 5870 29511, release_year, 35798 15489, has_genre, 5870 15489, release_year, 35798 39644, has_genre, 5870 2831, has_genre, 5870 2831, release_year, 35798 38244, has_genre, 5870 38244, release_year, 35798 39250, has_genre, 5870 39250, release_year, 35798 21830, has_genre, 5870 21830, release_year, 35798 36052, has_genre, 5870 36052, release_year, 35798 33753, has_genre, 5870 33753, release_year, 35798 11677, has_genre, 5870 11677, release_year, 35798 6924, has_genre, 5870 6924, has_tags, 5870 6924, release_year, 35798 1722, has_genre, 5870 1722, release_year, 35798 25057, release_year, 35798 25463, has_genre, 5870 25463, release_year, 35798 16105, has_genre, 5870 16105, release_year, 35798 14270, has_genre, 5870 14270, release_year, 35798 18609, has_genre, 5870 18609, release_year, 35798 1250, has_genre, 5870 1250, has_tags, 31812 28305, has_genre, 5870 28305, release_year, 35798 12531, has_genre, 5870 12531, release_year, 35798 22440, has_genre, 5870 22440, release_year, 35798 16147, has_genre, 5870 16147, release_year, 35798 12533, has_genre, 5870 12533, release_year, 35798 497, has_genre, 5870 497, release_year, 35798 8494, has_genre, 5870 8494, release_year, 35798 35250, has_genre, 5870 35250, release_year, 35798 23343, has_genre, 5870 23343, release_year, 35798 15490, has_genre, 5870 15490, release_year, 35798 18049, has_genre, 5870 18049, has_tags, 5870 18049, release_year, 35798 Question: In what context are MEGUMI ODAKA, STREETDANCE 3D, and THE LAST MAN ON EARTH connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "MEGUMI ODAKA", "STREETDANCE 3D", "THE LAST MAN ON EARTH" ], "valid_edges": [ [ "A HORRIBLE WAY TO DIE", "has_genre", "HORROR" ], [ "A HORRIBLE WAY TO DIE", "release_year", "2010" ], [ "A NIGHTMARE ON ELM STREET", "has_genre", "HORROR" ], [ "A NIGHTMARE ON ELM STREET", "has_tags", "HORROR" ], [ "A NIGHTMARE ON ELM STREET", "release_year", "2010" ], [ "ALICE IN MURDERLAND", "has_genre", "HORROR" ], [ "ALICE IN MURDERLAND", "release_year", "2010" ], [ "ATROCIOUS", "has_genre", "HORROR" ], [ "ATROCIOUS", "release_year", "2010" ], [ "BLACK DEATH", "has_genre", "HORROR" ], [ "BLACK DEATH", "release_year", "2010" ], [ "BLACK SWAN", "has_tags", "HORROR" ], [ "BLACK SWAN", "release_year", "2010" ], [ "BURNING BRIGHT", "has_genre", "HORROR" ], [ "BURNING BRIGHT", "release_year", "2010" ], [ "CHERRY TREE LANE", "has_genre", "HORROR" ], [ "CHERRY TREE LANE", "release_year", "2010" ], [ "DEVIL", "has_genre", "HORROR" ], [ "DEVIL", "has_tags", "HORROR" ], [ "DEVIL", "release_year", "2010" ], [ "DON'T BE AFRAID OF THE DARK", "has_genre", "HORROR" ], [ "DON'T BE AFRAID OF THE DARK", "release_year", "2010" ], [ "EXORCISMUS", "has_genre", "HORROR" ], [ "EXORCISMUS", "release_year", "2010" ], [ "FORGET ME NOT", "has_genre", "HORROR" ], [ "FORGET ME NOT", "release_year", "2010" ], [ "GODZILLA", "has_genre", "HORROR" ], [ "GODZILLA VS. DESTOROYAH", "directed_by", "TAKAO OKAWARA" ], [ "GODZILLA VS. DESTOROYAH", "has_tags", "GODZILLA" ], [ "GODZILLA VS. DESTOROYAH", "has_tags", "KAIJU" ], [ "GODZILLA VS. DESTOROYAH", "has_tags", "TOHO" ], [ "GODZILLA VS. DESTOROYAH", "in_language", "JAPANESE" ], [ "GODZILLA VS. DESTOROYAH", "starred_actors", "MEGUMI ODAKA" ], [ "GODZILLA VS. KING GHIDORAH", "has_tags", "GODZILLA" ], [ "GODZILLA VS. KING GHIDORAH", "has_tags", "KAIJU" ], [ "GODZILLA VS. KING GHIDORAH", "has_tags", "TOHO" ], [ "GODZILLA VS. KING GHIDORAH", "in_language", "JAPANESE" ], [ "GODZILLA VS. KING GHIDORAH", "starred_actors", "MEGUMI ODAKA" ], [ "GODZILLA VS. MECHAGODZILLA II", "directed_by", "TAKAO OKAWARA" ], [ "GODZILLA VS. MECHAGODZILLA II", "has_tags", "GODZILLA" ], [ "GODZILLA VS. MECHAGODZILLA II", "has_tags", "KAIJU" ], [ "GODZILLA VS. MECHAGODZILLA II", "has_tags", "TOHO" ], [ "GODZILLA VS. MECHAGODZILLA II", "in_language", "JAPANESE" ], [ "GODZILLA VS. MECHAGODZILLA II", "starred_actors", "MEGUMI ODAKA" ], [ "GROWTH", "has_genre", "HORROR" ], [ "GROWTH", "release_year", "2010" ], [ "I AM LEGEND", "has_tags", "HORROR" ], [ "I AM LEGEND", "has_tags", "I AM LEGEND" ], [ "I AM LEGEND", "has_tags", "MUTANTS" ], [ "I SPIT ON YOUR GRAVE", "has_genre", "HORROR" ], [ "I SPIT ON YOUR GRAVE", "release_year", "2010" ], [ "INSIDIOUS", "has_genre", "HORROR" ], [ "INSIDIOUS", "has_tags", "HORROR" ], [ "INSIDIOUS", "release_year", "2010" ], [ "JULIA'S EYES", "has_genre", "HORROR" ], [ "JULIA'S EYES", "release_year", "2010" ], [ "LET ME IN", "has_genre", "HORROR" ], [ "LET ME IN", "has_tags", "HORROR" ], [ "LET ME IN", "release_year", "2010" ], [ "MEGA PIRANHA", "has_genre", "HORROR" ], [ "MEGA PIRANHA", "release_year", "2010" ], [ "MIRRORS 2", "has_genre", "HORROR" ], [ "MIRRORS 2", "release_year", "2010" ], [ "MUTANTS", "has_genre", "HORROR" ], [ "MY SOUL TO TAKE", "has_genre", "HORROR" ], [ "MY SOUL TO TAKE", "release_year", "2010" ], [ "NINE DEAD", "has_genre", "HORROR" ], [ "NINE DEAD", "release_year", "2010" ], [ "PARANORMAL ACTIVITY 2", "has_genre", "HORROR" ], [ "PARANORMAL ACTIVITY 2", "release_year", "2010" ], [ "PIRANHA 3D", "has_genre", "HORROR" ], [ "PIRANHA 3D", "release_year", "2010" ], [ "PROWL", "has_genre", "HORROR" ], [ "PROWL", "release_year", "2010" ], [ "PSYCHOSIS", "has_genre", "HORROR" ], [ "PSYCHOSIS", "release_year", "2010" ], [ "RAMMBOCK", "has_genre", "HORROR" ], [ "RAMMBOCK", "release_year", "2010" ], [ "ROAD KILL", "has_genre", "HORROR" ], [ "ROAD KILL", "has_tags", "HORROR" ], [ "ROAD KILL", "release_year", "2010" ], [ "SHARKTOPUS", "has_genre", "HORROR" ], [ "SHARKTOPUS", "release_year", "2010" ], [ "STREETDANCE 3D", "release_year", "2010" ], [ "THE CRAZIES", "has_genre", "HORROR" ], [ "THE CRAZIES", "release_year", "2010" ], [ "THE DEAD", "has_genre", "HORROR" ], [ "THE DEAD", "release_year", "2010" ], [ "THE FINAL", "has_genre", "HORROR" ], [ "THE FINAL", "release_year", "2010" ], [ "THE LAST EXORCISM", "has_genre", "HORROR" ], [ "THE LAST EXORCISM", "release_year", "2010" ], [ "THE LAST MAN ON EARTH", "has_genre", "HORROR" ], [ "THE LAST MAN ON EARTH", "has_tags", "I AM LEGEND" ], [ "THE MAZE", "has_genre", "HORROR" ], [ "THE MAZE", "release_year", "2010" ], [ "THE PRESENCE", "has_genre", "HORROR" ], [ "THE PRESENCE", "release_year", "2010" ], [ "THE REEF", "has_genre", "HORROR" ], [ "THE REEF", "release_year", "2010" ], [ "THE SILENT HOUSE", "has_genre", "HORROR" ], [ "THE SILENT HOUSE", "release_year", "2010" ], [ "THE TORTURED", "has_genre", "HORROR" ], [ "THE TORTURED", "release_year", "2010" ], [ "THE TRAVELER", "has_genre", "HORROR" ], [ "THE TRAVELER", "release_year", "2010" ], [ "THE VIOLENT KIND", "has_genre", "HORROR" ], [ "THE VIOLENT KIND", "release_year", "2010" ], [ "THE WARD", "has_genre", "HORROR" ], [ "THE WARD", "release_year", "2010" ], [ "THE WOLFMAN", "has_genre", "HORROR" ], [ "THE WOLFMAN", "release_year", "2010" ], [ "WE ARE THE NIGHT", "has_genre", "HORROR" ], [ "WE ARE THE NIGHT", "release_year", "2010" ], [ "WE ARE WHAT WE ARE", "has_genre", "HORROR" ], [ "WE ARE WHAT WE ARE", "has_tags", "HORROR" ], [ "WE ARE WHAT WE ARE", "release_year", "2010" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 1006, 1996 10067, BERNIE 10569, DANIEL CHUBA 21909, ROBIN TUNNEY 19392, SKIP HOLLANDSWORTH 31004, SUPERNOVA 20760, THE CRAFT 29745, WITCHCRAFT src, edge_attr, dst 10067, release_year, 1006 10067, written_by, 19392 31004, has_tags, 21909 31004, written_by, 10569 20760, has_tags, 21909 20760, has_tags, 29745 20760, release_year, 1006 20760, starred_actors, 21909 Question: How are DANIEL CHUBA, SKIP HOLLANDSWORTH, and WITCHCRAFT related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "DANIEL CHUBA", "SKIP HOLLANDSWORTH", "WITCHCRAFT" ], "valid_edges": [ [ "BERNIE", "release_year", "1996" ], [ "BERNIE", "written_by", "SKIP HOLLANDSWORTH" ], [ "SUPERNOVA", "has_tags", "ROBIN TUNNEY" ], [ "SUPERNOVA", "written_by", "DANIEL CHUBA" ], [ "THE CRAFT", "has_tags", "ROBIN TUNNEY" ], [ "THE CRAFT", "has_tags", "WITCHCRAFT" ], [ "THE CRAFT", "release_year", "1996" ], [ "THE CRAFT", "starred_actors", "ROBIN TUNNEY" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 26310, 1947 1567, 1954 35063, 1976 9152, A STAR IS BORN 21857, ALAN CAMPBELL 21816, APARTHEID 10045, BD-R 5189, CAPE FEAR 39676, CROSSFIRE 6692, DISTRICT 9 18693, FILM NOIR 37497, NATIONAL FILM REGISTRY 21620, NOIR 14276, OUT OF THE PAST 28729, REMAKE 18515, RIVER OF NO RETURN 17979, ROBERT DE NIRO 39935, ROBERT MITCHUM 697, ROBERT RYAN 30447, RYAN'S DAUGHTER 1899, SECRET CEREMONY 37368, THE BIG SLEEP 34379, THE HUNTERS 30201, THE LAST TYCOON 27599, THE NIGHT OF THE HUNTER 7478, THE RACKET 24811, THRILLER 27872, TRACK OF THE CAT 14843, WHERE DANGER LIVES 24706, WILLIAM A. WELLMAN src, edge_attr, dst 9152, directed_by, 24706 9152, has_tags, 10045 9152, has_tags, 37497 9152, has_tags, 24706 9152, release_year, 1567 9152, release_year, 35063 9152, written_by, 21857 9152, written_by, 24706 5189, has_genre, 24811 5189, has_tags, 28729 5189, has_tags, 17979 5189, has_tags, 39935 5189, has_tags, 24811 5189, starred_actors, 17979 5189, starred_actors, 39935 39676, has_tags, 10045 39676, release_year, 26310 39676, starred_actors, 39935 39676, starred_actors, 697 6692, has_genre, 24811 6692, has_tags, 21816 6692, has_tags, 24811 14276, has_tags, 18693 14276, has_tags, 37497 14276, has_tags, 21620 14276, release_year, 26310 14276, starred_actors, 39935 18515, release_year, 1567 18515, starred_actors, 39935 30447, has_tags, 10045 30447, has_tags, 39935 30447, starred_actors, 39935 1899, has_tags, 10045 1899, starred_actors, 39935 37368, has_tags, 10045 37368, has_tags, 18693 37368, has_tags, 37497 37368, has_tags, 21620 37368, has_tags, 28729 37368, starred_actors, 39935 34379, has_genre, 24811 34379, starred_actors, 39935 30201, release_year, 35063 30201, starred_actors, 17979 30201, starred_actors, 39935 27599, directed_by, 39935 27599, has_tags, 10045 27599, has_tags, 37497 27599, has_tags, 39935 27599, has_tags, 24811 27599, starred_actors, 39935 7478, has_tags, 10045 7478, starred_actors, 39935 7478, starred_actors, 697 27872, directed_by, 24706 27872, release_year, 1567 27872, starred_actors, 39935 14843, has_genre, 24811 14843, starred_actors, 39935 Question: In what context are ALAN CAMPBELL, APARTHEID, and ROBERT MITCHUM connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ALAN CAMPBELL", "APARTHEID", "ROBERT MITCHUM" ], "valid_edges": [ [ "A STAR IS BORN", "directed_by", "WILLIAM A. WELLMAN" ], [ "A STAR IS BORN", "has_tags", "BD-R" ], [ "A STAR IS BORN", "has_tags", "NATIONAL FILM REGISTRY" ], [ "A STAR IS BORN", "has_tags", "WILLIAM A. WELLMAN" ], [ "A STAR IS BORN", "release_year", "1954" ], [ "A STAR IS BORN", "release_year", "1976" ], [ "A STAR IS BORN", "written_by", "ALAN CAMPBELL" ], [ "A STAR IS BORN", "written_by", "WILLIAM A. WELLMAN" ], [ "CAPE FEAR", "has_genre", "THRILLER" ], [ "CAPE FEAR", "has_tags", "REMAKE" ], [ "CAPE FEAR", "has_tags", "ROBERT DE NIRO" ], [ "CAPE FEAR", "has_tags", "ROBERT MITCHUM" ], [ "CAPE FEAR", "has_tags", "THRILLER" ], [ "CAPE FEAR", "starred_actors", "ROBERT DE NIRO" ], [ "CAPE FEAR", "starred_actors", "ROBERT MITCHUM" ], [ "CROSSFIRE", "has_tags", "BD-R" ], [ "CROSSFIRE", "release_year", "1947" ], [ "CROSSFIRE", "starred_actors", "ROBERT MITCHUM" ], [ "CROSSFIRE", "starred_actors", "ROBERT RYAN" ], [ "DISTRICT 9", "has_genre", "THRILLER" ], [ "DISTRICT 9", "has_tags", "APARTHEID" ], [ "DISTRICT 9", "has_tags", "THRILLER" ], [ "OUT OF THE PAST", "has_tags", "FILM NOIR" ], [ "OUT OF THE PAST", "has_tags", "NATIONAL FILM REGISTRY" ], [ "OUT OF THE PAST", "has_tags", "NOIR" ], [ "OUT OF THE PAST", "release_year", "1947" ], [ "OUT OF THE PAST", "starred_actors", "ROBERT MITCHUM" ], [ "RIVER OF NO RETURN", "release_year", "1954" ], [ "RIVER OF NO RETURN", "starred_actors", "ROBERT MITCHUM" ], [ "RYAN'S DAUGHTER", "has_tags", "BD-R" ], [ "RYAN'S DAUGHTER", "has_tags", "ROBERT MITCHUM" ], [ "RYAN'S DAUGHTER", "starred_actors", "ROBERT MITCHUM" ], [ "SECRET CEREMONY", "has_tags", "BD-R" ], [ "SECRET CEREMONY", "starred_actors", "ROBERT MITCHUM" ], [ "THE BIG SLEEP", "has_tags", "BD-R" ], [ "THE BIG SLEEP", "has_tags", "FILM NOIR" ], [ "THE BIG SLEEP", "has_tags", "NATIONAL FILM REGISTRY" ], [ "THE BIG SLEEP", "has_tags", "NOIR" ], [ "THE BIG SLEEP", "has_tags", "REMAKE" ], [ "THE BIG SLEEP", "starred_actors", "ROBERT MITCHUM" ], [ "THE HUNTERS", "has_genre", "THRILLER" ], [ "THE HUNTERS", "starred_actors", "ROBERT MITCHUM" ], [ "THE LAST TYCOON", "release_year", "1976" ], [ "THE LAST TYCOON", "starred_actors", "ROBERT DE NIRO" ], [ "THE LAST TYCOON", "starred_actors", "ROBERT MITCHUM" ], [ "THE NIGHT OF THE HUNTER", "directed_by", "ROBERT MITCHUM" ], [ "THE NIGHT OF THE HUNTER", "has_tags", "BD-R" ], [ "THE NIGHT OF THE HUNTER", "has_tags", "NATIONAL FILM REGISTRY" ], [ "THE NIGHT OF THE HUNTER", "has_tags", "ROBERT MITCHUM" ], [ "THE NIGHT OF THE HUNTER", "has_tags", "THRILLER" ], [ "THE NIGHT OF THE HUNTER", "starred_actors", "ROBERT MITCHUM" ], [ "THE RACKET", "has_tags", "BD-R" ], [ "THE RACKET", "starred_actors", "ROBERT MITCHUM" ], [ "THE RACKET", "starred_actors", "ROBERT RYAN" ], [ "TRACK OF THE CAT", "directed_by", "WILLIAM A. WELLMAN" ], [ "TRACK OF THE CAT", "release_year", "1954" ], [ "TRACK OF THE CAT", "starred_actors", "ROBERT MITCHUM" ], [ "WHERE DANGER LIVES", "has_genre", "THRILLER" ], [ "WHERE DANGER LIVES", "starred_actors", "ROBERT MITCHUM" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 16055, 1983 1006, 1996 30269, AMITYVILLE 3-D 22951, DON'T BE A MENACE TO SOUTH CENTRAL WHILE DRINKING YOUR JUICE IN THE HOOD 37940, OCTOPUSSY 36554, ROGER MOORE 12863, THE QUEST 6911, THE WILD GEESE src, edge_attr, dst 30269, release_year, 16055 22951, release_year, 1006 37940, has_tags, 36554 37940, release_year, 16055 37940, starred_actors, 36554 12863, has_tags, 36554 12863, release_year, 1006 12863, starred_actors, 36554 6911, has_tags, 36554 6911, starred_actors, 36554 Question: For what reason are AMITYVILLE 3-D, DON'T BE A MENACE TO SOUTH CENTRAL WHILE DRINKING YOUR JUICE IN THE HOOD, and THE WILD GEESE associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "AMITYVILLE 3-D", "DON'T BE A MENACE TO SOUTH CENTRAL WHILE DRINKING YOUR JUICE IN THE HOOD", "THE WILD GEESE" ], "valid_edges": [ [ "AMITYVILLE 3-D", "release_year", "1983" ], [ "DON'T BE A MENACE TO SOUTH CENTRAL WHILE DRINKING YOUR JUICE IN THE HOOD", "release_year", "1996" ], [ "OCTOPUSSY", "has_tags", "ROGER MOORE" ], [ "OCTOPUSSY", "release_year", "1983" ], [ "OCTOPUSSY", "starred_actors", "ROGER MOORE" ], [ "THE QUEST", "has_tags", "ROGER MOORE" ], [ "THE QUEST", "release_year", "1996" ], [ "THE QUEST", "starred_actors", "ROGER MOORE" ], [ "THE WILD GEESE", "has_tags", "ROGER MOORE" ], [ "THE WILD GEESE", "starred_actors", "ROGER MOORE" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 4151, A THIN LINE BETWEEN LOVE AND HATE 30463, COMEDY 1221, GONE FISHIN' 6886, HOUSEFULL 2 36949, JALLA! JALLA! 37303, LALEH POURKARIM 28563, LYNN WHITFIELD 13879, SAJID NADIADWALA src, edge_attr, dst 4151, has_genre, 30463 4151, starred_actors, 28563 1221, has_genre, 30463 1221, starred_actors, 28563 6886, has_genre, 30463 6886, written_by, 13879 36949, has_genre, 30463 36949, starred_actors, 37303 Question: For what reason are LALEH POURKARIM, LYNN WHITFIELD, and SAJID NADIADWALA associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "LALEH POURKARIM", "LYNN WHITFIELD", "SAJID NADIADWALA" ], "valid_edges": [ [ "A THIN LINE BETWEEN LOVE AND HATE", "has_genre", "COMEDY" ], [ "A THIN LINE BETWEEN LOVE AND HATE", "starred_actors", "LYNN WHITFIELD" ], [ "GONE FISHIN'", "has_genre", "COMEDY" ], [ "GONE FISHIN'", "starred_actors", "LYNN WHITFIELD" ], [ "HOUSEFULL 2", "has_genre", "COMEDY" ], [ "HOUSEFULL 2", "written_by", "SAJID NADIADWALA" ], [ "JALLA! JALLA!", "has_genre", "COMEDY" ], [ "JALLA! JALLA!", "starred_actors", "LALEH POURKARIM" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6347, $ 7977, 1969 37484, 2004 1698, ALFIE 6360, AROUND THE BEND 10045, BD-R 16823, BITE THE BULLET 11108, BRUTE FORCE 32075, BURN! 35953, BUTCH CASSIDY AND THE SUNDANCE KID 3034, CAT ON A HOT TIN ROOF 39676, CROSSFIRE 13424, DON'T DRINK THE WATER 4728, EASY RIDER 26308, FLIPPER 32144, FRANKENSTEIN MUST BE DESTROYED 2048, GOODBYE, MR. CHIPS 4709, GREAT EXPECTATIONS 12244, GUYS AND DOLLS 20941, HAMLET 12087, HOWL'S MOVING CASTLE 30184, IN COLD BLOOD 36637, JAMES B. CLARK 25901, JEAN SIMMONS 18020, JOHN FORSYTHE 13763, KES 4929, KEY LARGO 14156, KING SOLOMON'S MINES 28933, LAST SUMMER 26370, MADHOUSE 22678, MARLOWE 6543, MODEL SHOP 16139, MY SIDE OF THE MOUNTAIN 18184, OKLAHOMA! 33617, RED DUST 8614, RICHARD BROOKS 38066, SHIRLEY JONES 420, SUPPORT YOUR LOCAL SHERIFF! 24926, SWEET CHARITY 31619, THE ARRANGEMENT 2915, THE BROTHERS KARAMAZOV 31280, THE CATERED AFFAIR 7763, THE CHEYENNE SOCIAL CLUB 557, THE HAPPY ENDING 7447, THE INCREDIBLES 1443, THE ITALIAN JOB 30690, THE LADYKILLERS 1368, THE MILKY WAY 28118, THE MUSIC MAN 31953, THE PRIME OF MISS JEAN BRODIE 19768, THE PROFESSIONALS 23847, THE STEPFORD WIVES 18433, THE WILD BUNCH 37252, WOMEN IN LOVE 31083, WRONG IS RIGHT 38760, Z src, edge_attr, dst 6347, directed_by, 8614 6347, has_tags, 10045 6347, written_by, 8614 1698, has_tags, 10045 1698, release_year, 37484 6360, release_year, 37484 16823, directed_by, 8614 16823, has_tags, 10045 16823, written_by, 8614 11108, has_tags, 10045 11108, written_by, 8614 32075, has_tags, 10045 32075, release_year, 7977 35953, has_tags, 10045 35953, release_year, 7977 3034, directed_by, 8614 3034, has_tags, 10045 3034, has_tags, 8614 3034, written_by, 8614 39676, has_tags, 10045 39676, written_by, 8614 13424, has_tags, 10045 13424, release_year, 7977 4728, has_tags, 10045 4728, release_year, 7977 26308, directed_by, 36637 26308, has_tags, 10045 32144, has_tags, 10045 32144, release_year, 7977 2048, has_tags, 10045 2048, release_year, 7977 4709, has_tags, 10045 4709, starred_actors, 25901 12244, has_tags, 10045 12244, starred_actors, 25901 20941, has_tags, 10045 20941, release_year, 7977 12087, has_tags, 10045 12087, release_year, 37484 30184, directed_by, 8614 30184, has_tags, 10045 30184, has_tags, 8614 30184, starred_actors, 18020 30184, written_by, 8614 13763, has_tags, 10045 13763, release_year, 7977 4929, has_tags, 10045 4929, written_by, 8614 14156, has_tags, 10045 14156, release_year, 37484 28933, has_tags, 10045 28933, release_year, 7977 26370, has_tags, 10045 26370, release_year, 37484 22678, has_tags, 10045 22678, release_year, 7977 6543, has_tags, 10045 6543, release_year, 7977 16139, directed_by, 36637 16139, release_year, 7977 18184, has_tags, 10045 18184, has_tags, 38066 33617, has_tags, 10045 33617, release_year, 37484 420, has_tags, 10045 420, release_year, 7977 24926, has_tags, 10045 24926, release_year, 7977 31619, has_tags, 10045 31619, release_year, 7977 2915, directed_by, 8614 2915, has_tags, 10045 2915, written_by, 8614 31280, directed_by, 8614 31280, has_tags, 10045 31280, has_tags, 8614 7763, has_tags, 10045 7763, starred_actors, 38066 557, directed_by, 8614 557, has_tags, 10045 557, release_year, 7977 557, starred_actors, 25901 557, starred_actors, 18020 557, starred_actors, 38066 557, written_by, 8614 7447, has_tags, 10045 7447, release_year, 37484 1443, has_tags, 10045 1443, release_year, 7977 30690, has_tags, 10045 30690, release_year, 37484 1368, has_tags, 10045 1368, release_year, 7977 28118, has_tags, 10045 28118, has_tags, 38066 28118, starred_actors, 38066 31953, has_tags, 10045 31953, release_year, 7977 19768, directed_by, 8614 19768, has_tags, 10045 19768, has_tags, 8614 19768, written_by, 8614 23847, has_tags, 10045 23847, release_year, 37484 18433, has_tags, 10045 18433, release_year, 7977 37252, has_tags, 10045 37252, release_year, 7977 31083, directed_by, 8614 31083, has_tags, 10045 31083, written_by, 8614 38760, has_tags, 10045 38760, release_year, 7977 Question: How are AROUND THE BEND, JAMES B. CLARK, and THE HAPPY ENDING related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "AROUND THE BEND", "JAMES B. CLARK", "THE HAPPY ENDING" ], "valid_edges": [ [ "$", "directed_by", "RICHARD BROOKS" ], [ "$", "has_tags", "BD-R" ], [ "$", "written_by", "RICHARD BROOKS" ], [ "ALFIE", "has_tags", "BD-R" ], [ "ALFIE", "release_year", "2004" ], [ "AROUND THE BEND", "release_year", "2004" ], [ "BITE THE BULLET", "directed_by", "RICHARD BROOKS" ], [ "BITE THE BULLET", "has_tags", "BD-R" ], [ "BITE THE BULLET", "written_by", "RICHARD BROOKS" ], [ "BRUTE FORCE", "has_tags", "BD-R" ], [ "BRUTE FORCE", "written_by", "RICHARD BROOKS" ], [ "BURN!", "has_tags", "BD-R" ], [ "BURN!", "release_year", "1969" ], [ "BUTCH CASSIDY AND THE SUNDANCE KID", "has_tags", "BD-R" ], [ "BUTCH CASSIDY AND THE SUNDANCE KID", "release_year", "1969" ], [ "CAT ON A HOT TIN ROOF", "directed_by", "RICHARD BROOKS" ], [ "CAT ON A HOT TIN ROOF", "has_tags", "BD-R" ], [ "CAT ON A HOT TIN ROOF", "has_tags", "RICHARD BROOKS" ], [ "CAT ON A HOT TIN ROOF", "written_by", "RICHARD BROOKS" ], [ "CROSSFIRE", "has_tags", "BD-R" ], [ "CROSSFIRE", "written_by", "RICHARD BROOKS" ], [ "DON'T DRINK THE WATER", "has_tags", "BD-R" ], [ "DON'T DRINK THE WATER", "release_year", "1969" ], [ "EASY RIDER", "has_tags", "BD-R" ], [ "EASY RIDER", "release_year", "1969" ], [ "FLIPPER", "directed_by", "JAMES B. CLARK" ], [ "FLIPPER", "has_tags", "BD-R" ], [ "FRANKENSTEIN MUST BE DESTROYED", "has_tags", "BD-R" ], [ "FRANKENSTEIN MUST BE DESTROYED", "release_year", "1969" ], [ "GOODBYE, MR. CHIPS", "has_tags", "BD-R" ], [ "GOODBYE, MR. CHIPS", "release_year", "1969" ], [ "GREAT EXPECTATIONS", "has_tags", "BD-R" ], [ "GREAT EXPECTATIONS", "starred_actors", "JEAN SIMMONS" ], [ "GUYS AND DOLLS", "has_tags", "BD-R" ], [ "GUYS AND DOLLS", "starred_actors", "JEAN SIMMONS" ], [ "HAMLET", "has_tags", "BD-R" ], [ "HAMLET", "release_year", "1969" ], [ "HOWL'S MOVING CASTLE", "has_tags", "BD-R" ], [ "HOWL'S MOVING CASTLE", "release_year", "2004" ], [ "IN COLD BLOOD", "directed_by", "RICHARD BROOKS" ], [ "IN COLD BLOOD", "has_tags", "BD-R" ], [ "IN COLD BLOOD", "has_tags", "RICHARD BROOKS" ], [ "IN COLD BLOOD", "starred_actors", "JOHN FORSYTHE" ], [ "IN COLD BLOOD", "written_by", "RICHARD BROOKS" ], [ "KES", "has_tags", "BD-R" ], [ "KES", "release_year", "1969" ], [ "KEY LARGO", "has_tags", "BD-R" ], [ "KEY LARGO", "written_by", "RICHARD BROOKS" ], [ "KING SOLOMON'S MINES", "has_tags", "BD-R" ], [ "KING SOLOMON'S MINES", "release_year", "2004" ], [ "LAST SUMMER", "has_tags", "BD-R" ], [ "LAST SUMMER", "release_year", "1969" ], [ "MADHOUSE", "has_tags", "BD-R" ], [ "MADHOUSE", "release_year", "2004" ], [ "MARLOWE", "has_tags", "BD-R" ], [ "MARLOWE", "release_year", "1969" ], [ "MODEL SHOP", "has_tags", "BD-R" ], [ "MODEL SHOP", "release_year", "1969" ], [ "MY SIDE OF THE MOUNTAIN", "directed_by", "JAMES B. CLARK" ], [ "MY SIDE OF THE MOUNTAIN", "release_year", "1969" ], [ "OKLAHOMA!", "has_tags", "BD-R" ], [ "OKLAHOMA!", "has_tags", "SHIRLEY JONES" ], [ "RED DUST", "has_tags", "BD-R" ], [ "RED DUST", "release_year", "2004" ], [ "SUPPORT YOUR LOCAL SHERIFF!", "has_tags", "BD-R" ], [ "SUPPORT YOUR LOCAL SHERIFF!", "release_year", "1969" ], [ "SWEET CHARITY", "has_tags", "BD-R" ], [ "SWEET CHARITY", "release_year", "1969" ], [ "THE ARRANGEMENT", "has_tags", "BD-R" ], [ "THE ARRANGEMENT", "release_year", "1969" ], [ "THE BROTHERS KARAMAZOV", "directed_by", "RICHARD BROOKS" ], [ "THE BROTHERS KARAMAZOV", "has_tags", "BD-R" ], [ "THE BROTHERS KARAMAZOV", "written_by", "RICHARD BROOKS" ], [ "THE CATERED AFFAIR", "directed_by", "RICHARD BROOKS" ], [ "THE CATERED AFFAIR", "has_tags", "BD-R" ], [ "THE CATERED AFFAIR", "has_tags", "RICHARD BROOKS" ], [ "THE CHEYENNE SOCIAL CLUB", "has_tags", "BD-R" ], [ "THE CHEYENNE SOCIAL CLUB", "starred_actors", "SHIRLEY JONES" ], [ "THE HAPPY ENDING", "directed_by", "RICHARD BROOKS" ], [ "THE HAPPY ENDING", "has_tags", "BD-R" ], [ "THE HAPPY ENDING", "release_year", "1969" ], [ "THE HAPPY ENDING", "starred_actors", "JEAN SIMMONS" ], [ "THE HAPPY ENDING", "starred_actors", "JOHN FORSYTHE" ], [ "THE HAPPY ENDING", "starred_actors", "SHIRLEY JONES" ], [ "THE HAPPY ENDING", "written_by", "RICHARD BROOKS" ], [ "THE INCREDIBLES", "has_tags", "BD-R" ], [ "THE INCREDIBLES", "release_year", "2004" ], [ "THE ITALIAN JOB", "has_tags", "BD-R" ], [ "THE ITALIAN JOB", "release_year", "1969" ], [ "THE LADYKILLERS", "has_tags", "BD-R" ], [ "THE LADYKILLERS", "release_year", "2004" ], [ "THE MILKY WAY", "has_tags", "BD-R" ], [ "THE MILKY WAY", "release_year", "1969" ], [ "THE MUSIC MAN", "has_tags", "BD-R" ], [ "THE MUSIC MAN", "has_tags", "SHIRLEY JONES" ], [ "THE MUSIC MAN", "starred_actors", "SHIRLEY JONES" ], [ "THE PRIME OF MISS JEAN BRODIE", "has_tags", "BD-R" ], [ "THE PRIME OF MISS JEAN BRODIE", "release_year", "1969" ], [ "THE PROFESSIONALS", "directed_by", "RICHARD BROOKS" ], [ "THE PROFESSIONALS", "has_tags", "BD-R" ], [ "THE PROFESSIONALS", "has_tags", "RICHARD BROOKS" ], [ "THE PROFESSIONALS", "written_by", "RICHARD BROOKS" ], [ "THE STEPFORD WIVES", "has_tags", "BD-R" ], [ "THE STEPFORD WIVES", "release_year", "2004" ], [ "THE WILD BUNCH", "has_tags", "BD-R" ], [ "THE WILD BUNCH", "release_year", "1969" ], [ "WOMEN IN LOVE", "has_tags", "BD-R" ], [ "WOMEN IN LOVE", "release_year", "1969" ], [ "WRONG IS RIGHT", "directed_by", "RICHARD BROOKS" ], [ "WRONG IS RIGHT", "has_tags", "BD-R" ], [ "WRONG IS RIGHT", "written_by", "RICHARD BROOKS" ], [ "Z", "has_tags", "BD-R" ], [ "Z", "release_year", "1969" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 13464, 10 THINGS I HATE ABOUT YOU 8486, 1999 5620, 200 CIGARETTES 30146, A CHRISTMAS CAROL 38367, A MAP OF THE WORLD 27672, A ROOM FOR ROMEO BRASS 5152, A SLIPPING-DOWN LIFE 30464, A WALK ON THE MOON 35603, AGNES BROWNE 30284, ALL ABOUT MY MOTHER 39271, ALL FALL DOWN 17254, AMERICAN BEAUTY 17481, ANGELA'S ASHES 29422, ANNA AND THE KING 17320, ANY GIVEN SUNDAY 25586, ARLINGTON ROAD 7908, AUDITION 10045, BD-R 26205, BEING JOHN MALKOVICH 2067, BETWEEN YOUR LEGS 8900, BICENTENNIAL MAN 37569, BOYS DON'T CRY 21035, BRINGING OUT THE DEAD 7918, BROKEDOWN PALACE 26642, BUS STOP 3291, CATFISH IN BLACK BEAN SAUCE 32880, COME BACK, LITTLE SHEBA 23710, CRADLE WILL ROCK 32140, CRAZY IN ALABAMA 27377, CRUEL INTENTIONS 13007, DETERRENCE 36212, DRAMA 12319, DREAMING OF JOSEPH LEES 7568, EAST IS EAST 34555, FLAWLESS 17478, FOOLISH 9161, FOR LOVE OF THE GAME 5820, FOREVER MINE 22532, GIRL, INTERRUPTED 35657, GLORIA 10457, GOYA IN BORDEAUX 19204, GUINEVERE 24044, IMMEDIATE FAMILY 27446, IN TOO DEEP 35335, IT ALL STARTS TODAY 4912, JAKOB THE LIAR 14391, JOE THE KING 4864, JOSHUA LOGAN 27249, JUDY BERLIN 21224, KIKUJIRO 22333, KING OF COMEDY 18023, LIBERTY HEIGHTS 7104, LIFE 21197, LIGHT IT UP 4052, LIMBO 28092, MAGNOLIA 37867, MAN ON THE MOON 6649, MANSFIELD PARK 6559, MESSAGE IN A BOTTLE 30008, MISS JULIE 19598, MOLLY 14977, MOLOCH 16428, MUMFORD 3018, MUSIC OF THE HEART 33718, MYSTERY, ALASKA 493, ONE MAN'S HERO 18987, ONEGIN 35341, ORFEU 24816, OXYGEN 439, PICNIC 35054, PLAY IT TO THE BONE 21386, POLA X 10039, PUPS 16964, PUSHING TIN 9963, RANDOM HEARTS 27631, RIDE WITH THE DEVIL 12234, RKO 281 27556, ROGUE TRADER 8379, ROMANCE 15252, SCREWED IN TALLINN 36167, SOFT FRUIT 1469, SOPHIE'S WORLD 8753, SPLENDOR IN THE GRASS 32984, SWEET AND LOWDOWN 4157, THE BEST MAN 27111, THE BIG KAHUNA 22372, THE CIDER HOUSE RULES 6150, THE CONFESSION 25509, THE DEBT 791, THE DEEP END OF THE OCEAN 8141, THE END OF THE AFFAIR 14220, THE FIVE SENSES 12748, THE GLASS AGENCY 35367, THE GREEN MILE 16209, THE HI-LINE 12439, THE INSIDER 34986, THE JOYRIDERS 37562, THE KING AND I 9799, THE LOST SON 27045, THE MISSION 5098, THE MOMENT AFTER 37200, THE STORY OF US 9301, THE STRAIGHT STORY 11235, THE SUBURBANS 13405, THE THIRD MIRACLE 12026, THE TIC CODE 12801, THE VIRGIN SUICIDES 39253, THE WAR ZONE 14983, THE WINSLOW BOY 332, TOPSY-TURVY 308, TRUE CRIME 13101, TUMBLEWEEDS 9202, VARSITY BLUES 16102, WILDFLOWERS 16030, WILLIAM INGE 30288, WISCONSIN DEATH TRIP 6567, WITH FIRE AND SWORD 31862, WONDERLAND src, edge_attr, dst 13464, has_genre, 36212 13464, release_year, 8486 5620, has_genre, 36212 5620, release_year, 8486 30146, has_genre, 36212 30146, release_year, 8486 38367, has_genre, 36212 38367, release_year, 8486 27672, has_genre, 36212 27672, release_year, 8486 5152, has_genre, 36212 5152, release_year, 8486 30464, has_genre, 36212 30464, release_year, 8486 35603, has_genre, 36212 35603, release_year, 8486 30284, has_genre, 36212 30284, release_year, 8486 39271, has_genre, 36212 39271, written_by, 16030 17254, has_genre, 36212 17254, has_tags, 36212 17254, release_year, 8486 17481, has_genre, 36212 17481, release_year, 8486 29422, has_genre, 36212 29422, release_year, 8486 17320, has_genre, 36212 17320, release_year, 8486 25586, has_genre, 36212 25586, release_year, 8486 7908, has_genre, 36212 7908, release_year, 8486 26205, has_genre, 36212 26205, has_tags, 36212 26205, release_year, 8486 2067, has_genre, 36212 2067, release_year, 8486 8900, has_genre, 36212 8900, release_year, 8486 37569, has_genre, 36212 37569, has_tags, 36212 37569, release_year, 8486 21035, has_genre, 36212 21035, has_tags, 36212 21035, release_year, 8486 7918, has_genre, 36212 7918, release_year, 8486 26642, directed_by, 4864 26642, has_genre, 36212 26642, has_tags, 10045 26642, written_by, 16030 3291, has_genre, 36212 3291, release_year, 8486 32880, has_genre, 36212 32880, written_by, 16030 23710, has_genre, 36212 23710, release_year, 8486 32140, has_genre, 36212 32140, release_year, 8486 27377, has_genre, 36212 27377, release_year, 8486 13007, has_genre, 36212 13007, release_year, 8486 12319, has_genre, 36212 12319, release_year, 8486 7568, has_genre, 36212 7568, release_year, 8486 34555, has_genre, 36212 34555, release_year, 8486 17478, has_genre, 36212 17478, release_year, 8486 9161, has_genre, 36212 9161, release_year, 8486 5820, has_genre, 36212 5820, release_year, 8486 22532, has_genre, 36212 22532, has_tags, 36212 22532, release_year, 8486 35657, has_genre, 36212 35657, release_year, 8486 10457, has_genre, 36212 10457, release_year, 8486 19204, has_genre, 36212 19204, release_year, 8486 24044, has_genre, 36212 27446, has_genre, 36212 27446, release_year, 8486 35335, has_genre, 36212 35335, release_year, 8486 4912, has_genre, 36212 4912, release_year, 8486 14391, has_genre, 36212 14391, release_year, 8486 27249, has_genre, 36212 27249, release_year, 8486 21224, has_genre, 36212 21224, release_year, 8486 22333, has_genre, 36212 22333, release_year, 8486 18023, has_genre, 36212 18023, release_year, 8486 7104, has_genre, 36212 7104, release_year, 8486 21197, has_genre, 36212 21197, release_year, 8486 4052, has_genre, 36212 4052, release_year, 8486 28092, has_genre, 36212 28092, release_year, 8486 37867, has_genre, 36212 37867, release_year, 8486 6649, has_genre, 36212 6649, release_year, 8486 6559, has_genre, 36212 6559, release_year, 8486 30008, has_genre, 36212 30008, release_year, 8486 19598, has_genre, 36212 19598, release_year, 8486 14977, has_genre, 36212 14977, release_year, 8486 16428, has_genre, 36212 16428, release_year, 8486 3018, has_genre, 36212 3018, release_year, 8486 33718, has_genre, 36212 33718, release_year, 8486 493, has_genre, 36212 493, release_year, 8486 18987, has_genre, 36212 18987, release_year, 8486 35341, has_genre, 36212 35341, release_year, 8486 24816, release_year, 8486 439, directed_by, 4864 439, has_genre, 36212 439, has_tags, 10045 439, has_tags, 4864 439, written_by, 16030 35054, has_genre, 36212 35054, release_year, 8486 21386, has_genre, 36212 21386, release_year, 8486 10039, has_genre, 36212 10039, release_year, 8486 16964, has_genre, 36212 16964, release_year, 8486 9963, has_genre, 36212 9963, release_year, 8486 27631, has_genre, 36212 27631, release_year, 8486 12234, has_genre, 36212 12234, release_year, 8486 27556, has_genre, 36212 27556, release_year, 8486 8379, has_genre, 36212 8379, release_year, 8486 15252, has_genre, 36212 15252, release_year, 8486 36167, has_genre, 36212 36167, release_year, 8486 1469, has_genre, 36212 1469, release_year, 8486 8753, has_genre, 36212 8753, has_tags, 10045 8753, written_by, 16030 32984, has_genre, 36212 32984, release_year, 8486 4157, has_genre, 36212 4157, release_year, 8486 27111, has_genre, 36212 27111, release_year, 8486 22372, has_genre, 36212 22372, has_tags, 36212 22372, release_year, 8486 6150, has_genre, 36212 6150, release_year, 8486 25509, has_genre, 36212 25509, release_year, 8486 791, has_genre, 36212 791, release_year, 8486 8141, has_genre, 36212 8141, release_year, 8486 14220, has_genre, 36212 14220, release_year, 8486 12748, has_genre, 36212 12748, release_year, 8486 35367, has_genre, 36212 35367, has_tags, 36212 35367, release_year, 8486 16209, has_genre, 36212 16209, release_year, 8486 12439, has_genre, 36212 12439, has_tags, 36212 12439, release_year, 8486 34986, has_genre, 36212 34986, release_year, 8486 37562, has_genre, 36212 37562, release_year, 8486 9799, has_genre, 36212 9799, release_year, 8486 27045, has_genre, 36212 27045, release_year, 8486 5098, has_genre, 36212 5098, release_year, 8486 37200, has_genre, 36212 37200, has_tags, 36212 37200, release_year, 8486 9301, has_genre, 36212 9301, release_year, 8486 11235, has_genre, 36212 11235, release_year, 8486 13405, has_genre, 36212 13405, release_year, 8486 12026, has_genre, 36212 12026, release_year, 8486 12801, has_genre, 36212 12801, release_year, 8486 39253, has_genre, 36212 39253, release_year, 8486 14983, has_genre, 36212 14983, release_year, 8486 332, has_genre, 36212 332, release_year, 8486 308, has_genre, 36212 308, release_year, 8486 13101, has_genre, 36212 13101, release_year, 8486 9202, has_genre, 36212 9202, release_year, 8486 16102, has_genre, 36212 16102, release_year, 8486 30288, has_genre, 36212 30288, release_year, 8486 6567, has_genre, 36212 6567, release_year, 8486 31862, has_genre, 36212 31862, release_year, 8486 Question: In what context are IMMEDIATE FAMILY, OXYGEN, and WILLIAM INGE connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "IMMEDIATE FAMILY", "OXYGEN", "WILLIAM INGE" ], "valid_edges": [ [ "10 THINGS I HATE ABOUT YOU", "has_genre", "DRAMA" ], [ "10 THINGS I HATE ABOUT YOU", "release_year", "1999" ], [ "200 CIGARETTES", "has_genre", "DRAMA" ], [ "200 CIGARETTES", "release_year", "1999" ], [ "A CHRISTMAS CAROL", "has_genre", "DRAMA" ], [ "A CHRISTMAS CAROL", "release_year", "1999" ], [ "A MAP OF THE WORLD", "has_genre", "DRAMA" ], [ "A MAP OF THE WORLD", "release_year", "1999" ], [ "A ROOM FOR ROMEO BRASS", "has_genre", "DRAMA" ], [ "A ROOM FOR ROMEO BRASS", "release_year", "1999" ], [ "A SLIPPING-DOWN LIFE", "has_genre", "DRAMA" ], [ "A SLIPPING-DOWN LIFE", "release_year", "1999" ], [ "A WALK ON THE MOON", "has_genre", "DRAMA" ], [ "A WALK ON THE MOON", "release_year", "1999" ], [ "AGNES BROWNE", "has_genre", "DRAMA" ], [ "AGNES BROWNE", "release_year", "1999" ], [ "ALL ABOUT MY MOTHER", "has_genre", "DRAMA" ], [ "ALL ABOUT MY MOTHER", "release_year", "1999" ], [ "ALL FALL DOWN", "has_genre", "DRAMA" ], [ "ALL FALL DOWN", "written_by", "WILLIAM INGE" ], [ "AMERICAN BEAUTY", "has_genre", "DRAMA" ], [ "AMERICAN BEAUTY", "has_tags", "DRAMA" ], [ "AMERICAN BEAUTY", "release_year", "1999" ], [ "ANGELA'S ASHES", "has_genre", "DRAMA" ], [ "ANGELA'S ASHES", "release_year", "1999" ], [ "ANNA AND THE KING", "has_genre", "DRAMA" ], [ "ANNA AND THE KING", "release_year", "1999" ], [ "ANY GIVEN SUNDAY", "has_genre", "DRAMA" ], [ "ANY GIVEN SUNDAY", "release_year", "1999" ], [ "ARLINGTON ROAD", "has_genre", "DRAMA" ], [ "ARLINGTON ROAD", "release_year", "1999" ], [ "AUDITION", "has_genre", "DRAMA" ], [ "AUDITION", "release_year", "1999" ], [ "BEING JOHN MALKOVICH", "has_genre", "DRAMA" ], [ "BEING JOHN MALKOVICH", "has_tags", "DRAMA" ], [ "BEING JOHN MALKOVICH", "release_year", "1999" ], [ "BETWEEN YOUR LEGS", "has_genre", "DRAMA" ], [ "BETWEEN YOUR LEGS", "release_year", "1999" ], [ "BICENTENNIAL MAN", "has_genre", "DRAMA" ], [ "BICENTENNIAL MAN", "release_year", "1999" ], [ "BOYS DON'T CRY", "has_genre", "DRAMA" ], [ "BOYS DON'T CRY", "has_tags", "DRAMA" ], [ "BOYS DON'T CRY", "release_year", "1999" ], [ "BRINGING OUT THE DEAD", "has_genre", "DRAMA" ], [ "BRINGING OUT THE DEAD", "has_tags", "DRAMA" ], [ "BRINGING OUT THE DEAD", "release_year", "1999" ], [ "BROKEDOWN PALACE", "has_genre", "DRAMA" ], [ "BROKEDOWN PALACE", "release_year", "1999" ], [ "BUS STOP", "directed_by", "JOSHUA LOGAN" ], [ "BUS STOP", "has_genre", "DRAMA" ], [ "BUS STOP", "has_tags", "BD-R" ], [ "BUS STOP", "written_by", "WILLIAM INGE" ], [ "CATFISH IN BLACK BEAN SAUCE", "has_genre", "DRAMA" ], [ "CATFISH IN BLACK BEAN SAUCE", "release_year", "1999" ], [ "COME BACK, LITTLE SHEBA", "has_genre", "DRAMA" ], [ "COME BACK, LITTLE SHEBA", "written_by", "WILLIAM INGE" ], [ "CRADLE WILL ROCK", "has_genre", "DRAMA" ], [ "CRADLE WILL ROCK", "release_year", "1999" ], [ "CRAZY IN ALABAMA", "has_genre", "DRAMA" ], [ "CRAZY IN ALABAMA", "release_year", "1999" ], [ "CRUEL INTENTIONS", "has_genre", "DRAMA" ], [ "CRUEL INTENTIONS", "release_year", "1999" ], [ "DETERRENCE", "has_genre", "DRAMA" ], [ "DETERRENCE", "release_year", "1999" ], [ "DREAMING OF JOSEPH LEES", "has_genre", "DRAMA" ], [ "DREAMING OF JOSEPH LEES", "release_year", "1999" ], [ "EAST IS EAST", "has_genre", "DRAMA" ], [ "EAST IS EAST", "release_year", "1999" ], [ "FLAWLESS", "has_genre", "DRAMA" ], [ "FLAWLESS", "release_year", "1999" ], [ "FOOLISH", "has_genre", "DRAMA" ], [ "FOOLISH", "release_year", "1999" ], [ "FOR LOVE OF THE GAME", "has_genre", "DRAMA" ], [ "FOR LOVE OF THE GAME", "release_year", "1999" ], [ "FOREVER MINE", "has_genre", "DRAMA" ], [ "FOREVER MINE", "release_year", "1999" ], [ "GIRL, INTERRUPTED", "has_genre", "DRAMA" ], [ "GIRL, INTERRUPTED", "has_tags", "DRAMA" ], [ "GIRL, INTERRUPTED", "release_year", "1999" ], [ "GLORIA", "has_genre", "DRAMA" ], [ "GLORIA", "release_year", "1999" ], [ "GOYA IN BORDEAUX", "has_genre", "DRAMA" ], [ "GOYA IN BORDEAUX", "release_year", "1999" ], [ "GUINEVERE", "has_genre", "DRAMA" ], [ "GUINEVERE", "release_year", "1999" ], [ "IMMEDIATE FAMILY", "has_genre", "DRAMA" ], [ "IN TOO DEEP", "has_genre", "DRAMA" ], [ "IN TOO DEEP", "release_year", "1999" ], [ "IT ALL STARTS TODAY", "has_genre", "DRAMA" ], [ "IT ALL STARTS TODAY", "release_year", "1999" ], [ "JAKOB THE LIAR", "has_genre", "DRAMA" ], [ "JAKOB THE LIAR", "release_year", "1999" ], [ "JOE THE KING", "has_genre", "DRAMA" ], [ "JOE THE KING", "release_year", "1999" ], [ "JUDY BERLIN", "has_genre", "DRAMA" ], [ "JUDY BERLIN", "release_year", "1999" ], [ "KIKUJIRO", "has_genre", "DRAMA" ], [ "KIKUJIRO", "release_year", "1999" ], [ "KING OF COMEDY", "has_genre", "DRAMA" ], [ "KING OF COMEDY", "release_year", "1999" ], [ "LIBERTY HEIGHTS", "has_genre", "DRAMA" ], [ "LIBERTY HEIGHTS", "release_year", "1999" ], [ "LIFE", "has_genre", "DRAMA" ], [ "LIFE", "release_year", "1999" ], [ "LIGHT IT UP", "has_genre", "DRAMA" ], [ "LIGHT IT UP", "release_year", "1999" ], [ "LIMBO", "has_genre", "DRAMA" ], [ "LIMBO", "release_year", "1999" ], [ "MAGNOLIA", "has_genre", "DRAMA" ], [ "MAGNOLIA", "release_year", "1999" ], [ "MAN ON THE MOON", "has_genre", "DRAMA" ], [ "MAN ON THE MOON", "release_year", "1999" ], [ "MANSFIELD PARK", "has_genre", "DRAMA" ], [ "MANSFIELD PARK", "release_year", "1999" ], [ "MESSAGE IN A BOTTLE", "has_genre", "DRAMA" ], [ "MESSAGE IN A BOTTLE", "release_year", "1999" ], [ "MISS JULIE", "has_genre", "DRAMA" ], [ "MISS JULIE", "release_year", "1999" ], [ "MOLLY", "has_genre", "DRAMA" ], [ "MOLLY", "release_year", "1999" ], [ "MOLOCH", "has_genre", "DRAMA" ], [ "MOLOCH", "release_year", "1999" ], [ "MUMFORD", "has_genre", "DRAMA" ], [ "MUMFORD", "release_year", "1999" ], [ "MUSIC OF THE HEART", "has_genre", "DRAMA" ], [ "MUSIC OF THE HEART", "release_year", "1999" ], [ "MYSTERY, ALASKA", "has_genre", "DRAMA" ], [ "MYSTERY, ALASKA", "release_year", "1999" ], [ "ONE MAN'S HERO", "has_genre", "DRAMA" ], [ "ONE MAN'S HERO", "release_year", "1999" ], [ "ONEGIN", "has_genre", "DRAMA" ], [ "ONEGIN", "release_year", "1999" ], [ "ORFEU", "has_genre", "DRAMA" ], [ "ORFEU", "release_year", "1999" ], [ "OXYGEN", "release_year", "1999" ], [ "PICNIC", "directed_by", "JOSHUA LOGAN" ], [ "PICNIC", "has_genre", "DRAMA" ], [ "PICNIC", "has_tags", "BD-R" ], [ "PICNIC", "has_tags", "JOSHUA LOGAN" ], [ "PICNIC", "written_by", "WILLIAM INGE" ], [ "PLAY IT TO THE BONE", "has_genre", "DRAMA" ], [ "PLAY IT TO THE BONE", "release_year", "1999" ], [ "POLA X", "has_genre", "DRAMA" ], [ "POLA X", "release_year", "1999" ], [ "PUPS", "has_genre", "DRAMA" ], [ "PUPS", "release_year", "1999" ], [ "PUSHING TIN", "has_genre", "DRAMA" ], [ "PUSHING TIN", "release_year", "1999" ], [ "RANDOM HEARTS", "has_genre", "DRAMA" ], [ "RANDOM HEARTS", "release_year", "1999" ], [ "RIDE WITH THE DEVIL", "has_genre", "DRAMA" ], [ "RIDE WITH THE DEVIL", "release_year", "1999" ], [ "RKO 281", "has_genre", "DRAMA" ], [ "RKO 281", "release_year", "1999" ], [ "ROGUE TRADER", "has_genre", "DRAMA" ], [ "ROGUE TRADER", "release_year", "1999" ], [ "ROMANCE", "has_genre", "DRAMA" ], [ "ROMANCE", "release_year", "1999" ], [ "SCREWED IN TALLINN", "has_genre", "DRAMA" ], [ "SCREWED IN TALLINN", "release_year", "1999" ], [ "SOFT FRUIT", "has_genre", "DRAMA" ], [ "SOFT FRUIT", "release_year", "1999" ], [ "SOPHIE'S WORLD", "has_genre", "DRAMA" ], [ "SOPHIE'S WORLD", "release_year", "1999" ], [ "SPLENDOR IN THE GRASS", "has_genre", "DRAMA" ], [ "SPLENDOR IN THE GRASS", "has_tags", "BD-R" ], [ "SPLENDOR IN THE GRASS", "written_by", "WILLIAM INGE" ], [ "SWEET AND LOWDOWN", "has_genre", "DRAMA" ], [ "SWEET AND LOWDOWN", "release_year", "1999" ], [ "THE BEST MAN", "has_genre", "DRAMA" ], [ "THE BEST MAN", "release_year", "1999" ], [ "THE BIG KAHUNA", "has_genre", "DRAMA" ], [ "THE BIG KAHUNA", "release_year", "1999" ], [ "THE CIDER HOUSE RULES", "has_genre", "DRAMA" ], [ "THE CIDER HOUSE RULES", "has_tags", "DRAMA" ], [ "THE CIDER HOUSE RULES", "release_year", "1999" ], [ "THE CONFESSION", "has_genre", "DRAMA" ], [ "THE CONFESSION", "release_year", "1999" ], [ "THE DEBT", "has_genre", "DRAMA" ], [ "THE DEBT", "release_year", "1999" ], [ "THE DEEP END OF THE OCEAN", "has_genre", "DRAMA" ], [ "THE DEEP END OF THE OCEAN", "release_year", "1999" ], [ "THE END OF THE AFFAIR", "has_genre", "DRAMA" ], [ "THE END OF THE AFFAIR", "release_year", "1999" ], [ "THE FIVE SENSES", "has_genre", "DRAMA" ], [ "THE FIVE SENSES", "release_year", "1999" ], [ "THE GLASS AGENCY", "has_genre", "DRAMA" ], [ "THE GLASS AGENCY", "release_year", "1999" ], [ "THE GREEN MILE", "has_genre", "DRAMA" ], [ "THE GREEN MILE", "has_tags", "DRAMA" ], [ "THE GREEN MILE", "release_year", "1999" ], [ "THE HI-LINE", "has_genre", "DRAMA" ], [ "THE HI-LINE", "release_year", "1999" ], [ "THE INSIDER", "has_genre", "DRAMA" ], [ "THE INSIDER", "has_tags", "DRAMA" ], [ "THE INSIDER", "release_year", "1999" ], [ "THE JOYRIDERS", "has_genre", "DRAMA" ], [ "THE JOYRIDERS", "release_year", "1999" ], [ "THE KING AND I", "has_genre", "DRAMA" ], [ "THE KING AND I", "release_year", "1999" ], [ "THE LOST SON", "has_genre", "DRAMA" ], [ "THE LOST SON", "release_year", "1999" ], [ "THE MISSION", "has_genre", "DRAMA" ], [ "THE MISSION", "release_year", "1999" ], [ "THE MOMENT AFTER", "has_genre", "DRAMA" ], [ "THE MOMENT AFTER", "release_year", "1999" ], [ "THE STORY OF US", "has_genre", "DRAMA" ], [ "THE STORY OF US", "has_tags", "DRAMA" ], [ "THE STORY OF US", "release_year", "1999" ], [ "THE STRAIGHT STORY", "has_genre", "DRAMA" ], [ "THE STRAIGHT STORY", "release_year", "1999" ], [ "THE SUBURBANS", "has_genre", "DRAMA" ], [ "THE SUBURBANS", "release_year", "1999" ], [ "THE THIRD MIRACLE", "has_genre", "DRAMA" ], [ "THE THIRD MIRACLE", "release_year", "1999" ], [ "THE TIC CODE", "has_genre", "DRAMA" ], [ "THE TIC CODE", "release_year", "1999" ], [ "THE VIRGIN SUICIDES", "has_genre", "DRAMA" ], [ "THE VIRGIN SUICIDES", "release_year", "1999" ], [ "THE WAR ZONE", "has_genre", "DRAMA" ], [ "THE WAR ZONE", "release_year", "1999" ], [ "THE WINSLOW BOY", "has_genre", "DRAMA" ], [ "THE WINSLOW BOY", "release_year", "1999" ], [ "TOPSY-TURVY", "has_genre", "DRAMA" ], [ "TOPSY-TURVY", "release_year", "1999" ], [ "TRUE CRIME", "has_genre", "DRAMA" ], [ "TRUE CRIME", "release_year", "1999" ], [ "TUMBLEWEEDS", "has_genre", "DRAMA" ], [ "TUMBLEWEEDS", "release_year", "1999" ], [ "VARSITY BLUES", "has_genre", "DRAMA" ], [ "VARSITY BLUES", "release_year", "1999" ], [ "WILDFLOWERS", "has_genre", "DRAMA" ], [ "WILDFLOWERS", "release_year", "1999" ], [ "WISCONSIN DEATH TRIP", "has_genre", "DRAMA" ], [ "WISCONSIN DEATH TRIP", "release_year", "1999" ], [ "WITH FIRE AND SWORD", "has_genre", "DRAMA" ], [ "WITH FIRE AND SWORD", "release_year", "1999" ], [ "WONDERLAND", "has_genre", "DRAMA" ], [ "WONDERLAND", "release_year", "1999" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35935, 2002 26646, 3000 MILES TO GRACELAND 6094, A CLOCKWORK ORANGE 6938, ALPHA DOG 20584, AMERICAN GANGSTER 26048, AMERICAN HUSTLE 446, ANTHONY HOPKINS 23328, APPALOOSA 23513, ASH WEDNESDAY 27270, ASSASSINATION TANGO 5247, BAD LIEUTENANT 8402, BANGKOK DANGEROUS 23466, BEFORE THE DEVIL KNOWS YOU'RE DEAD 12228, BETTER LUCK TOMORROW 16956, BLUE COLLAR 20906, BRETT RATNER 12698, BUDD BOETTICHER 13639, BUGSY 33362, CARLITO'S WAY 27059, CATCH ME IF YOU CAN 33387, CITY OF GOD 29115, CITY OF INDUSTRY 28269, CLOCKERS 6871, COP LAND 32049, CRASH 14724, CRIME 7393, CRIME AND PUNISHMENT 38495, CRIME SPREE 17819, DARK BLUE 14292, DEAD IN THE WATER 11826, DEEP COVER 36982, DESPERATE HOURS 23027, DEUCES WILD 5795, DINNER RUSH 15961, DON 15892, DONNIE BRASCO 21021, DRIVE 12628, EASTERN PROMISES 26366, EDWARD NORTON 34642, EMILY WATSON 21123, FARGO 19755, FBI 18217, FIND ME GUILTY 34555, FLAWLESS 1033, FRACTURE 8690, FROZEN RIVER 17400, GANGS OF NEW YORK 23299, GET CARTER 35838, GUNCRAZY 17938, HANNIBAL 25245, HANNIBAL LECTER 18431, HARD EIGHT 13846, HARSH TIMES 31948, HARVEY KEITEL 33673, HEAT 26297, HIGH CRIMES 34072, I'M NOT SCARED 978, INFERNAL AFFAIRS 7264, INSIDE MAN 24305, JCVD 15615, JENNIFER LEITZES 11346, KISS KISS BANG BANG 3854, LAYER CAKE 38120, LORD OF WAR 33950, LUCKY NUMBER SLEVIN 10560, MAN BITES DOG 37052, MANHUNTER 33561, MEAN STREETS 38677, MIAMI VICE 3487, MINORITY REPORT 27827, MONSTER 10883, MONTANA 13217, NARC 1922, NINE QUEENS 29893, NO GOOD DEED 32999, PAID IN FULL 8869, PAYBACK 14744, PEOPLE I KNOW 27139, PHILIP SEYMOUR HOFFMAN 26716, PRIDE AND GLORY 31741, PRIMAL FEAR 26174, PULP FICTION 13081, R 2169, RANSOM 4523, RED DRAGON 28729, REMAKE 32357, RESERVOIR DOGS 25098, RICOCHET 5844, RIGHTEOUS KILL 19362, RISING SUN 1507, ROAD TO PERDITION 8516, ROBOCOP 9206, ROCKNROLLA 31913, ROMEO IS BLEEDING 5362, ROUNDERS 32607, RUSH HOUR 8020, SAFE MEN 6056, SCARFACE 10981, SERIAL KILLER 27807, SHAFT 1472, SHOTTAS 12333, SMOKIN' ACES 7083, SONNY 35843, SPUN 4722, STATE PROPERTY 5170, STEALING HARVARD 33414, SWEET SIXTEEN 30932, SWORDFISH 12, TAXI DRIVER 2512, THE BANK JOB 26674, THE BIG EASY 14696, THE BIG LEBOWSKI 11948, THE BLACK DAHLIA 16900, THE CALL 32597, THE DANCER UPSTAIRS 18997, THE DEPARTED 6524, THE GETAWAY 35551, THE GODFATHER 17518, THE GOOD THIEF 13052, THE HARD WORD 29952, THE INTERNATIONAL 33807, THE KILLER IS LOOSE 16288, THE LARAMIE PROJECT 11559, THE LOOKOUT 13761, THE ROOKIE 14250, THE SALTON SEA 14886, THE SCORE 37148, THE SECRET IN THEIR EYES 27836, THE SILENCE OF THE LAMBS 19084, THE TOWN 22820, THE UNITED STATES OF LELAND 9390, THE UNTOUCHABLES 20880, THE USUAL SUSPECTS 26758, THICK AS THIEVES 9726, THOMAS HARRIS 37331, TO DIE FOR 33836, TRAFFIC 17169, TRAINING DAY 27559, TRAINSPOTTING 24487, TRAPPED 10720, TRIGGERMEN 6485, TRIXIE 22588, WANTED 6770, WE OWN THE NIGHT 37210, ZODIAC src, edge_attr, dst 26646, has_genre, 14724 26646, has_tags, 13081 6094, has_genre, 14724 6094, has_tags, 13081 6938, has_genre, 14724 6938, has_tags, 13081 20584, has_genre, 14724 20584, has_tags, 14724 20584, has_tags, 13081 26048, has_genre, 14724 26048, has_tags, 13081 23328, has_genre, 14724 23328, has_tags, 13081 23513, has_genre, 14724 23513, release_year, 35935 27270, has_genre, 14724 27270, release_year, 35935 5247, has_genre, 14724 5247, has_tags, 31948 5247, starred_actors, 31948 8402, has_genre, 14724 8402, has_tags, 14724 8402, has_tags, 13081 23466, has_genre, 14724 23466, has_tags, 27139 23466, has_tags, 13081 23466, starred_actors, 27139 12228, has_genre, 14724 12228, release_year, 35935 16956, has_genre, 14724 16956, starred_actors, 31948 13639, has_genre, 14724 13639, starred_actors, 31948 33362, has_genre, 14724 33362, has_tags, 14724 33362, has_tags, 13081 27059, has_genre, 14724 27059, has_tags, 14724 27059, release_year, 35935 33387, has_genre, 14724 33387, has_tags, 14724 33387, has_tags, 13081 33387, release_year, 35935 29115, has_genre, 14724 29115, starred_actors, 31948 28269, has_genre, 14724 28269, starred_actors, 31948 6871, has_genre, 14724 6871, has_tags, 14724 6871, starred_actors, 31948 32049, has_tags, 14724 32049, has_tags, 13081 7393, has_genre, 14724 7393, release_year, 35935 38495, has_genre, 14724 38495, starred_actors, 31948 17819, has_genre, 14724 17819, release_year, 35935 14292, has_genre, 14724 14292, release_year, 35935 11826, has_genre, 14724 11826, has_tags, 13081 36982, has_genre, 14724 36982, starred_actors, 446 23027, has_genre, 14724 23027, release_year, 35935 5795, has_genre, 14724 5795, has_tags, 13081 15961, has_genre, 14724 15961, has_tags, 28729 15892, has_genre, 14724 15892, has_tags, 19755 21021, has_genre, 14724 21021, has_tags, 14724 21021, has_tags, 13081 12628, has_genre, 14724 12628, has_tags, 13081 21123, has_genre, 14724 21123, has_tags, 14724 21123, has_tags, 13081 18217, has_genre, 14724 18217, has_tags, 13081 34555, has_genre, 14724 34555, has_tags, 27139 34555, starred_actors, 27139 1033, has_genre, 14724 1033, has_tags, 446 1033, has_tags, 13081 1033, starred_actors, 446 8690, has_genre, 14724 8690, has_tags, 13081 17400, has_genre, 14724 17400, has_tags, 13081 17400, release_year, 35935 23299, has_genre, 14724 23299, has_tags, 28729 35838, has_genre, 14724 35838, has_tags, 28729 17938, has_genre, 14724 17938, has_tags, 446 17938, has_tags, 25245 17938, has_tags, 13081 17938, has_tags, 10981 17938, has_tags, 9726 17938, starred_actors, 446 17938, written_by, 9726 18431, has_genre, 14724 18431, has_tags, 27139 13846, has_genre, 14724 13846, has_tags, 13081 33673, has_genre, 14724 33673, has_tags, 14724 33673, has_tags, 13081 26297, has_genre, 14724 26297, release_year, 35935 34072, has_genre, 14724 34072, has_tags, 14724 34072, has_tags, 13081 978, has_genre, 14724 978, has_tags, 13081 978, release_year, 35935 7264, has_genre, 14724 7264, has_tags, 13081 24305, has_genre, 14724 24305, has_tags, 13081 11346, has_genre, 14724 11346, has_tags, 13081 3854, has_genre, 14724 3854, has_tags, 13081 38120, has_genre, 14724 38120, has_tags, 14724 38120, has_tags, 13081 33950, has_genre, 14724 33950, has_tags, 14724 33950, has_tags, 13081 10560, has_genre, 14724 10560, has_tags, 10981 37052, has_genre, 14724 37052, has_tags, 19755 37052, has_tags, 25245 37052, has_tags, 10981 37052, written_by, 9726 33561, has_genre, 14724 33561, has_tags, 31948 33561, has_tags, 13081 33561, starred_actors, 31948 38677, has_genre, 14724 38677, has_tags, 13081 3487, has_tags, 14724 3487, release_year, 35935 27827, has_genre, 14724 27827, has_tags, 14724 27827, has_tags, 10981 10883, directed_by, 15615 10883, has_genre, 14724 13217, has_genre, 14724 13217, release_year, 35935 1922, has_genre, 14724 1922, has_tags, 14724 1922, has_tags, 13081 29893, has_genre, 14724 29893, release_year, 35935 32999, has_genre, 14724 32999, release_year, 35935 8869, has_genre, 14724 8869, has_tags, 13081 14744, has_genre, 14724 14744, has_tags, 13081 14744, release_year, 35935 26716, has_genre, 14724 26716, has_tags, 26366 26716, has_tags, 13081 26716, starred_actors, 26366 31741, has_genre, 14724 31741, has_tags, 14724 31741, has_tags, 26366 26174, has_genre, 14724 26174, has_tags, 14724 26174, has_tags, 13081 2169, has_genre, 14724 2169, has_tags, 13081 4523, directed_by, 20906 4523, has_genre, 14724 4523, has_tags, 446 4523, has_tags, 20906 4523, has_tags, 26366 4523, has_tags, 34642 4523, has_tags, 19755 4523, has_tags, 25245 4523, has_tags, 31948 4523, has_tags, 27139 4523, has_tags, 13081 4523, has_tags, 28729 4523, has_tags, 10981 4523, release_year, 35935 4523, starred_actors, 446 4523, starred_actors, 26366 4523, starred_actors, 31948 4523, written_by, 9726 32357, has_genre, 14724 32357, has_tags, 14724 32357, has_tags, 31948 32357, starred_actors, 31948 25098, has_genre, 14724 25098, has_tags, 13081 5844, has_genre, 14724 5844, has_tags, 13081 19362, has_genre, 14724 19362, has_tags, 14724 19362, starred_actors, 31948 1507, has_genre, 14724 1507, release_year, 35935 8516, has_tags, 14724 8516, has_tags, 28729 9206, has_genre, 14724 9206, has_tags, 14724 9206, has_tags, 13081 31913, has_genre, 14724 31913, has_tags, 13081 5362, has_genre, 14724 5362, has_tags, 26366 32607, directed_by, 20906 32607, has_tags, 20906 32607, has_tags, 14724 8020, has_genre, 14724 8020, has_tags, 13081 6056, has_genre, 14724 6056, has_tags, 14724 6056, has_tags, 28729 27807, has_genre, 14724 27807, has_tags, 28729 1472, has_genre, 14724 1472, release_year, 35935 12333, has_genre, 14724 12333, has_tags, 13081 7083, has_genre, 14724 7083, release_year, 35935 35843, has_genre, 14724 35843, release_year, 35935 4722, has_genre, 14724 4722, release_year, 35935 5170, has_genre, 14724 5170, release_year, 35935 33414, has_genre, 14724 33414, has_tags, 13081 33414, release_year, 35935 30932, has_genre, 14724 30932, has_tags, 13081 12, has_genre, 14724 12, has_tags, 31948 2512, has_genre, 14724 2512, has_tags, 13081 26674, has_genre, 14724 26674, has_tags, 13081 14696, has_genre, 14724 14696, has_tags, 14724 14696, has_tags, 27139 11948, has_genre, 14724 11948, has_tags, 13081 16900, has_genre, 14724 16900, has_tags, 10981 32597, has_genre, 14724 32597, release_year, 35935 18997, has_genre, 14724 18997, has_tags, 14724 18997, has_tags, 13081 18997, has_tags, 28729 6524, has_genre, 14724 6524, has_tags, 28729 35551, has_genre, 14724 35551, has_tags, 14724 35551, has_tags, 13081 17518, has_genre, 14724 17518, release_year, 35935 13052, has_genre, 14724 13052, release_year, 35935 29952, has_genre, 14724 29952, has_tags, 13081 33807, directed_by, 12698 33807, has_genre, 14724 16288, has_genre, 14724 16288, release_year, 35935 11559, has_genre, 14724 11559, has_tags, 13081 13761, has_genre, 14724 13761, release_year, 35935 14250, has_genre, 14724 14250, release_year, 35935 14886, has_genre, 14724 14886, has_tags, 26366 14886, starred_actors, 26366 37148, has_tags, 14724 37148, has_tags, 13081 27836, has_tags, 446 27836, has_tags, 14724 27836, has_tags, 19755 27836, has_tags, 25245 27836, has_tags, 10981 27836, has_tags, 9726 27836, written_by, 9726 19084, has_genre, 14724 19084, has_tags, 14724 19084, has_tags, 13081 22820, has_genre, 14724 22820, has_tags, 13081 9390, has_genre, 14724 9390, has_tags, 14724 9390, has_tags, 13081 20880, has_genre, 14724 20880, has_tags, 14724 20880, has_tags, 13081 26758, has_genre, 14724 26758, has_tags, 13081 37331, has_genre, 14724 37331, has_tags, 13081 33836, has_genre, 14724 33836, has_tags, 13081 17169, has_genre, 14724 17169, has_tags, 13081 27559, has_tags, 14724 27559, has_tags, 13081 24487, has_genre, 14724 24487, release_year, 35935 10720, has_genre, 14724 10720, has_tags, 14724 10720, release_year, 35935 6485, has_genre, 14724 6485, starred_actors, 34642 22588, has_genre, 14724 22588, has_tags, 13081 6770, has_genre, 14724 6770, has_tags, 13081 37210, has_genre, 14724 37210, has_tags, 14724 37210, has_tags, 13081 37210, has_tags, 10981 Question: For what reason are BUDD BOETTICHER, JENNIFER LEITZES, and RED DRAGON associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BUDD BOETTICHER", "JENNIFER LEITZES", "RED DRAGON" ], "valid_edges": [ [ "3000 MILES TO GRACELAND", "has_genre", "CRIME" ], [ "3000 MILES TO GRACELAND", "has_tags", "R" ], [ "A CLOCKWORK ORANGE", "has_genre", "CRIME" ], [ "A CLOCKWORK ORANGE", "has_tags", "R" ], [ "ALPHA DOG", "has_genre", "CRIME" ], [ "ALPHA DOG", "has_tags", "R" ], [ "AMERICAN GANGSTER", "has_genre", "CRIME" ], [ "AMERICAN GANGSTER", "has_tags", "CRIME" ], [ "AMERICAN GANGSTER", "has_tags", "R" ], [ "AMERICAN HUSTLE", "has_genre", "CRIME" ], [ "AMERICAN HUSTLE", "has_tags", "R" ], [ "APPALOOSA", "has_genre", "CRIME" ], [ "APPALOOSA", "has_tags", "R" ], [ "ASH WEDNESDAY", "has_genre", "CRIME" ], [ "ASH WEDNESDAY", "release_year", "2002" ], [ "ASSASSINATION TANGO", "has_genre", "CRIME" ], [ "ASSASSINATION TANGO", "release_year", "2002" ], [ "BAD LIEUTENANT", "has_genre", "CRIME" ], [ "BAD LIEUTENANT", "has_tags", "HARVEY KEITEL" ], [ "BAD LIEUTENANT", "starred_actors", "HARVEY KEITEL" ], [ "BANGKOK DANGEROUS", "has_genre", "CRIME" ], [ "BANGKOK DANGEROUS", "has_tags", "CRIME" ], [ "BANGKOK DANGEROUS", "has_tags", "R" ], [ "BEFORE THE DEVIL KNOWS YOU'RE DEAD", "has_genre", "CRIME" ], [ "BEFORE THE DEVIL KNOWS YOU'RE DEAD", "has_tags", "PHILIP SEYMOUR HOFFMAN" ], [ "BEFORE THE DEVIL KNOWS YOU'RE DEAD", "has_tags", "R" ], [ "BEFORE THE DEVIL KNOWS YOU'RE DEAD", "starred_actors", "PHILIP SEYMOUR HOFFMAN" ], [ "BETTER LUCK TOMORROW", "has_genre", "CRIME" ], [ "BETTER LUCK TOMORROW", "release_year", "2002" ], [ "BLUE COLLAR", "has_genre", "CRIME" ], [ "BLUE COLLAR", "starred_actors", "HARVEY KEITEL" ], [ "BUGSY", "has_genre", "CRIME" ], [ "BUGSY", "starred_actors", "HARVEY KEITEL" ], [ "CARLITO'S WAY", "has_genre", "CRIME" ], [ "CARLITO'S WAY", "has_tags", "CRIME" ], [ "CARLITO'S WAY", "has_tags", "R" ], [ "CATCH ME IF YOU CAN", "has_genre", "CRIME" ], [ "CATCH ME IF YOU CAN", "has_tags", "CRIME" ], [ "CATCH ME IF YOU CAN", "release_year", "2002" ], [ "CITY OF GOD", "has_genre", "CRIME" ], [ "CITY OF GOD", "has_tags", "CRIME" ], [ "CITY OF GOD", "has_tags", "R" ], [ "CITY OF GOD", "release_year", "2002" ], [ "CITY OF INDUSTRY", "has_genre", "CRIME" ], [ "CITY OF INDUSTRY", "starred_actors", "HARVEY KEITEL" ], [ "CLOCKERS", "has_genre", "CRIME" ], [ "CLOCKERS", "starred_actors", "HARVEY KEITEL" ], [ "COP LAND", "has_genre", "CRIME" ], [ "COP LAND", "has_tags", "CRIME" ], [ "COP LAND", "starred_actors", "HARVEY KEITEL" ], [ "CRASH", "has_tags", "CRIME" ], [ "CRASH", "has_tags", "R" ], [ "CRIME AND PUNISHMENT", "has_genre", "CRIME" ], [ "CRIME AND PUNISHMENT", "release_year", "2002" ], [ "CRIME SPREE", "has_genre", "CRIME" ], [ "CRIME SPREE", "starred_actors", "HARVEY KEITEL" ], [ "DARK BLUE", "has_genre", "CRIME" ], [ "DARK BLUE", "release_year", "2002" ], [ "DEAD IN THE WATER", "has_genre", "CRIME" ], [ "DEAD IN THE WATER", "release_year", "2002" ], [ "DEEP COVER", "has_genre", "CRIME" ], [ "DEEP COVER", "has_tags", "R" ], [ "DESPERATE HOURS", "has_genre", "CRIME" ], [ "DESPERATE HOURS", "starred_actors", "ANTHONY HOPKINS" ], [ "DEUCES WILD", "has_genre", "CRIME" ], [ "DEUCES WILD", "release_year", "2002" ], [ "DINNER RUSH", "has_genre", "CRIME" ], [ "DINNER RUSH", "has_tags", "R" ], [ "DON", "has_genre", "CRIME" ], [ "DON", "has_tags", "REMAKE" ], [ "DONNIE BRASCO", "has_genre", "CRIME" ], [ "DONNIE BRASCO", "has_tags", "FBI" ], [ "DRIVE", "has_genre", "CRIME" ], [ "DRIVE", "has_tags", "CRIME" ], [ "DRIVE", "has_tags", "R" ], [ "EASTERN PROMISES", "has_genre", "CRIME" ], [ "EASTERN PROMISES", "has_tags", "R" ], [ "FARGO", "has_genre", "CRIME" ], [ "FARGO", "has_tags", "CRIME" ], [ "FARGO", "has_tags", "R" ], [ "FIND ME GUILTY", "has_genre", "CRIME" ], [ "FIND ME GUILTY", "has_tags", "R" ], [ "FLAWLESS", "has_genre", "CRIME" ], [ "FLAWLESS", "has_tags", "PHILIP SEYMOUR HOFFMAN" ], [ "FLAWLESS", "starred_actors", "PHILIP SEYMOUR HOFFMAN" ], [ "FRACTURE", "has_genre", "CRIME" ], [ "FRACTURE", "has_tags", "ANTHONY HOPKINS" ], [ "FRACTURE", "has_tags", "R" ], [ "FRACTURE", "starred_actors", "ANTHONY HOPKINS" ], [ "FROZEN RIVER", "has_genre", "CRIME" ], [ "FROZEN RIVER", "has_tags", "R" ], [ "GANGS OF NEW YORK", "has_genre", "CRIME" ], [ "GANGS OF NEW YORK", "has_tags", "R" ], [ "GANGS OF NEW YORK", "release_year", "2002" ], [ "GET CARTER", "has_genre", "CRIME" ], [ "GET CARTER", "has_tags", "REMAKE" ], [ "GUNCRAZY", "has_genre", "CRIME" ], [ "GUNCRAZY", "has_tags", "REMAKE" ], [ "HANNIBAL", "has_genre", "CRIME" ], [ "HANNIBAL", "has_tags", "ANTHONY HOPKINS" ], [ "HANNIBAL", "has_tags", "HANNIBAL LECTER" ], [ "HANNIBAL", "has_tags", "R" ], [ "HANNIBAL", "has_tags", "SERIAL KILLER" ], [ "HANNIBAL", "has_tags", "THOMAS HARRIS" ], [ "HANNIBAL", "starred_actors", "ANTHONY HOPKINS" ], [ "HANNIBAL", "written_by", "THOMAS HARRIS" ], [ "HARD EIGHT", "has_genre", "CRIME" ], [ "HARD EIGHT", "has_tags", "PHILIP SEYMOUR HOFFMAN" ], [ "HARSH TIMES", "has_genre", "CRIME" ], [ "HARSH TIMES", "has_tags", "R" ], [ "HEAT", "has_genre", "CRIME" ], [ "HEAT", "has_tags", "CRIME" ], [ "HEAT", "has_tags", "R" ], [ "HIGH CRIMES", "has_genre", "CRIME" ], [ "HIGH CRIMES", "release_year", "2002" ], [ "I'M NOT SCARED", "has_genre", "CRIME" ], [ "I'M NOT SCARED", "has_tags", "CRIME" ], [ "I'M NOT SCARED", "has_tags", "R" ], [ "INFERNAL AFFAIRS", "has_genre", "CRIME" ], [ "INFERNAL AFFAIRS", "has_tags", "R" ], [ "INFERNAL AFFAIRS", "release_year", "2002" ], [ "INSIDE MAN", "has_genre", "CRIME" ], [ "INSIDE MAN", "has_tags", "R" ], [ "JCVD", "has_genre", "CRIME" ], [ "JCVD", "has_tags", "R" ], [ "KISS KISS BANG BANG", "has_genre", "CRIME" ], [ "KISS KISS BANG BANG", "has_tags", "R" ], [ "LAYER CAKE", "has_genre", "CRIME" ], [ "LAYER CAKE", "has_tags", "R" ], [ "LORD OF WAR", "has_genre", "CRIME" ], [ "LORD OF WAR", "has_tags", "CRIME" ], [ "LORD OF WAR", "has_tags", "R" ], [ "LUCKY NUMBER SLEVIN", "has_genre", "CRIME" ], [ "LUCKY NUMBER SLEVIN", "has_tags", "CRIME" ], [ "LUCKY NUMBER SLEVIN", "has_tags", "R" ], [ "MAN BITES DOG", "has_genre", "CRIME" ], [ "MAN BITES DOG", "has_tags", "SERIAL KILLER" ], [ "MANHUNTER", "has_genre", "CRIME" ], [ "MANHUNTER", "has_tags", "FBI" ], [ "MANHUNTER", "has_tags", "HANNIBAL LECTER" ], [ "MANHUNTER", "has_tags", "SERIAL KILLER" ], [ "MANHUNTER", "written_by", "THOMAS HARRIS" ], [ "MEAN STREETS", "has_genre", "CRIME" ], [ "MEAN STREETS", "has_tags", "HARVEY KEITEL" ], [ "MEAN STREETS", "has_tags", "R" ], [ "MEAN STREETS", "starred_actors", "HARVEY KEITEL" ], [ "MIAMI VICE", "has_genre", "CRIME" ], [ "MIAMI VICE", "has_tags", "R" ], [ "MINORITY REPORT", "has_tags", "CRIME" ], [ "MINORITY REPORT", "release_year", "2002" ], [ "MONSTER", "has_genre", "CRIME" ], [ "MONSTER", "has_tags", "CRIME" ], [ "MONSTER", "has_tags", "SERIAL KILLER" ], [ "MONTANA", "directed_by", "JENNIFER LEITZES" ], [ "MONTANA", "has_genre", "CRIME" ], [ "NARC", "has_genre", "CRIME" ], [ "NARC", "release_year", "2002" ], [ "NINE QUEENS", "has_genre", "CRIME" ], [ "NINE QUEENS", "has_tags", "CRIME" ], [ "NINE QUEENS", "has_tags", "R" ], [ "NO GOOD DEED", "has_genre", "CRIME" ], [ "NO GOOD DEED", "release_year", "2002" ], [ "PAID IN FULL", "has_genre", "CRIME" ], [ "PAID IN FULL", "release_year", "2002" ], [ "PAYBACK", "has_genre", "CRIME" ], [ "PAYBACK", "has_tags", "R" ], [ "PEOPLE I KNOW", "has_genre", "CRIME" ], [ "PEOPLE I KNOW", "has_tags", "R" ], [ "PEOPLE I KNOW", "release_year", "2002" ], [ "PRIDE AND GLORY", "has_genre", "CRIME" ], [ "PRIDE AND GLORY", "has_tags", "EDWARD NORTON" ], [ "PRIDE AND GLORY", "has_tags", "R" ], [ "PRIDE AND GLORY", "starred_actors", "EDWARD NORTON" ], [ "PRIMAL FEAR", "has_genre", "CRIME" ], [ "PRIMAL FEAR", "has_tags", "CRIME" ], [ "PRIMAL FEAR", "has_tags", "EDWARD NORTON" ], [ "PULP FICTION", "has_genre", "CRIME" ], [ "PULP FICTION", "has_tags", "CRIME" ], [ "PULP FICTION", "has_tags", "R" ], [ "RANSOM", "has_genre", "CRIME" ], [ "RANSOM", "has_tags", "R" ], [ "RED DRAGON", "directed_by", "BRETT RATNER" ], [ "RED DRAGON", "has_genre", "CRIME" ], [ "RED DRAGON", "has_tags", "ANTHONY HOPKINS" ], [ "RED DRAGON", "has_tags", "BRETT RATNER" ], [ "RED DRAGON", "has_tags", "EDWARD NORTON" ], [ "RED DRAGON", "has_tags", "EMILY WATSON" ], [ "RED DRAGON", "has_tags", "FBI" ], [ "RED DRAGON", "has_tags", "HANNIBAL LECTER" ], [ "RED DRAGON", "has_tags", "HARVEY KEITEL" ], [ "RED DRAGON", "has_tags", "PHILIP SEYMOUR HOFFMAN" ], [ "RED DRAGON", "has_tags", "R" ], [ "RED DRAGON", "has_tags", "REMAKE" ], [ "RED DRAGON", "has_tags", "SERIAL KILLER" ], [ "RED DRAGON", "release_year", "2002" ], [ "RED DRAGON", "starred_actors", "ANTHONY HOPKINS" ], [ "RED DRAGON", "starred_actors", "EDWARD NORTON" ], [ "RED DRAGON", "starred_actors", "HARVEY KEITEL" ], [ "RED DRAGON", "written_by", "THOMAS HARRIS" ], [ "RESERVOIR DOGS", "has_genre", "CRIME" ], [ "RESERVOIR DOGS", "has_tags", "CRIME" ], [ "RESERVOIR DOGS", "has_tags", "HARVEY KEITEL" ], [ "RESERVOIR DOGS", "starred_actors", "HARVEY KEITEL" ], [ "RICOCHET", "has_genre", "CRIME" ], [ "RICOCHET", "has_tags", "R" ], [ "RIGHTEOUS KILL", "has_genre", "CRIME" ], [ "RIGHTEOUS KILL", "has_tags", "R" ], [ "RISING SUN", "has_genre", "CRIME" ], [ "RISING SUN", "has_tags", "CRIME" ], [ "RISING SUN", "starred_actors", "HARVEY KEITEL" ], [ "ROAD TO PERDITION", "has_genre", "CRIME" ], [ "ROAD TO PERDITION", "release_year", "2002" ], [ "ROBOCOP", "has_tags", "CRIME" ], [ "ROBOCOP", "has_tags", "REMAKE" ], [ "ROCKNROLLA", "has_genre", "CRIME" ], [ "ROCKNROLLA", "has_tags", "CRIME" ], [ "ROCKNROLLA", "has_tags", "R" ], [ "ROMEO IS BLEEDING", "has_genre", "CRIME" ], [ "ROMEO IS BLEEDING", "has_tags", "R" ], [ "ROUNDERS", "has_genre", "CRIME" ], [ "ROUNDERS", "has_tags", "EDWARD NORTON" ], [ "RUSH HOUR", "directed_by", "BRETT RATNER" ], [ "RUSH HOUR", "has_tags", "BRETT RATNER" ], [ "RUSH HOUR", "has_tags", "CRIME" ], [ "SAFE MEN", "has_genre", "CRIME" ], [ "SAFE MEN", "has_tags", "R" ], [ "SCARFACE", "has_genre", "CRIME" ], [ "SCARFACE", "has_tags", "CRIME" ], [ "SCARFACE", "has_tags", "REMAKE" ], [ "SHAFT", "has_genre", "CRIME" ], [ "SHAFT", "has_tags", "REMAKE" ], [ "SHOTTAS", "has_genre", "CRIME" ], [ "SHOTTAS", "release_year", "2002" ], [ "SMOKIN' ACES", "has_genre", "CRIME" ], [ "SMOKIN' ACES", "has_tags", "R" ], [ "SONNY", "has_genre", "CRIME" ], [ "SONNY", "release_year", "2002" ], [ "SPUN", "has_genre", "CRIME" ], [ "SPUN", "release_year", "2002" ], [ "STATE PROPERTY", "has_genre", "CRIME" ], [ "STATE PROPERTY", "release_year", "2002" ], [ "STEALING HARVARD", "has_genre", "CRIME" ], [ "STEALING HARVARD", "release_year", "2002" ], [ "SWEET SIXTEEN", "has_genre", "CRIME" ], [ "SWEET SIXTEEN", "has_tags", "R" ], [ "SWEET SIXTEEN", "release_year", "2002" ], [ "SWORDFISH", "has_genre", "CRIME" ], [ "SWORDFISH", "has_tags", "R" ], [ "TAXI DRIVER", "has_genre", "CRIME" ], [ "TAXI DRIVER", "has_tags", "HARVEY KEITEL" ], [ "THE BANK JOB", "has_genre", "CRIME" ], [ "THE BANK JOB", "has_tags", "R" ], [ "THE BIG EASY", "has_genre", "CRIME" ], [ "THE BIG EASY", "has_tags", "R" ], [ "THE BIG LEBOWSKI", "has_genre", "CRIME" ], [ "THE BIG LEBOWSKI", "has_tags", "CRIME" ], [ "THE BIG LEBOWSKI", "has_tags", "PHILIP SEYMOUR HOFFMAN" ], [ "THE BLACK DAHLIA", "has_genre", "CRIME" ], [ "THE BLACK DAHLIA", "has_tags", "R" ], [ "THE CALL", "has_genre", "CRIME" ], [ "THE CALL", "has_tags", "SERIAL KILLER" ], [ "THE DANCER UPSTAIRS", "has_genre", "CRIME" ], [ "THE DANCER UPSTAIRS", "release_year", "2002" ], [ "THE DEPARTED", "has_genre", "CRIME" ], [ "THE DEPARTED", "has_tags", "CRIME" ], [ "THE DEPARTED", "has_tags", "R" ], [ "THE DEPARTED", "has_tags", "REMAKE" ], [ "THE GETAWAY", "has_genre", "CRIME" ], [ "THE GETAWAY", "has_tags", "REMAKE" ], [ "THE GODFATHER", "has_genre", "CRIME" ], [ "THE GODFATHER", "has_tags", "CRIME" ], [ "THE GODFATHER", "has_tags", "R" ], [ "THE GOOD THIEF", "has_genre", "CRIME" ], [ "THE GOOD THIEF", "release_year", "2002" ], [ "THE HARD WORD", "has_genre", "CRIME" ], [ "THE HARD WORD", "release_year", "2002" ], [ "THE INTERNATIONAL", "has_genre", "CRIME" ], [ "THE INTERNATIONAL", "has_tags", "R" ], [ "THE KILLER IS LOOSE", "directed_by", "BUDD BOETTICHER" ], [ "THE KILLER IS LOOSE", "has_genre", "CRIME" ], [ "THE LARAMIE PROJECT", "has_genre", "CRIME" ], [ "THE LARAMIE PROJECT", "release_year", "2002" ], [ "THE LOOKOUT", "has_genre", "CRIME" ], [ "THE LOOKOUT", "has_tags", "R" ], [ "THE ROOKIE", "has_genre", "CRIME" ], [ "THE ROOKIE", "release_year", "2002" ], [ "THE SALTON SEA", "has_genre", "CRIME" ], [ "THE SALTON SEA", "release_year", "2002" ], [ "THE SCORE", "has_genre", "CRIME" ], [ "THE SCORE", "has_tags", "EDWARD NORTON" ], [ "THE SCORE", "starred_actors", "EDWARD NORTON" ], [ "THE SECRET IN THEIR EYES", "has_tags", "CRIME" ], [ "THE SECRET IN THEIR EYES", "has_tags", "R" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "ANTHONY HOPKINS" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "CRIME" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "FBI" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "HANNIBAL LECTER" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "SERIAL KILLER" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "THOMAS HARRIS" ], [ "THE SILENCE OF THE LAMBS", "written_by", "THOMAS HARRIS" ], [ "THE TOWN", "has_genre", "CRIME" ], [ "THE TOWN", "has_tags", "CRIME" ], [ "THE TOWN", "has_tags", "R" ], [ "THE UNITED STATES OF LELAND", "has_genre", "CRIME" ], [ "THE UNITED STATES OF LELAND", "has_tags", "R" ], [ "THE UNTOUCHABLES", "has_genre", "CRIME" ], [ "THE UNTOUCHABLES", "has_tags", "CRIME" ], [ "THE UNTOUCHABLES", "has_tags", "R" ], [ "THE USUAL SUSPECTS", "has_genre", "CRIME" ], [ "THE USUAL SUSPECTS", "has_tags", "CRIME" ], [ "THE USUAL SUSPECTS", "has_tags", "R" ], [ "THICK AS THIEVES", "has_genre", "CRIME" ], [ "THICK AS THIEVES", "has_tags", "R" ], [ "TO DIE FOR", "has_genre", "CRIME" ], [ "TO DIE FOR", "has_tags", "R" ], [ "TRAFFIC", "has_genre", "CRIME" ], [ "TRAFFIC", "has_tags", "R" ], [ "TRAINING DAY", "has_genre", "CRIME" ], [ "TRAINING DAY", "has_tags", "R" ], [ "TRAINSPOTTING", "has_tags", "CRIME" ], [ "TRAINSPOTTING", "has_tags", "R" ], [ "TRAPPED", "has_genre", "CRIME" ], [ "TRAPPED", "release_year", "2002" ], [ "TRIGGERMEN", "has_genre", "CRIME" ], [ "TRIGGERMEN", "has_tags", "CRIME" ], [ "TRIGGERMEN", "release_year", "2002" ], [ "TRIXIE", "has_genre", "CRIME" ], [ "TRIXIE", "starred_actors", "EMILY WATSON" ], [ "WANTED", "has_genre", "CRIME" ], [ "WANTED", "has_tags", "R" ], [ "WE OWN THE NIGHT", "has_genre", "CRIME" ], [ "WE OWN THE NIGHT", "has_tags", "R" ], [ "ZODIAC", "has_genre", "CRIME" ], [ "ZODIAC", "has_tags", "CRIME" ], [ "ZODIAC", "has_tags", "R" ], [ "ZODIAC", "has_tags", "SERIAL KILLER" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 5658, BAD BOY BUBBY 35468, CLEAN SLATE 30463, COMEDY 36280, MR. BEAN'S HOLIDAY 20191, NICHOLAS HOPE src, edge_attr, dst 5658, has_genre, 30463 5658, starred_actors, 20191 35468, has_genre, 30463 36280, has_genre, 30463 Question: For what reason are CLEAN SLATE, MR. BEAN'S HOLIDAY, and NICHOLAS HOPE associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "CLEAN SLATE", "MR. BEAN'S HOLIDAY", "NICHOLAS HOPE" ], "valid_edges": [ [ "BAD BOY BUBBY", "has_genre", "COMEDY" ], [ "BAD BOY BUBBY", "starred_actors", "NICHOLAS HOPE" ], [ "CLEAN SLATE", "has_genre", "COMEDY" ], [ "MR. BEAN'S HOLIDAY", "has_genre", "COMEDY" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 17315, 2007 7795, BEN CHAPLIN 6012, FRENCH 6480, GERMAN 5870, HORROR 35224, LOST SOULS 31784, PAUL A. PARTAIN 16348, PERSEPOLIS 36258, TALI-IHANTALA 1944 19649, THE COUNTERFEITERS 429, THE SEARCH 38051, THE TEXAS CHAIN SAW MASSACRE 31126, THE WATER HORSE 19779, UNDER THE BOMBS 22214, WAR src, edge_attr, dst 35224, has_genre, 5870 35224, starred_actors, 7795 16348, has_tags, 6012 16348, has_tags, 22214 16348, in_language, 6012 36258, has_genre, 22214 36258, in_language, 6480 19649, has_genre, 22214 19649, in_language, 6480 429, has_genre, 22214 429, in_language, 6012 429, in_language, 6480 38051, has_genre, 5870 38051, has_tags, 5870 38051, starred_actors, 31784 31126, release_year, 17315 31126, starred_actors, 7795 19779, has_genre, 22214 19779, in_language, 6012 22214, release_year, 17315 Question: In what context are BEN CHAPLIN, PAUL A. PARTAIN, and THE SEARCH connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BEN CHAPLIN", "PAUL A. PARTAIN", "THE SEARCH" ], "valid_edges": [ [ "LOST SOULS", "has_genre", "HORROR" ], [ "LOST SOULS", "starred_actors", "BEN CHAPLIN" ], [ "PERSEPOLIS", "has_tags", "FRENCH" ], [ "PERSEPOLIS", "has_tags", "WAR" ], [ "PERSEPOLIS", "in_language", "FRENCH" ], [ "TALI-IHANTALA 1944", "has_genre", "WAR" ], [ "TALI-IHANTALA 1944", "in_language", "GERMAN" ], [ "THE COUNTERFEITERS", "has_genre", "WAR" ], [ "THE COUNTERFEITERS", "in_language", "GERMAN" ], [ "THE SEARCH", "has_genre", "WAR" ], [ "THE SEARCH", "in_language", "FRENCH" ], [ "THE SEARCH", "in_language", "GERMAN" ], [ "THE TEXAS CHAIN SAW MASSACRE", "has_genre", "HORROR" ], [ "THE TEXAS CHAIN SAW MASSACRE", "has_tags", "HORROR" ], [ "THE TEXAS CHAIN SAW MASSACRE", "starred_actors", "PAUL A. PARTAIN" ], [ "THE WATER HORSE", "release_year", "2007" ], [ "THE WATER HORSE", "starred_actors", "BEN CHAPLIN" ], [ "UNDER THE BOMBS", "has_genre", "WAR" ], [ "UNDER THE BOMBS", "in_language", "FRENCH" ], [ "WAR", "release_year", "2007" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 17480, 1988 27261, 2009 18599, BILOXI BLUES 9551, KYLE MCCULLOCH 25508, MATTHEW BRODERICK 3977, TALES FROM THE GIMLI HOSPITAL 18158, THE PERFECT GAME 11574, TORCH SONG TRILOGY 37971, W. WILLIAM WINOKUR 31035, WONDERFUL WORLD src, edge_attr, dst 18599, release_year, 17480 18599, starred_actors, 25508 3977, release_year, 17480 3977, starred_actors, 9551 18158, release_year, 27261 18158, written_by, 37971 11574, has_tags, 25508 11574, release_year, 17480 11574, starred_actors, 25508 31035, release_year, 27261 31035, starred_actors, 25508 Question: How are KYLE MCCULLOCH, MATTHEW BRODERICK, and W. WILLIAM WINOKUR related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "KYLE MCCULLOCH", "MATTHEW BRODERICK", "W. WILLIAM WINOKUR" ], "valid_edges": [ [ "BILOXI BLUES", "release_year", "1988" ], [ "BILOXI BLUES", "starred_actors", "MATTHEW BRODERICK" ], [ "TALES FROM THE GIMLI HOSPITAL", "release_year", "1988" ], [ "TALES FROM THE GIMLI HOSPITAL", "starred_actors", "KYLE MCCULLOCH" ], [ "THE PERFECT GAME", "release_year", "2009" ], [ "THE PERFECT GAME", "written_by", "W. WILLIAM WINOKUR" ], [ "TORCH SONG TRILOGY", "has_tags", "MATTHEW BRODERICK" ], [ "TORCH SONG TRILOGY", "release_year", "1988" ], [ "TORCH SONG TRILOGY", "starred_actors", "MATTHEW BRODERICK" ], [ "WONDERFUL WORLD", "release_year", "2009" ], [ "WONDERFUL WORLD", "starred_actors", "MATTHEW BRODERICK" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 27810, 1968 4816, BANDOLERO! 29521, EASY VIRTUE 8406, FIRECREEK 39775, IN YOUR EYES 7736, JAMES STEWART 23224, KRISTIN SCOTT THOMAS 8379, ROMANCE 2738, ROMEO AND JULIET 22580, SUITE FRANÇAISE 27995, THE FAR COUNTRY src, edge_attr, dst 4816, release_year, 27810 4816, starred_actors, 7736 29521, has_genre, 8379 29521, starred_actors, 23224 8406, release_year, 27810 8406, starred_actors, 7736 39775, has_genre, 8379 2738, has_genre, 8379 2738, has_tags, 8379 2738, release_year, 27810 22580, has_genre, 8379 22580, starred_actors, 23224 27995, has_genre, 8379 27995, starred_actors, 7736 Question: For what reason are FIRECREEK, IN YOUR EYES, and KRISTIN SCOTT THOMAS associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "FIRECREEK", "IN YOUR EYES", "KRISTIN SCOTT THOMAS" ], "valid_edges": [ [ "BANDOLERO!", "release_year", "1968" ], [ "BANDOLERO!", "starred_actors", "JAMES STEWART" ], [ "EASY VIRTUE", "has_genre", "ROMANCE" ], [ "EASY VIRTUE", "starred_actors", "KRISTIN SCOTT THOMAS" ], [ "FIRECREEK", "release_year", "1968" ], [ "FIRECREEK", "starred_actors", "JAMES STEWART" ], [ "IN YOUR EYES", "has_genre", "ROMANCE" ], [ "ROMEO AND JULIET", "has_genre", "ROMANCE" ], [ "ROMEO AND JULIET", "has_tags", "ROMANCE" ], [ "ROMEO AND JULIET", "release_year", "1968" ], [ "SUITE FRANÇAISE", "has_genre", "ROMANCE" ], [ "SUITE FRANÇAISE", "starred_actors", "KRISTIN SCOTT THOMAS" ], [ "THE FAR COUNTRY", "has_genre", "ROMANCE" ], [ "THE FAR COUNTRY", "starred_actors", "JAMES STEWART" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 25221, 1981 27261, 2009 35798, 2010 673, 9 2992, A VERY POTTER MUSICAL 39013, A VERY POTTER SEQUEL 20762, BONNIE GRUESEN 3693, BRIAN HOLDEN 8709, CHRISTOPHER PLUMMER 14850, DARREN CRISS 11528, DEE HEPBURN 2807, GREGORY'S GIRL 5887, HARRY POTTER 7709, HELEN MIRREN 28207, JOEY RICHTER 8481, LAUREN LOPEZ 4018, MATT LANG 24593, MUSICAL 3347, MY DOG TULIP 18396, NICK LANG 27541, PARODY 38000, STATE OF PLAY 5081, THE IMAGINARIUM OF DOCTOR PARNASSUS 29682, THE LAST STATION src, edge_attr, dst 25221, release_year, 27261 35798, starred_actors, 7709 673, has_tags, 8709 673, release_year, 27261 673, starred_actors, 8709 2992, directed_by, 4018 2992, has_genre, 24593 2992, has_tags, 5887 2992, has_tags, 27541 2992, release_year, 27261 2992, starred_actors, 20762 2992, starred_actors, 14850 2992, starred_actors, 28207 2992, starred_actors, 8481 2992, written_by, 3693 2992, written_by, 4018 2992, written_by, 18396 39013, directed_by, 4018 39013, has_genre, 24593 39013, has_tags, 5887 39013, has_tags, 27541 39013, release_year, 35798 39013, starred_actors, 20762 39013, starred_actors, 14850 39013, starred_actors, 28207 39013, starred_actors, 8481 39013, written_by, 3693 39013, written_by, 4018 39013, written_by, 18396 2807, release_year, 25221 2807, starred_actors, 11528 3347, release_year, 27261 3347, starred_actors, 8709 38000, has_tags, 7709 38000, release_year, 27261 38000, starred_actors, 7709 5081, has_tags, 8709 5081, release_year, 27261 5081, starred_actors, 8709 29682, has_tags, 7709 29682, release_year, 27261 29682, starred_actors, 8709 29682, starred_actors, 7709 Question: For what reason are BRIAN HOLDEN, DEE HEPBURN, and THE LAST STATION associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BRIAN HOLDEN", "DEE HEPBURN", "THE LAST STATION" ], "valid_edges": [ [ "1981", "release_year", "2009" ], [ "2010", "starred_actors", "HELEN MIRREN" ], [ "9", "has_tags", "CHRISTOPHER PLUMMER" ], [ "9", "release_year", "2009" ], [ "9", "starred_actors", "CHRISTOPHER PLUMMER" ], [ "A VERY POTTER MUSICAL", "directed_by", "MATT LANG" ], [ "A VERY POTTER MUSICAL", "has_genre", "MUSICAL" ], [ "A VERY POTTER MUSICAL", "has_tags", "HARRY POTTER" ], [ "A VERY POTTER MUSICAL", "has_tags", "PARODY" ], [ "A VERY POTTER MUSICAL", "release_year", "2009" ], [ "A VERY POTTER MUSICAL", "starred_actors", "BONNIE GRUESEN" ], [ "A VERY POTTER MUSICAL", "starred_actors", "DARREN CRISS" ], [ "A VERY POTTER MUSICAL", "starred_actors", "JOEY RICHTER" ], [ "A VERY POTTER MUSICAL", "starred_actors", "LAUREN LOPEZ" ], [ "A VERY POTTER MUSICAL", "written_by", "BRIAN HOLDEN" ], [ "A VERY POTTER MUSICAL", "written_by", "MATT LANG" ], [ "A VERY POTTER MUSICAL", "written_by", "NICK LANG" ], [ "A VERY POTTER SEQUEL", "directed_by", "MATT LANG" ], [ "A VERY POTTER SEQUEL", "has_genre", "MUSICAL" ], [ "A VERY POTTER SEQUEL", "has_tags", "HARRY POTTER" ], [ "A VERY POTTER SEQUEL", "has_tags", "PARODY" ], [ "A VERY POTTER SEQUEL", "release_year", "2010" ], [ "A VERY POTTER SEQUEL", "starred_actors", "BONNIE GRUESEN" ], [ "A VERY POTTER SEQUEL", "starred_actors", "DARREN CRISS" ], [ "A VERY POTTER SEQUEL", "starred_actors", "JOEY RICHTER" ], [ "A VERY POTTER SEQUEL", "starred_actors", "LAUREN LOPEZ" ], [ "A VERY POTTER SEQUEL", "written_by", "BRIAN HOLDEN" ], [ "A VERY POTTER SEQUEL", "written_by", "MATT LANG" ], [ "A VERY POTTER SEQUEL", "written_by", "NICK LANG" ], [ "GREGORY'S GIRL", "release_year", "1981" ], [ "GREGORY'S GIRL", "starred_actors", "DEE HEPBURN" ], [ "MY DOG TULIP", "release_year", "2009" ], [ "MY DOG TULIP", "starred_actors", "CHRISTOPHER PLUMMER" ], [ "STATE OF PLAY", "has_tags", "HELEN MIRREN" ], [ "STATE OF PLAY", "release_year", "2009" ], [ "STATE OF PLAY", "starred_actors", "HELEN MIRREN" ], [ "THE IMAGINARIUM OF DOCTOR PARNASSUS", "has_tags", "CHRISTOPHER PLUMMER" ], [ "THE IMAGINARIUM OF DOCTOR PARNASSUS", "release_year", "2009" ], [ "THE IMAGINARIUM OF DOCTOR PARNASSUS", "starred_actors", "CHRISTOPHER PLUMMER" ], [ "THE LAST STATION", "has_tags", "HELEN MIRREN" ], [ "THE LAST STATION", "release_year", "2009" ], [ "THE LAST STATION", "starred_actors", "CHRISTOPHER PLUMMER" ], [ "THE LAST STATION", "starred_actors", "HELEN MIRREN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 10045, BD-R 2890, DESERT HEARTS 6565, DESPERATE JOURNEY 36212, DRAMA 37934, HELEN SHAVER 27893, POSSESSED 11501, RAYMOND MASSEY 24053, RUGGERO MACCARI 40001, SCENT OF A WOMAN 1277, THE COLOR OF MONEY 30987, THE FOUNTAINHEAD 31851, THE PRISONER OF ZENDA src, edge_attr, dst 2890, has_genre, 36212 2890, starred_actors, 37934 6565, has_genre, 36212 6565, starred_actors, 11501 27893, has_genre, 36212 27893, has_tags, 10045 27893, starred_actors, 11501 40001, has_genre, 36212 40001, has_tags, 36212 40001, written_by, 24053 1277, has_genre, 36212 1277, starred_actors, 37934 30987, has_genre, 36212 30987, starred_actors, 11501 31851, has_genre, 36212 31851, has_tags, 10045 31851, starred_actors, 11501 Question: In what context are HELEN SHAVER, RAYMOND MASSEY, and RUGGERO MACCARI connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "HELEN SHAVER", "RAYMOND MASSEY", "RUGGERO MACCARI" ], "valid_edges": [ [ "DESERT HEARTS", "has_genre", "DRAMA" ], [ "DESERT HEARTS", "starred_actors", "HELEN SHAVER" ], [ "DESPERATE JOURNEY", "has_genre", "DRAMA" ], [ "DESPERATE JOURNEY", "starred_actors", "RAYMOND MASSEY" ], [ "POSSESSED", "has_genre", "DRAMA" ], [ "POSSESSED", "has_tags", "BD-R" ], [ "POSSESSED", "starred_actors", "RAYMOND MASSEY" ], [ "SCENT OF A WOMAN", "has_genre", "DRAMA" ], [ "SCENT OF A WOMAN", "has_tags", "DRAMA" ], [ "SCENT OF A WOMAN", "written_by", "RUGGERO MACCARI" ], [ "THE COLOR OF MONEY", "has_genre", "DRAMA" ], [ "THE COLOR OF MONEY", "starred_actors", "HELEN SHAVER" ], [ "THE FOUNTAINHEAD", "has_genre", "DRAMA" ], [ "THE FOUNTAINHEAD", "starred_actors", "RAYMOND MASSEY" ], [ "THE PRISONER OF ZENDA", "has_genre", "DRAMA" ], [ "THE PRISONER OF ZENDA", "has_tags", "BD-R" ], [ "THE PRISONER OF ZENDA", "starred_actors", "RAYMOND MASSEY" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 658, 2012 22920, CHUNGKING EXPRESS 26193, ELECTION 31783, ENGLISH 24535, GAME CHANGE 12060, GARY LOCKWOOD 26001, HONG KONG 978, INFERNAL AFFAIRS 39252, JOHNNIE TO 6543, MODEL SHOP 31252, V/H/S 10735, VENGEANCE src, edge_attr, dst 22920, has_tags, 26001 22920, in_language, 31783 26193, directed_by, 39252 26193, has_tags, 26001 26193, has_tags, 39252 24535, has_tags, 26193 24535, release_year, 658 978, has_tags, 26001 978, in_language, 31783 6543, in_language, 31783 6543, starred_actors, 12060 31252, release_year, 658 10735, directed_by, 39252 10735, has_tags, 26001 10735, has_tags, 39252 10735, has_tags, 10735 10735, in_language, 31783 Question: How are GARY LOCKWOOD, HONG KONG, and V/H/S related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "GARY LOCKWOOD", "HONG KONG", "V/H/S" ], "valid_edges": [ [ "CHUNGKING EXPRESS", "has_tags", "HONG KONG" ], [ "CHUNGKING EXPRESS", "in_language", "ENGLISH" ], [ "ELECTION", "directed_by", "JOHNNIE TO" ], [ "ELECTION", "has_tags", "HONG KONG" ], [ "ELECTION", "has_tags", "JOHNNIE TO" ], [ "GAME CHANGE", "has_tags", "ELECTION" ], [ "GAME CHANGE", "release_year", "2012" ], [ "INFERNAL AFFAIRS", "has_tags", "HONG KONG" ], [ "INFERNAL AFFAIRS", "in_language", "ENGLISH" ], [ "MODEL SHOP", "in_language", "ENGLISH" ], [ "MODEL SHOP", "starred_actors", "GARY LOCKWOOD" ], [ "V/H/S", "release_year", "2012" ], [ "VENGEANCE", "directed_by", "JOHNNIE TO" ], [ "VENGEANCE", "has_tags", "HONG KONG" ], [ "VENGEANCE", "has_tags", "JOHNNIE TO" ], [ "VENGEANCE", "has_tags", "VENGEANCE" ], [ "VENGEANCE", "in_language", "ENGLISH" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 26257, 1994 1097, 2003 12144, BAD BOYS 34486, EILA 6666, FINNISH 39394, SHOPPING 1124, TAKE CARE OF YOUR SCARF, TATIANA 38099, THE HOME OF DARK BUTTERFLIES 29084, TWENTYNINE PALMS 6867, YOUNG GODS src, edge_attr, dst 12144, in_language, 6666 12144, release_year, 1097 34486, in_language, 6666 34486, release_year, 1097 39394, release_year, 26257 1124, in_language, 6666 1124, release_year, 26257 38099, in_language, 6666 29084, release_year, 1097 6867, in_language, 6666 6867, release_year, 1097 Question: How are SHOPPING, THE HOME OF DARK BUTTERFLIES, and TWENTYNINE PALMS related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "SHOPPING", "THE HOME OF DARK BUTTERFLIES", "TWENTYNINE PALMS" ], "valid_edges": [ [ "BAD BOYS", "in_language", "FINNISH" ], [ "BAD BOYS", "release_year", "2003" ], [ "EILA", "in_language", "FINNISH" ], [ "EILA", "release_year", "2003" ], [ "SHOPPING", "release_year", "1994" ], [ "TAKE CARE OF YOUR SCARF, TATIANA", "in_language", "FINNISH" ], [ "TAKE CARE OF YOUR SCARF, TATIANA", "release_year", "1994" ], [ "THE HOME OF DARK BUTTERFLIES", "in_language", "FINNISH" ], [ "TWENTYNINE PALMS", "release_year", "2003" ], [ "YOUNG GODS", "in_language", "FINNISH" ], [ "YOUNG GODS", "release_year", "2003" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 14259, 1997 35288, ALL OR NOTHING 37672, ANOTHER YEAR 16654, BRITISH 33121, CAREER GIRLS 31728, CHARLY 30463, COMEDY 30946, DANIEL BENZALI 36212, DRAMA 36942, HAPPY-GO-LUCKY 10555, HIGH HOPES 24353, MIKE LEIGH 9635, MR. TURNER 33425, MURDER AT 1600 3159, NAKED 4629, RUTH SHEEN 332, TOPSY-TURVY 22949, VERA DRAKE src, edge_attr, dst 35288, directed_by, 24353 35288, has_genre, 36212 35288, has_tags, 24353 35288, written_by, 24353 37672, directed_by, 24353 37672, has_genre, 36212 37672, has_tags, 16654 37672, has_tags, 24353 37672, starred_actors, 4629 37672, written_by, 24353 33121, directed_by, 24353 33121, has_genre, 36212 33121, has_tags, 24353 33121, release_year, 14259 33121, written_by, 24353 31728, has_genre, 36212 36942, directed_by, 24353 36942, has_genre, 30463 36942, has_genre, 36212 36942, has_tags, 16654 36942, has_tags, 36212 36942, has_tags, 24353 36942, written_by, 24353 10555, directed_by, 24353 10555, has_genre, 30463 10555, has_genre, 36212 10555, has_tags, 24353 10555, starred_actors, 4629 10555, written_by, 24353 9635, directed_by, 24353 9635, has_genre, 36212 9635, has_tags, 24353 9635, written_by, 24353 33425, release_year, 14259 33425, starred_actors, 30946 3159, directed_by, 24353 3159, has_genre, 36212 3159, has_tags, 16654 3159, has_tags, 24353 3159, written_by, 24353 332, directed_by, 24353 332, has_genre, 36212 332, has_tags, 24353 332, written_by, 24353 22949, directed_by, 24353 22949, has_genre, 36212 22949, has_tags, 24353 22949, written_by, 24353 Question: In what context are CHARLY, DANIEL BENZALI, and MIKE LEIGH connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "CHARLY", "DANIEL BENZALI", "MIKE LEIGH" ], "valid_edges": [ [ "ALL OR NOTHING", "directed_by", "MIKE LEIGH" ], [ "ALL OR NOTHING", "has_genre", "DRAMA" ], [ "ALL OR NOTHING", "has_tags", "MIKE LEIGH" ], [ "ALL OR NOTHING", "written_by", "MIKE LEIGH" ], [ "ANOTHER YEAR", "directed_by", "MIKE LEIGH" ], [ "ANOTHER YEAR", "has_genre", "DRAMA" ], [ "ANOTHER YEAR", "has_tags", "BRITISH" ], [ "ANOTHER YEAR", "has_tags", "MIKE LEIGH" ], [ "ANOTHER YEAR", "starred_actors", "RUTH SHEEN" ], [ "ANOTHER YEAR", "written_by", "MIKE LEIGH" ], [ "CAREER GIRLS", "directed_by", "MIKE LEIGH" ], [ "CAREER GIRLS", "has_genre", "DRAMA" ], [ "CAREER GIRLS", "has_tags", "MIKE LEIGH" ], [ "CAREER GIRLS", "release_year", "1997" ], [ "CAREER GIRLS", "written_by", "MIKE LEIGH" ], [ "CHARLY", "has_genre", "DRAMA" ], [ "HAPPY-GO-LUCKY", "directed_by", "MIKE LEIGH" ], [ "HAPPY-GO-LUCKY", "has_genre", "COMEDY" ], [ "HAPPY-GO-LUCKY", "has_genre", "DRAMA" ], [ "HAPPY-GO-LUCKY", "has_tags", "BRITISH" ], [ "HAPPY-GO-LUCKY", "has_tags", "DRAMA" ], [ "HAPPY-GO-LUCKY", "has_tags", "MIKE LEIGH" ], [ "HAPPY-GO-LUCKY", "written_by", "MIKE LEIGH" ], [ "HIGH HOPES", "directed_by", "MIKE LEIGH" ], [ "HIGH HOPES", "has_genre", "COMEDY" ], [ "HIGH HOPES", "has_genre", "DRAMA" ], [ "HIGH HOPES", "has_tags", "MIKE LEIGH" ], [ "HIGH HOPES", "starred_actors", "RUTH SHEEN" ], [ "HIGH HOPES", "written_by", "MIKE LEIGH" ], [ "MR. TURNER", "directed_by", "MIKE LEIGH" ], [ "MR. TURNER", "has_genre", "DRAMA" ], [ "MR. TURNER", "has_tags", "MIKE LEIGH" ], [ "MR. TURNER", "written_by", "MIKE LEIGH" ], [ "MURDER AT 1600", "release_year", "1997" ], [ "MURDER AT 1600", "starred_actors", "DANIEL BENZALI" ], [ "NAKED", "directed_by", "MIKE LEIGH" ], [ "NAKED", "has_genre", "DRAMA" ], [ "NAKED", "has_tags", "BRITISH" ], [ "NAKED", "has_tags", "MIKE LEIGH" ], [ "NAKED", "written_by", "MIKE LEIGH" ], [ "TOPSY-TURVY", "directed_by", "MIKE LEIGH" ], [ "TOPSY-TURVY", "has_genre", "DRAMA" ], [ "TOPSY-TURVY", "has_tags", "MIKE LEIGH" ], [ "TOPSY-TURVY", "written_by", "MIKE LEIGH" ], [ "VERA DRAKE", "directed_by", "MIKE LEIGH" ], [ "VERA DRAKE", "has_genre", "DRAMA" ], [ "VERA DRAKE", "has_tags", "MIKE LEIGH" ], [ "VERA DRAKE", "written_by", "MIKE LEIGH" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 26762, 2008 24116, COLLEGE 14144, CRAZY LOVE 18087, DAN KLORES 12841, DOCUMENTARY 17682, EXAMINED LIFE 18916, JAMES W. HORNE 4238, JUDITH BUTLER src, edge_attr, dst 24116, directed_by, 18916 24116, has_tags, 18916 24116, release_year, 26762 14144, directed_by, 18087 14144, has_genre, 12841 14144, written_by, 18087 17682, has_genre, 12841 17682, release_year, 26762 17682, starred_actors, 4238 Question: For what reason are DAN KLORES, JAMES W. HORNE, and JUDITH BUTLER associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "DAN KLORES", "JAMES W. HORNE", "JUDITH BUTLER" ], "valid_edges": [ [ "COLLEGE", "directed_by", "JAMES W. HORNE" ], [ "COLLEGE", "has_tags", "JAMES W. HORNE" ], [ "COLLEGE", "release_year", "2008" ], [ "CRAZY LOVE", "directed_by", "DAN KLORES" ], [ "CRAZY LOVE", "has_genre", "DOCUMENTARY" ], [ "CRAZY LOVE", "written_by", "DAN KLORES" ], [ "EXAMINED LIFE", "has_genre", "DOCUMENTARY" ], [ "EXAMINED LIFE", "release_year", "2008" ], [ "EXAMINED LIFE", "starred_actors", "JUDITH BUTLER" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 3702, 1995 8486, 1999 22189, BEFORE SUNRISE 6230, BYE-BYE 30463, COMEDY 36212, DRAMA 20071, FLIRT 21828, HUMAN TRAFFIC 11499, JOHN SIMM 33519, LA HAINE 14601, LES MISÉRABLES 36083, MIRANDA 35637, MITCH MULLANY 22915, MONDO 8379, ROMANCE 4689, SABRINA 8605, THE BREAKS src, edge_attr, dst 22189, has_genre, 36212 22189, release_year, 3702 6230, has_genre, 36212 6230, release_year, 3702 20071, has_genre, 36212 20071, release_year, 3702 21828, release_year, 8486 21828, starred_actors, 11499 33519, has_genre, 36212 33519, release_year, 3702 14601, has_genre, 36212 14601, release_year, 3702 36083, has_genre, 30463 36083, has_genre, 8379 36083, starred_actors, 11499 22915, has_genre, 36212 22915, release_year, 3702 8379, has_genre, 36212 4689, has_genre, 36212 4689, has_tags, 36212 4689, release_year, 3702 8605, has_genre, 30463 8605, release_year, 8486 8605, written_by, 35637 Question: How are FLIRT, JOHN SIMM, and MITCH MULLANY related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "FLIRT", "JOHN SIMM", "MITCH MULLANY" ], "valid_edges": [ [ "BEFORE SUNRISE", "has_genre", "DRAMA" ], [ "BEFORE SUNRISE", "release_year", "1995" ], [ "BYE-BYE", "has_genre", "DRAMA" ], [ "BYE-BYE", "release_year", "1995" ], [ "FLIRT", "has_genre", "DRAMA" ], [ "FLIRT", "release_year", "1995" ], [ "HUMAN TRAFFIC", "release_year", "1999" ], [ "HUMAN TRAFFIC", "starred_actors", "JOHN SIMM" ], [ "LA HAINE", "has_genre", "DRAMA" ], [ "LA HAINE", "release_year", "1995" ], [ "LES MISÉRABLES", "has_genre", "DRAMA" ], [ "LES MISÉRABLES", "release_year", "1995" ], [ "MIRANDA", "has_genre", "COMEDY" ], [ "MIRANDA", "has_genre", "ROMANCE" ], [ "MIRANDA", "starred_actors", "JOHN SIMM" ], [ "MONDO", "has_genre", "DRAMA" ], [ "MONDO", "release_year", "1995" ], [ "ROMANCE", "has_genre", "DRAMA" ], [ "SABRINA", "has_genre", "DRAMA" ], [ "SABRINA", "has_tags", "DRAMA" ], [ "SABRINA", "release_year", "1995" ], [ "THE BREAKS", "has_genre", "COMEDY" ], [ "THE BREAKS", "release_year", "1999" ], [ "THE BREAKS", "written_by", "MITCH MULLANY" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 26762, 2008 30721, A BEAUTIFUL MIND 9197, AKIVA GOLDSMAN 37752, ASIMOV 5496, BOOK 22958, FAMOUS 11339, FANTASTIC VOYAGE 11565, GOOD 20625, I, ROBOT 29267, ISAAC ASIMOV 14017, JURASSIC PARK 995, MIDNIGHT IN THE GARDEN OF GOOD AND EVIL 31134, PARIS 19982, PAUL BETTANY 26434, RON HOWARD 32533, SCIENCE FICTION 1059, THE DA VINCI CODE 33995, THE GOOD EARTH 16999, ZACK AND MIRI MAKE A PORNO src, edge_attr, dst 30721, directed_by, 26434 30721, has_tags, 5496 30721, has_tags, 19982 30721, has_tags, 26434 30721, written_by, 9197 11339, has_tags, 37752 11339, has_tags, 29267 11339, has_tags, 32533 11565, release_year, 26762 20625, has_tags, 37752 20625, has_tags, 29267 20625, written_by, 9197 20625, written_by, 29267 14017, has_tags, 5496 14017, has_tags, 32533 995, has_imdb_rating, 11565 995, has_imdb_votes, 22958 995, has_tags, 5496 31134, release_year, 26762 1059, directed_by, 26434 1059, has_imdb_votes, 22958 1059, has_tags, 5496 1059, has_tags, 31134 1059, has_tags, 19982 1059, has_tags, 26434 1059, written_by, 9197 33995, has_imdb_rating, 11565 33995, has_tags, 5496 16999, release_year, 26762 Question: In what context are ASIMOV, BOOK, and ZACK AND MIRI MAKE A PORNO connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ASIMOV", "BOOK", "ZACK AND MIRI MAKE A PORNO" ], "valid_edges": [ [ "A BEAUTIFUL MIND", "directed_by", "RON HOWARD" ], [ "A BEAUTIFUL MIND", "has_tags", "BOOK" ], [ "A BEAUTIFUL MIND", "has_tags", "PAUL BETTANY" ], [ "A BEAUTIFUL MIND", "has_tags", "RON HOWARD" ], [ "A BEAUTIFUL MIND", "written_by", "AKIVA GOLDSMAN" ], [ "FANTASTIC VOYAGE", "has_tags", "ASIMOV" ], [ "FANTASTIC VOYAGE", "has_tags", "ISAAC ASIMOV" ], [ "FANTASTIC VOYAGE", "has_tags", "SCIENCE FICTION" ], [ "GOOD", "release_year", "2008" ], [ "I, ROBOT", "has_tags", "ASIMOV" ], [ "I, ROBOT", "has_tags", "ISAAC ASIMOV" ], [ "I, ROBOT", "written_by", "AKIVA GOLDSMAN" ], [ "I, ROBOT", "written_by", "ISAAC ASIMOV" ], [ "JURASSIC PARK", "has_tags", "BOOK" ], [ "JURASSIC PARK", "has_tags", "SCIENCE FICTION" ], [ "MIDNIGHT IN THE GARDEN OF GOOD AND EVIL", "has_imdb_rating", "GOOD" ], [ "MIDNIGHT IN THE GARDEN OF GOOD AND EVIL", "has_imdb_votes", "FAMOUS" ], [ "MIDNIGHT IN THE GARDEN OF GOOD AND EVIL", "has_tags", "BOOK" ], [ "PARIS", "release_year", "2008" ], [ "THE DA VINCI CODE", "directed_by", "RON HOWARD" ], [ "THE DA VINCI CODE", "has_imdb_votes", "FAMOUS" ], [ "THE DA VINCI CODE", "has_tags", "BOOK" ], [ "THE DA VINCI CODE", "has_tags", "PARIS" ], [ "THE DA VINCI CODE", "has_tags", "PAUL BETTANY" ], [ "THE DA VINCI CODE", "has_tags", "RON HOWARD" ], [ "THE DA VINCI CODE", "written_by", "AKIVA GOLDSMAN" ], [ "THE GOOD EARTH", "has_imdb_rating", "GOOD" ], [ "THE GOOD EARTH", "has_tags", "BOOK" ], [ "ZACK AND MIRI MAKE A PORNO", "release_year", "2008" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 37967, 13 SINS 26762, 2008 9377, 2014 26212, 3 DAYS TO KILL 29141, A MOST WANTED MAN 14987, A SECOND CHANCE 38646, ACHILLES AND THE TORTOISE 23806, ADDICTED 36359, AFTERWARDS 36494, ANYTHING FOR HER 8402, BANGKOK DANGEROUS 22475, BOOT CAMP 5376, CAPE NO. 7 38311, CHAOS 24400, DARK WATER 5148, DEATH RACE 24410, DEMONLOVER 2710, DEPARTURES 2827, DETROIT METAL CITY 9456, DOOMSDAY 21692, EAGLE EYE 25595, EDEN LAKE 28064, FEAR ME NOT 16516, FIFTY DEAD MEN WALKING 17653, FINE, TOTALLY FINE 26691, FREEZER 3553, GODZILLA 9642, GONE GIRL 13851, GOOD PEOPLE 10147, HOUSE 35436, HUSH 10121, IP MAN 36874, JAPANESE 33200, JESSABELLE 38731, KIKI'S DELIVERY SERVICE 26839, KILLERS 35352, KILLSHOT 7415, KISS OF DEATH 35280, LAKEVIEW TERRACE 19523, LEFT BEHIND 9396, LINEWATCH 20189, LOFT 22804, LUST, CAUTION 30350, NEXT 1128, NICOLAS CAGE 22868, NIGHTCRAWLER 29893, NO GOOD DEED 31027, NOT SAFE FOR WORK 36326, ONE MISSED CALL 12482, OPEN WINDOWS 36532, OVER YOUR DEAD BODY 21178, PATHOLOGY 20144, PONYO 12428, RAGE 29252, REASONABLE DOUBT 16595, RED 19630, RESTRAINT 37097, SABOTAGE 10303, SEEKING JUSTICE 12787, STAND BY ME DORAEMON 26269, STEREO 14619, STONEHEARST ASYLUM 32584, SUSPECT X 30003, TAKEN 23782, TAKEN 3 19565, THE 39 STEPS 20129, THE ALPHABET KILLER 23821, THE BAG MAN 30049, THE CAPTIVE 6633, THE DEAD OUTSIDE 33457, THE DEAL 10912, THE EQUALIZER 9522, THE ESCAPIST 34975, THE GUEST 9187, THE HAPPENING 8194, THE HEADLESS WOMAN 10421, THE HORSEMAN 6126, THE INCITE MILL 4223, THE INTERVIEW 13613, THE LAZARUS PROJECT 30751, THE LOFT 27657, THE MACHINE GIRL 30336, THE MIDNIGHT MEAT TRAIN 39061, THE OXFORD MURDERS 27529, THE POKER CLUB 30014, THE RAID 2 8660, THE RECKONING 20527, THE SCRIBBLER 28095, THE SIGNAL 10017, THE SKY CRAWLERS 8659, THE SNOW WHITE MURDER CASE 1330, THE SQUARE 23525, THE TWO FACES OF JANUARY 17568, THE VANISHING 21593, THE VOICES 22751, THE WICKER MAN 36616, THE WORLD OF KANAKO 24811, THRILLER 37444, TIM KRABBÉ 37920, TOKYO! 34541, TORTURED 8334, TRANSSIBERIAN 22110, TRESPASS 18451, UNTRACEABLE 33011, VALKYRIE 4375, WHEN MARNIE WAS THERE 20222, WHILE SHE WAS OUT 6262, WHITE BIRD IN A BLIZZARD 28356, WICKED BLOOD 15405, ZANDALEE src, edge_attr, dst 37967, has_genre, 24811 37967, release_year, 9377 26212, has_genre, 24811 26212, release_year, 9377 29141, has_genre, 24811 29141, has_tags, 24811 29141, release_year, 9377 14987, has_genre, 24811 14987, release_year, 9377 38646, in_language, 36874 38646, release_year, 26762 23806, has_genre, 24811 23806, release_year, 9377 36359, has_genre, 24811 36359, release_year, 26762 36494, has_genre, 24811 36494, release_year, 26762 8402, has_genre, 24811 8402, has_tags, 1128 8402, release_year, 26762 8402, starred_actors, 1128 22475, has_genre, 24811 22475, release_year, 26762 5376, in_language, 36874 5376, release_year, 26762 38311, has_genre, 24811 38311, in_language, 36874 24400, has_genre, 24811 24400, in_language, 36874 5148, has_genre, 24811 5148, release_year, 26762 24410, has_genre, 24811 24410, in_language, 36874 2710, in_language, 36874 2710, release_year, 26762 2827, in_language, 36874 2827, release_year, 26762 9456, has_genre, 24811 9456, release_year, 26762 21692, has_genre, 24811 21692, release_year, 26762 25595, has_genre, 24811 25595, release_year, 26762 28064, has_genre, 24811 28064, release_year, 26762 16516, has_genre, 24811 16516, release_year, 26762 17653, in_language, 36874 17653, release_year, 26762 26691, has_genre, 24811 26691, release_year, 9377 3553, in_language, 36874 3553, release_year, 9377 9642, has_genre, 24811 9642, has_tags, 9377 9642, has_tags, 24811 9642, release_year, 9377 13851, has_genre, 24811 13851, release_year, 9377 10147, has_tags, 36874 10147, in_language, 36874 10147, release_year, 26762 35436, has_genre, 24811 35436, release_year, 26762 10121, in_language, 36874 10121, release_year, 26762 33200, has_genre, 24811 33200, release_year, 9377 38731, in_language, 36874 38731, release_year, 9377 26839, in_language, 36874 26839, release_year, 9377 35352, has_genre, 24811 35352, release_year, 26762 7415, has_genre, 24811 7415, starred_actors, 1128 35280, has_genre, 24811 35280, release_year, 26762 19523, has_genre, 24811 19523, has_tags, 1128 19523, release_year, 9377 19523, starred_actors, 1128 9396, has_genre, 24811 9396, release_year, 26762 20189, in_language, 36874 20189, release_year, 26762 22804, has_genre, 24811 22804, in_language, 36874 30350, has_genre, 24811 30350, has_tags, 1128 30350, starred_actors, 1128 22868, has_genre, 24811 22868, has_tags, 24811 22868, release_year, 9377 29893, has_genre, 24811 29893, release_year, 9377 31027, has_genre, 24811 31027, release_year, 9377 36326, in_language, 36874 36326, release_year, 26762 12482, has_genre, 24811 12482, release_year, 9377 36532, in_language, 36874 36532, release_year, 9377 21178, has_genre, 24811 21178, release_year, 26762 20144, in_language, 36874 20144, release_year, 26762 12428, has_genre, 24811 12428, release_year, 9377 12428, starred_actors, 1128 29252, has_genre, 24811 29252, release_year, 9377 16595, has_genre, 24811 16595, release_year, 26762 19630, has_genre, 24811 19630, release_year, 26762 37097, has_genre, 24811 37097, release_year, 9377 10303, has_genre, 24811 10303, has_tags, 1128 10303, starred_actors, 1128 12787, in_language, 36874 12787, release_year, 9377 26269, has_genre, 24811 26269, release_year, 9377 14619, has_genre, 24811 14619, release_year, 9377 32584, in_language, 36874 32584, release_year, 26762 30003, has_genre, 24811 30003, has_tags, 24811 30003, release_year, 26762 23782, has_genre, 24811 23782, release_year, 9377 19565, has_genre, 24811 19565, has_tags, 24811 19565, release_year, 26762 20129, has_genre, 24811 20129, release_year, 26762 23821, has_genre, 24811 23821, release_year, 9377 30049, has_genre, 24811 30049, release_year, 9377 6633, has_genre, 24811 6633, release_year, 26762 33457, has_genre, 24811 33457, release_year, 26762 10912, has_genre, 24811 10912, release_year, 9377 9522, has_genre, 24811 9522, release_year, 26762 34975, has_genre, 24811 34975, release_year, 9377 9187, has_genre, 24811 9187, release_year, 26762 8194, has_genre, 24811 8194, release_year, 26762 10421, has_genre, 24811 10421, release_year, 26762 6126, has_genre, 24811 6126, in_language, 36874 4223, has_genre, 24811 4223, release_year, 9377 13613, has_genre, 24811 13613, release_year, 26762 30751, has_genre, 24811 30751, release_year, 9377 27657, in_language, 36874 27657, release_year, 26762 30336, has_genre, 24811 30336, release_year, 26762 39061, has_genre, 24811 39061, release_year, 26762 27529, has_genre, 24811 27529, release_year, 26762 30014, in_language, 36874 30014, release_year, 9377 8660, has_genre, 24811 8660, release_year, 9377 20527, has_genre, 24811 20527, release_year, 9377 28095, has_genre, 24811 28095, release_year, 9377 10017, has_tags, 36874 10017, in_language, 36874 10017, release_year, 26762 8659, in_language, 36874 8659, release_year, 9377 1330, has_genre, 24811 1330, release_year, 26762 23525, has_genre, 24811 23525, release_year, 9377 17568, has_genre, 24811 17568, written_by, 37444 21593, has_genre, 24811 21593, release_year, 9377 22751, has_genre, 24811 22751, has_tags, 1128 22751, starred_actors, 1128 36616, in_language, 36874 36616, release_year, 9377 37920, in_language, 36874 37920, release_year, 26762 34541, has_genre, 24811 34541, release_year, 26762 8334, has_tags, 24811 8334, release_year, 26762 22110, has_genre, 24811 22110, has_tags, 1128 22110, starred_actors, 1128 18451, has_genre, 24811 18451, release_year, 26762 33011, has_genre, 24811 33011, release_year, 26762 4375, in_language, 36874 4375, release_year, 9377 20222, has_genre, 24811 20222, release_year, 26762 6262, has_genre, 24811 6262, release_year, 9377 28356, has_genre, 24811 28356, release_year, 9377 15405, has_genre, 24811 15405, starred_actors, 1128 Question: For what reason are LEFT BEHIND, THE MACHINE GIRL, and TIM KRABBÉ associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "LEFT BEHIND", "THE MACHINE GIRL", "TIM KRABBÉ" ], "valid_edges": [ [ "13 SINS", "has_genre", "THRILLER" ], [ "13 SINS", "release_year", "2014" ], [ "3 DAYS TO KILL", "has_genre", "THRILLER" ], [ "3 DAYS TO KILL", "release_year", "2014" ], [ "A MOST WANTED MAN", "has_genre", "THRILLER" ], [ "A MOST WANTED MAN", "has_tags", "THRILLER" ], [ "A MOST WANTED MAN", "release_year", "2014" ], [ "A SECOND CHANCE", "has_genre", "THRILLER" ], [ "A SECOND CHANCE", "release_year", "2014" ], [ "ACHILLES AND THE TORTOISE", "in_language", "JAPANESE" ], [ "ACHILLES AND THE TORTOISE", "release_year", "2008" ], [ "ADDICTED", "has_genre", "THRILLER" ], [ "ADDICTED", "release_year", "2014" ], [ "AFTERWARDS", "has_genre", "THRILLER" ], [ "AFTERWARDS", "release_year", "2008" ], [ "ANYTHING FOR HER", "has_genre", "THRILLER" ], [ "ANYTHING FOR HER", "release_year", "2008" ], [ "BANGKOK DANGEROUS", "has_genre", "THRILLER" ], [ "BANGKOK DANGEROUS", "has_tags", "NICOLAS CAGE" ], [ "BANGKOK DANGEROUS", "release_year", "2008" ], [ "BANGKOK DANGEROUS", "starred_actors", "NICOLAS CAGE" ], [ "BOOT CAMP", "has_genre", "THRILLER" ], [ "BOOT CAMP", "release_year", "2008" ], [ "CAPE NO. 7", "in_language", "JAPANESE" ], [ "CAPE NO. 7", "release_year", "2008" ], [ "CHAOS", "has_genre", "THRILLER" ], [ "CHAOS", "in_language", "JAPANESE" ], [ "DARK WATER", "has_genre", "THRILLER" ], [ "DARK WATER", "in_language", "JAPANESE" ], [ "DEATH RACE", "has_genre", "THRILLER" ], [ "DEATH RACE", "release_year", "2008" ], [ "DEMONLOVER", "has_genre", "THRILLER" ], [ "DEMONLOVER", "in_language", "JAPANESE" ], [ "DEPARTURES", "in_language", "JAPANESE" ], [ "DEPARTURES", "release_year", "2008" ], [ "DETROIT METAL CITY", "in_language", "JAPANESE" ], [ "DETROIT METAL CITY", "release_year", "2008" ], [ "DOOMSDAY", "has_genre", "THRILLER" ], [ "DOOMSDAY", "release_year", "2008" ], [ "EAGLE EYE", "has_genre", "THRILLER" ], [ "EAGLE EYE", "release_year", "2008" ], [ "EDEN LAKE", "has_genre", "THRILLER" ], [ "EDEN LAKE", "release_year", "2008" ], [ "FEAR ME NOT", "has_genre", "THRILLER" ], [ "FEAR ME NOT", "release_year", "2008" ], [ "FIFTY DEAD MEN WALKING", "has_genre", "THRILLER" ], [ "FIFTY DEAD MEN WALKING", "release_year", "2008" ], [ "FINE, TOTALLY FINE", "in_language", "JAPANESE" ], [ "FINE, TOTALLY FINE", "release_year", "2008" ], [ "FREEZER", "has_genre", "THRILLER" ], [ "FREEZER", "release_year", "2014" ], [ "GODZILLA", "in_language", "JAPANESE" ], [ "GODZILLA", "release_year", "2014" ], [ "GONE GIRL", "has_genre", "THRILLER" ], [ "GONE GIRL", "has_tags", "2014" ], [ "GONE GIRL", "has_tags", "THRILLER" ], [ "GONE GIRL", "release_year", "2014" ], [ "GOOD PEOPLE", "has_genre", "THRILLER" ], [ "GOOD PEOPLE", "release_year", "2014" ], [ "HOUSE", "has_tags", "JAPANESE" ], [ "HOUSE", "in_language", "JAPANESE" ], [ "HOUSE", "release_year", "2008" ], [ "HUSH", "has_genre", "THRILLER" ], [ "HUSH", "release_year", "2008" ], [ "IP MAN", "in_language", "JAPANESE" ], [ "IP MAN", "release_year", "2008" ], [ "JESSABELLE", "has_genre", "THRILLER" ], [ "JESSABELLE", "release_year", "2014" ], [ "KIKI'S DELIVERY SERVICE", "in_language", "JAPANESE" ], [ "KIKI'S DELIVERY SERVICE", "release_year", "2014" ], [ "KILLERS", "in_language", "JAPANESE" ], [ "KILLERS", "release_year", "2014" ], [ "KILLSHOT", "has_genre", "THRILLER" ], [ "KILLSHOT", "release_year", "2008" ], [ "KISS OF DEATH", "has_genre", "THRILLER" ], [ "KISS OF DEATH", "starred_actors", "NICOLAS CAGE" ], [ "LAKEVIEW TERRACE", "has_genre", "THRILLER" ], [ "LAKEVIEW TERRACE", "release_year", "2008" ], [ "LEFT BEHIND", "has_genre", "THRILLER" ], [ "LEFT BEHIND", "has_tags", "NICOLAS CAGE" ], [ "LEFT BEHIND", "release_year", "2014" ], [ "LEFT BEHIND", "starred_actors", "NICOLAS CAGE" ], [ "LINEWATCH", "has_genre", "THRILLER" ], [ "LINEWATCH", "release_year", "2008" ], [ "LOFT", "in_language", "JAPANESE" ], [ "LOFT", "release_year", "2008" ], [ "LUST, CAUTION", "has_genre", "THRILLER" ], [ "LUST, CAUTION", "in_language", "JAPANESE" ], [ "NEXT", "has_genre", "THRILLER" ], [ "NEXT", "has_tags", "NICOLAS CAGE" ], [ "NEXT", "starred_actors", "NICOLAS CAGE" ], [ "NIGHTCRAWLER", "has_genre", "THRILLER" ], [ "NIGHTCRAWLER", "has_tags", "THRILLER" ], [ "NIGHTCRAWLER", "release_year", "2014" ], [ "NO GOOD DEED", "has_genre", "THRILLER" ], [ "NO GOOD DEED", "release_year", "2014" ], [ "NOT SAFE FOR WORK", "has_genre", "THRILLER" ], [ "NOT SAFE FOR WORK", "release_year", "2014" ], [ "ONE MISSED CALL", "in_language", "JAPANESE" ], [ "ONE MISSED CALL", "release_year", "2008" ], [ "OPEN WINDOWS", "has_genre", "THRILLER" ], [ "OPEN WINDOWS", "release_year", "2014" ], [ "OVER YOUR DEAD BODY", "in_language", "JAPANESE" ], [ "OVER YOUR DEAD BODY", "release_year", "2014" ], [ "PATHOLOGY", "has_genre", "THRILLER" ], [ "PATHOLOGY", "release_year", "2008" ], [ "PONYO", "in_language", "JAPANESE" ], [ "PONYO", "release_year", "2008" ], [ "RAGE", "has_genre", "THRILLER" ], [ "RAGE", "release_year", "2014" ], [ "RAGE", "starred_actors", "NICOLAS CAGE" ], [ "REASONABLE DOUBT", "has_genre", "THRILLER" ], [ "REASONABLE DOUBT", "release_year", "2014" ], [ "RED", "has_genre", "THRILLER" ], [ "RED", "release_year", "2008" ], [ "RESTRAINT", "has_genre", "THRILLER" ], [ "RESTRAINT", "release_year", "2008" ], [ "SABOTAGE", "has_genre", "THRILLER" ], [ "SABOTAGE", "release_year", "2014" ], [ "SEEKING JUSTICE", "has_genre", "THRILLER" ], [ "SEEKING JUSTICE", "has_tags", "NICOLAS CAGE" ], [ "SEEKING JUSTICE", "starred_actors", "NICOLAS CAGE" ], [ "STAND BY ME DORAEMON", "in_language", "JAPANESE" ], [ "STAND BY ME DORAEMON", "release_year", "2014" ], [ "STEREO", "has_genre", "THRILLER" ], [ "STEREO", "release_year", "2014" ], [ "STONEHEARST ASYLUM", "has_genre", "THRILLER" ], [ "STONEHEARST ASYLUM", "release_year", "2014" ], [ "SUSPECT X", "in_language", "JAPANESE" ], [ "SUSPECT X", "release_year", "2008" ], [ "TAKEN", "has_genre", "THRILLER" ], [ "TAKEN", "has_tags", "THRILLER" ], [ "TAKEN", "release_year", "2008" ], [ "TAKEN 3", "has_genre", "THRILLER" ], [ "TAKEN 3", "release_year", "2014" ], [ "THE 39 STEPS", "has_genre", "THRILLER" ], [ "THE 39 STEPS", "has_tags", "THRILLER" ], [ "THE 39 STEPS", "release_year", "2008" ], [ "THE ALPHABET KILLER", "has_genre", "THRILLER" ], [ "THE ALPHABET KILLER", "release_year", "2008" ], [ "THE BAG MAN", "has_genre", "THRILLER" ], [ "THE BAG MAN", "release_year", "2014" ], [ "THE CAPTIVE", "has_genre", "THRILLER" ], [ "THE CAPTIVE", "release_year", "2014" ], [ "THE DEAD OUTSIDE", "has_genre", "THRILLER" ], [ "THE DEAD OUTSIDE", "release_year", "2008" ], [ "THE DEAL", "has_genre", "THRILLER" ], [ "THE DEAL", "release_year", "2008" ], [ "THE EQUALIZER", "has_genre", "THRILLER" ], [ "THE EQUALIZER", "release_year", "2014" ], [ "THE ESCAPIST", "has_genre", "THRILLER" ], [ "THE ESCAPIST", "release_year", "2008" ], [ "THE GUEST", "has_genre", "THRILLER" ], [ "THE GUEST", "release_year", "2014" ], [ "THE HAPPENING", "has_genre", "THRILLER" ], [ "THE HAPPENING", "release_year", "2008" ], [ "THE HEADLESS WOMAN", "has_genre", "THRILLER" ], [ "THE HEADLESS WOMAN", "release_year", "2008" ], [ "THE HORSEMAN", "has_genre", "THRILLER" ], [ "THE HORSEMAN", "release_year", "2008" ], [ "THE INCITE MILL", "has_genre", "THRILLER" ], [ "THE INCITE MILL", "in_language", "JAPANESE" ], [ "THE INTERVIEW", "has_genre", "THRILLER" ], [ "THE INTERVIEW", "release_year", "2014" ], [ "THE LAZARUS PROJECT", "has_genre", "THRILLER" ], [ "THE LAZARUS PROJECT", "release_year", "2008" ], [ "THE LOFT", "has_genre", "THRILLER" ], [ "THE LOFT", "release_year", "2014" ], [ "THE MACHINE GIRL", "in_language", "JAPANESE" ], [ "THE MACHINE GIRL", "release_year", "2008" ], [ "THE MIDNIGHT MEAT TRAIN", "has_genre", "THRILLER" ], [ "THE MIDNIGHT MEAT TRAIN", "release_year", "2008" ], [ "THE OXFORD MURDERS", "has_genre", "THRILLER" ], [ "THE OXFORD MURDERS", "release_year", "2008" ], [ "THE POKER CLUB", "has_genre", "THRILLER" ], [ "THE POKER CLUB", "release_year", "2008" ], [ "THE RAID 2", "in_language", "JAPANESE" ], [ "THE RAID 2", "release_year", "2014" ], [ "THE RECKONING", "has_genre", "THRILLER" ], [ "THE RECKONING", "release_year", "2014" ], [ "THE SCRIBBLER", "has_genre", "THRILLER" ], [ "THE SCRIBBLER", "release_year", "2014" ], [ "THE SIGNAL", "has_genre", "THRILLER" ], [ "THE SIGNAL", "release_year", "2014" ], [ "THE SKY CRAWLERS", "has_tags", "JAPANESE" ], [ "THE SKY CRAWLERS", "in_language", "JAPANESE" ], [ "THE SKY CRAWLERS", "release_year", "2008" ], [ "THE SNOW WHITE MURDER CASE", "in_language", "JAPANESE" ], [ "THE SNOW WHITE MURDER CASE", "release_year", "2014" ], [ "THE SQUARE", "has_genre", "THRILLER" ], [ "THE SQUARE", "release_year", "2008" ], [ "THE TWO FACES OF JANUARY", "has_genre", "THRILLER" ], [ "THE TWO FACES OF JANUARY", "release_year", "2014" ], [ "THE VANISHING", "has_genre", "THRILLER" ], [ "THE VANISHING", "written_by", "TIM KRABBÉ" ], [ "THE VOICES", "has_genre", "THRILLER" ], [ "THE VOICES", "release_year", "2014" ], [ "THE WICKER MAN", "has_genre", "THRILLER" ], [ "THE WICKER MAN", "has_tags", "NICOLAS CAGE" ], [ "THE WICKER MAN", "starred_actors", "NICOLAS CAGE" ], [ "THE WORLD OF KANAKO", "in_language", "JAPANESE" ], [ "THE WORLD OF KANAKO", "release_year", "2014" ], [ "TOKYO!", "in_language", "JAPANESE" ], [ "TOKYO!", "release_year", "2008" ], [ "TORTURED", "has_genre", "THRILLER" ], [ "TORTURED", "release_year", "2008" ], [ "TRANSSIBERIAN", "has_tags", "THRILLER" ], [ "TRANSSIBERIAN", "release_year", "2008" ], [ "TRESPASS", "has_genre", "THRILLER" ], [ "TRESPASS", "has_tags", "NICOLAS CAGE" ], [ "TRESPASS", "starred_actors", "NICOLAS CAGE" ], [ "UNTRACEABLE", "has_genre", "THRILLER" ], [ "UNTRACEABLE", "release_year", "2008" ], [ "VALKYRIE", "has_genre", "THRILLER" ], [ "VALKYRIE", "release_year", "2008" ], [ "WHEN MARNIE WAS THERE", "in_language", "JAPANESE" ], [ "WHEN MARNIE WAS THERE", "release_year", "2014" ], [ "WHILE SHE WAS OUT", "has_genre", "THRILLER" ], [ "WHILE SHE WAS OUT", "release_year", "2008" ], [ "WHITE BIRD IN A BLIZZARD", "has_genre", "THRILLER" ], [ "WHITE BIRD IN A BLIZZARD", "release_year", "2014" ], [ "WICKED BLOOD", "has_genre", "THRILLER" ], [ "WICKED BLOOD", "release_year", "2014" ], [ "ZANDALEE", "has_genre", "THRILLER" ], [ "ZANDALEE", "starred_actors", "NICOLAS CAGE" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 36212, DRAMA 25127, GLENN WITHROW 35364, NORTH DALLAS FORTY 39846, OLEG DRACH 39963, PETER GENT 25509, THE DEBT 38801, THE MOORING 24811, THRILLER src, edge_attr, dst 35364, has_genre, 36212 35364, written_by, 39963 25509, has_genre, 36212 25509, has_genre, 24811 25509, starred_actors, 39846 38801, directed_by, 25127 38801, has_genre, 24811 38801, written_by, 25127 Question: How are GLENN WITHROW, OLEG DRACH, and PETER GENT related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "GLENN WITHROW", "OLEG DRACH", "PETER GENT" ], "valid_edges": [ [ "NORTH DALLAS FORTY", "has_genre", "DRAMA" ], [ "NORTH DALLAS FORTY", "written_by", "PETER GENT" ], [ "THE DEBT", "has_genre", "DRAMA" ], [ "THE DEBT", "has_genre", "THRILLER" ], [ "THE DEBT", "starred_actors", "OLEG DRACH" ], [ "THE MOORING", "directed_by", "GLENN WITHROW" ], [ "THE MOORING", "has_genre", "THRILLER" ], [ "THE MOORING", "written_by", "GLENN WITHROW" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8486, 1999 29422, ANNA AND THE KING 32474, EMMANUELLE 6012, FRENCH 38857, JEAN POIRET 31026, LA CAGE AUX FOLLES 24110, ONLY GOD FORGIVES 8379, ROMANCE 13090, THAI 37261, THAILAND 22505, THE HANGOVER PART II 6207, THE LOVE OF SIAM 38179, THE UNDERGROUND COMEDY MOVIE src, edge_attr, dst 29422, has_tags, 37261 29422, in_language, 13090 29422, release_year, 8486 32474, has_tags, 37261 32474, in_language, 6012 31026, has_tags, 6012 31026, in_language, 6012 31026, written_by, 38857 24110, has_tags, 37261 24110, in_language, 13090 8379, release_year, 8486 22505, has_tags, 37261 22505, in_language, 13090 6207, has_genre, 8379 6207, has_tags, 8379 6207, has_tags, 37261 6207, in_language, 13090 38179, release_year, 8486 Question: In what context are JEAN POIRET, THAILAND, and THE UNDERGROUND COMEDY MOVIE connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "JEAN POIRET", "THAILAND", "THE UNDERGROUND COMEDY MOVIE" ], "valid_edges": [ [ "ANNA AND THE KING", "has_tags", "THAILAND" ], [ "ANNA AND THE KING", "in_language", "THAI" ], [ "ANNA AND THE KING", "release_year", "1999" ], [ "EMMANUELLE", "has_tags", "THAILAND" ], [ "EMMANUELLE", "in_language", "FRENCH" ], [ "LA CAGE AUX FOLLES", "has_tags", "FRENCH" ], [ "LA CAGE AUX FOLLES", "in_language", "FRENCH" ], [ "LA CAGE AUX FOLLES", "written_by", "JEAN POIRET" ], [ "ONLY GOD FORGIVES", "has_tags", "THAILAND" ], [ "ONLY GOD FORGIVES", "in_language", "THAI" ], [ "ROMANCE", "release_year", "1999" ], [ "THE HANGOVER PART II", "has_tags", "THAILAND" ], [ "THE HANGOVER PART II", "in_language", "THAI" ], [ "THE LOVE OF SIAM", "has_genre", "ROMANCE" ], [ "THE LOVE OF SIAM", "has_tags", "ROMANCE" ], [ "THE LOVE OF SIAM", "has_tags", "THAILAND" ], [ "THE LOVE OF SIAM", "in_language", "THAI" ], [ "THE UNDERGROUND COMEDY MOVIE", "release_year", "1999" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 13408, 2001 36212, DRAMA 6012, FRENCH 28028, JESSICA CAPSHAW 25430, KARIN VIARD 5493, POLISSE 31405, THE CARDINAL 28275, THE UNHOLY WIFE 25783, TIME OUT 2194, TOM TRYON 6988, VALENTINE src, edge_attr, dst 5493, has_genre, 36212 5493, in_language, 6012 5493, starred_actors, 25430 31405, has_genre, 36212 31405, starred_actors, 2194 28275, has_genre, 36212 28275, starred_actors, 2194 25783, has_genre, 36212 25783, in_language, 6012 25783, release_year, 13408 25783, starred_actors, 25430 6988, release_year, 13408 6988, starred_actors, 28028 Question: For what reason are JESSICA CAPSHAW, KARIN VIARD, and TOM TRYON associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "JESSICA CAPSHAW", "KARIN VIARD", "TOM TRYON" ], "valid_edges": [ [ "POLISSE", "has_genre", "DRAMA" ], [ "POLISSE", "in_language", "FRENCH" ], [ "POLISSE", "starred_actors", "KARIN VIARD" ], [ "THE CARDINAL", "has_genre", "DRAMA" ], [ "THE CARDINAL", "starred_actors", "TOM TRYON" ], [ "THE UNHOLY WIFE", "has_genre", "DRAMA" ], [ "THE UNHOLY WIFE", "starred_actors", "TOM TRYON" ], [ "TIME OUT", "has_genre", "DRAMA" ], [ "TIME OUT", "in_language", "FRENCH" ], [ "TIME OUT", "release_year", "2001" ], [ "TIME OUT", "starred_actors", "KARIN VIARD" ], [ "VALENTINE", "release_year", "2001" ], [ "VALENTINE", "starred_actors", "JESSICA CAPSHAW" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15421, 100 BLOODY ACRES 24580, 2-HEADED SHARK ATTACK 658, 2012 30586, A FANTASTIC FEAR OF EVERYTHING 24466, A FUNNY MAN 25327, AFTERSHOCK 34229, ANTIVIRAL 3449, APARTMENT 1303 3D 13736, BLACK ROCK 5946, BLOODY BLOODY BIBLE CAMP 30191, CHERNOBYL DIARIES 30339, CITADEL 38598, COME OUT AND PLAY 22349, CRAWLSPACE 7316, DANISH 30529, DARK SHADOWS 33477, DARKEST NIGHT 17466, DETENTION OF THE DEAD 28770, DRACULA 3D 36212, DRAMA 6394, EXCISION 938, FRANZ SCHULZ 1041, GALLOWWALKERS 24098, GRAVE ENCOUNTERS 2 5870, HORROR 4823, HOUSE AT THE END OF THE STREET 36133, JOHN DIES AT THE END 24437, MANIAC 10848, MARTIN ZANDVLIET 38276, MIDNIGHT 10573, NO ONE LIVES 36239, PARANORMAL ACTIVITY 4 35289, PARANORMAN 17916, PIRANHA 3DD 38226, RESOLUTION 31339, RISE OF THE ZOMBIES 38815, SADAKO 3D 24992, SCARY OR DIE 15926, SILENT NIGHT 8359, SINISTER 9247, STITCHES 39265, STORAGE 24 30456, TEDDY BEAR 31162, THE ABCS OF DEATH 39296, THE APPARITION 25250, THE BARRENS 19623, THE BATTERY 16753, THE BAY 19938, THE CABIN IN THE WOODS 2974, THE COLLECTION 18592, THE DEVIL INSIDE 7153, THE DEVIL'S CARNIVAL 19064, THE HAUNTING OF HELENA 11861, THE LORDS OF SALEM 24652, THE MAN WHO LAUGHS 29525, THE PACT 2023, THE POSSESSION 18162, THE RAVEN 27003, THE THOMPSONS 3437, THE WOMAN IN BLACK 31252, V/H/S 24570, VAMPS 39464, WOULD YOU RATHER src, edge_attr, dst 15421, has_genre, 5870 15421, release_year, 658 24580, has_genre, 5870 24580, release_year, 658 30586, has_genre, 5870 30586, has_tags, 5870 30586, release_year, 658 24466, directed_by, 10848 24466, has_genre, 36212 24466, in_language, 7316 24466, written_by, 10848 25327, has_genre, 5870 25327, release_year, 658 34229, has_genre, 5870 34229, release_year, 658 3449, has_genre, 5870 3449, release_year, 658 13736, has_genre, 5870 13736, release_year, 658 5946, has_genre, 5870 5946, release_year, 658 30191, has_genre, 5870 30191, release_year, 658 30339, has_genre, 5870 30339, release_year, 658 38598, has_genre, 5870 38598, release_year, 658 22349, has_genre, 5870 22349, release_year, 658 30529, has_genre, 5870 30529, release_year, 658 33477, has_genre, 5870 33477, has_tags, 5870 33477, release_year, 658 17466, has_genre, 5870 17466, release_year, 658 28770, has_genre, 5870 28770, release_year, 658 6394, has_genre, 5870 6394, release_year, 658 1041, has_genre, 5870 1041, release_year, 658 24098, has_genre, 5870 24098, release_year, 658 4823, has_genre, 5870 4823, release_year, 658 36133, has_genre, 5870 36133, release_year, 658 24437, has_genre, 5870 24437, release_year, 658 38276, has_genre, 36212 38276, written_by, 938 10573, has_genre, 5870 10573, release_year, 658 36239, has_genre, 5870 36239, release_year, 658 35289, has_tags, 5870 35289, release_year, 658 17916, has_genre, 5870 17916, release_year, 658 38226, has_genre, 5870 38226, release_year, 658 31339, has_genre, 5870 31339, release_year, 658 38815, has_genre, 5870 38815, release_year, 658 24992, has_genre, 5870 24992, release_year, 658 15926, has_genre, 5870 15926, release_year, 658 8359, has_genre, 5870 8359, has_tags, 5870 8359, release_year, 658 9247, has_genre, 5870 9247, release_year, 658 39265, has_genre, 5870 39265, release_year, 658 30456, in_language, 7316 30456, release_year, 658 30456, written_by, 10848 31162, has_genre, 5870 31162, release_year, 658 39296, has_genre, 5870 39296, release_year, 658 25250, has_genre, 5870 25250, release_year, 658 19623, has_genre, 5870 19623, release_year, 658 16753, has_genre, 5870 16753, release_year, 658 19938, has_genre, 5870 19938, has_tags, 5870 19938, release_year, 658 2974, has_genre, 5870 2974, release_year, 658 18592, has_genre, 5870 18592, release_year, 658 7153, has_genre, 5870 7153, release_year, 658 19064, has_genre, 5870 19064, release_year, 658 11861, has_genre, 5870 11861, release_year, 658 24652, has_genre, 5870 24652, release_year, 658 29525, has_genre, 5870 29525, release_year, 658 2023, has_genre, 5870 2023, has_tags, 5870 2023, release_year, 658 18162, has_genre, 5870 18162, has_tags, 5870 18162, release_year, 658 27003, has_genre, 5870 27003, release_year, 658 3437, has_genre, 5870 3437, has_tags, 5870 3437, release_year, 658 31252, has_genre, 5870 31252, has_tags, 5870 31252, release_year, 658 24570, has_genre, 5870 24570, release_year, 658 39464, has_genre, 5870 39464, has_tags, 5870 39464, release_year, 658 Question: For what reason are DARKEST NIGHT, FRANZ SCHULZ, and MARTIN ZANDVLIET associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "DARKEST NIGHT", "FRANZ SCHULZ", "MARTIN ZANDVLIET" ], "valid_edges": [ [ "100 BLOODY ACRES", "has_genre", "HORROR" ], [ "100 BLOODY ACRES", "release_year", "2012" ], [ "2-HEADED SHARK ATTACK", "has_genre", "HORROR" ], [ "2-HEADED SHARK ATTACK", "release_year", "2012" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_genre", "HORROR" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_tags", "HORROR" ], [ "A FANTASTIC FEAR OF EVERYTHING", "release_year", "2012" ], [ "A FUNNY MAN", "directed_by", "MARTIN ZANDVLIET" ], [ "A FUNNY MAN", "has_genre", "DRAMA" ], [ "A FUNNY MAN", "in_language", "DANISH" ], [ "A FUNNY MAN", "written_by", "MARTIN ZANDVLIET" ], [ "AFTERSHOCK", "has_genre", "HORROR" ], [ "AFTERSHOCK", "release_year", "2012" ], [ "ANTIVIRAL", "has_genre", "HORROR" ], [ "ANTIVIRAL", "release_year", "2012" ], [ "APARTMENT 1303 3D", "has_genre", "HORROR" ], [ "APARTMENT 1303 3D", "release_year", "2012" ], [ "BLACK ROCK", "has_genre", "HORROR" ], [ "BLACK ROCK", "release_year", "2012" ], [ "BLOODY BLOODY BIBLE CAMP", "has_genre", "HORROR" ], [ "BLOODY BLOODY BIBLE CAMP", "release_year", "2012" ], [ "CHERNOBYL DIARIES", "has_genre", "HORROR" ], [ "CHERNOBYL DIARIES", "release_year", "2012" ], [ "CITADEL", "has_genre", "HORROR" ], [ "CITADEL", "release_year", "2012" ], [ "COME OUT AND PLAY", "has_genre", "HORROR" ], [ "COME OUT AND PLAY", "release_year", "2012" ], [ "CRAWLSPACE", "has_genre", "HORROR" ], [ "CRAWLSPACE", "release_year", "2012" ], [ "DARK SHADOWS", "has_genre", "HORROR" ], [ "DARK SHADOWS", "release_year", "2012" ], [ "DARKEST NIGHT", "has_genre", "HORROR" ], [ "DARKEST NIGHT", "has_tags", "HORROR" ], [ "DARKEST NIGHT", "release_year", "2012" ], [ "DETENTION OF THE DEAD", "has_genre", "HORROR" ], [ "DETENTION OF THE DEAD", "release_year", "2012" ], [ "DRACULA 3D", "has_genre", "HORROR" ], [ "DRACULA 3D", "release_year", "2012" ], [ "EXCISION", "has_genre", "HORROR" ], [ "EXCISION", "release_year", "2012" ], [ "GALLOWWALKERS", "has_genre", "HORROR" ], [ "GALLOWWALKERS", "release_year", "2012" ], [ "GRAVE ENCOUNTERS 2", "has_genre", "HORROR" ], [ "GRAVE ENCOUNTERS 2", "release_year", "2012" ], [ "HOUSE AT THE END OF THE STREET", "has_genre", "HORROR" ], [ "HOUSE AT THE END OF THE STREET", "release_year", "2012" ], [ "JOHN DIES AT THE END", "has_genre", "HORROR" ], [ "JOHN DIES AT THE END", "release_year", "2012" ], [ "MANIAC", "has_genre", "HORROR" ], [ "MANIAC", "release_year", "2012" ], [ "MIDNIGHT", "has_genre", "DRAMA" ], [ "MIDNIGHT", "written_by", "FRANZ SCHULZ" ], [ "NO ONE LIVES", "has_genre", "HORROR" ], [ "NO ONE LIVES", "release_year", "2012" ], [ "PARANORMAL ACTIVITY 4", "has_genre", "HORROR" ], [ "PARANORMAL ACTIVITY 4", "release_year", "2012" ], [ "PARANORMAN", "has_tags", "HORROR" ], [ "PARANORMAN", "release_year", "2012" ], [ "PIRANHA 3DD", "has_genre", "HORROR" ], [ "PIRANHA 3DD", "release_year", "2012" ], [ "RESOLUTION", "has_genre", "HORROR" ], [ "RESOLUTION", "release_year", "2012" ], [ "RISE OF THE ZOMBIES", "has_genre", "HORROR" ], [ "RISE OF THE ZOMBIES", "release_year", "2012" ], [ "SADAKO 3D", "has_genre", "HORROR" ], [ "SADAKO 3D", "release_year", "2012" ], [ "SCARY OR DIE", "has_genre", "HORROR" ], [ "SCARY OR DIE", "release_year", "2012" ], [ "SILENT NIGHT", "has_genre", "HORROR" ], [ "SILENT NIGHT", "release_year", "2012" ], [ "SINISTER", "has_genre", "HORROR" ], [ "SINISTER", "has_tags", "HORROR" ], [ "SINISTER", "release_year", "2012" ], [ "STITCHES", "has_genre", "HORROR" ], [ "STITCHES", "release_year", "2012" ], [ "STORAGE 24", "has_genre", "HORROR" ], [ "STORAGE 24", "release_year", "2012" ], [ "TEDDY BEAR", "in_language", "DANISH" ], [ "TEDDY BEAR", "release_year", "2012" ], [ "TEDDY BEAR", "written_by", "MARTIN ZANDVLIET" ], [ "THE ABCS OF DEATH", "has_genre", "HORROR" ], [ "THE ABCS OF DEATH", "release_year", "2012" ], [ "THE APPARITION", "has_genre", "HORROR" ], [ "THE APPARITION", "release_year", "2012" ], [ "THE BARRENS", "has_genre", "HORROR" ], [ "THE BARRENS", "release_year", "2012" ], [ "THE BATTERY", "has_genre", "HORROR" ], [ "THE BATTERY", "release_year", "2012" ], [ "THE BAY", "has_genre", "HORROR" ], [ "THE BAY", "release_year", "2012" ], [ "THE CABIN IN THE WOODS", "has_genre", "HORROR" ], [ "THE CABIN IN THE WOODS", "has_tags", "HORROR" ], [ "THE CABIN IN THE WOODS", "release_year", "2012" ], [ "THE COLLECTION", "has_genre", "HORROR" ], [ "THE COLLECTION", "release_year", "2012" ], [ "THE DEVIL INSIDE", "has_genre", "HORROR" ], [ "THE DEVIL INSIDE", "release_year", "2012" ], [ "THE DEVIL'S CARNIVAL", "has_genre", "HORROR" ], [ "THE DEVIL'S CARNIVAL", "release_year", "2012" ], [ "THE HAUNTING OF HELENA", "has_genre", "HORROR" ], [ "THE HAUNTING OF HELENA", "release_year", "2012" ], [ "THE LORDS OF SALEM", "has_genre", "HORROR" ], [ "THE LORDS OF SALEM", "release_year", "2012" ], [ "THE MAN WHO LAUGHS", "has_genre", "HORROR" ], [ "THE MAN WHO LAUGHS", "release_year", "2012" ], [ "THE PACT", "has_genre", "HORROR" ], [ "THE PACT", "release_year", "2012" ], [ "THE POSSESSION", "has_genre", "HORROR" ], [ "THE POSSESSION", "has_tags", "HORROR" ], [ "THE POSSESSION", "release_year", "2012" ], [ "THE RAVEN", "has_genre", "HORROR" ], [ "THE RAVEN", "has_tags", "HORROR" ], [ "THE RAVEN", "release_year", "2012" ], [ "THE THOMPSONS", "has_genre", "HORROR" ], [ "THE THOMPSONS", "release_year", "2012" ], [ "THE WOMAN IN BLACK", "has_genre", "HORROR" ], [ "THE WOMAN IN BLACK", "has_tags", "HORROR" ], [ "THE WOMAN IN BLACK", "release_year", "2012" ], [ "V/H/S", "has_genre", "HORROR" ], [ "V/H/S", "has_tags", "HORROR" ], [ "V/H/S", "release_year", "2012" ], [ "VAMPS", "has_genre", "HORROR" ], [ "VAMPS", "release_year", "2012" ], [ "WOULD YOU RATHER", "has_genre", "HORROR" ], [ "WOULD YOU RATHER", "has_tags", "HORROR" ], [ "WOULD YOU RATHER", "release_year", "2012" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8486, 1999 1421, 2013 35260, A CASE OF YOU 18943, BRIAN BLESSED 35657, GLORIA 67, TARZAN 12439, THE INSIDER src, edge_attr, dst 35260, release_year, 1421 35657, release_year, 8486 35657, release_year, 1421 67, release_year, 8486 67, release_year, 1421 67, starred_actors, 18943 12439, release_year, 8486 Question: How are A CASE OF YOU, BRIAN BLESSED, and THE INSIDER related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "A CASE OF YOU", "BRIAN BLESSED", "THE INSIDER" ], "valid_edges": [ [ "A CASE OF YOU", "release_year", "2013" ], [ "GLORIA", "release_year", "1999" ], [ "GLORIA", "release_year", "2013" ], [ "TARZAN", "release_year", "1999" ], [ "TARZAN", "release_year", "2013" ], [ "TARZAN", "starred_actors", "BRIAN BLESSED" ], [ "THE INSIDER", "release_year", "1999" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6776, 2000 33688, ABERDEEN 11856, COLE PORTER 17606, DAMIEN CHAZELLE 33486, DE-LOVELY 36212, DRAMA 32380, GOSSIP 16354, GUY AND MADELINE ON A PARK BENCH 15017, LENA HEADEY 22845, MUSIC 24593, MUSICAL 8546, NIGHT AND DAY 38804, WHIPLASH src, edge_attr, dst 33688, has_genre, 36212 33688, release_year, 6776 33688, starred_actors, 15017 33486, has_genre, 22845 33486, has_tags, 11856 32380, has_genre, 36212 32380, has_tags, 32380 32380, release_year, 6776 32380, starred_actors, 15017 16354, directed_by, 17606 16354, has_genre, 24593 16354, has_tags, 17606 16354, written_by, 17606 8546, has_genre, 24593 8546, has_tags, 11856 38804, directed_by, 17606 38804, has_genre, 36212 38804, has_genre, 22845 38804, has_tags, 17606 38804, has_tags, 22845 38804, written_by, 17606 Question: In what context are COLE PORTER, DAMIEN CHAZELLE, and LENA HEADEY connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "COLE PORTER", "DAMIEN CHAZELLE", "LENA HEADEY" ], "valid_edges": [ [ "ABERDEEN", "has_genre", "DRAMA" ], [ "ABERDEEN", "release_year", "2000" ], [ "ABERDEEN", "starred_actors", "LENA HEADEY" ], [ "DE-LOVELY", "has_genre", "MUSIC" ], [ "DE-LOVELY", "has_tags", "COLE PORTER" ], [ "GOSSIP", "has_genre", "DRAMA" ], [ "GOSSIP", "has_tags", "GOSSIP" ], [ "GOSSIP", "release_year", "2000" ], [ "GOSSIP", "starred_actors", "LENA HEADEY" ], [ "GUY AND MADELINE ON A PARK BENCH", "directed_by", "DAMIEN CHAZELLE" ], [ "GUY AND MADELINE ON A PARK BENCH", "has_genre", "MUSICAL" ], [ "GUY AND MADELINE ON A PARK BENCH", "has_tags", "DAMIEN CHAZELLE" ], [ "GUY AND MADELINE ON A PARK BENCH", "written_by", "DAMIEN CHAZELLE" ], [ "NIGHT AND DAY", "has_genre", "MUSICAL" ], [ "NIGHT AND DAY", "has_tags", "COLE PORTER" ], [ "WHIPLASH", "directed_by", "DAMIEN CHAZELLE" ], [ "WHIPLASH", "has_genre", "DRAMA" ], [ "WHIPLASH", "has_genre", "MUSIC" ], [ "WHIPLASH", "has_tags", "DAMIEN CHAZELLE" ], [ "WHIPLASH", "has_tags", "MUSIC" ], [ "WHIPLASH", "written_by", "DAMIEN CHAZELLE" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 658, 2012 34699, CHRONICLE 10377, CIVIC DUTY 30806, IMAGINAERUM 17791, JEFF RENFROE 22063, MAX LANDIS 32395, STOBE HARJU 24811, THRILLER src, edge_attr, dst 34699, has_genre, 24811 34699, release_year, 658 34699, written_by, 22063 10377, directed_by, 17791 10377, has_genre, 24811 30806, directed_by, 32395 30806, release_year, 658 30806, written_by, 32395 Question: How are JEFF RENFROE, MAX LANDIS, and STOBE HARJU related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "JEFF RENFROE", "MAX LANDIS", "STOBE HARJU" ], "valid_edges": [ [ "CHRONICLE", "has_genre", "THRILLER" ], [ "CHRONICLE", "release_year", "2012" ], [ "CHRONICLE", "written_by", "MAX LANDIS" ], [ "CIVIC DUTY", "directed_by", "JEFF RENFROE" ], [ "CIVIC DUTY", "has_genre", "THRILLER" ], [ "IMAGINAERUM", "directed_by", "STOBE HARJU" ], [ "IMAGINAERUM", "release_year", "2012" ], [ "IMAGINAERUM", "written_by", "STOBE HARJU" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6718, A FAREWELL TO ARMS 32006, ARCH OF TRIUMPH 25805, DOCTOR ZHIVAGO 24981, FOR THE MOMENT 20693, GONE WITH THE WIND 234, PEARL HARBOR 8379, ROMANCE 22580, SUITE FRANÇAISE 11696, THE CUCKOO 36390, THE ENGLISH PATIENT 7831, THE SHEIK 32898, THIS ABOVE ALL 19813, VERBOTEN! 22214, WAR src, edge_attr, dst 6718, has_genre, 8379 6718, has_genre, 22214 32006, has_genre, 8379 32006, has_genre, 22214 25805, has_genre, 8379 25805, has_genre, 22214 25805, has_tags, 22214 24981, has_genre, 8379 24981, has_genre, 22214 20693, has_genre, 8379 20693, has_genre, 22214 20693, has_tags, 8379 20693, has_tags, 22214 234, has_genre, 8379 234, has_tags, 8379 234, has_tags, 22214 22580, has_genre, 8379 22580, has_genre, 22214 11696, has_genre, 22214 36390, has_genre, 8379 36390, has_genre, 22214 36390, has_tags, 22214 7831, has_genre, 8379 32898, has_genre, 8379 32898, has_genre, 22214 19813, has_genre, 22214 Question: In what context are THE CUCKOO, THE SHEIK, and VERBOTEN! connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "THE CUCKOO", "THE SHEIK", "VERBOTEN!" ], "valid_edges": [ [ "A FAREWELL TO ARMS", "has_genre", "ROMANCE" ], [ "A FAREWELL TO ARMS", "has_genre", "WAR" ], [ "ARCH OF TRIUMPH", "has_genre", "ROMANCE" ], [ "ARCH OF TRIUMPH", "has_genre", "WAR" ], [ "DOCTOR ZHIVAGO", "has_genre", "ROMANCE" ], [ "DOCTOR ZHIVAGO", "has_genre", "WAR" ], [ "DOCTOR ZHIVAGO", "has_tags", "WAR" ], [ "FOR THE MOMENT", "has_genre", "ROMANCE" ], [ "FOR THE MOMENT", "has_genre", "WAR" ], [ "GONE WITH THE WIND", "has_genre", "ROMANCE" ], [ "GONE WITH THE WIND", "has_genre", "WAR" ], [ "GONE WITH THE WIND", "has_tags", "ROMANCE" ], [ "GONE WITH THE WIND", "has_tags", "WAR" ], [ "PEARL HARBOR", "has_genre", "ROMANCE" ], [ "PEARL HARBOR", "has_tags", "ROMANCE" ], [ "PEARL HARBOR", "has_tags", "WAR" ], [ "SUITE FRANÇAISE", "has_genre", "ROMANCE" ], [ "SUITE FRANÇAISE", "has_genre", "WAR" ], [ "THE CUCKOO", "has_genre", "WAR" ], [ "THE ENGLISH PATIENT", "has_genre", "ROMANCE" ], [ "THE ENGLISH PATIENT", "has_genre", "WAR" ], [ "THE ENGLISH PATIENT", "has_tags", "WAR" ], [ "THE SHEIK", "has_genre", "ROMANCE" ], [ "THIS ABOVE ALL", "has_genre", "ROMANCE" ], [ "THIS ABOVE ALL", "has_genre", "WAR" ], [ "VERBOTEN!", "has_genre", "WAR" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 30172, 1964 26257, 1994 4269, A HARD DAY'S NIGHT 17505, A LOW DOWN DIRTY SHAME 431, A MAN OF NO IMPORTANCE 36629, A MILLION TO JUAN 31850, A SHOT IN THE DARK 29838, A SIMPLE TWIST OF FATE 7687, ADVANCE TO THE REAR 8837, AIRHEADS 33883, ALL THESE WOMEN 27410, ANGELS IN THE OUTFIELD 24704, ANGIE 35639, BABY'S DAY OUT 1868, BARCELONA 37702, BEDTIME STORY 10890, BEVERLY HILLS COP III 29301, BLANK CHECK 24639, BLANKMAN 34157, BLINK 34318, CABIN BOY 22539, CAR 54, WHERE ARE YOU? 26883, CEMETERY MAN 16350, CHASERS 35468, CLEAN SLATE 35351, CLERKS 9387, CLIFFORD 30463, COMEDY 10092, CONTINENTAL DIVIDE 21391, CRACKERJACK 5277, CRITICAL CONDITION 25978, DEADLY ADVICE 19009, DEAR HEART 13424, DON'T DRINK THE WATER 3893, ED WOOD 26709, ERNEST GOES TO SCHOOL 20441, EXIT TO EDEN 22720, FATHER GOOSE 24028, FLOUNDERING 22371, FORREST GUMP 6215, FOUR WEDDINGS AND A FUNERAL 36927, FROM BEIJING WITH LOVE 19795, GET YOURSELF A COLLEGE GIRL 34775, GETTING EVEN WITH DAD 9142, GETTING IN 26124, GOOD NEIGHBOR SAM 12502, GOODBYE CHARLIE 5585, GREEDY 34489, GUARDING TESS 25670, HAIL CAESAR 20990, HOLY MATRIMONY 34412, I LIKE IT LIKE THAT 13163, I LOVE TROUBLE 18096, I.Q. 24832, IN THE ARMY NOW 14341, IT COULD HAPPEN TO YOU 39987, IT RUNS IN THE FAMILY 37202, IT'S PAT 29404, JUNIOR 14878, KABHI HAAN KABHI NAA 4285, KISS ME, STUPID 13469, KISSES FOR MY PRESIDENT 18648, LEPRECHAUN 2 19862, LIGHTNING JACK 16780, LITTLE GIANTS 5975, MAN'S FAVORITE SPORT? 25283, MAVERICK 35439, MICHAEL APTED 12620, MILK MONEY 12371, MIXED NUTS 24172, MO OGRODNIK 9817, MONKEY TROUBLE 25796, MURIEL'S WEDDING 28963, MY GIRL 2 10609, NELL 4398, NOBODY'S FOOL 32358, NORTH 36883, ONLY YOU 7364, PARIS WHEN IT SIZZLES 24732, PCU 11042, PRINCESS CARABOO 26174, PULP FICTION 22395, RADIOLAND MURDERS 26180, REALITY BITES 25577, RENAISSANCE MAN 14486, SANTA CLAUS CONQUERS THE MARTIANS 24232, SEND ME NO FLOWERS 24642, SERIAL MOM 7941, SEX AND THE SINGLE GIRL 1374, SLEEP WITH ME 6144, SPANKING THE MONKEY 7010, SPEECHLESS 35026, STAGGERED 20877, SWIMMING WITH SHARKS 2567, TAMMY AND THE T-REX 25270, THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT 30090, THE AIR UP THERE 36790, THE AMERICANIZATION OF EMILY 4157, THE BEST MAN 13685, THE CHASE 22164, THE COWBOY WAY 39794, THE DISORDERLY ORDERLY 9215, THE FAVOR 17956, THE FLINTSTONES 10878, THE HUDSUCKER PROXY 38210, THE INKWELL 38550, THE LITTLE RASCALS 23802, THE MASK 29233, THE MONSTER 33982, THE PAPER 2732, THE PATSY 26061, THE REF 13917, THE ROAD TO WELLVILLE 17314, THE SANTA CLAUSE 36289, THE SCOUT 33905, THE SEARCH FOR ONE-EYE JIMMY 4767, THE SUM OF US 29878, THE WORLD OF HENRY ORIENT 16762, THREESOME 18195, TRAPPED IN PARADISE 12860, TRUE LIES 33767, TWIN SITTERS 35371, UPTOWN GIRLS 12130, WELCOME, OR NO TRESPASSING 37603, WHAT A WAY TO GO! 39623, WITH HONORS src, edge_attr, dst 4269, has_genre, 30463 4269, has_tags, 30463 4269, release_year, 30172 17505, has_genre, 30463 17505, release_year, 26257 431, has_genre, 30463 431, release_year, 26257 36629, has_genre, 30463 36629, release_year, 26257 31850, has_genre, 30463 31850, release_year, 30172 29838, has_genre, 30463 29838, release_year, 26257 7687, has_genre, 30463 7687, release_year, 30172 8837, has_genre, 30463 8837, has_tags, 30463 8837, release_year, 26257 33883, has_genre, 30463 33883, release_year, 30172 27410, has_genre, 30463 27410, release_year, 26257 24704, has_genre, 30463 24704, release_year, 26257 35639, has_genre, 30463 35639, release_year, 26257 1868, has_genre, 30463 1868, release_year, 26257 37702, has_genre, 30463 37702, release_year, 30172 10890, has_genre, 30463 10890, has_tags, 30463 10890, release_year, 26257 29301, has_genre, 30463 29301, release_year, 26257 24639, has_genre, 30463 24639, release_year, 26257 34157, directed_by, 35439 34157, release_year, 26257 34318, has_genre, 30463 34318, release_year, 26257 22539, has_genre, 30463 22539, release_year, 26257 26883, has_genre, 30463 26883, release_year, 26257 16350, has_genre, 30463 16350, release_year, 26257 35468, has_genre, 30463 35468, release_year, 26257 35351, has_genre, 30463 35351, has_tags, 30463 35351, release_year, 26257 9387, has_genre, 30463 9387, release_year, 26257 10092, directed_by, 35439 10092, has_genre, 30463 21391, has_genre, 30463 21391, release_year, 26257 5277, directed_by, 35439 5277, has_genre, 30463 25978, has_genre, 30463 25978, release_year, 26257 19009, has_genre, 30463 19009, release_year, 30172 13424, has_genre, 30463 13424, release_year, 26257 3893, has_genre, 30463 3893, release_year, 26257 26709, has_genre, 30463 26709, release_year, 26257 20441, has_genre, 30463 20441, release_year, 26257 22720, has_genre, 30463 22720, has_tags, 30463 22720, release_year, 30172 24028, has_genre, 30463 24028, release_year, 26257 22371, has_tags, 30463 22371, release_year, 26257 6215, has_genre, 30463 6215, has_tags, 30463 6215, release_year, 26257 36927, has_genre, 30463 36927, release_year, 26257 19795, has_genre, 30463 19795, release_year, 30172 34775, has_genre, 30463 34775, release_year, 26257 9142, has_genre, 30463 9142, release_year, 26257 26124, has_genre, 30463 26124, release_year, 30172 12502, has_genre, 30463 12502, release_year, 30172 5585, has_genre, 30463 5585, release_year, 26257 34489, has_genre, 30463 34489, release_year, 26257 25670, has_genre, 30463 25670, release_year, 26257 20990, has_genre, 30463 20990, release_year, 26257 34412, has_genre, 30463 34412, release_year, 26257 13163, has_genre, 30463 13163, release_year, 26257 18096, has_genre, 30463 18096, release_year, 26257 24832, has_genre, 30463 24832, release_year, 26257 14341, has_genre, 30463 14341, has_tags, 30463 14341, release_year, 26257 39987, has_genre, 30463 39987, release_year, 26257 37202, has_genre, 30463 37202, release_year, 26257 29404, has_genre, 30463 29404, release_year, 26257 14878, has_genre, 30463 14878, release_year, 26257 4285, has_genre, 30463 4285, release_year, 30172 13469, has_genre, 30463 13469, release_year, 30172 18648, has_genre, 30463 18648, release_year, 26257 19862, has_genre, 30463 19862, release_year, 26257 16780, has_genre, 30463 16780, release_year, 26257 5975, has_genre, 30463 5975, release_year, 30172 25283, has_genre, 30463 25283, has_tags, 30463 25283, release_year, 26257 12620, has_genre, 30463 12620, release_year, 26257 12371, has_genre, 30463 12371, release_year, 26257 9817, has_genre, 30463 9817, release_year, 26257 25796, has_genre, 30463 25796, has_tags, 30463 25796, release_year, 26257 28963, has_genre, 30463 28963, release_year, 26257 10609, directed_by, 35439 10609, release_year, 26257 4398, has_genre, 30463 4398, release_year, 26257 32358, has_genre, 30463 32358, release_year, 26257 36883, has_genre, 30463 36883, release_year, 26257 7364, has_genre, 30463 7364, release_year, 30172 24732, has_genre, 30463 24732, release_year, 26257 11042, has_genre, 30463 11042, release_year, 26257 26174, has_tags, 30463 26174, release_year, 26257 22395, has_genre, 30463 22395, release_year, 26257 26180, has_genre, 30463 26180, release_year, 26257 25577, has_genre, 30463 25577, release_year, 26257 14486, has_genre, 30463 14486, release_year, 30172 24232, has_genre, 30463 24232, release_year, 30172 24642, has_genre, 30463 24642, has_tags, 30463 24642, release_year, 26257 7941, has_genre, 30463 7941, release_year, 30172 1374, has_genre, 30463 1374, release_year, 26257 6144, has_genre, 30463 6144, release_year, 26257 7010, has_genre, 30463 7010, release_year, 26257 35026, has_genre, 30463 35026, release_year, 26257 20877, has_genre, 30463 20877, release_year, 26257 2567, has_genre, 30463 2567, release_year, 26257 25270, has_genre, 30463 25270, release_year, 26257 30090, has_genre, 30463 30090, has_tags, 30463 30090, release_year, 26257 36790, has_genre, 30463 36790, release_year, 30172 4157, has_genre, 30463 4157, release_year, 30172 13685, has_genre, 30463 13685, release_year, 26257 22164, has_genre, 30463 22164, release_year, 26257 39794, has_genre, 30463 39794, release_year, 30172 9215, has_genre, 30463 9215, release_year, 26257 17956, has_genre, 30463 17956, has_tags, 30463 17956, release_year, 26257 10878, has_genre, 30463 10878, has_tags, 30463 10878, release_year, 26257 38210, has_genre, 30463 38210, release_year, 26257 38550, has_genre, 30463 38550, release_year, 26257 23802, has_genre, 30463 23802, has_tags, 30463 23802, release_year, 26257 29233, has_genre, 30463 29233, has_tags, 30463 29233, release_year, 26257 33982, has_genre, 30463 33982, release_year, 26257 2732, has_genre, 30463 2732, release_year, 30172 26061, has_genre, 30463 26061, has_tags, 30463 26061, release_year, 26257 13917, has_genre, 30463 13917, release_year, 26257 17314, has_genre, 30463 17314, release_year, 26257 36289, has_genre, 30463 36289, release_year, 26257 33905, has_genre, 30463 33905, release_year, 26257 4767, has_genre, 30463 4767, release_year, 26257 29878, has_genre, 30463 29878, has_tags, 30463 29878, release_year, 30172 16762, has_genre, 30463 16762, has_tags, 30463 16762, release_year, 26257 18195, has_genre, 30463 18195, release_year, 26257 12860, has_genre, 30463 12860, has_tags, 30463 12860, release_year, 26257 33767, has_genre, 30463 33767, release_year, 26257 35371, has_genre, 30463 35371, written_by, 24172 12130, has_genre, 30463 12130, release_year, 30172 37603, has_genre, 30463 37603, release_year, 30172 39623, has_genre, 30463 39623, release_year, 26257 Question: In what context are BLINK, MO OGRODNIK, and WELCOME, OR NO TRESPASSING connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BLINK", "MO OGRODNIK", "WELCOME, OR NO TRESPASSING" ], "valid_edges": [ [ "A HARD DAY'S NIGHT", "has_genre", "COMEDY" ], [ "A HARD DAY'S NIGHT", "has_tags", "COMEDY" ], [ "A HARD DAY'S NIGHT", "release_year", "1964" ], [ "A LOW DOWN DIRTY SHAME", "has_genre", "COMEDY" ], [ "A LOW DOWN DIRTY SHAME", "release_year", "1994" ], [ "A MAN OF NO IMPORTANCE", "has_genre", "COMEDY" ], [ "A MAN OF NO IMPORTANCE", "release_year", "1994" ], [ "A MILLION TO JUAN", "has_genre", "COMEDY" ], [ "A MILLION TO JUAN", "release_year", "1994" ], [ "A SHOT IN THE DARK", "has_genre", "COMEDY" ], [ "A SHOT IN THE DARK", "release_year", "1964" ], [ "A SIMPLE TWIST OF FATE", "has_genre", "COMEDY" ], [ "A SIMPLE TWIST OF FATE", "release_year", "1994" ], [ "ADVANCE TO THE REAR", "has_genre", "COMEDY" ], [ "ADVANCE TO THE REAR", "release_year", "1964" ], [ "AIRHEADS", "has_genre", "COMEDY" ], [ "AIRHEADS", "has_tags", "COMEDY" ], [ "AIRHEADS", "release_year", "1994" ], [ "ALL THESE WOMEN", "has_genre", "COMEDY" ], [ "ALL THESE WOMEN", "release_year", "1964" ], [ "ANGELS IN THE OUTFIELD", "has_genre", "COMEDY" ], [ "ANGELS IN THE OUTFIELD", "release_year", "1994" ], [ "ANGIE", "has_genre", "COMEDY" ], [ "ANGIE", "release_year", "1994" ], [ "BABY'S DAY OUT", "has_genre", "COMEDY" ], [ "BABY'S DAY OUT", "release_year", "1994" ], [ "BARCELONA", "has_genre", "COMEDY" ], [ "BARCELONA", "release_year", "1994" ], [ "BEDTIME STORY", "has_genre", "COMEDY" ], [ "BEDTIME STORY", "release_year", "1964" ], [ "BEVERLY HILLS COP III", "has_genre", "COMEDY" ], [ "BEVERLY HILLS COP III", "has_tags", "COMEDY" ], [ "BEVERLY HILLS COP III", "release_year", "1994" ], [ "BLANK CHECK", "has_genre", "COMEDY" ], [ "BLANK CHECK", "release_year", "1994" ], [ "BLANKMAN", "has_genre", "COMEDY" ], [ "BLANKMAN", "release_year", "1994" ], [ "BLINK", "directed_by", "MICHAEL APTED" ], [ "BLINK", "release_year", "1994" ], [ "CABIN BOY", "has_genre", "COMEDY" ], [ "CABIN BOY", "release_year", "1994" ], [ "CAR 54, WHERE ARE YOU?", "has_genre", "COMEDY" ], [ "CAR 54, WHERE ARE YOU?", "release_year", "1994" ], [ "CEMETERY MAN", "has_genre", "COMEDY" ], [ "CEMETERY MAN", "release_year", "1994" ], [ "CHASERS", "has_genre", "COMEDY" ], [ "CHASERS", "release_year", "1994" ], [ "CLEAN SLATE", "has_genre", "COMEDY" ], [ "CLEAN SLATE", "release_year", "1994" ], [ "CLERKS", "has_genre", "COMEDY" ], [ "CLERKS", "has_tags", "COMEDY" ], [ "CLERKS", "release_year", "1994" ], [ "CLIFFORD", "has_genre", "COMEDY" ], [ "CLIFFORD", "release_year", "1994" ], [ "CONTINENTAL DIVIDE", "directed_by", "MICHAEL APTED" ], [ "CONTINENTAL DIVIDE", "has_genre", "COMEDY" ], [ "CRACKERJACK", "has_genre", "COMEDY" ], [ "CRACKERJACK", "release_year", "1994" ], [ "CRITICAL CONDITION", "directed_by", "MICHAEL APTED" ], [ "CRITICAL CONDITION", "has_genre", "COMEDY" ], [ "DEADLY ADVICE", "has_genre", "COMEDY" ], [ "DEADLY ADVICE", "release_year", "1994" ], [ "DEAR HEART", "has_genre", "COMEDY" ], [ "DEAR HEART", "release_year", "1964" ], [ "DON'T DRINK THE WATER", "has_genre", "COMEDY" ], [ "DON'T DRINK THE WATER", "release_year", "1994" ], [ "ED WOOD", "has_genre", "COMEDY" ], [ "ED WOOD", "release_year", "1994" ], [ "ERNEST GOES TO SCHOOL", "has_genre", "COMEDY" ], [ "ERNEST GOES TO SCHOOL", "release_year", "1994" ], [ "EXIT TO EDEN", "has_genre", "COMEDY" ], [ "EXIT TO EDEN", "release_year", "1994" ], [ "FATHER GOOSE", "has_genre", "COMEDY" ], [ "FATHER GOOSE", "has_tags", "COMEDY" ], [ "FATHER GOOSE", "release_year", "1964" ], [ "FLOUNDERING", "has_genre", "COMEDY" ], [ "FLOUNDERING", "release_year", "1994" ], [ "FORREST GUMP", "has_tags", "COMEDY" ], [ "FORREST GUMP", "release_year", "1994" ], [ "FOUR WEDDINGS AND A FUNERAL", "has_genre", "COMEDY" ], [ "FOUR WEDDINGS AND A FUNERAL", "has_tags", "COMEDY" ], [ "FOUR WEDDINGS AND A FUNERAL", "release_year", "1994" ], [ "FROM BEIJING WITH LOVE", "has_genre", "COMEDY" ], [ "FROM BEIJING WITH LOVE", "release_year", "1994" ], [ "GET YOURSELF A COLLEGE GIRL", "has_genre", "COMEDY" ], [ "GET YOURSELF A COLLEGE GIRL", "release_year", "1964" ], [ "GETTING EVEN WITH DAD", "has_genre", "COMEDY" ], [ "GETTING EVEN WITH DAD", "release_year", "1994" ], [ "GETTING IN", "has_genre", "COMEDY" ], [ "GETTING IN", "release_year", "1994" ], [ "GOOD NEIGHBOR SAM", "has_genre", "COMEDY" ], [ "GOOD NEIGHBOR SAM", "release_year", "1964" ], [ "GOODBYE CHARLIE", "has_genre", "COMEDY" ], [ "GOODBYE CHARLIE", "release_year", "1964" ], [ "GREEDY", "has_genre", "COMEDY" ], [ "GREEDY", "release_year", "1994" ], [ "GUARDING TESS", "has_genre", "COMEDY" ], [ "GUARDING TESS", "release_year", "1994" ], [ "HAIL CAESAR", "has_genre", "COMEDY" ], [ "HAIL CAESAR", "release_year", "1994" ], [ "HOLY MATRIMONY", "has_genre", "COMEDY" ], [ "HOLY MATRIMONY", "release_year", "1994" ], [ "I LIKE IT LIKE THAT", "has_genre", "COMEDY" ], [ "I LIKE IT LIKE THAT", "release_year", "1994" ], [ "I LOVE TROUBLE", "has_genre", "COMEDY" ], [ "I LOVE TROUBLE", "release_year", "1994" ], [ "I.Q.", "has_genre", "COMEDY" ], [ "I.Q.", "release_year", "1994" ], [ "IN THE ARMY NOW", "has_genre", "COMEDY" ], [ "IN THE ARMY NOW", "release_year", "1994" ], [ "IT COULD HAPPEN TO YOU", "has_genre", "COMEDY" ], [ "IT COULD HAPPEN TO YOU", "has_tags", "COMEDY" ], [ "IT COULD HAPPEN TO YOU", "release_year", "1994" ], [ "IT RUNS IN THE FAMILY", "has_genre", "COMEDY" ], [ "IT RUNS IN THE FAMILY", "release_year", "1994" ], [ "IT'S PAT", "has_genre", "COMEDY" ], [ "IT'S PAT", "release_year", "1994" ], [ "JUNIOR", "has_genre", "COMEDY" ], [ "JUNIOR", "release_year", "1994" ], [ "KABHI HAAN KABHI NAA", "has_genre", "COMEDY" ], [ "KABHI HAAN KABHI NAA", "release_year", "1994" ], [ "KISS ME, STUPID", "has_genre", "COMEDY" ], [ "KISS ME, STUPID", "release_year", "1964" ], [ "KISSES FOR MY PRESIDENT", "has_genre", "COMEDY" ], [ "KISSES FOR MY PRESIDENT", "release_year", "1964" ], [ "LEPRECHAUN 2", "has_genre", "COMEDY" ], [ "LEPRECHAUN 2", "release_year", "1994" ], [ "LIGHTNING JACK", "has_genre", "COMEDY" ], [ "LIGHTNING JACK", "release_year", "1994" ], [ "LITTLE GIANTS", "has_genre", "COMEDY" ], [ "LITTLE GIANTS", "release_year", "1994" ], [ "MAN'S FAVORITE SPORT?", "has_genre", "COMEDY" ], [ "MAN'S FAVORITE SPORT?", "release_year", "1964" ], [ "MAVERICK", "has_genre", "COMEDY" ], [ "MAVERICK", "has_tags", "COMEDY" ], [ "MAVERICK", "release_year", "1994" ], [ "MILK MONEY", "has_genre", "COMEDY" ], [ "MILK MONEY", "release_year", "1994" ], [ "MIXED NUTS", "has_genre", "COMEDY" ], [ "MIXED NUTS", "release_year", "1994" ], [ "MONKEY TROUBLE", "has_genre", "COMEDY" ], [ "MONKEY TROUBLE", "release_year", "1994" ], [ "MURIEL'S WEDDING", "has_genre", "COMEDY" ], [ "MURIEL'S WEDDING", "has_tags", "COMEDY" ], [ "MURIEL'S WEDDING", "release_year", "1994" ], [ "MY GIRL 2", "has_genre", "COMEDY" ], [ "MY GIRL 2", "release_year", "1994" ], [ "NELL", "directed_by", "MICHAEL APTED" ], [ "NELL", "release_year", "1994" ], [ "NOBODY'S FOOL", "has_genre", "COMEDY" ], [ "NOBODY'S FOOL", "release_year", "1994" ], [ "NORTH", "has_genre", "COMEDY" ], [ "NORTH", "release_year", "1994" ], [ "ONLY YOU", "has_genre", "COMEDY" ], [ "ONLY YOU", "release_year", "1994" ], [ "PARIS WHEN IT SIZZLES", "has_genre", "COMEDY" ], [ "PARIS WHEN IT SIZZLES", "release_year", "1964" ], [ "PCU", "has_genre", "COMEDY" ], [ "PCU", "release_year", "1994" ], [ "PRINCESS CARABOO", "has_genre", "COMEDY" ], [ "PRINCESS CARABOO", "release_year", "1994" ], [ "PULP FICTION", "has_tags", "COMEDY" ], [ "PULP FICTION", "release_year", "1994" ], [ "RADIOLAND MURDERS", "has_genre", "COMEDY" ], [ "RADIOLAND MURDERS", "release_year", "1994" ], [ "REALITY BITES", "has_genre", "COMEDY" ], [ "REALITY BITES", "release_year", "1994" ], [ "RENAISSANCE MAN", "has_genre", "COMEDY" ], [ "RENAISSANCE MAN", "release_year", "1994" ], [ "SANTA CLAUS CONQUERS THE MARTIANS", "has_genre", "COMEDY" ], [ "SANTA CLAUS CONQUERS THE MARTIANS", "release_year", "1964" ], [ "SEND ME NO FLOWERS", "has_genre", "COMEDY" ], [ "SEND ME NO FLOWERS", "release_year", "1964" ], [ "SERIAL MOM", "has_genre", "COMEDY" ], [ "SERIAL MOM", "has_tags", "COMEDY" ], [ "SERIAL MOM", "release_year", "1994" ], [ "SEX AND THE SINGLE GIRL", "has_genre", "COMEDY" ], [ "SEX AND THE SINGLE GIRL", "release_year", "1964" ], [ "SLEEP WITH ME", "has_genre", "COMEDY" ], [ "SLEEP WITH ME", "release_year", "1994" ], [ "SPANKING THE MONKEY", "has_genre", "COMEDY" ], [ "SPANKING THE MONKEY", "release_year", "1994" ], [ "SPEECHLESS", "has_genre", "COMEDY" ], [ "SPEECHLESS", "release_year", "1994" ], [ "STAGGERED", "has_genre", "COMEDY" ], [ "STAGGERED", "release_year", "1994" ], [ "SWIMMING WITH SHARKS", "has_genre", "COMEDY" ], [ "SWIMMING WITH SHARKS", "release_year", "1994" ], [ "TAMMY AND THE T-REX", "has_genre", "COMEDY" ], [ "TAMMY AND THE T-REX", "release_year", "1994" ], [ "THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT", "has_genre", "COMEDY" ], [ "THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT", "release_year", "1994" ], [ "THE AIR UP THERE", "has_genre", "COMEDY" ], [ "THE AIR UP THERE", "has_tags", "COMEDY" ], [ "THE AIR UP THERE", "release_year", "1994" ], [ "THE AMERICANIZATION OF EMILY", "has_genre", "COMEDY" ], [ "THE AMERICANIZATION OF EMILY", "release_year", "1964" ], [ "THE BEST MAN", "has_genre", "COMEDY" ], [ "THE BEST MAN", "release_year", "1964" ], [ "THE CHASE", "has_genre", "COMEDY" ], [ "THE CHASE", "release_year", "1994" ], [ "THE COWBOY WAY", "has_genre", "COMEDY" ], [ "THE COWBOY WAY", "release_year", "1994" ], [ "THE DISORDERLY ORDERLY", "has_genre", "COMEDY" ], [ "THE DISORDERLY ORDERLY", "release_year", "1964" ], [ "THE FAVOR", "has_genre", "COMEDY" ], [ "THE FAVOR", "release_year", "1994" ], [ "THE FLINTSTONES", "has_genre", "COMEDY" ], [ "THE FLINTSTONES", "has_tags", "COMEDY" ], [ "THE FLINTSTONES", "release_year", "1994" ], [ "THE HUDSUCKER PROXY", "has_genre", "COMEDY" ], [ "THE HUDSUCKER PROXY", "has_tags", "COMEDY" ], [ "THE HUDSUCKER PROXY", "release_year", "1994" ], [ "THE INKWELL", "has_genre", "COMEDY" ], [ "THE INKWELL", "release_year", "1994" ], [ "THE LITTLE RASCALS", "has_genre", "COMEDY" ], [ "THE LITTLE RASCALS", "release_year", "1994" ], [ "THE MASK", "has_genre", "COMEDY" ], [ "THE MASK", "has_tags", "COMEDY" ], [ "THE MASK", "release_year", "1994" ], [ "THE MONSTER", "has_genre", "COMEDY" ], [ "THE MONSTER", "has_tags", "COMEDY" ], [ "THE MONSTER", "release_year", "1994" ], [ "THE PAPER", "has_genre", "COMEDY" ], [ "THE PAPER", "release_year", "1994" ], [ "THE PATSY", "has_genre", "COMEDY" ], [ "THE PATSY", "release_year", "1964" ], [ "THE REF", "has_genre", "COMEDY" ], [ "THE REF", "has_tags", "COMEDY" ], [ "THE REF", "release_year", "1994" ], [ "THE ROAD TO WELLVILLE", "has_genre", "COMEDY" ], [ "THE ROAD TO WELLVILLE", "release_year", "1994" ], [ "THE SANTA CLAUSE", "has_genre", "COMEDY" ], [ "THE SANTA CLAUSE", "release_year", "1994" ], [ "THE SCOUT", "has_genre", "COMEDY" ], [ "THE SCOUT", "release_year", "1994" ], [ "THE SEARCH FOR ONE-EYE JIMMY", "has_genre", "COMEDY" ], [ "THE SEARCH FOR ONE-EYE JIMMY", "release_year", "1994" ], [ "THE SUM OF US", "has_genre", "COMEDY" ], [ "THE SUM OF US", "release_year", "1994" ], [ "THE WORLD OF HENRY ORIENT", "has_genre", "COMEDY" ], [ "THE WORLD OF HENRY ORIENT", "has_tags", "COMEDY" ], [ "THE WORLD OF HENRY ORIENT", "release_year", "1964" ], [ "THREESOME", "has_genre", "COMEDY" ], [ "THREESOME", "has_tags", "COMEDY" ], [ "THREESOME", "release_year", "1994" ], [ "TRAPPED IN PARADISE", "has_genre", "COMEDY" ], [ "TRAPPED IN PARADISE", "release_year", "1994" ], [ "TRUE LIES", "has_genre", "COMEDY" ], [ "TRUE LIES", "has_tags", "COMEDY" ], [ "TRUE LIES", "release_year", "1994" ], [ "TWIN SITTERS", "has_genre", "COMEDY" ], [ "TWIN SITTERS", "release_year", "1994" ], [ "UPTOWN GIRLS", "has_genre", "COMEDY" ], [ "UPTOWN GIRLS", "written_by", "MO OGRODNIK" ], [ "WELCOME, OR NO TRESPASSING", "has_genre", "COMEDY" ], [ "WELCOME, OR NO TRESPASSING", "release_year", "1964" ], [ "WHAT A WAY TO GO!", "has_genre", "COMEDY" ], [ "WHAT A WAY TO GO!", "release_year", "1964" ], [ "WITH HONORS", "has_genre", "COMEDY" ], [ "WITH HONORS", "release_year", "1994" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 4210, A FINE MADNESS 30463, COMEDY 80, LABOR PAINS 33319, LARA SHAPIRO 24096, MARY WALSH 16645, NEW WATERFORD GIRL 36591, SEAN CONNERY src, edge_attr, dst 4210, has_genre, 30463 4210, starred_actors, 36591 80, directed_by, 33319 80, has_genre, 30463 80, written_by, 33319 16645, has_genre, 30463 16645, starred_actors, 24096 Question: How are LARA SHAPIRO, MARY WALSH, and SEAN CONNERY related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "LARA SHAPIRO", "MARY WALSH", "SEAN CONNERY" ], "valid_edges": [ [ "A FINE MADNESS", "has_genre", "COMEDY" ], [ "A FINE MADNESS", "starred_actors", "SEAN CONNERY" ], [ "LABOR PAINS", "directed_by", "LARA SHAPIRO" ], [ "LABOR PAINS", "has_genre", "COMEDY" ], [ "LABOR PAINS", "written_by", "LARA SHAPIRO" ], [ "NEW WATERFORD GIRL", "has_genre", "COMEDY" ], [ "NEW WATERFORD GIRL", "starred_actors", "MARY WALSH" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 38680, COMPULSION 952, HEATHER GRAHAM 14037, MARY 27490, PHILIPPE ARACTINGI 22652, SEBASTIAN SHAW 15966, THE SPY IN BLACK 24811, THRILLER 19779, UNDER THE BOMBS 22214, WAR src, edge_attr, dst 38680, has_genre, 24811 38680, starred_actors, 952 14037, has_genre, 24811 14037, starred_actors, 952 15966, has_genre, 24811 15966, has_genre, 22214 15966, starred_actors, 22652 19779, directed_by, 27490 19779, has_genre, 22214 19779, written_by, 27490 Question: In what context are HEATHER GRAHAM, PHILIPPE ARACTINGI, and SEBASTIAN SHAW connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "HEATHER GRAHAM", "PHILIPPE ARACTINGI", "SEBASTIAN SHAW" ], "valid_edges": [ [ "COMPULSION", "has_genre", "THRILLER" ], [ "COMPULSION", "starred_actors", "HEATHER GRAHAM" ], [ "MARY", "has_genre", "THRILLER" ], [ "MARY", "starred_actors", "HEATHER GRAHAM" ], [ "THE SPY IN BLACK", "has_genre", "THRILLER" ], [ "THE SPY IN BLACK", "has_genre", "WAR" ], [ "THE SPY IN BLACK", "starred_actors", "SEBASTIAN SHAW" ], [ "UNDER THE BOMBS", "directed_by", "PHILIPPE ARACTINGI" ], [ "UNDER THE BOMBS", "has_genre", "WAR" ], [ "UNDER THE BOMBS", "written_by", "PHILIPPE ARACTINGI" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 7034, CAN'T HELP SINGING 30615, CHRIS SANDERS 3461, IN OLD OKLAHOMA 10422, MARTHA SCOTT 23695, MULAN 24593, MUSICAL 35406, ROBERT PAIGE 36026, WESTERN src, edge_attr, dst 7034, has_genre, 24593 7034, has_genre, 36026 7034, starred_actors, 35406 3461, has_genre, 36026 3461, starred_actors, 10422 23695, has_tags, 24593 23695, written_by, 30615 Question: How are CHRIS SANDERS, MARTHA SCOTT, and ROBERT PAIGE related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "CHRIS SANDERS", "MARTHA SCOTT", "ROBERT PAIGE" ], "valid_edges": [ [ "CAN'T HELP SINGING", "has_genre", "MUSICAL" ], [ "CAN'T HELP SINGING", "has_genre", "WESTERN" ], [ "CAN'T HELP SINGING", "starred_actors", "ROBERT PAIGE" ], [ "IN OLD OKLAHOMA", "has_genre", "WESTERN" ], [ "IN OLD OKLAHOMA", "starred_actors", "MARTHA SCOTT" ], [ "MULAN", "has_tags", "MUSICAL" ], [ "MULAN", "written_by", "CHRIS SANDERS" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 20854, ALBERT BEICH 10045, BD-R 34664, BLOOD FROM THE MUMMY'S TOMB 29300, DEAD RINGER 21801, ROB EPSTEIN 9266, THE TIMES OF HARVEY MILK 12899, VALERIE LEON src, edge_attr, dst 34664, has_tags, 10045 34664, starred_actors, 12899 29300, has_tags, 10045 29300, written_by, 20854 9266, directed_by, 21801 9266, has_tags, 10045 9266, has_tags, 21801 Question: For what reason are ALBERT BEICH, ROB EPSTEIN, and VALERIE LEON associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ALBERT BEICH", "ROB EPSTEIN", "VALERIE LEON" ], "valid_edges": [ [ "BLOOD FROM THE MUMMY'S TOMB", "has_tags", "BD-R" ], [ "BLOOD FROM THE MUMMY'S TOMB", "starred_actors", "VALERIE LEON" ], [ "DEAD RINGER", "has_tags", "BD-R" ], [ "DEAD RINGER", "written_by", "ALBERT BEICH" ], [ "THE TIMES OF HARVEY MILK", "directed_by", "ROB EPSTEIN" ], [ "THE TIMES OF HARVEY MILK", "has_tags", "BD-R" ], [ "THE TIMES OF HARVEY MILK", "has_tags", "ROB EPSTEIN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 25221, 1981 24818, 1992 9005, 3 NINJAS 30146, A CHRISTMAS CAROL 16150, A CHRISTMAS STORY 9698, A CHRISTMAS TALE 31344, A LEAGUE OF THEIR OWN 10158, A PRINCESS FOR CHRISTMAS 28540, AN AMERICAN CAROL 26858, ARMY OF DARKNESS 14159, ARTHUR CHRISTMAS 34109, BAD SANTA 14271, BEETHOVEN 9127, BIG GIRLS DON'T CRY... THEY GET EVEN 32193, BLAME IT ON THE BELLBOY 14984, BLOW OUT 19829, BOOMERANG 8606, BRAIN DONORS 8065, BRIAN DE PALMA 23319, BUFFY THE VAMPIRE SLAYER 33773, BÉBÉ'S KIDS 10877, CAPTAIN RON 17280, CHRISTMAS 32407, CHRISTMAS IN CONNECTICUT 24598, CHRISTMAS WITH THE KRANKS 15721, CIAO, PROFESSORE! 12356, CLASS ACT 30463, COMEDY 11807, CRITTERS 4 27380, DEATH BECOMES HER 38345, DECK THE HALLS 771, DRESSED TO KILL 9505, ELF 9457, ENCINO MAN 29258, ERNEST SAVES CHRISTMAS 31630, EVIL TOONS 5636, FOLKS! 22005, FOUR CHRISTMASES 32553, FROZEN ASSETS 34177, GREMLINS 3829, HERO 18278, HOLIDAY IN HANDCUFFS 30886, HOME ALONE 18546, HONEYMOON IN VEGAS 25277, HUSBANDS AND WIVES 26137, I WANNA HOLD YOUR HAND 32168, ILLUSIVE TRACKS 4419, IN THE SOUP 19868, JACK FROST 4798, JINGLE ALL THE WAY 11719, JUST FRIENDS 25510, KING OF BEGGARS 13704, KUFFS 18950, LADYBUGS 191, LEAVING NORMAL 24500, LITTLE SISTER 3591, LIVE NUDE GIRLS 13672, LOVE ACTUALLY 19507, MAKE THE YULETIDE GAY 8268, MALEDETTO IL GIORNO CHE T'HO INCONTRATO 10560, MAN BITES DOG 24068, MAN TROUBLE 4561, MEMOIRS OF AN INVISIBLE MAN 34536, MISTRESS 12371, MIXED NUTS 39399, MO' MONEY 17135, MOM AND DAD SAVE THE WORLD 815, MR. BASEBALL 21919, MY COUSIN VINNY 2581, MY NEW GUN 27242, NANCY ALLEN 14917, OUT ON A LIMB 16910, PARENTI SERPENTI 25678, PETER'S FRIENDS 23220, REMEMBER THE NIGHT 4524, SANTA WITH MUSCLES 21320, SANTA'S SLAY 4187, SCROOGED 21512, SHERLOCK HOLMES 3358, SINGLES 8652, SISTER ACT 17370, STAY TUNED 2033, STOP! OR MY MOM WILL SHOOT 22993, STRAIGHT TALK 1432, STRICTLY BALLROOM 36928, THE BIKINI CARWASH COMPANY 13869, THE DISTINGUISHED GENTLEMAN 28948, THE FAMILY STONE 21831, THE GUN IN BETTY LOU'S HANDBAG 26955, THE HOLIDAY 24724, THE MIGHTY DUCKS 26128, THE MUPPET CHRISTMAS CAROL 24035, THE NORTHERNERS 28470, THE PERFECT HOLIDAY 10859, THE PREACHER'S WIFE 17314, THE SANTA CLAUSE 16918, THIS CHRISTMAS 24811, THRILLER 22911, TO GRANDMOTHER'S HOUSE WE GO 2281, TOYS 7279, TWIN DRAGONS 10376, USED PEOPLE 38839, WAYNE'S WORLD 22241, WE'RE NO ANGELS 20751, WHITE MEN CAN'T JUMP src, edge_attr, dst 25221, has_genre, 30463 9005, has_genre, 30463 9005, release_year, 24818 30146, has_genre, 30463 30146, has_tags, 17280 16150, has_genre, 30463 16150, has_tags, 17280 9698, has_genre, 30463 9698, has_tags, 17280 31344, has_genre, 30463 31344, release_year, 24818 10158, has_genre, 30463 10158, has_tags, 17280 28540, has_genre, 30463 28540, has_tags, 17280 26858, has_genre, 30463 26858, has_tags, 30463 26858, release_year, 24818 14159, has_genre, 30463 14159, has_tags, 17280 34109, has_genre, 30463 34109, has_tags, 17280 14271, has_genre, 30463 14271, has_tags, 30463 14271, release_year, 24818 9127, has_genre, 30463 9127, release_year, 24818 32193, has_genre, 30463 32193, release_year, 24818 14984, directed_by, 8065 14984, has_genre, 24811 14984, has_tags, 8065 14984, release_year, 25221 14984, starred_actors, 27242 14984, written_by, 8065 19829, has_genre, 30463 19829, release_year, 24818 8606, has_genre, 30463 8606, release_year, 24818 23319, has_genre, 30463 23319, has_tags, 30463 23319, release_year, 24818 33773, has_genre, 30463 33773, release_year, 24818 10877, has_genre, 30463 10877, has_tags, 30463 10877, release_year, 24818 32407, has_genre, 30463 32407, has_tags, 17280 24598, has_genre, 30463 24598, has_tags, 17280 15721, has_genre, 30463 15721, release_year, 24818 12356, has_genre, 30463 12356, release_year, 24818 11807, has_genre, 30463 11807, release_year, 24818 27380, has_genre, 30463 27380, release_year, 24818 38345, has_genre, 30463 38345, has_tags, 17280 771, directed_by, 8065 771, has_genre, 24811 771, has_tags, 8065 771, has_tags, 21512 771, starred_actors, 27242 771, written_by, 8065 9505, has_genre, 30463 9505, has_tags, 17280 9505, has_tags, 30463 9457, has_genre, 30463 9457, release_year, 24818 29258, has_genre, 30463 29258, has_tags, 17280 31630, has_genre, 30463 31630, release_year, 24818 5636, has_genre, 30463 5636, release_year, 24818 22005, has_genre, 30463 22005, has_tags, 17280 32553, has_genre, 30463 32553, release_year, 24818 34177, has_genre, 30463 34177, has_tags, 17280 3829, has_genre, 30463 3829, release_year, 24818 18278, has_genre, 30463 18278, has_tags, 17280 30886, has_genre, 30463 30886, has_tags, 17280 30886, has_tags, 30463 18546, has_genre, 30463 18546, release_year, 24818 25277, has_genre, 30463 25277, release_year, 24818 26137, has_genre, 30463 26137, starred_actors, 27242 32168, has_genre, 30463 32168, has_tags, 17280 4419, has_genre, 30463 4419, release_year, 24818 19868, has_genre, 30463 19868, has_tags, 17280 4798, has_genre, 30463 4798, has_tags, 17280 11719, has_genre, 30463 11719, has_tags, 17280 25510, has_genre, 30463 25510, release_year, 24818 13704, has_genre, 30463 13704, release_year, 24818 18950, has_genre, 30463 18950, release_year, 24818 191, has_genre, 30463 191, release_year, 24818 24500, has_genre, 30463 24500, release_year, 24818 3591, has_genre, 30463 13672, has_genre, 30463 13672, has_tags, 17280 13672, has_tags, 30463 19507, has_genre, 30463 19507, has_tags, 17280 8268, has_genre, 30463 8268, release_year, 24818 10560, has_genre, 30463 10560, release_year, 24818 24068, has_genre, 30463 24068, release_year, 24818 4561, has_genre, 30463 4561, release_year, 24818 34536, has_genre, 30463 34536, release_year, 24818 12371, has_genre, 30463 12371, has_tags, 17280 39399, has_genre, 30463 39399, release_year, 24818 17135, has_genre, 30463 17135, release_year, 24818 815, has_genre, 30463 815, release_year, 24818 21919, has_genre, 30463 21919, has_tags, 30463 21919, release_year, 24818 2581, has_genre, 30463 2581, release_year, 24818 14917, has_genre, 30463 14917, release_year, 24818 16910, has_genre, 30463 16910, release_year, 24818 25678, has_genre, 30463 25678, release_year, 24818 23220, has_genre, 30463 23220, has_tags, 17280 4524, has_genre, 30463 4524, has_tags, 17280 21320, has_genre, 30463 21320, has_tags, 17280 4187, has_genre, 30463 4187, has_tags, 17280 4187, has_tags, 30463 21512, has_tags, 30463 3358, has_genre, 30463 3358, release_year, 24818 8652, has_genre, 30463 8652, release_year, 24818 17370, has_genre, 30463 17370, release_year, 24818 2033, has_genre, 30463 2033, has_tags, 30463 2033, release_year, 24818 22993, has_genre, 30463 22993, release_year, 24818 1432, has_genre, 30463 1432, release_year, 24818 36928, has_genre, 30463 36928, release_year, 24818 13869, has_genre, 30463 13869, release_year, 24818 28948, has_genre, 30463 28948, has_tags, 17280 28948, has_tags, 30463 21831, has_genre, 30463 21831, release_year, 24818 26955, has_genre, 30463 26955, has_tags, 17280 24724, has_genre, 30463 24724, release_year, 24818 26128, has_genre, 30463 26128, has_tags, 17280 26128, release_year, 24818 24035, has_genre, 30463 24035, release_year, 24818 28470, has_genre, 30463 28470, has_tags, 17280 10859, has_genre, 30463 10859, has_tags, 17280 17314, has_genre, 30463 17314, has_tags, 17280 16918, has_genre, 30463 16918, has_tags, 17280 22911, has_tags, 17280 22911, release_year, 24818 2281, has_genre, 30463 2281, release_year, 24818 7279, has_genre, 30463 7279, release_year, 24818 10376, has_genre, 30463 10376, release_year, 24818 38839, has_genre, 30463 38839, has_tags, 30463 38839, release_year, 24818 22241, has_genre, 30463 22241, has_tags, 17280 22241, has_tags, 30463 20751, has_genre, 30463 20751, has_tags, 30463 20751, release_year, 24818 Question: In what context are LIVE NUDE GIRLS, NANCY ALLEN, and TO GRANDMOTHER'S HOUSE WE GO connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "LIVE NUDE GIRLS", "NANCY ALLEN", "TO GRANDMOTHER'S HOUSE WE GO" ], "valid_edges": [ [ "1981", "has_genre", "COMEDY" ], [ "3 NINJAS", "has_genre", "COMEDY" ], [ "3 NINJAS", "release_year", "1992" ], [ "A CHRISTMAS CAROL", "has_genre", "COMEDY" ], [ "A CHRISTMAS CAROL", "has_tags", "CHRISTMAS" ], [ "A CHRISTMAS STORY", "has_genre", "COMEDY" ], [ "A CHRISTMAS STORY", "has_tags", "CHRISTMAS" ], [ "A CHRISTMAS TALE", "has_genre", "COMEDY" ], [ "A CHRISTMAS TALE", "has_tags", "CHRISTMAS" ], [ "A LEAGUE OF THEIR OWN", "has_genre", "COMEDY" ], [ "A LEAGUE OF THEIR OWN", "release_year", "1992" ], [ "A PRINCESS FOR CHRISTMAS", "has_genre", "COMEDY" ], [ "A PRINCESS FOR CHRISTMAS", "has_tags", "CHRISTMAS" ], [ "AN AMERICAN CAROL", "has_genre", "COMEDY" ], [ "AN AMERICAN CAROL", "has_tags", "CHRISTMAS" ], [ "ARMY OF DARKNESS", "has_genre", "COMEDY" ], [ "ARMY OF DARKNESS", "has_tags", "COMEDY" ], [ "ARMY OF DARKNESS", "release_year", "1992" ], [ "ARTHUR CHRISTMAS", "has_genre", "COMEDY" ], [ "ARTHUR CHRISTMAS", "has_tags", "CHRISTMAS" ], [ "BAD SANTA", "has_genre", "COMEDY" ], [ "BAD SANTA", "has_tags", "CHRISTMAS" ], [ "BEETHOVEN", "has_genre", "COMEDY" ], [ "BEETHOVEN", "has_tags", "COMEDY" ], [ "BEETHOVEN", "release_year", "1992" ], [ "BIG GIRLS DON'T CRY... THEY GET EVEN", "has_genre", "COMEDY" ], [ "BIG GIRLS DON'T CRY... THEY GET EVEN", "release_year", "1992" ], [ "BLAME IT ON THE BELLBOY", "has_genre", "COMEDY" ], [ "BLAME IT ON THE BELLBOY", "release_year", "1992" ], [ "BLOW OUT", "directed_by", "BRIAN DE PALMA" ], [ "BLOW OUT", "has_genre", "THRILLER" ], [ "BLOW OUT", "has_tags", "BRIAN DE PALMA" ], [ "BLOW OUT", "release_year", "1981" ], [ "BLOW OUT", "starred_actors", "NANCY ALLEN" ], [ "BLOW OUT", "written_by", "BRIAN DE PALMA" ], [ "BOOMERANG", "has_genre", "COMEDY" ], [ "BOOMERANG", "release_year", "1992" ], [ "BRAIN DONORS", "has_genre", "COMEDY" ], [ "BRAIN DONORS", "release_year", "1992" ], [ "BUFFY THE VAMPIRE SLAYER", "has_genre", "COMEDY" ], [ "BUFFY THE VAMPIRE SLAYER", "has_tags", "COMEDY" ], [ "BUFFY THE VAMPIRE SLAYER", "release_year", "1992" ], [ "BÉBÉ'S KIDS", "has_genre", "COMEDY" ], [ "BÉBÉ'S KIDS", "release_year", "1992" ], [ "CAPTAIN RON", "has_genre", "COMEDY" ], [ "CAPTAIN RON", "has_tags", "COMEDY" ], [ "CAPTAIN RON", "release_year", "1992" ], [ "CHRISTMAS IN CONNECTICUT", "has_genre", "COMEDY" ], [ "CHRISTMAS IN CONNECTICUT", "has_tags", "CHRISTMAS" ], [ "CHRISTMAS WITH THE KRANKS", "has_genre", "COMEDY" ], [ "CHRISTMAS WITH THE KRANKS", "has_tags", "CHRISTMAS" ], [ "CIAO, PROFESSORE!", "has_genre", "COMEDY" ], [ "CIAO, PROFESSORE!", "release_year", "1992" ], [ "CLASS ACT", "has_genre", "COMEDY" ], [ "CLASS ACT", "release_year", "1992" ], [ "CRITTERS 4", "has_genre", "COMEDY" ], [ "CRITTERS 4", "release_year", "1992" ], [ "DEATH BECOMES HER", "has_genre", "COMEDY" ], [ "DEATH BECOMES HER", "release_year", "1992" ], [ "DECK THE HALLS", "has_genre", "COMEDY" ], [ "DECK THE HALLS", "has_tags", "CHRISTMAS" ], [ "DRESSED TO KILL", "directed_by", "BRIAN DE PALMA" ], [ "DRESSED TO KILL", "has_genre", "THRILLER" ], [ "DRESSED TO KILL", "has_tags", "BRIAN DE PALMA" ], [ "DRESSED TO KILL", "has_tags", "SHERLOCK HOLMES" ], [ "DRESSED TO KILL", "starred_actors", "NANCY ALLEN" ], [ "DRESSED TO KILL", "written_by", "BRIAN DE PALMA" ], [ "ELF", "has_genre", "COMEDY" ], [ "ELF", "has_tags", "CHRISTMAS" ], [ "ELF", "has_tags", "COMEDY" ], [ "ENCINO MAN", "has_genre", "COMEDY" ], [ "ENCINO MAN", "release_year", "1992" ], [ "ERNEST SAVES CHRISTMAS", "has_genre", "COMEDY" ], [ "ERNEST SAVES CHRISTMAS", "has_tags", "CHRISTMAS" ], [ "EVIL TOONS", "has_genre", "COMEDY" ], [ "EVIL TOONS", "release_year", "1992" ], [ "FOLKS!", "has_genre", "COMEDY" ], [ "FOLKS!", "release_year", "1992" ], [ "FOUR CHRISTMASES", "has_genre", "COMEDY" ], [ "FOUR CHRISTMASES", "has_tags", "CHRISTMAS" ], [ "FROZEN ASSETS", "has_genre", "COMEDY" ], [ "FROZEN ASSETS", "release_year", "1992" ], [ "GREMLINS", "has_genre", "COMEDY" ], [ "GREMLINS", "has_tags", "CHRISTMAS" ], [ "HERO", "has_genre", "COMEDY" ], [ "HERO", "release_year", "1992" ], [ "HOLIDAY IN HANDCUFFS", "has_genre", "COMEDY" ], [ "HOLIDAY IN HANDCUFFS", "has_tags", "CHRISTMAS" ], [ "HOME ALONE", "has_genre", "COMEDY" ], [ "HOME ALONE", "has_tags", "CHRISTMAS" ], [ "HOME ALONE", "has_tags", "COMEDY" ], [ "HONEYMOON IN VEGAS", "has_genre", "COMEDY" ], [ "HONEYMOON IN VEGAS", "release_year", "1992" ], [ "HUSBANDS AND WIVES", "has_genre", "COMEDY" ], [ "HUSBANDS AND WIVES", "release_year", "1992" ], [ "I WANNA HOLD YOUR HAND", "has_genre", "COMEDY" ], [ "I WANNA HOLD YOUR HAND", "starred_actors", "NANCY ALLEN" ], [ "ILLUSIVE TRACKS", "has_genre", "COMEDY" ], [ "ILLUSIVE TRACKS", "has_tags", "CHRISTMAS" ], [ "IN THE SOUP", "has_genre", "COMEDY" ], [ "IN THE SOUP", "release_year", "1992" ], [ "JACK FROST", "has_genre", "COMEDY" ], [ "JACK FROST", "has_tags", "CHRISTMAS" ], [ "JINGLE ALL THE WAY", "has_genre", "COMEDY" ], [ "JINGLE ALL THE WAY", "has_tags", "CHRISTMAS" ], [ "JUST FRIENDS", "has_genre", "COMEDY" ], [ "JUST FRIENDS", "has_tags", "CHRISTMAS" ], [ "KING OF BEGGARS", "has_genre", "COMEDY" ], [ "KING OF BEGGARS", "release_year", "1992" ], [ "KUFFS", "has_genre", "COMEDY" ], [ "KUFFS", "release_year", "1992" ], [ "LADYBUGS", "has_genre", "COMEDY" ], [ "LADYBUGS", "release_year", "1992" ], [ "LEAVING NORMAL", "has_genre", "COMEDY" ], [ "LEAVING NORMAL", "release_year", "1992" ], [ "LITTLE SISTER", "has_genre", "COMEDY" ], [ "LITTLE SISTER", "release_year", "1992" ], [ "LIVE NUDE GIRLS", "has_genre", "COMEDY" ], [ "LOVE ACTUALLY", "has_genre", "COMEDY" ], [ "LOVE ACTUALLY", "has_tags", "CHRISTMAS" ], [ "LOVE ACTUALLY", "has_tags", "COMEDY" ], [ "MAKE THE YULETIDE GAY", "has_genre", "COMEDY" ], [ "MAKE THE YULETIDE GAY", "has_tags", "CHRISTMAS" ], [ "MALEDETTO IL GIORNO CHE T'HO INCONTRATO", "has_genre", "COMEDY" ], [ "MALEDETTO IL GIORNO CHE T'HO INCONTRATO", "release_year", "1992" ], [ "MAN BITES DOG", "has_genre", "COMEDY" ], [ "MAN BITES DOG", "release_year", "1992" ], [ "MAN TROUBLE", "has_genre", "COMEDY" ], [ "MAN TROUBLE", "release_year", "1992" ], [ "MEMOIRS OF AN INVISIBLE MAN", "has_genre", "COMEDY" ], [ "MEMOIRS OF AN INVISIBLE MAN", "release_year", "1992" ], [ "MISTRESS", "has_genre", "COMEDY" ], [ "MISTRESS", "release_year", "1992" ], [ "MIXED NUTS", "has_genre", "COMEDY" ], [ "MIXED NUTS", "has_tags", "CHRISTMAS" ], [ "MO' MONEY", "has_genre", "COMEDY" ], [ "MO' MONEY", "release_year", "1992" ], [ "MOM AND DAD SAVE THE WORLD", "has_genre", "COMEDY" ], [ "MOM AND DAD SAVE THE WORLD", "release_year", "1992" ], [ "MR. BASEBALL", "has_genre", "COMEDY" ], [ "MR. BASEBALL", "release_year", "1992" ], [ "MY COUSIN VINNY", "has_genre", "COMEDY" ], [ "MY COUSIN VINNY", "has_tags", "COMEDY" ], [ "MY COUSIN VINNY", "release_year", "1992" ], [ "MY NEW GUN", "has_genre", "COMEDY" ], [ "MY NEW GUN", "release_year", "1992" ], [ "OUT ON A LIMB", "has_genre", "COMEDY" ], [ "OUT ON A LIMB", "release_year", "1992" ], [ "PARENTI SERPENTI", "has_genre", "COMEDY" ], [ "PARENTI SERPENTI", "release_year", "1992" ], [ "PETER'S FRIENDS", "has_genre", "COMEDY" ], [ "PETER'S FRIENDS", "release_year", "1992" ], [ "REMEMBER THE NIGHT", "has_genre", "COMEDY" ], [ "REMEMBER THE NIGHT", "has_tags", "CHRISTMAS" ], [ "SANTA WITH MUSCLES", "has_genre", "COMEDY" ], [ "SANTA WITH MUSCLES", "has_tags", "CHRISTMAS" ], [ "SANTA'S SLAY", "has_genre", "COMEDY" ], [ "SANTA'S SLAY", "has_tags", "CHRISTMAS" ], [ "SCROOGED", "has_genre", "COMEDY" ], [ "SCROOGED", "has_tags", "CHRISTMAS" ], [ "SCROOGED", "has_tags", "COMEDY" ], [ "SHERLOCK HOLMES", "has_tags", "COMEDY" ], [ "SINGLES", "has_genre", "COMEDY" ], [ "SINGLES", "release_year", "1992" ], [ "SISTER ACT", "has_genre", "COMEDY" ], [ "SISTER ACT", "release_year", "1992" ], [ "STAY TUNED", "has_genre", "COMEDY" ], [ "STAY TUNED", "release_year", "1992" ], [ "STOP! OR MY MOM WILL SHOOT", "has_genre", "COMEDY" ], [ "STOP! OR MY MOM WILL SHOOT", "has_tags", "COMEDY" ], [ "STOP! OR MY MOM WILL SHOOT", "release_year", "1992" ], [ "STRAIGHT TALK", "has_genre", "COMEDY" ], [ "STRAIGHT TALK", "release_year", "1992" ], [ "STRICTLY BALLROOM", "has_genre", "COMEDY" ], [ "STRICTLY BALLROOM", "release_year", "1992" ], [ "THE BIKINI CARWASH COMPANY", "has_genre", "COMEDY" ], [ "THE BIKINI CARWASH COMPANY", "release_year", "1992" ], [ "THE DISTINGUISHED GENTLEMAN", "has_genre", "COMEDY" ], [ "THE DISTINGUISHED GENTLEMAN", "release_year", "1992" ], [ "THE FAMILY STONE", "has_genre", "COMEDY" ], [ "THE FAMILY STONE", "has_tags", "CHRISTMAS" ], [ "THE FAMILY STONE", "has_tags", "COMEDY" ], [ "THE GUN IN BETTY LOU'S HANDBAG", "has_genre", "COMEDY" ], [ "THE GUN IN BETTY LOU'S HANDBAG", "release_year", "1992" ], [ "THE HOLIDAY", "has_genre", "COMEDY" ], [ "THE HOLIDAY", "has_tags", "CHRISTMAS" ], [ "THE MIGHTY DUCKS", "has_genre", "COMEDY" ], [ "THE MIGHTY DUCKS", "release_year", "1992" ], [ "THE MUPPET CHRISTMAS CAROL", "has_genre", "COMEDY" ], [ "THE MUPPET CHRISTMAS CAROL", "has_tags", "CHRISTMAS" ], [ "THE MUPPET CHRISTMAS CAROL", "release_year", "1992" ], [ "THE NORTHERNERS", "has_genre", "COMEDY" ], [ "THE NORTHERNERS", "release_year", "1992" ], [ "THE PERFECT HOLIDAY", "has_genre", "COMEDY" ], [ "THE PERFECT HOLIDAY", "has_tags", "CHRISTMAS" ], [ "THE PREACHER'S WIFE", "has_genre", "COMEDY" ], [ "THE PREACHER'S WIFE", "has_tags", "CHRISTMAS" ], [ "THE SANTA CLAUSE", "has_genre", "COMEDY" ], [ "THE SANTA CLAUSE", "has_tags", "CHRISTMAS" ], [ "THIS CHRISTMAS", "has_genre", "COMEDY" ], [ "THIS CHRISTMAS", "has_tags", "CHRISTMAS" ], [ "TO GRANDMOTHER'S HOUSE WE GO", "has_tags", "CHRISTMAS" ], [ "TO GRANDMOTHER'S HOUSE WE GO", "release_year", "1992" ], [ "TOYS", "has_genre", "COMEDY" ], [ "TOYS", "release_year", "1992" ], [ "TWIN DRAGONS", "has_genre", "COMEDY" ], [ "TWIN DRAGONS", "release_year", "1992" ], [ "USED PEOPLE", "has_genre", "COMEDY" ], [ "USED PEOPLE", "release_year", "1992" ], [ "WAYNE'S WORLD", "has_genre", "COMEDY" ], [ "WAYNE'S WORLD", "has_tags", "COMEDY" ], [ "WAYNE'S WORLD", "release_year", "1992" ], [ "WE'RE NO ANGELS", "has_genre", "COMEDY" ], [ "WE'RE NO ANGELS", "has_tags", "CHRISTMAS" ], [ "WE'RE NO ANGELS", "has_tags", "COMEDY" ], [ "WHITE MEN CAN'T JUMP", "has_genre", "COMEDY" ], [ "WHITE MEN CAN'T JUMP", "has_tags", "COMEDY" ], [ "WHITE MEN CAN'T JUMP", "release_year", "1992" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15271, ARIA 3957, BLUTZBRÜDAZ 33088, CARMEN 19500, FIRST SNOW 6480, GERMAN 7930, GUY PEARCE 1292, HEDWIG AND THE ANGRY INCH 18279, METROPOLIS 22845, MUSIC 21918, SIMON MACCORKINDALE 10522, TAKING SIDES 25270, THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT 34277, THE APPLE 25230, THE RIDDLE OF THE SANDS 37901, THE SOUND OF MUSIC src, edge_attr, dst 15271, has_genre, 22845 15271, in_language, 6480 3957, has_genre, 22845 3957, in_language, 6480 33088, has_genre, 22845 33088, in_language, 6480 19500, starred_actors, 7930 1292, has_genre, 22845 1292, has_tags, 22845 1292, in_language, 6480 18279, has_tags, 22845 18279, in_language, 6480 10522, has_genre, 22845 10522, in_language, 6480 25270, has_genre, 22845 25270, has_tags, 7930 25270, starred_actors, 7930 34277, has_genre, 22845 34277, has_tags, 22845 34277, in_language, 6480 25230, in_language, 6480 25230, starred_actors, 21918 37901, has_tags, 22845 37901, in_language, 6480 Question: For what reason are BLUTZBRÜDAZ, FIRST SNOW, and SIMON MACCORKINDALE associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BLUTZBRÜDAZ", "FIRST SNOW", "SIMON MACCORKINDALE" ], "valid_edges": [ [ "ARIA", "has_genre", "MUSIC" ], [ "ARIA", "in_language", "GERMAN" ], [ "BLUTZBRÜDAZ", "has_genre", "MUSIC" ], [ "BLUTZBRÜDAZ", "in_language", "GERMAN" ], [ "CARMEN", "has_genre", "MUSIC" ], [ "CARMEN", "in_language", "GERMAN" ], [ "FIRST SNOW", "starred_actors", "GUY PEARCE" ], [ "HEDWIG AND THE ANGRY INCH", "has_genre", "MUSIC" ], [ "HEDWIG AND THE ANGRY INCH", "has_tags", "MUSIC" ], [ "HEDWIG AND THE ANGRY INCH", "in_language", "GERMAN" ], [ "METROPOLIS", "has_tags", "MUSIC" ], [ "METROPOLIS", "in_language", "GERMAN" ], [ "TAKING SIDES", "has_genre", "MUSIC" ], [ "TAKING SIDES", "in_language", "GERMAN" ], [ "THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT", "has_genre", "MUSIC" ], [ "THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT", "has_tags", "GUY PEARCE" ], [ "THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT", "starred_actors", "GUY PEARCE" ], [ "THE APPLE", "has_genre", "MUSIC" ], [ "THE APPLE", "has_tags", "MUSIC" ], [ "THE APPLE", "in_language", "GERMAN" ], [ "THE RIDDLE OF THE SANDS", "in_language", "GERMAN" ], [ "THE RIDDLE OF THE SANDS", "starred_actors", "SIMON MACCORKINDALE" ], [ "THE SOUND OF MUSIC", "has_tags", "MUSIC" ], [ "THE SOUND OF MUSIC", "in_language", "GERMAN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 4177, 2 DAYS IN PARIS 13747, 25 WATTS 31344, A LEAGUE OF THEIR OWN 37090, A LITTLE HELP 20033, ANGEL 28344, ANGUS 16052, ANNE OF THE THOUSAND DAYS 5593, BABY BOY 23952, BANDITS 27714, BARNEY'S VERSION 13418, BARTLEBY 28846, BEGINNERS 31957, BOY 17892, CAMOUFLAGE 18996, CEMETERY JUNCTION 13257, CHARLIE BARTLETT 13901, CHARLIE WILSON'S WAR 10349, CHICAGO 30463, COMEDY 23734, COUPE DE VILLE 656, CRUSH 16600, CYRUS 22663, DAN IN REAL LIFE 22667, DAYS OF DARKNESS 1915, DIL CHAHTA HAI 3172, DIRTY GIRL 36212, DRAMA 25651, DUMMY 14240, EVERYTHING MUST GO 15188, EXPIRED 2969, FATHER OF INVENTION 34555, FLAWLESS 24880, FLIPPED 6915, FOCUS 3079, FREEDOM WRITERS 5287, FROZEN 2155, GEORGIA RULE 12664, GHOST WORLD 27638, GREENBERG 19912, GRIFF THE INVISIBLE 33701, HAPPYTHANKYOUMOREPLEASE 21060, HARVARD MAN 1292, HEDWIG AND THE ANGRY INCH 3829, HERO 19039, HESHER 38901, HIGH HEELS AND LOW LIFES 37827, HIGH SCHOOL 4931, HOW DO YOU KNOW 35625, HUMAN NATURE 25277, HUSBANDS AND WIVES 34856, I THINK I LOVE MY WIFE 9359, IMELDA STAUNTON 21788, IT'S KIND OF A FUNNY STORY 23526, JACKPOT 12610, JOE SOMEBODY 37372, JUNO 4619, KING OF CALIFORNIA 7699, KINGDOM COME 37238, KNOCKED UP 39069, LARS AND THE REAL GIRL 191, LEAVING NORMAL 9452, LIFE AS WE KNOW IT 29734, LITTLE SECRETS 16814, LITTLE WHITE LIES 33238, MARTIAN CHILD 33714, MEAN MACHINE 22924, MEET MONICA VELOUR 2990, MERMAID 18214, MISTER LONELY 34536, MISTRESS 16662, MORNING GLORY 24616, MOSTLY MARTHA 9519, NO RESERVATIONS 33677, NOISE 32186, ONE MAN UP 28044, PAULINE AND PAULETTE 11129, PEEP WORLD 25678, PETER'S FRIENDS 31652, PRETTY IN PINK 28182, RARE BIRDS 18177, REIGN OVER ME 38381, ROCK STAR 15095, ROCKET SCIENCE 12153, RUSHMORE 22312, SAY ANYTHING... 26699, SHORT CUTS 37133, SOMEWHERE 2407, SON OF THE BRIDE 19971, SPIN 7949, STORYTELLING 1432, STRICTLY BALLROOM 25002, STRUCK BY LIGHTNING 30733, SUBMARINE 25865, SUBURBIA 27762, SWEET NOVEMBER 17992, SWING VOTE 34308, TEACHERS 15795, THE ANNIVERSARY PARTY 17931, THE BREAKFAST CLUB 25674, THE BROTHERS 13582, THE BUCKET LIST 32654, THE CHOSEN ONE 8512, THE DUKES 36722, THE FOUR-FACED LIAR 2507, THE KIDS ARE ALL RIGHT 28107, THE LAST KISS 25623, THE MATRIARCH 1345, THE NANNY DIARIES 20728, THE ROYAL TENENBAUMS 27067, THEN SHE FOUND ME 16918, THIS CHRISTMAS 1903, TINY FURNITURE 35988, TORTILLA SOUP 11987, TRUST 26275, TRUST ME 9436, VIVA 35443, VIZONTELE 4460, WAITRESS 33258, WEST IS WEST 29999, WHY DID I GET MARRIED TOO? 6287, WHY DID I GET MARRIED? 1287, WILDER NAPALM 29421, YEAR OF THE DOG 6279, YOU WILL MEET A TALL DARK STRANGER src, edge_attr, dst 4177, has_genre, 30463 4177, has_genre, 36212 13747, has_genre, 30463 13747, has_genre, 36212 31344, has_genre, 30463 31344, has_genre, 36212 31344, has_tags, 36212 37090, has_genre, 30463 37090, has_genre, 36212 20033, has_genre, 30463 20033, has_genre, 36212 28344, has_genre, 30463 28344, has_genre, 36212 16052, has_genre, 36212 5593, has_genre, 30463 5593, has_genre, 36212 23952, has_genre, 30463 23952, has_genre, 36212 27714, has_genre, 30463 27714, has_genre, 36212 13418, has_genre, 30463 13418, has_genre, 36212 28846, has_genre, 30463 28846, has_genre, 36212 28846, has_tags, 36212 31957, has_genre, 30463 31957, has_genre, 36212 17892, has_genre, 30463 17892, has_genre, 36212 18996, has_genre, 30463 18996, has_genre, 36212 18996, has_tags, 30463 13257, has_genre, 30463 13257, has_genre, 36212 13901, has_genre, 30463 13901, has_genre, 36212 10349, has_genre, 30463 10349, has_genre, 36212 23734, has_genre, 30463 23734, has_genre, 36212 656, has_genre, 36212 656, starred_actors, 9359 16600, has_genre, 30463 16600, has_genre, 36212 22663, has_genre, 30463 22663, has_genre, 36212 22667, has_genre, 30463 22667, has_genre, 36212 1915, has_genre, 30463 1915, has_genre, 36212 1915, has_tags, 30463 3172, has_genre, 30463 3172, has_genre, 36212 25651, has_genre, 30463 25651, has_genre, 36212 14240, has_genre, 30463 14240, has_genre, 36212 15188, has_genre, 30463 15188, has_genre, 36212 2969, has_genre, 30463 2969, has_genre, 36212 34555, has_genre, 30463 34555, has_genre, 36212 24880, has_genre, 30463 24880, has_genre, 36212 6915, has_genre, 30463 6915, has_genre, 36212 3079, has_genre, 36212 3079, has_tags, 37827 3079, starred_actors, 9359 3079, written_by, 3079 5287, has_genre, 30463 5287, has_genre, 36212 2155, has_genre, 30463 2155, has_genre, 36212 12664, has_genre, 30463 12664, has_genre, 36212 27638, has_genre, 30463 27638, has_genre, 36212 19912, has_genre, 30463 19912, has_genre, 36212 33701, has_genre, 30463 33701, has_genre, 36212 21060, has_genre, 30463 21060, has_genre, 36212 1292, has_genre, 30463 1292, has_genre, 36212 3829, has_genre, 30463 3829, has_genre, 36212 19039, has_genre, 30463 19039, has_genre, 36212 38901, has_genre, 30463 38901, has_genre, 36212 37827, has_genre, 30463 4931, has_genre, 30463 4931, has_genre, 36212 35625, has_genre, 30463 35625, has_genre, 36212 25277, has_genre, 30463 25277, has_genre, 36212 34856, has_genre, 30463 34856, has_genre, 36212 21788, has_genre, 30463 21788, has_genre, 36212 23526, has_genre, 30463 23526, has_genre, 36212 12610, has_genre, 30463 12610, has_genre, 36212 37372, has_genre, 30463 37372, has_genre, 36212 37372, has_tags, 30463 4619, has_genre, 30463 4619, has_genre, 36212 7699, has_genre, 30463 7699, has_genre, 36212 37238, has_genre, 30463 37238, has_genre, 36212 39069, has_genre, 30463 39069, has_genre, 36212 191, has_genre, 30463 191, has_genre, 36212 9452, has_genre, 30463 9452, has_genre, 36212 9452, has_tags, 30463 9452, has_tags, 36212 29734, has_genre, 30463 29734, has_genre, 36212 16814, has_genre, 30463 16814, has_genre, 36212 33238, has_genre, 30463 33238, has_genre, 36212 33714, has_genre, 30463 33714, has_genre, 36212 22924, has_genre, 30463 22924, has_genre, 36212 2990, has_genre, 30463 2990, has_genre, 36212 18214, has_genre, 30463 18214, has_genre, 36212 34536, has_genre, 30463 34536, has_genre, 36212 16662, has_genre, 30463 16662, has_genre, 36212 24616, has_genre, 30463 24616, has_genre, 36212 9519, has_genre, 30463 9519, has_genre, 36212 33677, has_genre, 30463 33677, has_genre, 36212 33677, has_tags, 30463 32186, has_genre, 30463 32186, has_genre, 36212 28044, has_genre, 30463 28044, has_genre, 36212 11129, has_genre, 30463 11129, has_genre, 36212 25678, has_genre, 30463 25678, has_genre, 36212 31652, has_genre, 30463 31652, has_genre, 36212 31652, has_tags, 30463 28182, has_genre, 30463 28182, has_genre, 36212 18177, has_genre, 36212 18177, has_tags, 30463 18177, has_tags, 36212 38381, has_genre, 30463 38381, has_genre, 36212 15095, has_genre, 30463 15095, has_genre, 36212 12153, has_genre, 30463 12153, has_genre, 36212 12153, has_tags, 30463 22312, has_genre, 30463 22312, has_genre, 36212 26699, has_genre, 30463 26699, has_genre, 36212 37133, has_genre, 30463 37133, has_genre, 36212 2407, has_genre, 30463 2407, has_genre, 36212 19971, has_genre, 30463 19971, has_genre, 36212 7949, has_genre, 30463 7949, has_genre, 36212 1432, has_genre, 30463 1432, has_genre, 36212 25002, has_genre, 30463 25002, has_genre, 36212 30733, has_genre, 30463 30733, has_genre, 36212 25865, has_genre, 30463 25865, has_genre, 36212 27762, has_genre, 30463 27762, has_genre, 36212 17992, has_genre, 30463 17992, has_genre, 36212 34308, has_genre, 30463 34308, has_genre, 36212 15795, has_genre, 30463 15795, has_genre, 36212 17931, has_genre, 30463 17931, has_genre, 36212 17931, has_tags, 30463 17931, has_tags, 36212 25674, has_genre, 30463 25674, has_genre, 36212 13582, has_genre, 30463 13582, has_genre, 36212 32654, has_genre, 30463 32654, has_genre, 36212 8512, has_genre, 30463 8512, has_genre, 36212 36722, has_genre, 30463 36722, has_genre, 36212 2507, has_genre, 30463 2507, has_genre, 36212 2507, has_tags, 30463 2507, has_tags, 36212 28107, has_genre, 30463 28107, has_genre, 36212 25623, has_genre, 30463 25623, has_genre, 36212 25623, has_tags, 36212 1345, has_genre, 30463 1345, has_genre, 36212 20728, has_genre, 30463 20728, has_genre, 36212 20728, has_tags, 30463 27067, has_genre, 30463 27067, has_genre, 36212 16918, has_genre, 30463 16918, has_genre, 36212 1903, has_genre, 30463 1903, has_genre, 36212 35988, has_genre, 30463 35988, has_genre, 36212 11987, has_genre, 30463 11987, has_genre, 36212 26275, has_genre, 30463 26275, has_genre, 36212 9436, has_genre, 30463 9436, has_genre, 36212 35443, has_genre, 30463 35443, has_genre, 36212 35443, has_tags, 30463 4460, has_genre, 30463 4460, has_genre, 36212 33258, has_genre, 30463 33258, has_genre, 36212 29999, has_genre, 30463 29999, has_genre, 36212 6287, has_genre, 30463 6287, has_genre, 36212 1287, has_genre, 30463 29421, has_genre, 30463 29421, has_genre, 36212 6279, has_genre, 30463 6279, has_genre, 36212 6279, has_tags, 30463 6279, has_tags, 36212 Question: In what context are ANNE OF THE THOUSAND DAYS, IMELDA STAUNTON, and WILDER NAPALM connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ANNE OF THE THOUSAND DAYS", "IMELDA STAUNTON", "WILDER NAPALM" ], "valid_edges": [ [ "2 DAYS IN PARIS", "has_genre", "COMEDY" ], [ "2 DAYS IN PARIS", "has_genre", "DRAMA" ], [ "25 WATTS", "has_genre", "COMEDY" ], [ "25 WATTS", "has_genre", "DRAMA" ], [ "A LEAGUE OF THEIR OWN", "has_genre", "COMEDY" ], [ "A LEAGUE OF THEIR OWN", "has_genre", "DRAMA" ], [ "A LEAGUE OF THEIR OWN", "has_tags", "DRAMA" ], [ "A LITTLE HELP", "has_genre", "COMEDY" ], [ "A LITTLE HELP", "has_genre", "DRAMA" ], [ "ANGEL", "has_genre", "COMEDY" ], [ "ANGEL", "has_genre", "DRAMA" ], [ "ANGUS", "has_genre", "COMEDY" ], [ "ANGUS", "has_genre", "DRAMA" ], [ "ANNE OF THE THOUSAND DAYS", "has_genre", "DRAMA" ], [ "BABY BOY", "has_genre", "COMEDY" ], [ "BABY BOY", "has_genre", "DRAMA" ], [ "BANDITS", "has_genre", "COMEDY" ], [ "BANDITS", "has_genre", "DRAMA" ], [ "BARNEY'S VERSION", "has_genre", "COMEDY" ], [ "BARNEY'S VERSION", "has_genre", "DRAMA" ], [ "BARTLEBY", "has_genre", "COMEDY" ], [ "BARTLEBY", "has_genre", "DRAMA" ], [ "BEGINNERS", "has_genre", "COMEDY" ], [ "BEGINNERS", "has_genre", "DRAMA" ], [ "BEGINNERS", "has_tags", "DRAMA" ], [ "BOY", "has_genre", "COMEDY" ], [ "BOY", "has_genre", "DRAMA" ], [ "CAMOUFLAGE", "has_genre", "COMEDY" ], [ "CAMOUFLAGE", "has_genre", "DRAMA" ], [ "CEMETERY JUNCTION", "has_genre", "COMEDY" ], [ "CEMETERY JUNCTION", "has_genre", "DRAMA" ], [ "CEMETERY JUNCTION", "has_tags", "COMEDY" ], [ "CHARLIE BARTLETT", "has_genre", "COMEDY" ], [ "CHARLIE BARTLETT", "has_genre", "DRAMA" ], [ "CHARLIE WILSON'S WAR", "has_genre", "COMEDY" ], [ "CHARLIE WILSON'S WAR", "has_genre", "DRAMA" ], [ "CHICAGO", "has_genre", "COMEDY" ], [ "CHICAGO", "has_genre", "DRAMA" ], [ "COUPE DE VILLE", "has_genre", "COMEDY" ], [ "COUPE DE VILLE", "has_genre", "DRAMA" ], [ "CRUSH", "has_genre", "DRAMA" ], [ "CRUSH", "starred_actors", "IMELDA STAUNTON" ], [ "CYRUS", "has_genre", "COMEDY" ], [ "CYRUS", "has_genre", "DRAMA" ], [ "DAN IN REAL LIFE", "has_genre", "COMEDY" ], [ "DAN IN REAL LIFE", "has_genre", "DRAMA" ], [ "DAYS OF DARKNESS", "has_genre", "COMEDY" ], [ "DAYS OF DARKNESS", "has_genre", "DRAMA" ], [ "DIL CHAHTA HAI", "has_genre", "COMEDY" ], [ "DIL CHAHTA HAI", "has_genre", "DRAMA" ], [ "DIL CHAHTA HAI", "has_tags", "COMEDY" ], [ "DIRTY GIRL", "has_genre", "COMEDY" ], [ "DIRTY GIRL", "has_genre", "DRAMA" ], [ "DUMMY", "has_genre", "COMEDY" ], [ "DUMMY", "has_genre", "DRAMA" ], [ "EVERYTHING MUST GO", "has_genre", "COMEDY" ], [ "EVERYTHING MUST GO", "has_genre", "DRAMA" ], [ "EXPIRED", "has_genre", "COMEDY" ], [ "EXPIRED", "has_genre", "DRAMA" ], [ "FATHER OF INVENTION", "has_genre", "COMEDY" ], [ "FATHER OF INVENTION", "has_genre", "DRAMA" ], [ "FLAWLESS", "has_genre", "COMEDY" ], [ "FLAWLESS", "has_genre", "DRAMA" ], [ "FLIPPED", "has_genre", "COMEDY" ], [ "FLIPPED", "has_genre", "DRAMA" ], [ "FOCUS", "has_genre", "COMEDY" ], [ "FOCUS", "has_genre", "DRAMA" ], [ "FREEDOM WRITERS", "has_genre", "DRAMA" ], [ "FREEDOM WRITERS", "has_tags", "HIGH SCHOOL" ], [ "FREEDOM WRITERS", "starred_actors", "IMELDA STAUNTON" ], [ "FREEDOM WRITERS", "written_by", "FREEDOM WRITERS" ], [ "FROZEN", "has_genre", "COMEDY" ], [ "FROZEN", "has_genre", "DRAMA" ], [ "GEORGIA RULE", "has_genre", "COMEDY" ], [ "GEORGIA RULE", "has_genre", "DRAMA" ], [ "GHOST WORLD", "has_genre", "COMEDY" ], [ "GHOST WORLD", "has_genre", "DRAMA" ], [ "GREENBERG", "has_genre", "COMEDY" ], [ "GREENBERG", "has_genre", "DRAMA" ], [ "GRIFF THE INVISIBLE", "has_genre", "COMEDY" ], [ "GRIFF THE INVISIBLE", "has_genre", "DRAMA" ], [ "HAPPYTHANKYOUMOREPLEASE", "has_genre", "COMEDY" ], [ "HAPPYTHANKYOUMOREPLEASE", "has_genre", "DRAMA" ], [ "HARVARD MAN", "has_genre", "COMEDY" ], [ "HARVARD MAN", "has_genre", "DRAMA" ], [ "HEDWIG AND THE ANGRY INCH", "has_genre", "COMEDY" ], [ "HEDWIG AND THE ANGRY INCH", "has_genre", "DRAMA" ], [ "HERO", "has_genre", "COMEDY" ], [ "HERO", "has_genre", "DRAMA" ], [ "HESHER", "has_genre", "COMEDY" ], [ "HESHER", "has_genre", "DRAMA" ], [ "HIGH HEELS AND LOW LIFES", "has_genre", "COMEDY" ], [ "HIGH HEELS AND LOW LIFES", "has_genre", "DRAMA" ], [ "HIGH SCHOOL", "has_genre", "COMEDY" ], [ "HOW DO YOU KNOW", "has_genre", "COMEDY" ], [ "HOW DO YOU KNOW", "has_genre", "DRAMA" ], [ "HUMAN NATURE", "has_genre", "COMEDY" ], [ "HUMAN NATURE", "has_genre", "DRAMA" ], [ "HUSBANDS AND WIVES", "has_genre", "COMEDY" ], [ "HUSBANDS AND WIVES", "has_genre", "DRAMA" ], [ "I THINK I LOVE MY WIFE", "has_genre", "COMEDY" ], [ "I THINK I LOVE MY WIFE", "has_genre", "DRAMA" ], [ "IT'S KIND OF A FUNNY STORY", "has_genre", "COMEDY" ], [ "IT'S KIND OF A FUNNY STORY", "has_genre", "DRAMA" ], [ "JACKPOT", "has_genre", "COMEDY" ], [ "JACKPOT", "has_genre", "DRAMA" ], [ "JOE SOMEBODY", "has_genre", "COMEDY" ], [ "JOE SOMEBODY", "has_genre", "DRAMA" ], [ "JUNO", "has_genre", "COMEDY" ], [ "JUNO", "has_genre", "DRAMA" ], [ "JUNO", "has_tags", "COMEDY" ], [ "KING OF CALIFORNIA", "has_genre", "COMEDY" ], [ "KING OF CALIFORNIA", "has_genre", "DRAMA" ], [ "KINGDOM COME", "has_genre", "COMEDY" ], [ "KINGDOM COME", "has_genre", "DRAMA" ], [ "KNOCKED UP", "has_genre", "COMEDY" ], [ "KNOCKED UP", "has_genre", "DRAMA" ], [ "LARS AND THE REAL GIRL", "has_genre", "COMEDY" ], [ "LARS AND THE REAL GIRL", "has_genre", "DRAMA" ], [ "LEAVING NORMAL", "has_genre", "COMEDY" ], [ "LEAVING NORMAL", "has_genre", "DRAMA" ], [ "LIFE AS WE KNOW IT", "has_genre", "COMEDY" ], [ "LIFE AS WE KNOW IT", "has_genre", "DRAMA" ], [ "LIFE AS WE KNOW IT", "has_tags", "COMEDY" ], [ "LIFE AS WE KNOW IT", "has_tags", "DRAMA" ], [ "LITTLE SECRETS", "has_genre", "COMEDY" ], [ "LITTLE SECRETS", "has_genre", "DRAMA" ], [ "LITTLE WHITE LIES", "has_genre", "COMEDY" ], [ "LITTLE WHITE LIES", "has_genre", "DRAMA" ], [ "MARTIAN CHILD", "has_genre", "COMEDY" ], [ "MARTIAN CHILD", "has_genre", "DRAMA" ], [ "MEAN MACHINE", "has_genre", "COMEDY" ], [ "MEAN MACHINE", "has_genre", "DRAMA" ], [ "MEET MONICA VELOUR", "has_genre", "COMEDY" ], [ "MEET MONICA VELOUR", "has_genre", "DRAMA" ], [ "MERMAID", "has_genre", "COMEDY" ], [ "MERMAID", "has_genre", "DRAMA" ], [ "MISTER LONELY", "has_genre", "COMEDY" ], [ "MISTER LONELY", "has_genre", "DRAMA" ], [ "MISTRESS", "has_genre", "COMEDY" ], [ "MISTRESS", "has_genre", "DRAMA" ], [ "MORNING GLORY", "has_genre", "COMEDY" ], [ "MORNING GLORY", "has_genre", "DRAMA" ], [ "MOSTLY MARTHA", "has_genre", "COMEDY" ], [ "MOSTLY MARTHA", "has_genre", "DRAMA" ], [ "NO RESERVATIONS", "has_genre", "COMEDY" ], [ "NO RESERVATIONS", "has_genre", "DRAMA" ], [ "NOISE", "has_genre", "COMEDY" ], [ "NOISE", "has_genre", "DRAMA" ], [ "NOISE", "has_tags", "COMEDY" ], [ "ONE MAN UP", "has_genre", "COMEDY" ], [ "ONE MAN UP", "has_genre", "DRAMA" ], [ "PAULINE AND PAULETTE", "has_genre", "COMEDY" ], [ "PAULINE AND PAULETTE", "has_genre", "DRAMA" ], [ "PEEP WORLD", "has_genre", "COMEDY" ], [ "PEEP WORLD", "has_genre", "DRAMA" ], [ "PETER'S FRIENDS", "has_genre", "COMEDY" ], [ "PETER'S FRIENDS", "has_genre", "DRAMA" ], [ "PRETTY IN PINK", "has_genre", "COMEDY" ], [ "PRETTY IN PINK", "has_genre", "DRAMA" ], [ "PRETTY IN PINK", "has_tags", "COMEDY" ], [ "RARE BIRDS", "has_genre", "COMEDY" ], [ "RARE BIRDS", "has_genre", "DRAMA" ], [ "REIGN OVER ME", "has_genre", "DRAMA" ], [ "REIGN OVER ME", "has_tags", "COMEDY" ], [ "REIGN OVER ME", "has_tags", "DRAMA" ], [ "ROCK STAR", "has_genre", "COMEDY" ], [ "ROCK STAR", "has_genre", "DRAMA" ], [ "ROCKET SCIENCE", "has_genre", "COMEDY" ], [ "ROCKET SCIENCE", "has_genre", "DRAMA" ], [ "RUSHMORE", "has_genre", "COMEDY" ], [ "RUSHMORE", "has_genre", "DRAMA" ], [ "RUSHMORE", "has_tags", "COMEDY" ], [ "SAY ANYTHING...", "has_genre", "COMEDY" ], [ "SAY ANYTHING...", "has_genre", "DRAMA" ], [ "SHORT CUTS", "has_genre", "COMEDY" ], [ "SHORT CUTS", "has_genre", "DRAMA" ], [ "SOMEWHERE", "has_genre", "COMEDY" ], [ "SOMEWHERE", "has_genre", "DRAMA" ], [ "SON OF THE BRIDE", "has_genre", "COMEDY" ], [ "SON OF THE BRIDE", "has_genre", "DRAMA" ], [ "SPIN", "has_genre", "COMEDY" ], [ "SPIN", "has_genre", "DRAMA" ], [ "STORYTELLING", "has_genre", "COMEDY" ], [ "STORYTELLING", "has_genre", "DRAMA" ], [ "STRICTLY BALLROOM", "has_genre", "COMEDY" ], [ "STRICTLY BALLROOM", "has_genre", "DRAMA" ], [ "STRUCK BY LIGHTNING", "has_genre", "COMEDY" ], [ "STRUCK BY LIGHTNING", "has_genre", "DRAMA" ], [ "SUBMARINE", "has_genre", "COMEDY" ], [ "SUBMARINE", "has_genre", "DRAMA" ], [ "SUBURBIA", "has_genre", "COMEDY" ], [ "SUBURBIA", "has_genre", "DRAMA" ], [ "SWEET NOVEMBER", "has_genre", "COMEDY" ], [ "SWEET NOVEMBER", "has_genre", "DRAMA" ], [ "SWING VOTE", "has_genre", "COMEDY" ], [ "SWING VOTE", "has_genre", "DRAMA" ], [ "TEACHERS", "has_genre", "COMEDY" ], [ "TEACHERS", "has_genre", "DRAMA" ], [ "THE ANNIVERSARY PARTY", "has_genre", "COMEDY" ], [ "THE ANNIVERSARY PARTY", "has_genre", "DRAMA" ], [ "THE BREAKFAST CLUB", "has_genre", "COMEDY" ], [ "THE BREAKFAST CLUB", "has_genre", "DRAMA" ], [ "THE BREAKFAST CLUB", "has_tags", "COMEDY" ], [ "THE BREAKFAST CLUB", "has_tags", "DRAMA" ], [ "THE BROTHERS", "has_genre", "COMEDY" ], [ "THE BROTHERS", "has_genre", "DRAMA" ], [ "THE BUCKET LIST", "has_genre", "COMEDY" ], [ "THE BUCKET LIST", "has_genre", "DRAMA" ], [ "THE CHOSEN ONE", "has_genre", "COMEDY" ], [ "THE CHOSEN ONE", "has_genre", "DRAMA" ], [ "THE DUKES", "has_genre", "COMEDY" ], [ "THE DUKES", "has_genre", "DRAMA" ], [ "THE FOUR-FACED LIAR", "has_genre", "COMEDY" ], [ "THE FOUR-FACED LIAR", "has_genre", "DRAMA" ], [ "THE KIDS ARE ALL RIGHT", "has_genre", "COMEDY" ], [ "THE KIDS ARE ALL RIGHT", "has_genre", "DRAMA" ], [ "THE KIDS ARE ALL RIGHT", "has_tags", "COMEDY" ], [ "THE KIDS ARE ALL RIGHT", "has_tags", "DRAMA" ], [ "THE LAST KISS", "has_genre", "COMEDY" ], [ "THE LAST KISS", "has_genre", "DRAMA" ], [ "THE MATRIARCH", "has_genre", "COMEDY" ], [ "THE MATRIARCH", "has_genre", "DRAMA" ], [ "THE MATRIARCH", "has_tags", "DRAMA" ], [ "THE NANNY DIARIES", "has_genre", "COMEDY" ], [ "THE NANNY DIARIES", "has_genre", "DRAMA" ], [ "THE ROYAL TENENBAUMS", "has_genre", "COMEDY" ], [ "THE ROYAL TENENBAUMS", "has_genre", "DRAMA" ], [ "THE ROYAL TENENBAUMS", "has_tags", "COMEDY" ], [ "THEN SHE FOUND ME", "has_genre", "COMEDY" ], [ "THEN SHE FOUND ME", "has_genre", "DRAMA" ], [ "THIS CHRISTMAS", "has_genre", "COMEDY" ], [ "THIS CHRISTMAS", "has_genre", "DRAMA" ], [ "TINY FURNITURE", "has_genre", "COMEDY" ], [ "TINY FURNITURE", "has_genre", "DRAMA" ], [ "TORTILLA SOUP", "has_genre", "COMEDY" ], [ "TORTILLA SOUP", "has_genre", "DRAMA" ], [ "TRUST", "has_genre", "COMEDY" ], [ "TRUST", "has_genre", "DRAMA" ], [ "TRUST ME", "has_genre", "COMEDY" ], [ "TRUST ME", "has_genre", "DRAMA" ], [ "VIVA", "has_genre", "COMEDY" ], [ "VIVA", "has_genre", "DRAMA" ], [ "VIZONTELE", "has_genre", "COMEDY" ], [ "VIZONTELE", "has_genre", "DRAMA" ], [ "VIZONTELE", "has_tags", "COMEDY" ], [ "WAITRESS", "has_genre", "COMEDY" ], [ "WAITRESS", "has_genre", "DRAMA" ], [ "WEST IS WEST", "has_genre", "COMEDY" ], [ "WEST IS WEST", "has_genre", "DRAMA" ], [ "WHY DID I GET MARRIED TOO?", "has_genre", "COMEDY" ], [ "WHY DID I GET MARRIED TOO?", "has_genre", "DRAMA" ], [ "WHY DID I GET MARRIED?", "has_genre", "COMEDY" ], [ "WHY DID I GET MARRIED?", "has_genre", "DRAMA" ], [ "WILDER NAPALM", "has_genre", "COMEDY" ], [ "YEAR OF THE DOG", "has_genre", "COMEDY" ], [ "YEAR OF THE DOG", "has_genre", "DRAMA" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_genre", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_genre", "DRAMA" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_tags", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_tags", "DRAMA" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35935, 2002 29424, 2011 28374, ANTWONE FISHER 23286, CARNAGE 12903, DISNEY 2670, FOOTLOOSE 3859, I AM NUMBER FOUR 3429, MISS BALA 14026, PINOCCHIO 21773, POLLYANNA 729, SLEEPING BEAUTY 3578, SMALL TOWN 19102, SUPER 8 7816, THE THREE MUSKETEERS 10921, TREASURE PLANET 37351, WINNIE THE POOH src, edge_attr, dst 28374, release_year, 35935 28374, written_by, 28374 23286, release_year, 35935 23286, release_year, 29424 2670, has_tags, 3578 2670, release_year, 29424 3859, has_tags, 12903 3859, release_year, 29424 3429, release_year, 29424 14026, has_tags, 12903 14026, release_year, 35935 21773, has_tags, 12903 21773, has_tags, 3578 729, has_tags, 12903 729, release_year, 29424 19102, has_tags, 3578 19102, release_year, 29424 7816, has_tags, 12903 7816, release_year, 29424 10921, has_tags, 12903 10921, release_year, 35935 37351, has_tags, 12903 37351, release_year, 29424 Question: For what reason are ANTWONE FISHER, MISS BALA, and POLLYANNA associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ANTWONE FISHER", "MISS BALA", "POLLYANNA" ], "valid_edges": [ [ "ANTWONE FISHER", "release_year", "2002" ], [ "ANTWONE FISHER", "written_by", "ANTWONE FISHER" ], [ "CARNAGE", "release_year", "2002" ], [ "CARNAGE", "release_year", "2011" ], [ "FOOTLOOSE", "has_tags", "SMALL TOWN" ], [ "FOOTLOOSE", "release_year", "2011" ], [ "I AM NUMBER FOUR", "has_tags", "DISNEY" ], [ "I AM NUMBER FOUR", "release_year", "2011" ], [ "MISS BALA", "release_year", "2011" ], [ "PINOCCHIO", "has_tags", "DISNEY" ], [ "PINOCCHIO", "release_year", "2002" ], [ "POLLYANNA", "has_tags", "DISNEY" ], [ "POLLYANNA", "has_tags", "SMALL TOWN" ], [ "SLEEPING BEAUTY", "has_tags", "DISNEY" ], [ "SLEEPING BEAUTY", "release_year", "2011" ], [ "SUPER 8", "has_tags", "SMALL TOWN" ], [ "SUPER 8", "release_year", "2011" ], [ "THE THREE MUSKETEERS", "has_tags", "DISNEY" ], [ "THE THREE MUSKETEERS", "release_year", "2011" ], [ "TREASURE PLANET", "has_tags", "DISNEY" ], [ "TREASURE PLANET", "release_year", "2002" ], [ "WINNIE THE POOH", "has_tags", "DISNEY" ], [ "WINNIE THE POOH", "release_year", "2011" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 10702, 1991 15407, 29TH STREET 39705, A LITTLE STIFF 18051, AFTER HOURS 23683, ALL I WANT FOR CHRISTMAS 27592, ANIMAL HOUSE 30578, ANOTHER YOU 24301, ART SCHOOL CONFIDENTIAL 35639, BABY'S DAY OUT 26205, BEING JOHN MALKOVICH 9414, BINGO 35599, BURN AFTER READING 29437, CAN'T BUY ME LOVE 28295, CAREER OPPORTUNITIES 15585, CITY SLICKERS 30463, COMEDY 29148, CRAZY SAFARI 8100, CURLY SUE 31214, DEFENDING YOUR LIFE 29485, DELIRIOUS 37395, DEN OFRIVILLIGE GOLFAREN 3160, DOC HOLLYWOOD 21407, DOGMA 16978, DON'T TELL MOM THE BABYSITTER'S DEAD 20846, DROP DEAD FRED 31008, DUTCH 10757, EDDIE 36202, ERNEST SCARED STUPID 31630, EVIL TOONS 38250, FATHER OF THE BRIDE 25551, FAVORITE DEADLY SINS 3010, FORGET PARIS 30356, FRED OLEN RAY 35150, FRIED GREEN TOMATOES 28778, GOTCHA! 18119, HE SAID, SHE SAID 34320, HEAR MY SONG 25610, HIGH STRUNG 6546, HOT SHOTS! 39726, HUDSON HAWK 3909, IF LOOKS COULD KILL 25363, JERRY AND TOM 1841, JOE MANTEGNA 28149, JOHN MALKOVICH 37963, JOHNNY ENGLISH 528, JOHNNY STECCHINO 29911, KEVIN BACON 30015, KING RALPH 7439, L.A. STORY 29323, LIFE STINKS 23416, LINDA FIORENTINO 11652, LONELY STREET 22842, MAKING MR. RIGHT 32627, MYSTERY DATE 25620, NECESSARY ROUGHNESS 40059, ONCE AROUND 23735, ONLY THE LONELY 16225, ORDINARY DECENT CRIMINAL 30662, OSCAR 17667, OTHER PEOPLE'S MONEY 25824, PARADISE 32423, PERFECTLY NORMAL 10910, PICTURE PERFECT 23297, PROBLEM CHILD 2 39, PURE LUCK 25772, PYRATES 29601, QUEENS LOGIC 31731, R.I.P.D. 31600, RUBIN AND ED 28824, SENTA BERGER 33607, SEX AND ZEN 32127, SHE'S HAVING A BABY 8932, SLACKER 11883, SOAPDISH 38762, SPEAKING OF THE DEVIL 22375, STEVE RASH 29906, SUBURBAN COMMANDO 5700, SUPER 35064, SWITCH 30090, THE AIR UP THERE 35152, THE AMBUSHERS 32454, THE BIG PICTURE 8210, THE BUTCHER'S WIFE 4345, THE COMMITMENTS 252, THE DARK BACKWARD 20229, THE FISHER KING 25305, THE GREAT BUCK HOWARD 32102, THE HARD WAY 37565, THE LAST BOY SCOUT 12947, THE MARRYING MAN 1545, THE SUPER 21375, THINGS CHANGE 10238, UNDERWORLD 7593, WHAT ABOUT BOB? src, edge_attr, dst 15407, has_genre, 30463 15407, release_year, 10702 39705, has_genre, 30463 39705, release_year, 10702 18051, has_genre, 30463 18051, has_tags, 23416 23683, has_genre, 30463 23683, release_year, 10702 27592, has_genre, 30463 27592, has_tags, 30463 27592, has_tags, 29911 30578, has_genre, 30463 30578, release_year, 10702 24301, has_genre, 30463 24301, has_tags, 28149 24301, starred_actors, 28149 35639, has_genre, 30463 35639, starred_actors, 1841 26205, has_genre, 30463 26205, has_tags, 30463 26205, has_tags, 28149 9414, has_genre, 30463 9414, release_year, 10702 35599, has_genre, 30463 35599, has_tags, 30463 35599, has_tags, 28149 35599, starred_actors, 28149 29437, directed_by, 22375 29437, has_genre, 30463 29437, has_tags, 22375 28295, has_genre, 30463 28295, release_year, 10702 15585, has_genre, 30463 15585, release_year, 10702 29148, has_genre, 30463 29148, release_year, 10702 8100, has_genre, 30463 8100, release_year, 10702 31214, has_genre, 30463 31214, release_year, 10702 29485, has_genre, 30463 29485, release_year, 10702 37395, has_genre, 30463 37395, release_year, 10702 3160, has_genre, 30463 3160, has_tags, 30463 3160, release_year, 10702 21407, has_genre, 30463 21407, has_tags, 30463 21407, has_tags, 23416 16978, has_genre, 30463 16978, release_year, 10702 20846, has_genre, 30463 20846, release_year, 10702 31008, has_genre, 30463 31008, release_year, 10702 10757, directed_by, 22375 10757, has_genre, 30463 10757, has_tags, 22375 36202, has_genre, 30463 36202, release_year, 10702 31630, directed_by, 30356 31630, has_genre, 30463 31630, written_by, 30356 38250, has_genre, 30463 38250, has_tags, 30463 38250, release_year, 10702 25551, has_genre, 30463 25551, starred_actors, 1841 3010, has_genre, 30463 3010, starred_actors, 1841 35150, has_genre, 30463 35150, release_year, 10702 28778, has_genre, 30463 28778, starred_actors, 23416 18119, has_genre, 30463 18119, release_year, 10702 18119, starred_actors, 29911 34320, has_genre, 30463 34320, release_year, 10702 25610, has_genre, 30463 25610, release_year, 10702 6546, has_genre, 30463 6546, has_tags, 30463 6546, release_year, 10702 39726, has_genre, 30463 39726, has_tags, 30463 39726, release_year, 10702 3909, has_genre, 30463 3909, release_year, 10702 25363, has_genre, 30463 25363, starred_actors, 1841 37963, has_genre, 30463 37963, has_tags, 30463 37963, has_tags, 28149 528, has_genre, 30463 528, release_year, 10702 30015, has_genre, 30463 30015, has_tags, 30463 30015, release_year, 10702 7439, has_genre, 30463 7439, release_year, 10702 29323, has_genre, 30463 29323, release_year, 10702 11652, has_genre, 30463 11652, starred_actors, 1841 22842, has_genre, 30463 22842, has_tags, 28149 22842, starred_actors, 28149 32627, has_genre, 30463 32627, release_year, 10702 25620, has_genre, 30463 25620, release_year, 10702 40059, has_genre, 30463 40059, release_year, 10702 23735, has_genre, 30463 23735, release_year, 10702 16225, has_genre, 30463 16225, starred_actors, 23416 30662, has_genre, 30463 30662, release_year, 10702 17667, has_genre, 30463 17667, release_year, 10702 25824, has_genre, 30463 25824, release_year, 10702 32423, has_genre, 30463 32423, release_year, 10702 10910, has_genre, 30463 10910, starred_actors, 29911 23297, has_genre, 30463 23297, release_year, 10702 39, has_genre, 30463 39, release_year, 10702 25772, has_genre, 30463 25772, release_year, 10702 25772, starred_actors, 29911 29601, directed_by, 22375 29601, has_genre, 30463 29601, has_tags, 28149 29601, release_year, 10702 29601, starred_actors, 1841 29601, starred_actors, 28149 29601, starred_actors, 29911 29601, starred_actors, 23416 31731, has_genre, 30463 31731, starred_actors, 29911 31600, has_genre, 30463 31600, release_year, 10702 33607, has_genre, 30463 33607, release_year, 10702 32127, has_genre, 30463 32127, starred_actors, 29911 8932, has_genre, 30463 8932, release_year, 10702 11883, has_genre, 30463 11883, has_tags, 30463 11883, release_year, 10702 38762, has_genre, 30463 38762, release_year, 10702 29906, has_genre, 30463 29906, release_year, 10702 5700, has_genre, 30463 5700, has_tags, 29911 5700, starred_actors, 29911 35064, has_genre, 30463 35064, release_year, 10702 30090, has_genre, 30463 30090, has_tags, 30463 30090, has_tags, 29911 30090, starred_actors, 29911 35152, has_genre, 30463 35152, starred_actors, 28824 32454, has_genre, 30463 32454, starred_actors, 29911 8210, has_genre, 30463 8210, release_year, 10702 4345, has_genre, 30463 4345, release_year, 10702 252, has_genre, 30463 252, release_year, 10702 20229, has_genre, 30463 20229, release_year, 10702 25305, has_genre, 30463 25305, starred_actors, 28149 32102, has_genre, 30463 32102, release_year, 10702 37565, has_genre, 30463 37565, release_year, 10702 12947, has_genre, 30463 12947, release_year, 10702 1545, has_genre, 30463 1545, release_year, 10702 21375, has_genre, 30463 21375, starred_actors, 1841 10238, has_genre, 30463 10238, starred_actors, 1841 7593, has_genre, 30463 7593, release_year, 10702 Question: How are FRED OLEN RAY, QUEENS LOGIC, and SENTA BERGER related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "FRED OLEN RAY", "QUEENS LOGIC", "SENTA BERGER" ], "valid_edges": [ [ "29TH STREET", "has_genre", "COMEDY" ], [ "29TH STREET", "release_year", "1991" ], [ "A LITTLE STIFF", "has_genre", "COMEDY" ], [ "A LITTLE STIFF", "release_year", "1991" ], [ "AFTER HOURS", "has_genre", "COMEDY" ], [ "AFTER HOURS", "has_tags", "LINDA FIORENTINO" ], [ "ALL I WANT FOR CHRISTMAS", "has_genre", "COMEDY" ], [ "ALL I WANT FOR CHRISTMAS", "release_year", "1991" ], [ "ANIMAL HOUSE", "has_genre", "COMEDY" ], [ "ANIMAL HOUSE", "has_tags", "COMEDY" ], [ "ANIMAL HOUSE", "has_tags", "KEVIN BACON" ], [ "ANOTHER YOU", "has_genre", "COMEDY" ], [ "ANOTHER YOU", "release_year", "1991" ], [ "ART SCHOOL CONFIDENTIAL", "has_genre", "COMEDY" ], [ "ART SCHOOL CONFIDENTIAL", "has_tags", "JOHN MALKOVICH" ], [ "ART SCHOOL CONFIDENTIAL", "starred_actors", "JOHN MALKOVICH" ], [ "BABY'S DAY OUT", "has_genre", "COMEDY" ], [ "BABY'S DAY OUT", "starred_actors", "JOE MANTEGNA" ], [ "BEING JOHN MALKOVICH", "has_genre", "COMEDY" ], [ "BEING JOHN MALKOVICH", "has_tags", "COMEDY" ], [ "BEING JOHN MALKOVICH", "has_tags", "JOHN MALKOVICH" ], [ "BINGO", "has_genre", "COMEDY" ], [ "BINGO", "release_year", "1991" ], [ "BURN AFTER READING", "has_genre", "COMEDY" ], [ "BURN AFTER READING", "has_tags", "COMEDY" ], [ "BURN AFTER READING", "has_tags", "JOHN MALKOVICH" ], [ "BURN AFTER READING", "starred_actors", "JOHN MALKOVICH" ], [ "CAN'T BUY ME LOVE", "directed_by", "STEVE RASH" ], [ "CAN'T BUY ME LOVE", "has_genre", "COMEDY" ], [ "CAN'T BUY ME LOVE", "has_tags", "STEVE RASH" ], [ "CAREER OPPORTUNITIES", "has_genre", "COMEDY" ], [ "CAREER OPPORTUNITIES", "release_year", "1991" ], [ "CITY SLICKERS", "has_genre", "COMEDY" ], [ "CITY SLICKERS", "release_year", "1991" ], [ "CRAZY SAFARI", "has_genre", "COMEDY" ], [ "CRAZY SAFARI", "release_year", "1991" ], [ "CURLY SUE", "has_genre", "COMEDY" ], [ "CURLY SUE", "release_year", "1991" ], [ "DEFENDING YOUR LIFE", "has_genre", "COMEDY" ], [ "DEFENDING YOUR LIFE", "release_year", "1991" ], [ "DELIRIOUS", "has_genre", "COMEDY" ], [ "DELIRIOUS", "release_year", "1991" ], [ "DEN OFRIVILLIGE GOLFAREN", "has_genre", "COMEDY" ], [ "DEN OFRIVILLIGE GOLFAREN", "release_year", "1991" ], [ "DOC HOLLYWOOD", "has_genre", "COMEDY" ], [ "DOC HOLLYWOOD", "has_tags", "COMEDY" ], [ "DOC HOLLYWOOD", "release_year", "1991" ], [ "DOGMA", "has_genre", "COMEDY" ], [ "DOGMA", "has_tags", "COMEDY" ], [ "DOGMA", "has_tags", "LINDA FIORENTINO" ], [ "DON'T TELL MOM THE BABYSITTER'S DEAD", "has_genre", "COMEDY" ], [ "DON'T TELL MOM THE BABYSITTER'S DEAD", "release_year", "1991" ], [ "DROP DEAD FRED", "has_genre", "COMEDY" ], [ "DROP DEAD FRED", "release_year", "1991" ], [ "DUTCH", "has_genre", "COMEDY" ], [ "DUTCH", "release_year", "1991" ], [ "EDDIE", "directed_by", "STEVE RASH" ], [ "EDDIE", "has_genre", "COMEDY" ], [ "EDDIE", "has_tags", "STEVE RASH" ], [ "ERNEST SCARED STUPID", "has_genre", "COMEDY" ], [ "ERNEST SCARED STUPID", "release_year", "1991" ], [ "EVIL TOONS", "directed_by", "FRED OLEN RAY" ], [ "EVIL TOONS", "has_genre", "COMEDY" ], [ "EVIL TOONS", "written_by", "FRED OLEN RAY" ], [ "FATHER OF THE BRIDE", "has_genre", "COMEDY" ], [ "FATHER OF THE BRIDE", "has_tags", "COMEDY" ], [ "FATHER OF THE BRIDE", "release_year", "1991" ], [ "FAVORITE DEADLY SINS", "has_genre", "COMEDY" ], [ "FAVORITE DEADLY SINS", "starred_actors", "JOE MANTEGNA" ], [ "FORGET PARIS", "has_genre", "COMEDY" ], [ "FORGET PARIS", "starred_actors", "JOE MANTEGNA" ], [ "FRIED GREEN TOMATOES", "has_genre", "COMEDY" ], [ "FRIED GREEN TOMATOES", "release_year", "1991" ], [ "GOTCHA!", "has_genre", "COMEDY" ], [ "GOTCHA!", "starred_actors", "LINDA FIORENTINO" ], [ "HE SAID, SHE SAID", "has_genre", "COMEDY" ], [ "HE SAID, SHE SAID", "release_year", "1991" ], [ "HE SAID, SHE SAID", "starred_actors", "KEVIN BACON" ], [ "HEAR MY SONG", "has_genre", "COMEDY" ], [ "HEAR MY SONG", "release_year", "1991" ], [ "HIGH STRUNG", "has_genre", "COMEDY" ], [ "HIGH STRUNG", "release_year", "1991" ], [ "HOT SHOTS!", "has_genre", "COMEDY" ], [ "HOT SHOTS!", "has_tags", "COMEDY" ], [ "HOT SHOTS!", "release_year", "1991" ], [ "HUDSON HAWK", "has_genre", "COMEDY" ], [ "HUDSON HAWK", "has_tags", "COMEDY" ], [ "HUDSON HAWK", "release_year", "1991" ], [ "IF LOOKS COULD KILL", "has_genre", "COMEDY" ], [ "IF LOOKS COULD KILL", "release_year", "1991" ], [ "JERRY AND TOM", "has_genre", "COMEDY" ], [ "JERRY AND TOM", "starred_actors", "JOE MANTEGNA" ], [ "JOHNNY ENGLISH", "has_genre", "COMEDY" ], [ "JOHNNY ENGLISH", "has_tags", "COMEDY" ], [ "JOHNNY ENGLISH", "has_tags", "JOHN MALKOVICH" ], [ "JOHNNY STECCHINO", "has_genre", "COMEDY" ], [ "JOHNNY STECCHINO", "release_year", "1991" ], [ "KING RALPH", "has_genre", "COMEDY" ], [ "KING RALPH", "has_tags", "COMEDY" ], [ "KING RALPH", "release_year", "1991" ], [ "L.A. STORY", "has_genre", "COMEDY" ], [ "L.A. STORY", "release_year", "1991" ], [ "LIFE STINKS", "has_genre", "COMEDY" ], [ "LIFE STINKS", "release_year", "1991" ], [ "LONELY STREET", "has_genre", "COMEDY" ], [ "LONELY STREET", "starred_actors", "JOE MANTEGNA" ], [ "MAKING MR. RIGHT", "has_genre", "COMEDY" ], [ "MAKING MR. RIGHT", "has_tags", "JOHN MALKOVICH" ], [ "MAKING MR. RIGHT", "starred_actors", "JOHN MALKOVICH" ], [ "MYSTERY DATE", "has_genre", "COMEDY" ], [ "MYSTERY DATE", "release_year", "1991" ], [ "NECESSARY ROUGHNESS", "has_genre", "COMEDY" ], [ "NECESSARY ROUGHNESS", "release_year", "1991" ], [ "ONCE AROUND", "has_genre", "COMEDY" ], [ "ONCE AROUND", "release_year", "1991" ], [ "ONLY THE LONELY", "has_genre", "COMEDY" ], [ "ONLY THE LONELY", "release_year", "1991" ], [ "ORDINARY DECENT CRIMINAL", "has_genre", "COMEDY" ], [ "ORDINARY DECENT CRIMINAL", "starred_actors", "LINDA FIORENTINO" ], [ "OSCAR", "has_genre", "COMEDY" ], [ "OSCAR", "release_year", "1991" ], [ "OTHER PEOPLE'S MONEY", "has_genre", "COMEDY" ], [ "OTHER PEOPLE'S MONEY", "release_year", "1991" ], [ "PARADISE", "has_genre", "COMEDY" ], [ "PARADISE", "release_year", "1991" ], [ "PERFECTLY NORMAL", "has_genre", "COMEDY" ], [ "PERFECTLY NORMAL", "release_year", "1991" ], [ "PICTURE PERFECT", "has_genre", "COMEDY" ], [ "PICTURE PERFECT", "starred_actors", "KEVIN BACON" ], [ "PROBLEM CHILD 2", "has_genre", "COMEDY" ], [ "PROBLEM CHILD 2", "release_year", "1991" ], [ "PURE LUCK", "has_genre", "COMEDY" ], [ "PURE LUCK", "release_year", "1991" ], [ "PYRATES", "has_genre", "COMEDY" ], [ "PYRATES", "release_year", "1991" ], [ "PYRATES", "starred_actors", "KEVIN BACON" ], [ "QUEENS LOGIC", "directed_by", "STEVE RASH" ], [ "QUEENS LOGIC", "has_genre", "COMEDY" ], [ "QUEENS LOGIC", "has_tags", "JOHN MALKOVICH" ], [ "QUEENS LOGIC", "release_year", "1991" ], [ "QUEENS LOGIC", "starred_actors", "JOE MANTEGNA" ], [ "QUEENS LOGIC", "starred_actors", "JOHN MALKOVICH" ], [ "QUEENS LOGIC", "starred_actors", "KEVIN BACON" ], [ "QUEENS LOGIC", "starred_actors", "LINDA FIORENTINO" ], [ "R.I.P.D.", "has_genre", "COMEDY" ], [ "R.I.P.D.", "starred_actors", "KEVIN BACON" ], [ "RUBIN AND ED", "has_genre", "COMEDY" ], [ "RUBIN AND ED", "release_year", "1991" ], [ "SEX AND ZEN", "has_genre", "COMEDY" ], [ "SEX AND ZEN", "release_year", "1991" ], [ "SHE'S HAVING A BABY", "has_genre", "COMEDY" ], [ "SHE'S HAVING A BABY", "starred_actors", "KEVIN BACON" ], [ "SLACKER", "has_genre", "COMEDY" ], [ "SLACKER", "release_year", "1991" ], [ "SOAPDISH", "has_genre", "COMEDY" ], [ "SOAPDISH", "has_tags", "COMEDY" ], [ "SOAPDISH", "release_year", "1991" ], [ "SPEAKING OF THE DEVIL", "has_genre", "COMEDY" ], [ "SPEAKING OF THE DEVIL", "release_year", "1991" ], [ "SUBURBAN COMMANDO", "has_genre", "COMEDY" ], [ "SUBURBAN COMMANDO", "release_year", "1991" ], [ "SUPER", "has_genre", "COMEDY" ], [ "SUPER", "has_tags", "KEVIN BACON" ], [ "SUPER", "starred_actors", "KEVIN BACON" ], [ "SWITCH", "has_genre", "COMEDY" ], [ "SWITCH", "release_year", "1991" ], [ "THE AIR UP THERE", "has_genre", "COMEDY" ], [ "THE AIR UP THERE", "has_tags", "COMEDY" ], [ "THE AIR UP THERE", "has_tags", "KEVIN BACON" ], [ "THE AIR UP THERE", "starred_actors", "KEVIN BACON" ], [ "THE AMBUSHERS", "has_genre", "COMEDY" ], [ "THE AMBUSHERS", "starred_actors", "SENTA BERGER" ], [ "THE BIG PICTURE", "has_genre", "COMEDY" ], [ "THE BIG PICTURE", "starred_actors", "KEVIN BACON" ], [ "THE BUTCHER'S WIFE", "has_genre", "COMEDY" ], [ "THE BUTCHER'S WIFE", "release_year", "1991" ], [ "THE COMMITMENTS", "has_genre", "COMEDY" ], [ "THE COMMITMENTS", "release_year", "1991" ], [ "THE DARK BACKWARD", "has_genre", "COMEDY" ], [ "THE DARK BACKWARD", "release_year", "1991" ], [ "THE FISHER KING", "has_genre", "COMEDY" ], [ "THE FISHER KING", "release_year", "1991" ], [ "THE GREAT BUCK HOWARD", "has_genre", "COMEDY" ], [ "THE GREAT BUCK HOWARD", "starred_actors", "JOHN MALKOVICH" ], [ "THE HARD WAY", "has_genre", "COMEDY" ], [ "THE HARD WAY", "release_year", "1991" ], [ "THE LAST BOY SCOUT", "has_genre", "COMEDY" ], [ "THE LAST BOY SCOUT", "release_year", "1991" ], [ "THE MARRYING MAN", "has_genre", "COMEDY" ], [ "THE MARRYING MAN", "release_year", "1991" ], [ "THE SUPER", "has_genre", "COMEDY" ], [ "THE SUPER", "release_year", "1991" ], [ "THINGS CHANGE", "has_genre", "COMEDY" ], [ "THINGS CHANGE", "starred_actors", "JOE MANTEGNA" ], [ "UNDERWORLD", "has_genre", "COMEDY" ], [ "UNDERWORLD", "starred_actors", "JOE MANTEGNA" ], [ "WHAT ABOUT BOB?", "has_genre", "COMEDY" ], [ "WHAT ABOUT BOB?", "release_year", "1991" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 1421, 2013 20629, A SINGLE SHOT 30769, AIN'T THEM BODIES SAINTS 1824, ALL GOOD THINGS 26048, AMERICAN HUSTLE 18880, BANGKOK 8402, BANGKOK DANGEROUS 30870, BEAUTIFUL CREATURES 6809, BLOOD OF REDEMPTION 7483, BLOOD TIES 31203, BRAZILIAN WESTERN 38211, BROKEN CITY 12604, CIRCUS OF HORRORS 33784, CLOSED CIRCUIT 25131, COLD COMES THE NIGHT 38680, COMPULSION 8511, COTTAGE COUNTRY 14724, CRIME 17284, DEAD MAN DOWN 20056, DEBRA HILL 31232, DEVIL'S KNOT 33382, DOM HEMINGWAY 14268, DONALD PLEASENCE 30262, DRACULA 21021, DRIVE 13986, EMPIRE STATE 28256, FILTH 4372, FORCE OF EXECUTION 1033, FRACTURE 24481, FRANCHISE 35657, GLORIA 4830, HALLOWEEN 15753, HALLOWEEN II 12915, HELI 5870, HORROR 39037, HUNTING ELEPHANTS 8667, IDENTITY THIEF 4472, JAMIE LEE CURTIS 24988, JOHN CARPENTER 23224, KRISTIN SCOTT THOMAS 29765, LIFE OF CRIME 9792, LOVE CRIME 31769, MALCOLM MCDOWELL 16074, METRO MANILA 25918, MICHAEL MYERS 7925, MYSTERY ROAD 34844, NICOLAS WINDING REFN 24110, ONLY GOD FORGIVES 32279, PARKER 29586, PAWN SHOP CHRONICLES 38043, PUSHER 35405, ROB ZOMBIE 22512, RUNNER RUNNER 11468, RUSH 20331, RYAN GOSLING 5409, SLASHER 13712, TAMMY LAUREN 13090, THAI 13431, THE BLING RING 16900, THE CALL 38918, THE FAMILY 30294, THE NIGHT OF THE GENERALS 40086, THE PLACE BEYOND THE PINES 12738, TRAFFIC DEPARTMENT 5750, VAMPIRE 919, VAMPIRE IN VENICE 13357, WISHMASTER 17777, ZULU src, edge_attr, dst 20629, has_genre, 14724 20629, release_year, 1421 30769, has_genre, 14724 30769, release_year, 1421 1824, has_genre, 14724 1824, has_tags, 20331 1824, starred_actors, 20331 26048, has_genre, 14724 26048, release_year, 1421 8402, has_genre, 14724 8402, has_tags, 18880 8402, has_tags, 14724 8402, in_language, 13090 30870, has_genre, 14724 30870, release_year, 1421 6809, has_genre, 14724 6809, release_year, 1421 7483, has_genre, 14724 7483, release_year, 1421 31203, has_genre, 14724 31203, release_year, 1421 38211, has_genre, 14724 38211, release_year, 1421 12604, has_genre, 5870 12604, starred_actors, 14268 33784, has_genre, 14724 33784, release_year, 1421 25131, has_genre, 14724 25131, release_year, 1421 38680, has_genre, 14724 38680, release_year, 1421 8511, has_genre, 14724 8511, release_year, 1421 17284, has_genre, 14724 17284, release_year, 1421 31232, has_genre, 14724 31232, release_year, 1421 33382, has_genre, 14724 33382, release_year, 1421 30262, has_genre, 5870 30262, has_tags, 30262 30262, has_tags, 5870 30262, has_tags, 5750 30262, starred_actors, 14268 21021, directed_by, 34844 21021, has_genre, 14724 21021, has_tags, 14724 21021, has_tags, 34844 21021, has_tags, 20331 21021, starred_actors, 20331 13986, has_genre, 14724 13986, release_year, 1421 28256, has_genre, 14724 28256, release_year, 1421 4372, has_genre, 14724 4372, release_year, 1421 1033, has_genre, 14724 1033, has_tags, 20331 1033, starred_actors, 20331 35657, has_genre, 14724 35657, release_year, 1421 4830, directed_by, 24988 4830, directed_by, 35405 4830, has_genre, 5870 4830, has_tags, 14268 4830, has_tags, 24481 4830, has_tags, 4830 4830, has_tags, 5870 4830, has_tags, 4472 4830, has_tags, 24988 4830, has_tags, 31769 4830, has_tags, 25918 4830, has_tags, 35405 4830, has_tags, 5409 4830, starred_actors, 14268 4830, starred_actors, 4472 4830, starred_actors, 31769 4830, written_by, 20056 4830, written_by, 24988 4830, written_by, 35405 15753, directed_by, 35405 15753, has_genre, 5870 15753, has_tags, 14268 15753, has_tags, 24481 15753, has_tags, 4830 15753, has_tags, 5870 15753, has_tags, 4472 15753, has_tags, 31769 15753, has_tags, 25918 15753, has_tags, 5409 15753, starred_actors, 14268 15753, starred_actors, 4472 15753, written_by, 20056 15753, written_by, 24988 15753, written_by, 35405 12915, has_genre, 14724 12915, release_year, 1421 39037, has_genre, 14724 39037, release_year, 1421 8667, has_genre, 14724 8667, has_tags, 14724 8667, release_year, 1421 29765, has_genre, 14724 29765, release_year, 1421 9792, has_genre, 14724 9792, starred_actors, 23224 16074, has_genre, 14724 16074, release_year, 1421 7925, has_genre, 14724 7925, release_year, 1421 24110, directed_by, 34844 24110, has_genre, 14724 24110, has_tags, 18880 24110, has_tags, 34844 24110, has_tags, 20331 24110, in_language, 13090 24110, release_year, 1421 24110, starred_actors, 23224 24110, starred_actors, 20331 24110, written_by, 34844 32279, has_genre, 14724 32279, has_tags, 14724 32279, release_year, 1421 29586, has_genre, 14724 29586, release_year, 1421 38043, directed_by, 34844 38043, has_genre, 14724 38043, has_tags, 34844 38043, written_by, 34844 22512, has_genre, 14724 22512, release_year, 1421 11468, has_genre, 14724 11468, release_year, 1421 13431, has_genre, 14724 13431, has_tags, 14724 13431, release_year, 1421 16900, has_genre, 14724 16900, release_year, 1421 38918, has_genre, 14724 38918, release_year, 1421 30294, has_genre, 14724 30294, starred_actors, 14268 40086, has_genre, 14724 40086, has_tags, 20331 40086, starred_actors, 20331 12738, has_genre, 14724 12738, release_year, 1421 919, has_genre, 5870 919, has_tags, 5750 919, starred_actors, 14268 13357, has_genre, 5870 13357, has_tags, 5870 13357, starred_actors, 13712 17777, has_genre, 14724 17777, release_year, 1421 Question: In what context are DONALD PLEASENCE, ONLY GOD FORGIVES, and TAMMY LAUREN connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "DONALD PLEASENCE", "ONLY GOD FORGIVES", "TAMMY LAUREN" ], "valid_edges": [ [ "A SINGLE SHOT", "has_genre", "CRIME" ], [ "A SINGLE SHOT", "release_year", "2013" ], [ "AIN'T THEM BODIES SAINTS", "has_genre", "CRIME" ], [ "AIN'T THEM BODIES SAINTS", "release_year", "2013" ], [ "ALL GOOD THINGS", "has_genre", "CRIME" ], [ "ALL GOOD THINGS", "has_tags", "RYAN GOSLING" ], [ "ALL GOOD THINGS", "starred_actors", "RYAN GOSLING" ], [ "AMERICAN HUSTLE", "has_genre", "CRIME" ], [ "AMERICAN HUSTLE", "release_year", "2013" ], [ "BANGKOK DANGEROUS", "has_genre", "CRIME" ], [ "BANGKOK DANGEROUS", "has_tags", "BANGKOK" ], [ "BANGKOK DANGEROUS", "has_tags", "CRIME" ], [ "BANGKOK DANGEROUS", "in_language", "THAI" ], [ "BEAUTIFUL CREATURES", "has_genre", "CRIME" ], [ "BEAUTIFUL CREATURES", "release_year", "2013" ], [ "BLOOD OF REDEMPTION", "has_genre", "CRIME" ], [ "BLOOD OF REDEMPTION", "release_year", "2013" ], [ "BLOOD TIES", "has_genre", "CRIME" ], [ "BLOOD TIES", "release_year", "2013" ], [ "BRAZILIAN WESTERN", "has_genre", "CRIME" ], [ "BRAZILIAN WESTERN", "release_year", "2013" ], [ "BROKEN CITY", "has_genre", "CRIME" ], [ "BROKEN CITY", "release_year", "2013" ], [ "CIRCUS OF HORRORS", "has_genre", "HORROR" ], [ "CIRCUS OF HORRORS", "starred_actors", "DONALD PLEASENCE" ], [ "CLOSED CIRCUIT", "has_genre", "CRIME" ], [ "CLOSED CIRCUIT", "release_year", "2013" ], [ "COLD COMES THE NIGHT", "has_genre", "CRIME" ], [ "COLD COMES THE NIGHT", "release_year", "2013" ], [ "COMPULSION", "has_genre", "CRIME" ], [ "COMPULSION", "release_year", "2013" ], [ "COTTAGE COUNTRY", "has_genre", "CRIME" ], [ "COTTAGE COUNTRY", "release_year", "2013" ], [ "DEAD MAN DOWN", "has_genre", "CRIME" ], [ "DEAD MAN DOWN", "release_year", "2013" ], [ "DEVIL'S KNOT", "has_genre", "CRIME" ], [ "DEVIL'S KNOT", "release_year", "2013" ], [ "DOM HEMINGWAY", "has_genre", "CRIME" ], [ "DOM HEMINGWAY", "release_year", "2013" ], [ "DRACULA", "has_genre", "HORROR" ], [ "DRACULA", "has_tags", "DRACULA" ], [ "DRACULA", "has_tags", "HORROR" ], [ "DRACULA", "has_tags", "VAMPIRE" ], [ "DRACULA", "starred_actors", "DONALD PLEASENCE" ], [ "DRIVE", "directed_by", "NICOLAS WINDING REFN" ], [ "DRIVE", "has_genre", "CRIME" ], [ "DRIVE", "has_tags", "CRIME" ], [ "DRIVE", "has_tags", "NICOLAS WINDING REFN" ], [ "DRIVE", "has_tags", "RYAN GOSLING" ], [ "DRIVE", "starred_actors", "RYAN GOSLING" ], [ "EMPIRE STATE", "has_genre", "CRIME" ], [ "EMPIRE STATE", "release_year", "2013" ], [ "FILTH", "has_genre", "CRIME" ], [ "FILTH", "release_year", "2013" ], [ "FORCE OF EXECUTION", "has_genre", "CRIME" ], [ "FORCE OF EXECUTION", "release_year", "2013" ], [ "FRACTURE", "has_genre", "CRIME" ], [ "FRACTURE", "has_tags", "RYAN GOSLING" ], [ "FRACTURE", "starred_actors", "RYAN GOSLING" ], [ "GLORIA", "has_genre", "CRIME" ], [ "GLORIA", "release_year", "2013" ], [ "HALLOWEEN", "directed_by", "JOHN CARPENTER" ], [ "HALLOWEEN", "directed_by", "ROB ZOMBIE" ], [ "HALLOWEEN", "has_genre", "HORROR" ], [ "HALLOWEEN", "has_tags", "DONALD PLEASENCE" ], [ "HALLOWEEN", "has_tags", "FRANCHISE" ], [ "HALLOWEEN", "has_tags", "HALLOWEEN" ], [ "HALLOWEEN", "has_tags", "HORROR" ], [ "HALLOWEEN", "has_tags", "JAMIE LEE CURTIS" ], [ "HALLOWEEN", "has_tags", "JOHN CARPENTER" ], [ "HALLOWEEN", "has_tags", "MALCOLM MCDOWELL" ], [ "HALLOWEEN", "has_tags", "MICHAEL MYERS" ], [ "HALLOWEEN", "has_tags", "ROB ZOMBIE" ], [ "HALLOWEEN", "has_tags", "SLASHER" ], [ "HALLOWEEN", "starred_actors", "DONALD PLEASENCE" ], [ "HALLOWEEN", "starred_actors", "JAMIE LEE CURTIS" ], [ "HALLOWEEN", "starred_actors", "MALCOLM MCDOWELL" ], [ "HALLOWEEN", "written_by", "DEBRA HILL" ], [ "HALLOWEEN", "written_by", "JOHN CARPENTER" ], [ "HALLOWEEN", "written_by", "ROB ZOMBIE" ], [ "HALLOWEEN II", "directed_by", "ROB ZOMBIE" ], [ "HALLOWEEN II", "has_genre", "HORROR" ], [ "HALLOWEEN II", "has_tags", "DONALD PLEASENCE" ], [ "HALLOWEEN II", "has_tags", "FRANCHISE" ], [ "HALLOWEEN II", "has_tags", "HALLOWEEN" ], [ "HALLOWEEN II", "has_tags", "HORROR" ], [ "HALLOWEEN II", "has_tags", "JAMIE LEE CURTIS" ], [ "HALLOWEEN II", "has_tags", "MALCOLM MCDOWELL" ], [ "HALLOWEEN II", "has_tags", "MICHAEL MYERS" ], [ "HALLOWEEN II", "has_tags", "SLASHER" ], [ "HALLOWEEN II", "starred_actors", "DONALD PLEASENCE" ], [ "HALLOWEEN II", "starred_actors", "JAMIE LEE CURTIS" ], [ "HALLOWEEN II", "written_by", "DEBRA HILL" ], [ "HALLOWEEN II", "written_by", "JOHN CARPENTER" ], [ "HALLOWEEN II", "written_by", "ROB ZOMBIE" ], [ "HELI", "has_genre", "CRIME" ], [ "HELI", "release_year", "2013" ], [ "HUNTING ELEPHANTS", "has_genre", "CRIME" ], [ "HUNTING ELEPHANTS", "release_year", "2013" ], [ "IDENTITY THIEF", "has_genre", "CRIME" ], [ "IDENTITY THIEF", "has_tags", "CRIME" ], [ "IDENTITY THIEF", "release_year", "2013" ], [ "LIFE OF CRIME", "has_genre", "CRIME" ], [ "LIFE OF CRIME", "release_year", "2013" ], [ "LOVE CRIME", "has_genre", "CRIME" ], [ "LOVE CRIME", "starred_actors", "KRISTIN SCOTT THOMAS" ], [ "METRO MANILA", "has_genre", "CRIME" ], [ "METRO MANILA", "release_year", "2013" ], [ "MYSTERY ROAD", "has_genre", "CRIME" ], [ "MYSTERY ROAD", "release_year", "2013" ], [ "ONLY GOD FORGIVES", "directed_by", "NICOLAS WINDING REFN" ], [ "ONLY GOD FORGIVES", "has_genre", "CRIME" ], [ "ONLY GOD FORGIVES", "has_tags", "BANGKOK" ], [ "ONLY GOD FORGIVES", "has_tags", "NICOLAS WINDING REFN" ], [ "ONLY GOD FORGIVES", "has_tags", "RYAN GOSLING" ], [ "ONLY GOD FORGIVES", "in_language", "THAI" ], [ "ONLY GOD FORGIVES", "release_year", "2013" ], [ "ONLY GOD FORGIVES", "starred_actors", "KRISTIN SCOTT THOMAS" ], [ "ONLY GOD FORGIVES", "starred_actors", "RYAN GOSLING" ], [ "ONLY GOD FORGIVES", "written_by", "NICOLAS WINDING REFN" ], [ "PARKER", "has_genre", "CRIME" ], [ "PARKER", "has_tags", "CRIME" ], [ "PARKER", "release_year", "2013" ], [ "PAWN SHOP CHRONICLES", "has_genre", "CRIME" ], [ "PAWN SHOP CHRONICLES", "release_year", "2013" ], [ "PUSHER", "directed_by", "NICOLAS WINDING REFN" ], [ "PUSHER", "has_genre", "CRIME" ], [ "PUSHER", "has_tags", "NICOLAS WINDING REFN" ], [ "PUSHER", "written_by", "NICOLAS WINDING REFN" ], [ "RUNNER RUNNER", "has_genre", "CRIME" ], [ "RUNNER RUNNER", "release_year", "2013" ], [ "RUSH", "has_genre", "CRIME" ], [ "RUSH", "release_year", "2013" ], [ "THE BLING RING", "has_genre", "CRIME" ], [ "THE BLING RING", "has_tags", "CRIME" ], [ "THE BLING RING", "release_year", "2013" ], [ "THE CALL", "has_genre", "CRIME" ], [ "THE CALL", "release_year", "2013" ], [ "THE FAMILY", "has_genre", "CRIME" ], [ "THE FAMILY", "release_year", "2013" ], [ "THE NIGHT OF THE GENERALS", "has_genre", "CRIME" ], [ "THE NIGHT OF THE GENERALS", "starred_actors", "DONALD PLEASENCE" ], [ "THE PLACE BEYOND THE PINES", "has_genre", "CRIME" ], [ "THE PLACE BEYOND THE PINES", "has_tags", "RYAN GOSLING" ], [ "THE PLACE BEYOND THE PINES", "starred_actors", "RYAN GOSLING" ], [ "TRAFFIC DEPARTMENT", "has_genre", "CRIME" ], [ "TRAFFIC DEPARTMENT", "release_year", "2013" ], [ "VAMPIRE IN VENICE", "has_genre", "HORROR" ], [ "VAMPIRE IN VENICE", "has_tags", "VAMPIRE" ], [ "VAMPIRE IN VENICE", "starred_actors", "DONALD PLEASENCE" ], [ "WISHMASTER", "has_genre", "HORROR" ], [ "WISHMASTER", "has_tags", "HORROR" ], [ "WISHMASTER", "starred_actors", "TAMMY LAUREN" ], [ "ZULU", "has_genre", "CRIME" ], [ "ZULU", "release_year", "2013" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 2452, 13 ASSASSINS 11, 1940 19543, 2001 MANIACS 37484, 2004 6314, A DAMSEL IN DISTRESS 24480, A NIGHTMARE ON ELM STREET 25465, APARTMENT 1303 7908, AUDITION 23324, BATTLEFIELD BASEBALL 4445, BITTER SWEET 36593, BLACK CHRISTMAS 3506, BLUE SKIES 25126, BODY SNATCHERS 16011, BROADWAY MELODY OF 1940 7381, CALVAIRE 8079, CAREFREE 38311, CHAOS 1551, CLUB DREAD 32408, CREEP 26635, CROWS ZERO 12155, DADDY LONG LEGS 24400, DARK WATER 21942, DAWN OF THE DEAD 5547, DEAD BIRDS 1353, DECOYS 25528, DEVIL'S PASS 23612, DOLLS 13272, DON'T BE AFRAID OF THE DARK 15286, DOWN ARGENTINE WAY 25662, DR. CYCLOPS 6662, DUMPLINGS 5799, EASTER PARADE 23129, EVIL DEAD 19728, EVILENKO 5038, FINIAN'S RAINBOW 16485, FOLLOW THE FLEET 29924, FRED ASTAIRE 22215, FRIDAY THE 13TH 31648, FRIGHT NIGHT 1578, FUNNY FACE 32864, GHOST STORY 3553, GODZILLA 4830, HALLOWEEN 11997, HELLBENT 23534, HOLIDAY INN 14279, HOLLY GOSS 5870, HORROR 10147, HOUSE 9686, HOUSE OF WAX 31551, HOUSE ON HAUNTED HILL 15439, I SPIT ON YOUR GRAVE 13174, INFECTION 25576, INNOCENCE 13070, INVADERS FROM MARS 14910, IT'S ALIVE 8164, IZO 8248, JAPAN 36874, JAPANESE 4418, KWAIDAN 29987, LET ME IN 8851, LITTLE SHOP OF HORRORS 20189, LOFT 26370, MADHOUSE 2461, MEMOIRS OF A GEISHA 35621, MILLENNIUM ACTRESS 21686, MOONLIGHT SERENADE 24593, MUSICAL 32988, NIGHT OF THE LIVING DEAD 11340, NIGHT OF THE LIVING DEAD 3D 18722, NOSFERATU THE VAMPYRE 3089, NOT OF THIS EARTH 36326, ONE MISSED CALL 6302, ONIBABA 36532, OVER YOUR DEAD BODY 234, PEARL HARBOR 14026, PINOCCHIO 6035, PRINCESS RACCOON 28091, PSYCHO 13237, PULSE 199, PUPPET MASTER VS DEMONIC TOYS 6175, QUARANTINE 28729, REMAKE 30416, REPO! THE GENETIC OPERA 32894, RIDING THE BULLET 28, RING 35249, RING 2 38241, RING OF DARKNESS 27308, ROBERTA 38307, ROYAL WEDDING 34022, SATAN'S LITTLE HELPER 36247, SAW 12071, SECOND CHORUS 2186, SEED OF CHUCKY 30996, SHUTTER 16848, SILK STOCKINGS 6260, SPIRITED AWAY 32367, STAGE FRIGHT 1584, STEPHEN SUSCO 13333, STRIKE UP THE BAND 7170, SWEET HOME 8998, SWING TIME 18903, TAKASHI SHIMIZU 20069, THAT'S ENTERTAINMENT! 31138, THAT'S ENTERTAINMENT, PART II 9015, THE AMITYVILLE HORROR 16069, THE APE 32851, THE BAND WAGON 3223, THE BARKLEYS OF BROADWAY 3543, THE BLOB 25463, THE CRAZIES 4694, THE CREATURE WASN'T NICE 3273, THE DEVIL BAT 7153, THE DEVIL'S CARNIVAL 20931, THE EYE 38052, THE GAY DIVORCEE 28048, THE GHOST BREAKERS 786, THE GRUDGE 9129, THE GRUDGE 2 19342, THE GRUDGE 3 17778, THE HAPPINESS OF THE KATAKURIS 11787, THE HAUNTING 15462, THE HILLS HAVE EYES 26776, THE INVISIBLE MAN RETURNS 15202, THE LAST HOUSE ON THE LEFT 29109, THE LODGER 17910, THE OMEN 2492, THE RING 12654, THE RING TWO 9923, THE SKY'S THE LIMIT 23847, THE STEPFORD WIVES 30082, THE STORY OF VERNON AND IRENE CASTLE 21548, THE SUN 9665, THE TEXAS CHAINSAW MASSACRE 22751, THE WICKER MAN 24040, THREE LITTLE WORDS 29819, TIN PAN ALLEY 19710, TOKYO ZOMBIE 12856, TOOLBOX MURDERS 32505, TOP HAT 19088, VAN HELSING 36374, VILLAGE OF THE DAMNED 29077, WASABI 8589, WICKED CITY 12029, WILD ZERO 38538, WILLARD 15472, YOLANDA AND THE THIEF 19677, YOU WERE NEVER LOVELIER 38863, YOU'LL NEVER GET RICH 14841, ZIEGFELD FOLLIES 38978, ZOMBIE HONEYMOON src, edge_attr, dst 2452, has_tags, 8248 2452, in_language, 36874 19543, has_genre, 5870 19543, has_tags, 28729 6314, has_genre, 24593 6314, starred_actors, 29924 24480, has_genre, 5870 24480, has_tags, 5870 24480, has_tags, 28729 25465, has_genre, 5870 25465, in_language, 36874 7908, has_genre, 5870 7908, in_language, 36874 23324, has_genre, 5870 23324, in_language, 36874 4445, has_genre, 24593 4445, release_year, 11 36593, has_genre, 5870 36593, has_tags, 5870 36593, has_tags, 28729 3506, has_genre, 24593 3506, starred_actors, 29924 25126, has_genre, 5870 25126, has_tags, 28729 16011, has_genre, 24593 16011, release_year, 11 16011, starred_actors, 29924 7381, has_genre, 5870 7381, has_tags, 5870 7381, release_year, 37484 8079, has_genre, 24593 8079, starred_actors, 29924 38311, has_genre, 5870 38311, in_language, 36874 1551, has_genre, 5870 1551, release_year, 37484 32408, has_genre, 5870 32408, release_year, 37484 26635, has_tags, 8248 26635, in_language, 36874 12155, has_genre, 24593 12155, has_tags, 29924 12155, has_tags, 24593 12155, starred_actors, 29924 24400, has_genre, 5870 24400, has_tags, 5870 24400, has_tags, 28729 24400, in_language, 36874 21942, has_genre, 5870 21942, has_tags, 5870 21942, has_tags, 28729 21942, release_year, 37484 5547, has_genre, 5870 5547, release_year, 37484 1353, has_genre, 5870 1353, release_year, 37484 25528, has_genre, 5870 25528, starred_actors, 14279 23612, has_genre, 5870 23612, has_tags, 8248 23612, in_language, 36874 13272, has_genre, 5870 13272, has_tags, 28729 15286, has_genre, 24593 15286, release_year, 11 25662, has_genre, 5870 25662, release_year, 11 6662, has_genre, 5870 6662, release_year, 37484 5799, has_genre, 24593 5799, has_tags, 29924 5799, starred_actors, 29924 23129, has_genre, 5870 23129, has_tags, 5870 23129, has_tags, 28729 19728, has_genre, 5870 19728, release_year, 37484 5038, has_genre, 24593 5038, has_tags, 29924 5038, has_tags, 24593 5038, starred_actors, 29924 16485, has_genre, 24593 16485, starred_actors, 29924 22215, has_genre, 5870 22215, has_tags, 28729 31648, has_genre, 5870 31648, has_tags, 28729 1578, has_genre, 24593 1578, has_tags, 29924 1578, has_tags, 24593 1578, starred_actors, 29924 32864, has_genre, 5870 32864, starred_actors, 29924 3553, has_genre, 5870 3553, has_tags, 8248 3553, in_language, 36874 4830, has_genre, 5870 4830, has_tags, 5870 4830, has_tags, 28729 11997, has_genre, 5870 11997, has_tags, 5870 11997, release_year, 37484 23534, has_genre, 24593 23534, has_tags, 29924 23534, starred_actors, 29924 10147, has_genre, 5870 10147, has_tags, 36874 10147, in_language, 36874 9686, has_genre, 5870 9686, has_tags, 28729 31551, has_genre, 5870 31551, has_tags, 28729 15439, has_genre, 5870 15439, has_tags, 28729 13174, has_genre, 5870 13174, in_language, 36874 13174, release_year, 37484 25576, has_genre, 5870 25576, release_year, 37484 13070, has_genre, 5870 13070, has_tags, 28729 14910, has_genre, 5870 14910, has_tags, 28729 8164, in_language, 36874 8164, release_year, 37484 4418, has_genre, 5870 4418, has_tags, 8248 4418, in_language, 36874 29987, has_genre, 5870 29987, has_tags, 5870 29987, has_tags, 28729 8851, has_genre, 5870 8851, has_genre, 24593 8851, has_tags, 24593 20189, has_genre, 5870 20189, in_language, 36874 26370, has_genre, 5870 26370, release_year, 37484 2461, has_tags, 8248 2461, has_tags, 36874 2461, in_language, 36874 35621, has_tags, 8248 35621, in_language, 36874 21686, has_genre, 24593 21686, in_language, 36874 32988, has_genre, 5870 32988, has_tags, 5870 32988, has_tags, 28729 11340, has_genre, 5870 11340, has_tags, 28729 18722, has_genre, 5870 18722, has_tags, 28729 3089, has_genre, 5870 3089, has_tags, 28729 36326, has_genre, 5870 36326, in_language, 36874 6302, has_genre, 5870 6302, in_language, 36874 36532, has_genre, 5870 36532, has_tags, 5870 36532, in_language, 36874 234, has_tags, 8248 234, in_language, 36874 14026, has_tags, 24593 14026, release_year, 11 6035, has_genre, 24593 6035, in_language, 36874 28091, has_genre, 5870 28091, has_tags, 5870 28091, has_tags, 28729 13237, has_genre, 5870 13237, has_tags, 5870 13237, has_tags, 36874 13237, in_language, 36874 199, has_genre, 5870 199, release_year, 37484 6175, has_genre, 5870 6175, has_tags, 28729 30416, has_genre, 5870 30416, has_genre, 24593 30416, has_tags, 24593 32894, has_genre, 5870 32894, release_year, 37484 28, has_genre, 5870 28, in_language, 36874 35249, has_genre, 5870 35249, has_tags, 5870 35249, in_language, 36874 38241, has_genre, 5870 38241, release_year, 37484 27308, has_genre, 24593 27308, has_tags, 29924 27308, starred_actors, 29924 38307, has_genre, 24593 38307, has_tags, 29924 38307, starred_actors, 29924 34022, has_genre, 5870 34022, release_year, 37484 36247, has_genre, 5870 36247, has_tags, 5870 36247, release_year, 37484 12071, has_genre, 24593 12071, release_year, 11 12071, starred_actors, 29924 2186, has_genre, 5870 2186, release_year, 37484 30996, has_genre, 5870 30996, has_tags, 5870 30996, has_tags, 28729 30996, release_year, 37484 16848, has_genre, 24593 16848, starred_actors, 29924 6260, has_tags, 8248 6260, has_tags, 36874 6260, in_language, 36874 32367, has_genre, 5870 32367, has_genre, 24593 13333, has_genre, 24593 13333, release_year, 11 7170, has_genre, 5870 7170, in_language, 36874 8998, has_genre, 24593 8998, starred_actors, 29924 20069, has_genre, 24593 20069, starred_actors, 29924 31138, has_genre, 24593 31138, starred_actors, 29924 9015, has_genre, 5870 9015, has_tags, 28729 16069, has_genre, 5870 16069, release_year, 11 32851, has_genre, 24593 32851, starred_actors, 29924 3223, has_genre, 24593 3223, has_tags, 29924 3223, starred_actors, 29924 3543, has_genre, 5870 3543, has_tags, 28729 25463, has_genre, 5870 25463, has_tags, 28729 4694, has_genre, 5870 4694, has_genre, 24593 3273, has_genre, 5870 3273, release_year, 11 7153, has_genre, 5870 7153, has_genre, 24593 20931, has_genre, 5870 20931, has_tags, 5870 20931, has_tags, 28729 38052, has_genre, 24593 38052, starred_actors, 29924 28048, has_genre, 5870 28048, release_year, 11 786, directed_by, 18903 786, has_genre, 5870 786, has_tags, 8248 786, has_tags, 28729 786, in_language, 36874 786, release_year, 37484 786, written_by, 1584 786, written_by, 18903 9129, directed_by, 18903 9129, has_genre, 5870 9129, written_by, 1584 9129, written_by, 18903 19342, has_genre, 5870 19342, written_by, 18903 17778, has_genre, 5870 17778, has_genre, 24593 17778, has_tags, 24593 11787, has_genre, 5870 11787, has_tags, 5870 11787, has_tags, 28729 15462, has_genre, 5870 15462, has_tags, 5870 15462, has_tags, 28729 26776, has_genre, 5870 26776, release_year, 11 15202, has_genre, 5870 15202, has_tags, 28729 29109, has_genre, 5870 29109, has_tags, 28729 17910, has_genre, 5870 17910, has_tags, 5870 17910, has_tags, 28729 2492, has_genre, 5870 2492, has_tags, 5870 2492, has_tags, 36874 2492, has_tags, 28729 12654, has_genre, 5870 12654, has_tags, 28729 9923, has_genre, 24593 9923, starred_actors, 29924 23847, has_genre, 5870 23847, has_tags, 28729 23847, release_year, 37484 30082, has_genre, 24593 30082, starred_actors, 29924 21548, has_tags, 8248 21548, in_language, 36874 9665, has_genre, 5870 9665, has_tags, 28729 22751, has_genre, 5870 22751, has_tags, 28729 24040, has_genre, 24593 24040, starred_actors, 29924 29819, has_genre, 24593 29819, release_year, 11 19710, has_tags, 8248 19710, in_language, 36874 12856, has_genre, 5870 12856, has_tags, 5870 12856, release_year, 37484 32505, has_genre, 24593 32505, has_tags, 29924 32505, starred_actors, 29924 19088, has_tags, 5870 19088, release_year, 37484 36374, has_genre, 5870 36374, has_tags, 28729 29077, has_tags, 8248 29077, in_language, 36874 8589, has_genre, 5870 8589, in_language, 36874 12029, has_genre, 5870 12029, in_language, 36874 38538, has_genre, 5870 38538, has_tags, 28729 15472, has_genre, 24593 15472, starred_actors, 29924 19677, has_genre, 24593 19677, has_tags, 29924 19677, starred_actors, 29924 38863, has_genre, 24593 38863, starred_actors, 29924 14841, has_genre, 24593 14841, starred_actors, 29924 38978, has_genre, 5870 38978, release_year, 37484 Question: How are BROADWAY MELODY OF 1940, HOLLY GOSS, and THE GRUDGE related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BROADWAY MELODY OF 1940", "HOLLY GOSS", "THE GRUDGE" ], "valid_edges": [ [ "13 ASSASSINS", "has_tags", "JAPAN" ], [ "13 ASSASSINS", "in_language", "JAPANESE" ], [ "2001 MANIACS", "has_genre", "HORROR" ], [ "2001 MANIACS", "has_tags", "REMAKE" ], [ "A DAMSEL IN DISTRESS", "has_genre", "MUSICAL" ], [ "A DAMSEL IN DISTRESS", "starred_actors", "FRED ASTAIRE" ], [ "A NIGHTMARE ON ELM STREET", "has_genre", "HORROR" ], [ "A NIGHTMARE ON ELM STREET", "has_tags", "HORROR" ], [ "A NIGHTMARE ON ELM STREET", "has_tags", "REMAKE" ], [ "APARTMENT 1303", "has_genre", "HORROR" ], [ "APARTMENT 1303", "in_language", "JAPANESE" ], [ "AUDITION", "has_genre", "HORROR" ], [ "AUDITION", "in_language", "JAPANESE" ], [ "BATTLEFIELD BASEBALL", "has_genre", "HORROR" ], [ "BATTLEFIELD BASEBALL", "in_language", "JAPANESE" ], [ "BITTER SWEET", "has_genre", "MUSICAL" ], [ "BITTER SWEET", "release_year", "1940" ], [ "BLACK CHRISTMAS", "has_genre", "HORROR" ], [ "BLACK CHRISTMAS", "has_tags", "HORROR" ], [ "BLACK CHRISTMAS", "has_tags", "REMAKE" ], [ "BLUE SKIES", "has_genre", "MUSICAL" ], [ "BLUE SKIES", "starred_actors", "FRED ASTAIRE" ], [ "BODY SNATCHERS", "has_genre", "HORROR" ], [ "BODY SNATCHERS", "has_tags", "REMAKE" ], [ "BROADWAY MELODY OF 1940", "has_genre", "MUSICAL" ], [ "BROADWAY MELODY OF 1940", "release_year", "1940" ], [ "BROADWAY MELODY OF 1940", "starred_actors", "FRED ASTAIRE" ], [ "CALVAIRE", "has_genre", "HORROR" ], [ "CALVAIRE", "has_tags", "HORROR" ], [ "CALVAIRE", "release_year", "2004" ], [ "CAREFREE", "has_genre", "MUSICAL" ], [ "CAREFREE", "starred_actors", "FRED ASTAIRE" ], [ "CHAOS", "has_genre", "HORROR" ], [ "CHAOS", "in_language", "JAPANESE" ], [ "CLUB DREAD", "has_genre", "HORROR" ], [ "CLUB DREAD", "release_year", "2004" ], [ "CREEP", "has_genre", "HORROR" ], [ "CREEP", "release_year", "2004" ], [ "CROWS ZERO", "has_tags", "JAPAN" ], [ "CROWS ZERO", "in_language", "JAPANESE" ], [ "DADDY LONG LEGS", "has_genre", "MUSICAL" ], [ "DADDY LONG LEGS", "has_tags", "FRED ASTAIRE" ], [ "DADDY LONG LEGS", "has_tags", "MUSICAL" ], [ "DADDY LONG LEGS", "starred_actors", "FRED ASTAIRE" ], [ "DARK WATER", "has_genre", "HORROR" ], [ "DARK WATER", "has_tags", "HORROR" ], [ "DARK WATER", "has_tags", "REMAKE" ], [ "DARK WATER", "in_language", "JAPANESE" ], [ "DAWN OF THE DEAD", "has_genre", "HORROR" ], [ "DAWN OF THE DEAD", "has_tags", "HORROR" ], [ "DAWN OF THE DEAD", "has_tags", "REMAKE" ], [ "DAWN OF THE DEAD", "release_year", "2004" ], [ "DEAD BIRDS", "has_genre", "HORROR" ], [ "DEAD BIRDS", "release_year", "2004" ], [ "DECOYS", "has_genre", "HORROR" ], [ "DECOYS", "release_year", "2004" ], [ "DEVIL'S PASS", "has_genre", "HORROR" ], [ "DEVIL'S PASS", "starred_actors", "HOLLY GOSS" ], [ "DOLLS", "has_genre", "HORROR" ], [ "DOLLS", "has_tags", "JAPAN" ], [ "DOLLS", "in_language", "JAPANESE" ], [ "DON'T BE AFRAID OF THE DARK", "has_genre", "HORROR" ], [ "DON'T BE AFRAID OF THE DARK", "has_tags", "REMAKE" ], [ "DOWN ARGENTINE WAY", "has_genre", "MUSICAL" ], [ "DOWN ARGENTINE WAY", "release_year", "1940" ], [ "DR. CYCLOPS", "has_genre", "HORROR" ], [ "DR. CYCLOPS", "release_year", "1940" ], [ "DUMPLINGS", "has_genre", "HORROR" ], [ "DUMPLINGS", "release_year", "2004" ], [ "EASTER PARADE", "has_genre", "MUSICAL" ], [ "EASTER PARADE", "has_tags", "FRED ASTAIRE" ], [ "EASTER PARADE", "starred_actors", "FRED ASTAIRE" ], [ "EVIL DEAD", "has_genre", "HORROR" ], [ "EVIL DEAD", "has_tags", "HORROR" ], [ "EVIL DEAD", "has_tags", "REMAKE" ], [ "EVILENKO", "has_genre", "HORROR" ], [ "EVILENKO", "release_year", "2004" ], [ "FINIAN'S RAINBOW", "has_genre", "MUSICAL" ], [ "FINIAN'S RAINBOW", "has_tags", "FRED ASTAIRE" ], [ "FINIAN'S RAINBOW", "has_tags", "MUSICAL" ], [ "FINIAN'S RAINBOW", "starred_actors", "FRED ASTAIRE" ], [ "FOLLOW THE FLEET", "has_genre", "MUSICAL" ], [ "FOLLOW THE FLEET", "starred_actors", "FRED ASTAIRE" ], [ "FRIDAY THE 13TH", "has_genre", "HORROR" ], [ "FRIDAY THE 13TH", "has_tags", "REMAKE" ], [ "FRIGHT NIGHT", "has_genre", "HORROR" ], [ "FRIGHT NIGHT", "has_tags", "REMAKE" ], [ "FUNNY FACE", "has_genre", "MUSICAL" ], [ "FUNNY FACE", "has_tags", "FRED ASTAIRE" ], [ "FUNNY FACE", "has_tags", "MUSICAL" ], [ "FUNNY FACE", "starred_actors", "FRED ASTAIRE" ], [ "GHOST STORY", "has_genre", "HORROR" ], [ "GHOST STORY", "starred_actors", "FRED ASTAIRE" ], [ "GODZILLA", "has_genre", "HORROR" ], [ "GODZILLA", "has_tags", "JAPAN" ], [ "GODZILLA", "in_language", "JAPANESE" ], [ "HALLOWEEN", "has_genre", "HORROR" ], [ "HALLOWEEN", "has_tags", "HORROR" ], [ "HALLOWEEN", "has_tags", "REMAKE" ], [ "HELLBENT", "has_genre", "HORROR" ], [ "HELLBENT", "has_tags", "HORROR" ], [ "HELLBENT", "release_year", "2004" ], [ "HOLIDAY INN", "has_genre", "MUSICAL" ], [ "HOLIDAY INN", "has_tags", "FRED ASTAIRE" ], [ "HOLIDAY INN", "starred_actors", "FRED ASTAIRE" ], [ "HOUSE", "has_genre", "HORROR" ], [ "HOUSE", "has_tags", "JAPANESE" ], [ "HOUSE", "in_language", "JAPANESE" ], [ "HOUSE OF WAX", "has_genre", "HORROR" ], [ "HOUSE OF WAX", "has_tags", "REMAKE" ], [ "HOUSE ON HAUNTED HILL", "has_genre", "HORROR" ], [ "HOUSE ON HAUNTED HILL", "has_tags", "REMAKE" ], [ "I SPIT ON YOUR GRAVE", "has_genre", "HORROR" ], [ "I SPIT ON YOUR GRAVE", "has_tags", "REMAKE" ], [ "INFECTION", "has_genre", "HORROR" ], [ "INFECTION", "in_language", "JAPANESE" ], [ "INFECTION", "release_year", "2004" ], [ "INNOCENCE", "has_genre", "HORROR" ], [ "INNOCENCE", "release_year", "2004" ], [ "INVADERS FROM MARS", "has_genre", "HORROR" ], [ "INVADERS FROM MARS", "has_tags", "REMAKE" ], [ "IT'S ALIVE", "has_genre", "HORROR" ], [ "IT'S ALIVE", "has_tags", "REMAKE" ], [ "IZO", "in_language", "JAPANESE" ], [ "IZO", "release_year", "2004" ], [ "KWAIDAN", "has_genre", "HORROR" ], [ "KWAIDAN", "has_tags", "JAPAN" ], [ "KWAIDAN", "in_language", "JAPANESE" ], [ "LET ME IN", "has_genre", "HORROR" ], [ "LET ME IN", "has_tags", "HORROR" ], [ "LET ME IN", "has_tags", "REMAKE" ], [ "LITTLE SHOP OF HORRORS", "has_genre", "HORROR" ], [ "LITTLE SHOP OF HORRORS", "has_genre", "MUSICAL" ], [ "LITTLE SHOP OF HORRORS", "has_tags", "MUSICAL" ], [ "LOFT", "has_genre", "HORROR" ], [ "LOFT", "in_language", "JAPANESE" ], [ "MADHOUSE", "has_genre", "HORROR" ], [ "MADHOUSE", "release_year", "2004" ], [ "MEMOIRS OF A GEISHA", "has_tags", "JAPAN" ], [ "MEMOIRS OF A GEISHA", "has_tags", "JAPANESE" ], [ "MEMOIRS OF A GEISHA", "in_language", "JAPANESE" ], [ "MILLENNIUM ACTRESS", "has_tags", "JAPAN" ], [ "MILLENNIUM ACTRESS", "in_language", "JAPANESE" ], [ "MOONLIGHT SERENADE", "has_genre", "MUSICAL" ], [ "MOONLIGHT SERENADE", "in_language", "JAPANESE" ], [ "NIGHT OF THE LIVING DEAD", "has_genre", "HORROR" ], [ "NIGHT OF THE LIVING DEAD", "has_tags", "HORROR" ], [ "NIGHT OF THE LIVING DEAD", "has_tags", "REMAKE" ], [ "NIGHT OF THE LIVING DEAD 3D", "has_genre", "HORROR" ], [ "NIGHT OF THE LIVING DEAD 3D", "has_tags", "REMAKE" ], [ "NOSFERATU THE VAMPYRE", "has_genre", "HORROR" ], [ "NOSFERATU THE VAMPYRE", "has_tags", "REMAKE" ], [ "NOT OF THIS EARTH", "has_genre", "HORROR" ], [ "NOT OF THIS EARTH", "has_tags", "REMAKE" ], [ "ONE MISSED CALL", "has_genre", "HORROR" ], [ "ONE MISSED CALL", "in_language", "JAPANESE" ], [ "ONIBABA", "has_genre", "HORROR" ], [ "ONIBABA", "in_language", "JAPANESE" ], [ "OVER YOUR DEAD BODY", "has_genre", "HORROR" ], [ "OVER YOUR DEAD BODY", "has_tags", "HORROR" ], [ "OVER YOUR DEAD BODY", "in_language", "JAPANESE" ], [ "PEARL HARBOR", "has_tags", "JAPAN" ], [ "PEARL HARBOR", "in_language", "JAPANESE" ], [ "PINOCCHIO", "has_tags", "MUSICAL" ], [ "PINOCCHIO", "release_year", "1940" ], [ "PRINCESS RACCOON", "has_genre", "MUSICAL" ], [ "PRINCESS RACCOON", "in_language", "JAPANESE" ], [ "PSYCHO", "has_genre", "HORROR" ], [ "PSYCHO", "has_tags", "HORROR" ], [ "PSYCHO", "has_tags", "REMAKE" ], [ "PULSE", "has_genre", "HORROR" ], [ "PULSE", "has_tags", "HORROR" ], [ "PULSE", "has_tags", "JAPANESE" ], [ "PULSE", "in_language", "JAPANESE" ], [ "PUPPET MASTER VS DEMONIC TOYS", "has_genre", "HORROR" ], [ "PUPPET MASTER VS DEMONIC TOYS", "release_year", "2004" ], [ "QUARANTINE", "has_genre", "HORROR" ], [ "QUARANTINE", "has_tags", "REMAKE" ], [ "REPO! THE GENETIC OPERA", "has_genre", "HORROR" ], [ "REPO! THE GENETIC OPERA", "has_genre", "MUSICAL" ], [ "REPO! THE GENETIC OPERA", "has_tags", "MUSICAL" ], [ "RIDING THE BULLET", "has_genre", "HORROR" ], [ "RIDING THE BULLET", "release_year", "2004" ], [ "RING", "has_genre", "HORROR" ], [ "RING", "in_language", "JAPANESE" ], [ "RING 2", "has_genre", "HORROR" ], [ "RING 2", "has_tags", "HORROR" ], [ "RING 2", "in_language", "JAPANESE" ], [ "RING OF DARKNESS", "has_genre", "HORROR" ], [ "RING OF DARKNESS", "release_year", "2004" ], [ "ROBERTA", "has_genre", "MUSICAL" ], [ "ROBERTA", "has_tags", "FRED ASTAIRE" ], [ "ROBERTA", "starred_actors", "FRED ASTAIRE" ], [ "ROYAL WEDDING", "has_genre", "MUSICAL" ], [ "ROYAL WEDDING", "has_tags", "FRED ASTAIRE" ], [ "ROYAL WEDDING", "starred_actors", "FRED ASTAIRE" ], [ "SATAN'S LITTLE HELPER", "has_genre", "HORROR" ], [ "SATAN'S LITTLE HELPER", "release_year", "2004" ], [ "SAW", "has_genre", "HORROR" ], [ "SAW", "has_tags", "HORROR" ], [ "SAW", "release_year", "2004" ], [ "SECOND CHORUS", "has_genre", "MUSICAL" ], [ "SECOND CHORUS", "release_year", "1940" ], [ "SECOND CHORUS", "starred_actors", "FRED ASTAIRE" ], [ "SEED OF CHUCKY", "has_genre", "HORROR" ], [ "SEED OF CHUCKY", "release_year", "2004" ], [ "SHUTTER", "has_genre", "HORROR" ], [ "SHUTTER", "has_tags", "HORROR" ], [ "SHUTTER", "has_tags", "REMAKE" ], [ "SHUTTER", "release_year", "2004" ], [ "SILK STOCKINGS", "has_genre", "MUSICAL" ], [ "SILK STOCKINGS", "starred_actors", "FRED ASTAIRE" ], [ "SPIRITED AWAY", "has_tags", "JAPAN" ], [ "SPIRITED AWAY", "has_tags", "JAPANESE" ], [ "SPIRITED AWAY", "in_language", "JAPANESE" ], [ "STAGE FRIGHT", "has_genre", "HORROR" ], [ "STAGE FRIGHT", "has_genre", "MUSICAL" ], [ "STRIKE UP THE BAND", "has_genre", "MUSICAL" ], [ "STRIKE UP THE BAND", "release_year", "1940" ], [ "SWEET HOME", "has_genre", "HORROR" ], [ "SWEET HOME", "in_language", "JAPANESE" ], [ "SWING TIME", "has_genre", "MUSICAL" ], [ "SWING TIME", "starred_actors", "FRED ASTAIRE" ], [ "THAT'S ENTERTAINMENT!", "has_genre", "MUSICAL" ], [ "THAT'S ENTERTAINMENT!", "starred_actors", "FRED ASTAIRE" ], [ "THAT'S ENTERTAINMENT, PART II", "has_genre", "MUSICAL" ], [ "THAT'S ENTERTAINMENT, PART II", "starred_actors", "FRED ASTAIRE" ], [ "THE AMITYVILLE HORROR", "has_genre", "HORROR" ], [ "THE AMITYVILLE HORROR", "has_tags", "REMAKE" ], [ "THE APE", "has_genre", "HORROR" ], [ "THE APE", "release_year", "1940" ], [ "THE BAND WAGON", "has_genre", "MUSICAL" ], [ "THE BAND WAGON", "starred_actors", "FRED ASTAIRE" ], [ "THE BARKLEYS OF BROADWAY", "has_genre", "MUSICAL" ], [ "THE BARKLEYS OF BROADWAY", "has_tags", "FRED ASTAIRE" ], [ "THE BARKLEYS OF BROADWAY", "starred_actors", "FRED ASTAIRE" ], [ "THE BLOB", "has_genre", "HORROR" ], [ "THE BLOB", "has_tags", "REMAKE" ], [ "THE CRAZIES", "has_genre", "HORROR" ], [ "THE CRAZIES", "has_tags", "REMAKE" ], [ "THE CREATURE WASN'T NICE", "has_genre", "HORROR" ], [ "THE CREATURE WASN'T NICE", "has_genre", "MUSICAL" ], [ "THE DEVIL BAT", "has_genre", "HORROR" ], [ "THE DEVIL BAT", "release_year", "1940" ], [ "THE DEVIL'S CARNIVAL", "has_genre", "HORROR" ], [ "THE DEVIL'S CARNIVAL", "has_genre", "MUSICAL" ], [ "THE EYE", "has_genre", "HORROR" ], [ "THE EYE", "has_tags", "HORROR" ], [ "THE EYE", "has_tags", "REMAKE" ], [ "THE GAY DIVORCEE", "has_genre", "MUSICAL" ], [ "THE GAY DIVORCEE", "starred_actors", "FRED ASTAIRE" ], [ "THE GHOST BREAKERS", "has_genre", "HORROR" ], [ "THE GHOST BREAKERS", "release_year", "1940" ], [ "THE GRUDGE", "directed_by", "TAKASHI SHIMIZU" ], [ "THE GRUDGE", "has_genre", "HORROR" ], [ "THE GRUDGE", "has_tags", "JAPAN" ], [ "THE GRUDGE", "has_tags", "REMAKE" ], [ "THE GRUDGE", "in_language", "JAPANESE" ], [ "THE GRUDGE", "release_year", "2004" ], [ "THE GRUDGE", "written_by", "STEPHEN SUSCO" ], [ "THE GRUDGE", "written_by", "TAKASHI SHIMIZU" ], [ "THE GRUDGE 2", "directed_by", "TAKASHI SHIMIZU" ], [ "THE GRUDGE 2", "has_genre", "HORROR" ], [ "THE GRUDGE 2", "written_by", "STEPHEN SUSCO" ], [ "THE GRUDGE 2", "written_by", "TAKASHI SHIMIZU" ], [ "THE GRUDGE 3", "has_genre", "HORROR" ], [ "THE GRUDGE 3", "written_by", "TAKASHI SHIMIZU" ], [ "THE HAPPINESS OF THE KATAKURIS", "has_genre", "HORROR" ], [ "THE HAPPINESS OF THE KATAKURIS", "has_genre", "MUSICAL" ], [ "THE HAPPINESS OF THE KATAKURIS", "has_tags", "MUSICAL" ], [ "THE HAUNTING", "has_genre", "HORROR" ], [ "THE HAUNTING", "has_tags", "HORROR" ], [ "THE HAUNTING", "has_tags", "REMAKE" ], [ "THE HILLS HAVE EYES", "has_genre", "HORROR" ], [ "THE HILLS HAVE EYES", "has_tags", "HORROR" ], [ "THE HILLS HAVE EYES", "has_tags", "REMAKE" ], [ "THE INVISIBLE MAN RETURNS", "has_genre", "HORROR" ], [ "THE INVISIBLE MAN RETURNS", "release_year", "1940" ], [ "THE LAST HOUSE ON THE LEFT", "has_genre", "HORROR" ], [ "THE LAST HOUSE ON THE LEFT", "has_tags", "REMAKE" ], [ "THE LODGER", "has_genre", "HORROR" ], [ "THE LODGER", "has_tags", "REMAKE" ], [ "THE OMEN", "has_genre", "HORROR" ], [ "THE OMEN", "has_tags", "HORROR" ], [ "THE OMEN", "has_tags", "REMAKE" ], [ "THE RING", "has_genre", "HORROR" ], [ "THE RING", "has_tags", "HORROR" ], [ "THE RING", "has_tags", "JAPANESE" ], [ "THE RING", "has_tags", "REMAKE" ], [ "THE RING TWO", "has_genre", "HORROR" ], [ "THE RING TWO", "has_tags", "REMAKE" ], [ "THE SKY'S THE LIMIT", "has_genre", "MUSICAL" ], [ "THE SKY'S THE LIMIT", "starred_actors", "FRED ASTAIRE" ], [ "THE STEPFORD WIVES", "has_genre", "HORROR" ], [ "THE STEPFORD WIVES", "has_tags", "REMAKE" ], [ "THE STEPFORD WIVES", "release_year", "2004" ], [ "THE STORY OF VERNON AND IRENE CASTLE", "has_genre", "MUSICAL" ], [ "THE STORY OF VERNON AND IRENE CASTLE", "starred_actors", "FRED ASTAIRE" ], [ "THE SUN", "has_tags", "JAPAN" ], [ "THE SUN", "in_language", "JAPANESE" ], [ "THE TEXAS CHAINSAW MASSACRE", "has_genre", "HORROR" ], [ "THE TEXAS CHAINSAW MASSACRE", "has_tags", "REMAKE" ], [ "THE WICKER MAN", "has_genre", "HORROR" ], [ "THE WICKER MAN", "has_tags", "REMAKE" ], [ "THREE LITTLE WORDS", "has_genre", "MUSICAL" ], [ "THREE LITTLE WORDS", "starred_actors", "FRED ASTAIRE" ], [ "TIN PAN ALLEY", "has_genre", "MUSICAL" ], [ "TIN PAN ALLEY", "release_year", "1940" ], [ "TOKYO ZOMBIE", "has_tags", "JAPAN" ], [ "TOKYO ZOMBIE", "in_language", "JAPANESE" ], [ "TOOLBOX MURDERS", "has_genre", "HORROR" ], [ "TOOLBOX MURDERS", "has_tags", "HORROR" ], [ "TOOLBOX MURDERS", "release_year", "2004" ], [ "TOP HAT", "has_genre", "MUSICAL" ], [ "TOP HAT", "has_tags", "FRED ASTAIRE" ], [ "TOP HAT", "starred_actors", "FRED ASTAIRE" ], [ "VAN HELSING", "has_tags", "HORROR" ], [ "VAN HELSING", "release_year", "2004" ], [ "VILLAGE OF THE DAMNED", "has_genre", "HORROR" ], [ "VILLAGE OF THE DAMNED", "has_tags", "REMAKE" ], [ "WASABI", "has_tags", "JAPAN" ], [ "WASABI", "in_language", "JAPANESE" ], [ "WICKED CITY", "has_genre", "HORROR" ], [ "WICKED CITY", "in_language", "JAPANESE" ], [ "WILD ZERO", "has_genre", "HORROR" ], [ "WILD ZERO", "in_language", "JAPANESE" ], [ "WILLARD", "has_genre", "HORROR" ], [ "WILLARD", "has_tags", "REMAKE" ], [ "YOLANDA AND THE THIEF", "has_genre", "MUSICAL" ], [ "YOLANDA AND THE THIEF", "starred_actors", "FRED ASTAIRE" ], [ "YOU WERE NEVER LOVELIER", "has_genre", "MUSICAL" ], [ "YOU WERE NEVER LOVELIER", "has_tags", "FRED ASTAIRE" ], [ "YOU WERE NEVER LOVELIER", "starred_actors", "FRED ASTAIRE" ], [ "YOU'LL NEVER GET RICH", "has_genre", "MUSICAL" ], [ "YOU'LL NEVER GET RICH", "starred_actors", "FRED ASTAIRE" ], [ "ZIEGFELD FOLLIES", "has_genre", "MUSICAL" ], [ "ZIEGFELD FOLLIES", "starred_actors", "FRED ASTAIRE" ], [ "ZOMBIE HONEYMOON", "has_genre", "HORROR" ], [ "ZOMBIE HONEYMOON", "release_year", "2004" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 2133, 1998 6776, 2000 4476, CHOCOLAT 7091, FRANCE 32434, GEORGE WASHINGTON 22006, I WANT YOU 14601, LES MISÉRABLES 4182, THE MAN IN THE IRON MASK 12691, THE PATRIOT 23150, THE WOMEN ON THE 6TH FLOOR src, edge_attr, dst 4476, has_tags, 7091 4476, release_year, 6776 32434, release_year, 6776 22006, release_year, 2133 14601, has_tags, 7091 14601, release_year, 2133 4182, has_tags, 7091 4182, release_year, 2133 12691, release_year, 2133 12691, release_year, 6776 23150, has_tags, 7091 Question: For what reason are GEORGE WASHINGTON, I WANT YOU, and THE WOMEN ON THE 6TH FLOOR associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "GEORGE WASHINGTON", "I WANT YOU", "THE WOMEN ON THE 6TH FLOOR" ], "valid_edges": [ [ "CHOCOLAT", "has_tags", "FRANCE" ], [ "CHOCOLAT", "release_year", "2000" ], [ "GEORGE WASHINGTON", "release_year", "2000" ], [ "I WANT YOU", "release_year", "1998" ], [ "LES MISÉRABLES", "has_tags", "FRANCE" ], [ "LES MISÉRABLES", "release_year", "1998" ], [ "THE MAN IN THE IRON MASK", "has_tags", "FRANCE" ], [ "THE MAN IN THE IRON MASK", "release_year", "1998" ], [ "THE PATRIOT", "release_year", "1998" ], [ "THE PATRIOT", "release_year", "2000" ], [ "THE WOMEN ON THE 6TH FLOOR", "has_tags", "FRANCE" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 14588, CHRIS COLFER 14129, DIRTY DANCING 36212, DRAMA 14520, JENNIFER GREY 29961, PARKLAND 25002, STRUCK BY LIGHTNING 30391, VINCENT BUGLIOSI src, edge_attr, dst 14129, has_genre, 36212 14129, has_tags, 14520 14129, starred_actors, 14520 29961, has_genre, 36212 29961, written_by, 30391 25002, has_genre, 36212 25002, starred_actors, 14588 25002, written_by, 14588 Question: How are CHRIS COLFER, JENNIFER GREY, and VINCENT BUGLIOSI related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "CHRIS COLFER", "JENNIFER GREY", "VINCENT BUGLIOSI" ], "valid_edges": [ [ "DIRTY DANCING", "has_genre", "DRAMA" ], [ "DIRTY DANCING", "has_tags", "JENNIFER GREY" ], [ "DIRTY DANCING", "starred_actors", "JENNIFER GREY" ], [ "PARKLAND", "has_genre", "DRAMA" ], [ "PARKLAND", "written_by", "VINCENT BUGLIOSI" ], [ "STRUCK BY LIGHTNING", "has_genre", "DRAMA" ], [ "STRUCK BY LIGHTNING", "starred_actors", "CHRIS COLFER" ], [ "STRUCK BY LIGHTNING", "written_by", "CHRIS COLFER" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 25354, $9.99 26762, 2008 16060, 27 DRESSES 37783, A BUNCH OF AMATEURS 9698, A CHRISTMAS TALE 955, A FILM WITH ME IN IT 6602, A MATTER OF LOAF AND DEATH 38059, ABSURDISTAN 30643, ACROSS THE UNIVERSE 37010, AGE OF CONSENT 28540, AN AMERICAN CAROL 26658, ANOTHER CINDERELLA STORY 6336, ANTHONY LAPAGLIA 18169, APRIL FOOL'S DAY 28274, ASSASSINATION OF A HIGH SCHOOL PRESIDENT 37608, AUSTRALIA 9340, BABY MAMA 24827, BAGHEAD 10673, BART GOT A ROOM 26783, BE KIND REWIND 25639, BEDTIME STORIES 19159, BEER FOR MY HORSES 13412, BERLIN CALLING 25846, BEVERLY HILLS CHIHUAHUA 4902, BIRDS OF AMERICA 7314, BOLT 10081, BOTTLE SHOCK 35599, BURN AFTER READING 33360, CANDY 15437, CHAOS THEORY 33927, CHOKE 24116, COLLEGE 21862, COLLEGE ROAD TRIP 30463, COMEDY 22452, COMMANDMENTS 24877, DANCE OF THE DEAD 9709, DE L'AUTRE CÔTÉ DU LIT 35428, DEAD FURY 12702, DEFINITELY, MAYBE 18106, DIMINISHED CAPACITY 36212, DRAMA 19245, DRILLBIT TAYLOR 29521, EASY VIRTUE 25559, EXTREME MOVIE 22541, FATSO 14303, FINDING AMANDA 17653, FINE, TOTALLY FINE 17405, FIRST SUNDAY 6219, FORGETTING SARAH MARSHALL 22005, FOUR CHRISTMASES 25086, FRENCH FILM 21839, GEOFFREY RUSH 17416, GET SMART 2159, GHOST TOWN 31060, GIGANTIC 19912, GRIFF THE INVISIBLE 13778, HAMLET 2 9852, HANK AND MIKE 36942, HAPPY-GO-LUCKY 31330, HOME 7811, HORTON HEARS A WHO! 10147, HOUSE 31549, HOW TO BE 33499, HOW TO BE A SERIAL KILLER 3508, HUMBOLDT COUNTY 17763, I SELL THE DEAD 21265, IGOR 33279, IN BRUGES 25088, JUST ADD WATER 11089, KILLER MOVIE 35095, KILLER PAD 19602, KUNG FU PANDA 39347, LEATHERHEADS 31011, LYMELIFE 16378, MAD MONEY 24627, MADE OF HONOR 5163, MAMMA MIA! 3844, MANAGEMENT 7813, MARY AND MAX 20844, MEET DAVE 9678, MEET THE BROWNS 19216, MID-AUGUST LUNCH 31735, MIDDLE OF NOWHERE 20418, MISS PETTIGREW LIVES FOR A DAY 25796, MURIEL'S WEDDING 15428, MY BEST FRIEND'S GIRL 27683, MY SASSY GIRL 33751, NEW YORK, I LOVE YOU 34872, NINJA CHEERLEADERS 27725, ONE-EYED MONSTER 16554, OPEN SEASON 2 15037, OTIS 292, OVER HER DEAD BODY 1697, PINEAPPLE EXPRESS 30478, PRIVATE LESSONS 17627, RAB NE BANA DI JODI 34313, REMARKABLE POWER 9633, ROLE MODELS 28099, RUDO Y CURSI 37850, SEMI-PRO 7015, SEX AND THE CITY 12676, SEX DRIVE 2024, SHINE 10415, SILENT WEDDING 11074, SINGH IS KINNG 30145, SMART PEOPLE 25151, SNOW DOGS 22068, SOUL MEN 24506, SPACE CHIMPS 15449, STEP BROTHERS 31154, STRANGE WILDERNESS 1432, STRICTLY BALLROOM 13322, STRICTLY SEXUAL 29231, SUNSHINE CLEANING 21445, SUPERHERO MOVIE 19488, SURFER, DUDE 17992, SWING VOTE 10020, THE ACCIDENTAL HUSBAND 34206, THE ADVENTURES OF FOOD BOY 33436, THE BANK 11158, THE BEATLES 39238, THE BROTHERS BLOOM 33457, THE DEAL 25305, THE GREAT BUCK HOWARD 25624, THE HOUSE BUNNY 31058, THE LONGSHOTS 14591, THE LOVE GURU 7303, THE LUCKY ONES 39053, THE ONION MOVIE 3558, THE OTHER END OF THE LINE 6703, THE RAMEN GIRL 5156, THE ROCKER 2739, THE SAPPHIRES 20210, THE WOMEN 8393, TROPIC THUNDER 9639, UNDEAD 20713, VISIONEERS 10231, WHAT HAPPENS IN VEGAS 27612, WHAT JUST HAPPENED 1547, WIENERS 32981, WILD CHILD 33110, WITLESS PROTECTION 1945, YES MAN 23774, YOU DON'T MESS WITH THE ZOHAN 16999, ZACK AND MIRI MAKE A PORNO src, edge_attr, dst 25354, has_tags, 37608 25354, release_year, 26762 25354, starred_actors, 6336 25354, starred_actors, 21839 16060, has_genre, 30463 16060, release_year, 26762 37783, has_genre, 30463 37783, release_year, 26762 9698, has_genre, 30463 9698, release_year, 26762 955, has_genre, 30463 955, release_year, 26762 6602, has_genre, 30463 6602, has_tags, 30463 6602, release_year, 26762 38059, has_genre, 30463 38059, release_year, 26762 30643, has_genre, 36212 30643, has_tags, 11158 37010, has_genre, 30463 37010, has_tags, 37608 28540, has_genre, 30463 28540, release_year, 26762 26658, has_genre, 30463 26658, release_year, 26762 18169, has_genre, 30463 18169, release_year, 26762 28274, has_genre, 30463 28274, release_year, 26762 37608, has_genre, 36212 37608, has_tags, 37608 37608, release_year, 26762 9340, has_genre, 30463 9340, release_year, 26762 24827, has_genre, 30463 24827, release_year, 26762 10673, has_genre, 30463 10673, release_year, 26762 26783, has_genre, 30463 26783, release_year, 26762 25639, has_genre, 30463 25639, release_year, 26762 19159, has_genre, 30463 19159, release_year, 26762 13412, has_genre, 30463 13412, release_year, 26762 25846, has_genre, 30463 25846, has_tags, 30463 25846, release_year, 26762 4902, has_genre, 30463 4902, release_year, 26762 7314, has_genre, 30463 7314, release_year, 26762 10081, has_genre, 30463 10081, release_year, 26762 35599, has_genre, 30463 35599, has_tags, 30463 35599, release_year, 26762 33360, has_tags, 37608 33360, has_tags, 21839 33360, starred_actors, 21839 15437, has_genre, 30463 15437, release_year, 26762 33927, has_genre, 30463 33927, has_tags, 30463 33927, release_year, 26762 24116, has_genre, 30463 24116, release_year, 26762 21862, has_genre, 30463 21862, has_tags, 30463 21862, release_year, 26762 22452, has_genre, 30463 22452, starred_actors, 6336 24877, has_genre, 30463 24877, release_year, 26762 9709, has_genre, 30463 9709, release_year, 26762 35428, has_genre, 30463 35428, release_year, 26762 12702, has_genre, 30463 12702, release_year, 26762 18106, has_genre, 30463 18106, release_year, 26762 19245, has_genre, 30463 19245, release_year, 26762 29521, has_genre, 30463 29521, has_tags, 30463 29521, release_year, 26762 25559, has_genre, 30463 25559, release_year, 26762 22541, has_genre, 30463 22541, release_year, 26762 14303, has_genre, 30463 14303, release_year, 26762 17653, has_genre, 30463 17653, release_year, 26762 17405, has_genre, 30463 17405, release_year, 26762 6219, has_genre, 30463 6219, has_tags, 30463 6219, release_year, 26762 22005, has_genre, 30463 22005, release_year, 26762 25086, has_genre, 30463 25086, release_year, 26762 17416, has_genre, 30463 17416, release_year, 26762 2159, has_genre, 30463 2159, release_year, 26762 31060, has_genre, 30463 31060, release_year, 26762 19912, has_genre, 30463 19912, has_tags, 37608 13778, has_genre, 30463 13778, release_year, 26762 9852, has_genre, 30463 9852, release_year, 26762 36942, has_genre, 30463 36942, release_year, 26762 31330, has_genre, 30463 31330, release_year, 26762 7811, has_genre, 30463 7811, has_tags, 30463 7811, release_year, 26762 10147, has_genre, 30463 10147, release_year, 26762 31549, has_genre, 30463 31549, release_year, 26762 33499, has_genre, 30463 33499, release_year, 26762 3508, has_genre, 30463 3508, release_year, 26762 17763, has_genre, 30463 17763, release_year, 26762 21265, has_genre, 30463 21265, release_year, 26762 33279, has_genre, 30463 33279, has_tags, 30463 33279, release_year, 26762 25088, has_genre, 30463 25088, release_year, 26762 11089, has_genre, 30463 11089, release_year, 26762 35095, has_genre, 30463 35095, release_year, 26762 19602, has_tags, 30463 19602, release_year, 26762 39347, has_genre, 30463 39347, has_tags, 30463 39347, release_year, 26762 31011, has_genre, 30463 31011, release_year, 26762 16378, has_genre, 30463 16378, release_year, 26762 24627, has_genre, 30463 24627, release_year, 26762 5163, has_genre, 30463 5163, release_year, 26762 3844, has_genre, 30463 3844, release_year, 26762 7813, has_genre, 30463 7813, has_tags, 37608 20844, has_genre, 30463 20844, release_year, 26762 9678, has_genre, 30463 9678, release_year, 26762 19216, has_genre, 30463 19216, release_year, 26762 31735, has_genre, 30463 31735, release_year, 26762 20418, has_genre, 30463 20418, release_year, 26762 25796, has_genre, 30463 25796, has_tags, 37608 25796, has_tags, 30463 15428, has_genre, 30463 15428, release_year, 26762 27683, has_genre, 30463 27683, release_year, 26762 33751, has_genre, 30463 33751, release_year, 26762 34872, has_genre, 30463 34872, release_year, 26762 27725, has_genre, 30463 27725, release_year, 26762 16554, has_genre, 30463 16554, release_year, 26762 15037, has_genre, 30463 15037, release_year, 26762 292, has_genre, 30463 292, release_year, 26762 1697, has_genre, 30463 1697, has_tags, 30463 1697, release_year, 26762 30478, has_genre, 30463 30478, release_year, 26762 17627, has_genre, 30463 17627, release_year, 26762 34313, has_genre, 30463 34313, release_year, 26762 9633, has_genre, 30463 9633, has_tags, 30463 9633, release_year, 26762 28099, has_genre, 30463 28099, release_year, 26762 37850, has_genre, 30463 37850, release_year, 26762 7015, has_genre, 30463 7015, release_year, 26762 12676, has_genre, 30463 12676, has_tags, 30463 12676, release_year, 26762 2024, has_tags, 37608 2024, has_tags, 21839 2024, starred_actors, 21839 10415, has_genre, 30463 10415, release_year, 26762 11074, has_genre, 30463 11074, release_year, 26762 30145, has_genre, 30463 30145, release_year, 26762 25151, has_genre, 30463 22068, has_genre, 30463 22068, release_year, 26762 24506, has_genre, 30463 24506, release_year, 26762 15449, has_genre, 30463 15449, has_tags, 30463 15449, release_year, 26762 31154, has_genre, 30463 31154, release_year, 26762 1432, has_genre, 30463 1432, has_tags, 37608 13322, has_genre, 30463 13322, release_year, 26762 29231, has_genre, 30463 29231, release_year, 26762 21445, has_genre, 30463 21445, release_year, 26762 19488, has_genre, 30463 19488, release_year, 26762 17992, has_genre, 30463 17992, release_year, 26762 10020, has_genre, 30463 10020, release_year, 26762 34206, has_genre, 30463 34206, release_year, 26762 33436, has_tags, 37608 33436, starred_actors, 6336 39238, has_genre, 30463 39238, release_year, 26762 33457, has_genre, 30463 33457, release_year, 26762 25305, has_genre, 30463 25305, release_year, 26762 25624, has_genre, 30463 25624, has_tags, 30463 25624, release_year, 26762 31058, has_genre, 30463 31058, release_year, 26762 14591, has_genre, 30463 14591, release_year, 26762 7303, has_genre, 30463 7303, release_year, 26762 39053, has_genre, 30463 39053, has_tags, 30463 39053, release_year, 26762 3558, has_genre, 30463 3558, release_year, 26762 6703, has_genre, 30463 6703, release_year, 26762 5156, has_genre, 30463 5156, release_year, 26762 2739, has_genre, 30463 2739, has_tags, 37608 20210, has_genre, 30463 20210, release_year, 26762 8393, has_genre, 30463 8393, release_year, 26762 9639, has_genre, 30463 9639, has_tags, 37608 20713, has_genre, 30463 20713, release_year, 26762 10231, has_genre, 30463 10231, has_tags, 30463 10231, release_year, 26762 27612, has_genre, 30463 27612, release_year, 26762 1547, has_genre, 30463 1547, release_year, 26762 32981, has_genre, 30463 32981, release_year, 26762 33110, has_genre, 30463 33110, release_year, 26762 1945, has_genre, 30463 1945, release_year, 26762 23774, has_genre, 30463 23774, has_tags, 30463 23774, release_year, 26762 16999, has_genre, 30463 16999, release_year, 26762 Question: How are $9.99, SNOW DOGS, and THE BEATLES related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "$9.99", "SNOW DOGS", "THE BEATLES" ], "valid_edges": [ [ "$9.99", "has_tags", "AUSTRALIA" ], [ "$9.99", "release_year", "2008" ], [ "$9.99", "starred_actors", "ANTHONY LAPAGLIA" ], [ "$9.99", "starred_actors", "GEOFFREY RUSH" ], [ "27 DRESSES", "has_genre", "COMEDY" ], [ "27 DRESSES", "release_year", "2008" ], [ "A BUNCH OF AMATEURS", "has_genre", "COMEDY" ], [ "A BUNCH OF AMATEURS", "release_year", "2008" ], [ "A CHRISTMAS TALE", "has_genre", "COMEDY" ], [ "A CHRISTMAS TALE", "release_year", "2008" ], [ "A FILM WITH ME IN IT", "has_genre", "COMEDY" ], [ "A FILM WITH ME IN IT", "release_year", "2008" ], [ "A MATTER OF LOAF AND DEATH", "has_genre", "COMEDY" ], [ "A MATTER OF LOAF AND DEATH", "has_tags", "COMEDY" ], [ "A MATTER OF LOAF AND DEATH", "release_year", "2008" ], [ "ABSURDISTAN", "has_genre", "COMEDY" ], [ "ABSURDISTAN", "release_year", "2008" ], [ "ACROSS THE UNIVERSE", "has_genre", "DRAMA" ], [ "ACROSS THE UNIVERSE", "has_tags", "THE BEATLES" ], [ "AGE OF CONSENT", "has_genre", "COMEDY" ], [ "AGE OF CONSENT", "has_tags", "AUSTRALIA" ], [ "AN AMERICAN CAROL", "has_genre", "COMEDY" ], [ "AN AMERICAN CAROL", "release_year", "2008" ], [ "ANOTHER CINDERELLA STORY", "has_genre", "COMEDY" ], [ "ANOTHER CINDERELLA STORY", "release_year", "2008" ], [ "APRIL FOOL'S DAY", "has_genre", "COMEDY" ], [ "APRIL FOOL'S DAY", "release_year", "2008" ], [ "ASSASSINATION OF A HIGH SCHOOL PRESIDENT", "has_genre", "COMEDY" ], [ "ASSASSINATION OF A HIGH SCHOOL PRESIDENT", "release_year", "2008" ], [ "AUSTRALIA", "has_genre", "DRAMA" ], [ "AUSTRALIA", "has_tags", "AUSTRALIA" ], [ "AUSTRALIA", "release_year", "2008" ], [ "BABY MAMA", "has_genre", "COMEDY" ], [ "BABY MAMA", "release_year", "2008" ], [ "BAGHEAD", "has_genre", "COMEDY" ], [ "BAGHEAD", "release_year", "2008" ], [ "BART GOT A ROOM", "has_genre", "COMEDY" ], [ "BART GOT A ROOM", "release_year", "2008" ], [ "BE KIND REWIND", "has_genre", "COMEDY" ], [ "BE KIND REWIND", "release_year", "2008" ], [ "BEDTIME STORIES", "has_genre", "COMEDY" ], [ "BEDTIME STORIES", "release_year", "2008" ], [ "BEER FOR MY HORSES", "has_genre", "COMEDY" ], [ "BEER FOR MY HORSES", "release_year", "2008" ], [ "BERLIN CALLING", "has_genre", "COMEDY" ], [ "BERLIN CALLING", "release_year", "2008" ], [ "BEVERLY HILLS CHIHUAHUA", "has_genre", "COMEDY" ], [ "BEVERLY HILLS CHIHUAHUA", "has_tags", "COMEDY" ], [ "BEVERLY HILLS CHIHUAHUA", "release_year", "2008" ], [ "BIRDS OF AMERICA", "has_genre", "COMEDY" ], [ "BIRDS OF AMERICA", "release_year", "2008" ], [ "BOLT", "has_genre", "COMEDY" ], [ "BOLT", "release_year", "2008" ], [ "BOTTLE SHOCK", "has_genre", "COMEDY" ], [ "BOTTLE SHOCK", "release_year", "2008" ], [ "BURN AFTER READING", "has_genre", "COMEDY" ], [ "BURN AFTER READING", "has_tags", "COMEDY" ], [ "BURN AFTER READING", "release_year", "2008" ], [ "CANDY", "has_tags", "AUSTRALIA" ], [ "CANDY", "has_tags", "GEOFFREY RUSH" ], [ "CANDY", "starred_actors", "GEOFFREY RUSH" ], [ "CHAOS THEORY", "has_genre", "COMEDY" ], [ "CHAOS THEORY", "release_year", "2008" ], [ "CHOKE", "has_genre", "COMEDY" ], [ "CHOKE", "has_tags", "COMEDY" ], [ "CHOKE", "release_year", "2008" ], [ "COLLEGE", "has_genre", "COMEDY" ], [ "COLLEGE", "release_year", "2008" ], [ "COLLEGE ROAD TRIP", "has_genre", "COMEDY" ], [ "COLLEGE ROAD TRIP", "has_tags", "COMEDY" ], [ "COLLEGE ROAD TRIP", "release_year", "2008" ], [ "COMMANDMENTS", "has_genre", "COMEDY" ], [ "COMMANDMENTS", "starred_actors", "ANTHONY LAPAGLIA" ], [ "DANCE OF THE DEAD", "has_genre", "COMEDY" ], [ "DANCE OF THE DEAD", "release_year", "2008" ], [ "DE L'AUTRE CÔTÉ DU LIT", "has_genre", "COMEDY" ], [ "DE L'AUTRE CÔTÉ DU LIT", "release_year", "2008" ], [ "DEAD FURY", "has_genre", "COMEDY" ], [ "DEAD FURY", "release_year", "2008" ], [ "DEFINITELY, MAYBE", "has_genre", "COMEDY" ], [ "DEFINITELY, MAYBE", "release_year", "2008" ], [ "DIMINISHED CAPACITY", "has_genre", "COMEDY" ], [ "DIMINISHED CAPACITY", "release_year", "2008" ], [ "DRILLBIT TAYLOR", "has_genre", "COMEDY" ], [ "DRILLBIT TAYLOR", "release_year", "2008" ], [ "EASY VIRTUE", "has_genre", "COMEDY" ], [ "EASY VIRTUE", "has_tags", "COMEDY" ], [ "EASY VIRTUE", "release_year", "2008" ], [ "EXTREME MOVIE", "has_genre", "COMEDY" ], [ "EXTREME MOVIE", "release_year", "2008" ], [ "FATSO", "has_genre", "COMEDY" ], [ "FATSO", "release_year", "2008" ], [ "FINDING AMANDA", "has_genre", "COMEDY" ], [ "FINDING AMANDA", "release_year", "2008" ], [ "FINE, TOTALLY FINE", "has_genre", "COMEDY" ], [ "FINE, TOTALLY FINE", "release_year", "2008" ], [ "FIRST SUNDAY", "has_genre", "COMEDY" ], [ "FIRST SUNDAY", "release_year", "2008" ], [ "FORGETTING SARAH MARSHALL", "has_genre", "COMEDY" ], [ "FORGETTING SARAH MARSHALL", "has_tags", "COMEDY" ], [ "FORGETTING SARAH MARSHALL", "release_year", "2008" ], [ "FOUR CHRISTMASES", "has_genre", "COMEDY" ], [ "FOUR CHRISTMASES", "release_year", "2008" ], [ "FRENCH FILM", "has_genre", "COMEDY" ], [ "FRENCH FILM", "release_year", "2008" ], [ "GET SMART", "has_genre", "COMEDY" ], [ "GET SMART", "release_year", "2008" ], [ "GHOST TOWN", "has_genre", "COMEDY" ], [ "GHOST TOWN", "release_year", "2008" ], [ "GIGANTIC", "has_genre", "COMEDY" ], [ "GIGANTIC", "release_year", "2008" ], [ "GRIFF THE INVISIBLE", "has_genre", "COMEDY" ], [ "GRIFF THE INVISIBLE", "has_tags", "AUSTRALIA" ], [ "HAMLET 2", "has_genre", "COMEDY" ], [ "HAMLET 2", "release_year", "2008" ], [ "HANK AND MIKE", "has_genre", "COMEDY" ], [ "HANK AND MIKE", "release_year", "2008" ], [ "HAPPY-GO-LUCKY", "has_genre", "COMEDY" ], [ "HAPPY-GO-LUCKY", "release_year", "2008" ], [ "HOME", "has_genre", "COMEDY" ], [ "HOME", "release_year", "2008" ], [ "HORTON HEARS A WHO!", "has_genre", "COMEDY" ], [ "HORTON HEARS A WHO!", "has_tags", "COMEDY" ], [ "HORTON HEARS A WHO!", "release_year", "2008" ], [ "HOUSE", "has_genre", "COMEDY" ], [ "HOUSE", "release_year", "2008" ], [ "HOW TO BE", "has_genre", "COMEDY" ], [ "HOW TO BE", "release_year", "2008" ], [ "HOW TO BE A SERIAL KILLER", "has_genre", "COMEDY" ], [ "HOW TO BE A SERIAL KILLER", "release_year", "2008" ], [ "HUMBOLDT COUNTY", "has_genre", "COMEDY" ], [ "HUMBOLDT COUNTY", "release_year", "2008" ], [ "I SELL THE DEAD", "has_genre", "COMEDY" ], [ "I SELL THE DEAD", "release_year", "2008" ], [ "IGOR", "has_genre", "COMEDY" ], [ "IGOR", "release_year", "2008" ], [ "IN BRUGES", "has_genre", "COMEDY" ], [ "IN BRUGES", "has_tags", "COMEDY" ], [ "IN BRUGES", "release_year", "2008" ], [ "JUST ADD WATER", "has_genre", "COMEDY" ], [ "JUST ADD WATER", "release_year", "2008" ], [ "KILLER MOVIE", "has_genre", "COMEDY" ], [ "KILLER MOVIE", "release_year", "2008" ], [ "KILLER PAD", "has_genre", "COMEDY" ], [ "KILLER PAD", "release_year", "2008" ], [ "KUNG FU PANDA", "has_tags", "COMEDY" ], [ "KUNG FU PANDA", "release_year", "2008" ], [ "LEATHERHEADS", "has_genre", "COMEDY" ], [ "LEATHERHEADS", "has_tags", "COMEDY" ], [ "LEATHERHEADS", "release_year", "2008" ], [ "LYMELIFE", "has_genre", "COMEDY" ], [ "LYMELIFE", "release_year", "2008" ], [ "MAD MONEY", "has_genre", "COMEDY" ], [ "MAD MONEY", "release_year", "2008" ], [ "MADE OF HONOR", "has_genre", "COMEDY" ], [ "MADE OF HONOR", "release_year", "2008" ], [ "MAMMA MIA!", "has_genre", "COMEDY" ], [ "MAMMA MIA!", "release_year", "2008" ], [ "MANAGEMENT", "has_genre", "COMEDY" ], [ "MANAGEMENT", "release_year", "2008" ], [ "MARY AND MAX", "has_genre", "COMEDY" ], [ "MARY AND MAX", "has_tags", "AUSTRALIA" ], [ "MEET DAVE", "has_genre", "COMEDY" ], [ "MEET DAVE", "release_year", "2008" ], [ "MEET THE BROWNS", "has_genre", "COMEDY" ], [ "MEET THE BROWNS", "release_year", "2008" ], [ "MID-AUGUST LUNCH", "has_genre", "COMEDY" ], [ "MID-AUGUST LUNCH", "release_year", "2008" ], [ "MIDDLE OF NOWHERE", "has_genre", "COMEDY" ], [ "MIDDLE OF NOWHERE", "release_year", "2008" ], [ "MISS PETTIGREW LIVES FOR A DAY", "has_genre", "COMEDY" ], [ "MISS PETTIGREW LIVES FOR A DAY", "release_year", "2008" ], [ "MURIEL'S WEDDING", "has_genre", "COMEDY" ], [ "MURIEL'S WEDDING", "has_tags", "AUSTRALIA" ], [ "MURIEL'S WEDDING", "has_tags", "COMEDY" ], [ "MY BEST FRIEND'S GIRL", "has_genre", "COMEDY" ], [ "MY BEST FRIEND'S GIRL", "release_year", "2008" ], [ "MY SASSY GIRL", "has_genre", "COMEDY" ], [ "MY SASSY GIRL", "release_year", "2008" ], [ "NEW YORK, I LOVE YOU", "has_genre", "COMEDY" ], [ "NEW YORK, I LOVE YOU", "release_year", "2008" ], [ "NINJA CHEERLEADERS", "has_genre", "COMEDY" ], [ "NINJA CHEERLEADERS", "release_year", "2008" ], [ "ONE-EYED MONSTER", "has_genre", "COMEDY" ], [ "ONE-EYED MONSTER", "release_year", "2008" ], [ "OPEN SEASON 2", "has_genre", "COMEDY" ], [ "OPEN SEASON 2", "release_year", "2008" ], [ "OTIS", "has_genre", "COMEDY" ], [ "OTIS", "release_year", "2008" ], [ "OVER HER DEAD BODY", "has_genre", "COMEDY" ], [ "OVER HER DEAD BODY", "release_year", "2008" ], [ "PINEAPPLE EXPRESS", "has_genre", "COMEDY" ], [ "PINEAPPLE EXPRESS", "has_tags", "COMEDY" ], [ "PINEAPPLE EXPRESS", "release_year", "2008" ], [ "PRIVATE LESSONS", "has_genre", "COMEDY" ], [ "PRIVATE LESSONS", "release_year", "2008" ], [ "RAB NE BANA DI JODI", "has_genre", "COMEDY" ], [ "RAB NE BANA DI JODI", "release_year", "2008" ], [ "REMARKABLE POWER", "has_genre", "COMEDY" ], [ "REMARKABLE POWER", "release_year", "2008" ], [ "ROLE MODELS", "has_genre", "COMEDY" ], [ "ROLE MODELS", "has_tags", "COMEDY" ], [ "ROLE MODELS", "release_year", "2008" ], [ "RUDO Y CURSI", "has_genre", "COMEDY" ], [ "RUDO Y CURSI", "release_year", "2008" ], [ "SEMI-PRO", "has_genre", "COMEDY" ], [ "SEMI-PRO", "release_year", "2008" ], [ "SEX AND THE CITY", "has_genre", "COMEDY" ], [ "SEX AND THE CITY", "release_year", "2008" ], [ "SEX DRIVE", "has_genre", "COMEDY" ], [ "SEX DRIVE", "has_tags", "COMEDY" ], [ "SEX DRIVE", "release_year", "2008" ], [ "SHINE", "has_tags", "AUSTRALIA" ], [ "SHINE", "has_tags", "GEOFFREY RUSH" ], [ "SHINE", "starred_actors", "GEOFFREY RUSH" ], [ "SILENT WEDDING", "has_genre", "COMEDY" ], [ "SILENT WEDDING", "release_year", "2008" ], [ "SINGH IS KINNG", "has_genre", "COMEDY" ], [ "SINGH IS KINNG", "release_year", "2008" ], [ "SMART PEOPLE", "has_genre", "COMEDY" ], [ "SMART PEOPLE", "release_year", "2008" ], [ "SNOW DOGS", "has_genre", "COMEDY" ], [ "SOUL MEN", "has_genre", "COMEDY" ], [ "SOUL MEN", "release_year", "2008" ], [ "SPACE CHIMPS", "has_genre", "COMEDY" ], [ "SPACE CHIMPS", "release_year", "2008" ], [ "STEP BROTHERS", "has_genre", "COMEDY" ], [ "STEP BROTHERS", "has_tags", "COMEDY" ], [ "STEP BROTHERS", "release_year", "2008" ], [ "STRANGE WILDERNESS", "has_genre", "COMEDY" ], [ "STRANGE WILDERNESS", "release_year", "2008" ], [ "STRICTLY BALLROOM", "has_genre", "COMEDY" ], [ "STRICTLY BALLROOM", "has_tags", "AUSTRALIA" ], [ "STRICTLY SEXUAL", "has_genre", "COMEDY" ], [ "STRICTLY SEXUAL", "release_year", "2008" ], [ "SUNSHINE CLEANING", "has_genre", "COMEDY" ], [ "SUNSHINE CLEANING", "release_year", "2008" ], [ "SUPERHERO MOVIE", "has_genre", "COMEDY" ], [ "SUPERHERO MOVIE", "release_year", "2008" ], [ "SURFER, DUDE", "has_genre", "COMEDY" ], [ "SURFER, DUDE", "release_year", "2008" ], [ "SWING VOTE", "has_genre", "COMEDY" ], [ "SWING VOTE", "release_year", "2008" ], [ "THE ACCIDENTAL HUSBAND", "has_genre", "COMEDY" ], [ "THE ACCIDENTAL HUSBAND", "release_year", "2008" ], [ "THE ADVENTURES OF FOOD BOY", "has_genre", "COMEDY" ], [ "THE ADVENTURES OF FOOD BOY", "release_year", "2008" ], [ "THE BANK", "has_tags", "AUSTRALIA" ], [ "THE BANK", "starred_actors", "ANTHONY LAPAGLIA" ], [ "THE BROTHERS BLOOM", "has_genre", "COMEDY" ], [ "THE BROTHERS BLOOM", "release_year", "2008" ], [ "THE DEAL", "has_genre", "COMEDY" ], [ "THE DEAL", "release_year", "2008" ], [ "THE GREAT BUCK HOWARD", "has_genre", "COMEDY" ], [ "THE GREAT BUCK HOWARD", "release_year", "2008" ], [ "THE HOUSE BUNNY", "has_genre", "COMEDY" ], [ "THE HOUSE BUNNY", "has_tags", "COMEDY" ], [ "THE HOUSE BUNNY", "release_year", "2008" ], [ "THE LONGSHOTS", "has_genre", "COMEDY" ], [ "THE LONGSHOTS", "release_year", "2008" ], [ "THE LOVE GURU", "has_genre", "COMEDY" ], [ "THE LOVE GURU", "release_year", "2008" ], [ "THE LUCKY ONES", "has_genre", "COMEDY" ], [ "THE LUCKY ONES", "release_year", "2008" ], [ "THE ONION MOVIE", "has_genre", "COMEDY" ], [ "THE ONION MOVIE", "has_tags", "COMEDY" ], [ "THE ONION MOVIE", "release_year", "2008" ], [ "THE OTHER END OF THE LINE", "has_genre", "COMEDY" ], [ "THE OTHER END OF THE LINE", "release_year", "2008" ], [ "THE RAMEN GIRL", "has_genre", "COMEDY" ], [ "THE RAMEN GIRL", "release_year", "2008" ], [ "THE ROCKER", "has_genre", "COMEDY" ], [ "THE ROCKER", "release_year", "2008" ], [ "THE SAPPHIRES", "has_genre", "COMEDY" ], [ "THE SAPPHIRES", "has_tags", "AUSTRALIA" ], [ "THE WOMEN", "has_genre", "COMEDY" ], [ "THE WOMEN", "release_year", "2008" ], [ "TROPIC THUNDER", "has_genre", "COMEDY" ], [ "TROPIC THUNDER", "release_year", "2008" ], [ "UNDEAD", "has_genre", "COMEDY" ], [ "UNDEAD", "has_tags", "AUSTRALIA" ], [ "VISIONEERS", "has_genre", "COMEDY" ], [ "VISIONEERS", "release_year", "2008" ], [ "WHAT HAPPENS IN VEGAS", "has_genre", "COMEDY" ], [ "WHAT HAPPENS IN VEGAS", "has_tags", "COMEDY" ], [ "WHAT HAPPENS IN VEGAS", "release_year", "2008" ], [ "WHAT JUST HAPPENED", "has_genre", "COMEDY" ], [ "WHAT JUST HAPPENED", "release_year", "2008" ], [ "WIENERS", "has_genre", "COMEDY" ], [ "WIENERS", "release_year", "2008" ], [ "WILD CHILD", "has_genre", "COMEDY" ], [ "WILD CHILD", "release_year", "2008" ], [ "WITLESS PROTECTION", "has_genre", "COMEDY" ], [ "WITLESS PROTECTION", "release_year", "2008" ], [ "YES MAN", "has_genre", "COMEDY" ], [ "YES MAN", "release_year", "2008" ], [ "YOU DON'T MESS WITH THE ZOHAN", "has_genre", "COMEDY" ], [ "YOU DON'T MESS WITH THE ZOHAN", "has_tags", "COMEDY" ], [ "YOU DON'T MESS WITH THE ZOHAN", "release_year", "2008" ], [ "ZACK AND MIRI MAKE A PORNO", "has_genre", "COMEDY" ], [ "ZACK AND MIRI MAKE A PORNO", "release_year", "2008" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 32043, BROADCAST NEWS 30463, COMEDY 36212, DRAMA 24940, HOPE AND GLORY 23849, HOUSEKEEPING 19498, MERMAIDS 40064, MY OLD LADY 30044, PETER NELSON 1687, PURELY BELTER 25023, QUARTET 23599, THE BEST EXOTIC MARIGOLD HOTEL 33050, THE CAMPAIGN 1371, THE LONELY PASSION OF JUDITH HEARNE 8791, THE SECOND BEST EXOTIC MARIGOLD HOTEL 36468, TOUGH GUYS DON'T DANCE 6981, WISH YOU WERE HERE src, edge_attr, dst 32043, has_genre, 30463 32043, has_genre, 36212 24940, has_genre, 30463 24940, has_genre, 36212 23849, has_genre, 30463 23849, has_genre, 36212 19498, has_genre, 30463 19498, has_genre, 36212 19498, has_tags, 30463 19498, has_tags, 36212 40064, has_genre, 30463 40064, has_genre, 36212 1687, has_genre, 30463 1687, has_genre, 36212 25023, has_genre, 30463 25023, has_genre, 36212 25023, has_tags, 30463 23599, has_genre, 30463 23599, has_genre, 36212 33050, has_genre, 30463 1371, has_genre, 36212 1371, written_by, 30044 8791, has_genre, 30463 8791, has_genre, 36212 36468, has_genre, 30463 36468, has_genre, 36212 6981, has_genre, 30463 6981, has_genre, 36212 Question: For what reason are PETER NELSON, PURELY BELTER, and THE CAMPAIGN associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "PETER NELSON", "PURELY BELTER", "THE CAMPAIGN" ], "valid_edges": [ [ "BROADCAST NEWS", "has_genre", "COMEDY" ], [ "BROADCAST NEWS", "has_genre", "DRAMA" ], [ "HOPE AND GLORY", "has_genre", "COMEDY" ], [ "HOPE AND GLORY", "has_genre", "DRAMA" ], [ "HOUSEKEEPING", "has_genre", "COMEDY" ], [ "HOUSEKEEPING", "has_genre", "DRAMA" ], [ "MERMAIDS", "has_genre", "COMEDY" ], [ "MERMAIDS", "has_genre", "DRAMA" ], [ "MERMAIDS", "has_tags", "COMEDY" ], [ "MERMAIDS", "has_tags", "DRAMA" ], [ "MY OLD LADY", "has_genre", "COMEDY" ], [ "MY OLD LADY", "has_genre", "DRAMA" ], [ "PURELY BELTER", "has_genre", "COMEDY" ], [ "PURELY BELTER", "has_genre", "DRAMA" ], [ "QUARTET", "has_genre", "COMEDY" ], [ "QUARTET", "has_genre", "DRAMA" ], [ "QUARTET", "has_tags", "COMEDY" ], [ "THE BEST EXOTIC MARIGOLD HOTEL", "has_genre", "COMEDY" ], [ "THE BEST EXOTIC MARIGOLD HOTEL", "has_genre", "DRAMA" ], [ "THE CAMPAIGN", "has_genre", "COMEDY" ], [ "THE LONELY PASSION OF JUDITH HEARNE", "has_genre", "DRAMA" ], [ "THE LONELY PASSION OF JUDITH HEARNE", "written_by", "PETER NELSON" ], [ "THE SECOND BEST EXOTIC MARIGOLD HOTEL", "has_genre", "COMEDY" ], [ "THE SECOND BEST EXOTIC MARIGOLD HOTEL", "has_genre", "DRAMA" ], [ "TOUGH GUYS DON'T DANCE", "has_genre", "COMEDY" ], [ "TOUGH GUYS DON'T DANCE", "has_genre", "DRAMA" ], [ "WISH YOU WERE HERE", "has_genre", "COMEDY" ], [ "WISH YOU WERE HERE", "has_genre", "DRAMA" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 2452, 13 ASSASSINS 33013, A LETTER TO MOMO 29800, ACE ATTORNEY 29816, AIR DOLL 31491, AN AUTUMN AFTERNOON 35497, ANTARCTICA 7421, APART FROM YOU 7908, AUDITION 30907, BAREFOOT GEN 39878, BRIGHT FUTURE 5376, CAPE NO. 7 21907, COME SEE THE PARADISE 1148, CONFESSIONS 24410, DEMONLOVER 2710, DEPARTURES 38714, DOUBLE SUICIDE 36212, DRAMA 16970, EUREKA 18312, FABIÁN BIELINSKY 5931, FLOATING CLOUDS 1612, FROM UP ON POPPY HILL 38222, FUNERAL PARADE OF ROSES 14853, GO FOR BROKE! 22091, GRAVE OF THE FIREFLIES 12994, HARAKIRI 20207, HELENA BERGSTRÖM 22180, HIMIZU 9849, HOUSE OF ANGELS 11051, I AM WAITING 36874, JAPANESE 21614, JAPANESE STORY 28036, KAN SHIMOZAWA 21224, KIKUJIRO 1859, LATE AUTUMN 19809, LATE SPRING 25019, LIKE FATHER, LIKE SON 36020, LIKE SOMEONE IN LOVE 39818, MACHIBUSE 21686, MOONLIGHT SERENADE 21030, NANA 1922, NINE QUEENS 25709, NOBODY KNOWS 16592, NORWEGIAN WOOD 12009, ODD OBSESSION 34288, ONLY YESTERDAY 234, PEARL HARBOR 34639, POSTMAN BLUES 18584, PRINCESS MONONOKE 11570, RASHOMON 27361, RIDING ALONE FOR THOUSANDS OF MILES 35944, SAMURAI 33216, SAMURAI BANNERS 20932, SEVEN SAMURAI 4027, SHARA 9448, SILENCE 26949, SISTERS OF THE GION 32584, SUSPECT X 12462, THE ADVENTURES OF MILO AND OTIS 14688, THE FLOATING CASTLE 34462, THE FLOWERS OF WAR 24493, THE FUNERAL 31571, THE LOYAL 47 RONIN 25054, THE PILLOW BOOK 21548, THE SUN 8873, THE TALE OF ZATOICHI 18076, THE WIND RISES 13374, THRONE OF BLOOD 34804, TOKYO FIST 16940, TORA! TORA! TORA! 4527, UGETSU 14956, VOICES OF A DISTANT STAR 22783, WHEN A WOMAN ASCENDS THE STAIRS 24623, WHISPER OF THE HEART 54, YOJIMBO 29857, ZATOICHI MEETS YOJIMBO src, edge_attr, dst 2452, has_genre, 36212 2452, has_tags, 35944 2452, in_language, 36874 33013, has_genre, 36212 33013, in_language, 36874 29800, has_genre, 36212 29800, in_language, 36874 29816, has_genre, 36212 29816, in_language, 36874 31491, has_genre, 36212 31491, has_tags, 36874 31491, in_language, 36874 35497, has_genre, 36212 35497, in_language, 36874 7421, has_genre, 36212 7421, in_language, 36874 7908, has_genre, 36212 7908, in_language, 36874 30907, has_genre, 36212 30907, in_language, 36874 39878, has_genre, 36212 39878, in_language, 36874 5376, has_genre, 36212 5376, in_language, 36874 21907, has_genre, 36212 21907, in_language, 36874 1148, has_genre, 36212 1148, has_tags, 36874 1148, in_language, 36874 24410, has_genre, 36212 24410, in_language, 36874 2710, has_genre, 36212 2710, in_language, 36874 38714, has_genre, 36212 38714, in_language, 36874 16970, has_genre, 36212 16970, in_language, 36874 5931, has_genre, 36212 5931, in_language, 36874 1612, has_genre, 36212 1612, in_language, 36874 38222, has_genre, 36212 38222, in_language, 36874 14853, has_genre, 36212 14853, in_language, 36874 22091, has_genre, 36212 22091, in_language, 36874 12994, has_genre, 36212 12994, has_tags, 36874 12994, in_language, 36874 22180, has_genre, 36212 22180, in_language, 36874 9849, has_genre, 36212 9849, starred_actors, 20207 11051, has_genre, 36212 11051, in_language, 36874 21614, has_genre, 36212 21614, in_language, 36874 21224, has_genre, 36212 21224, in_language, 36874 1859, has_genre, 36212 1859, in_language, 36874 19809, has_genre, 36212 19809, in_language, 36874 25019, has_genre, 36212 25019, in_language, 36874 36020, has_genre, 36212 36020, in_language, 36874 39818, has_genre, 36212 39818, in_language, 36874 21686, has_genre, 36212 21686, in_language, 36874 21030, has_genre, 36212 21030, in_language, 36874 1922, directed_by, 18312 1922, has_genre, 36212 1922, written_by, 18312 25709, has_genre, 36212 25709, in_language, 36874 16592, has_genre, 36212 16592, has_tags, 36874 16592, in_language, 36874 12009, has_genre, 36212 12009, in_language, 36874 34288, has_genre, 36212 34288, in_language, 36874 234, has_genre, 36212 234, has_tags, 36212 234, in_language, 36874 34639, has_genre, 36212 34639, in_language, 36874 18584, has_tags, 36212 18584, has_tags, 36874 18584, in_language, 36874 11570, has_genre, 36212 11570, in_language, 36874 27361, has_genre, 36212 27361, in_language, 36874 33216, has_genre, 36212 33216, in_language, 36874 20932, has_genre, 36212 20932, has_tags, 36212 20932, has_tags, 35944 20932, in_language, 36874 4027, has_genre, 36212 4027, in_language, 36874 9448, has_genre, 36212 9448, in_language, 36874 26949, has_genre, 36212 26949, in_language, 36874 32584, has_genre, 36212 32584, in_language, 36874 12462, has_genre, 36212 12462, in_language, 36874 14688, has_genre, 36212 14688, in_language, 36874 34462, has_genre, 36212 34462, in_language, 36874 24493, has_genre, 36212 24493, in_language, 36874 31571, has_genre, 36212 31571, in_language, 36874 25054, has_genre, 36212 25054, in_language, 36874 21548, has_genre, 36212 21548, in_language, 36874 8873, has_genre, 36212 8873, has_tags, 35944 8873, in_language, 36874 8873, written_by, 28036 18076, has_genre, 36212 18076, in_language, 36874 13374, has_genre, 36212 13374, has_tags, 36874 13374, in_language, 36874 34804, has_genre, 36212 34804, in_language, 36874 16940, has_genre, 36212 16940, has_tags, 36874 16940, in_language, 36874 4527, has_genre, 36212 4527, in_language, 36874 14956, has_genre, 36212 14956, in_language, 36874 22783, has_genre, 36212 22783, in_language, 36874 24623, has_genre, 36212 24623, in_language, 36874 54, has_genre, 36212 54, in_language, 36874 29857, has_genre, 36212 29857, in_language, 36874 29857, written_by, 28036 Question: How are FABIÁN BIELINSKY, HELENA BERGSTRÖM, and THE TALE OF ZATOICHI related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "FABIÁN BIELINSKY", "HELENA BERGSTRÖM", "THE TALE OF ZATOICHI" ], "valid_edges": [ [ "13 ASSASSINS", "has_genre", "DRAMA" ], [ "13 ASSASSINS", "has_tags", "SAMURAI" ], [ "13 ASSASSINS", "in_language", "JAPANESE" ], [ "A LETTER TO MOMO", "has_genre", "DRAMA" ], [ "A LETTER TO MOMO", "in_language", "JAPANESE" ], [ "ACE ATTORNEY", "has_genre", "DRAMA" ], [ "ACE ATTORNEY", "in_language", "JAPANESE" ], [ "AIR DOLL", "has_genre", "DRAMA" ], [ "AIR DOLL", "in_language", "JAPANESE" ], [ "AN AUTUMN AFTERNOON", "has_genre", "DRAMA" ], [ "AN AUTUMN AFTERNOON", "has_tags", "JAPANESE" ], [ "AN AUTUMN AFTERNOON", "in_language", "JAPANESE" ], [ "ANTARCTICA", "has_genre", "DRAMA" ], [ "ANTARCTICA", "in_language", "JAPANESE" ], [ "APART FROM YOU", "has_genre", "DRAMA" ], [ "APART FROM YOU", "in_language", "JAPANESE" ], [ "AUDITION", "has_genre", "DRAMA" ], [ "AUDITION", "in_language", "JAPANESE" ], [ "BAREFOOT GEN", "has_genre", "DRAMA" ], [ "BAREFOOT GEN", "in_language", "JAPANESE" ], [ "BRIGHT FUTURE", "has_genre", "DRAMA" ], [ "BRIGHT FUTURE", "in_language", "JAPANESE" ], [ "CAPE NO. 7", "has_genre", "DRAMA" ], [ "CAPE NO. 7", "in_language", "JAPANESE" ], [ "COME SEE THE PARADISE", "has_genre", "DRAMA" ], [ "COME SEE THE PARADISE", "in_language", "JAPANESE" ], [ "CONFESSIONS", "has_genre", "DRAMA" ], [ "CONFESSIONS", "has_tags", "JAPANESE" ], [ "CONFESSIONS", "in_language", "JAPANESE" ], [ "DEMONLOVER", "has_genre", "DRAMA" ], [ "DEMONLOVER", "in_language", "JAPANESE" ], [ "DEPARTURES", "has_genre", "DRAMA" ], [ "DEPARTURES", "in_language", "JAPANESE" ], [ "DOUBLE SUICIDE", "has_genre", "DRAMA" ], [ "DOUBLE SUICIDE", "in_language", "JAPANESE" ], [ "EUREKA", "has_genre", "DRAMA" ], [ "EUREKA", "in_language", "JAPANESE" ], [ "FLOATING CLOUDS", "has_genre", "DRAMA" ], [ "FLOATING CLOUDS", "in_language", "JAPANESE" ], [ "FROM UP ON POPPY HILL", "has_genre", "DRAMA" ], [ "FROM UP ON POPPY HILL", "in_language", "JAPANESE" ], [ "FUNERAL PARADE OF ROSES", "has_genre", "DRAMA" ], [ "FUNERAL PARADE OF ROSES", "in_language", "JAPANESE" ], [ "GO FOR BROKE!", "has_genre", "DRAMA" ], [ "GO FOR BROKE!", "in_language", "JAPANESE" ], [ "GRAVE OF THE FIREFLIES", "has_genre", "DRAMA" ], [ "GRAVE OF THE FIREFLIES", "in_language", "JAPANESE" ], [ "HARAKIRI", "has_genre", "DRAMA" ], [ "HARAKIRI", "has_tags", "JAPANESE" ], [ "HARAKIRI", "in_language", "JAPANESE" ], [ "HIMIZU", "has_genre", "DRAMA" ], [ "HIMIZU", "in_language", "JAPANESE" ], [ "HOUSE OF ANGELS", "has_genre", "DRAMA" ], [ "HOUSE OF ANGELS", "starred_actors", "HELENA BERGSTRÖM" ], [ "I AM WAITING", "has_genre", "DRAMA" ], [ "I AM WAITING", "in_language", "JAPANESE" ], [ "JAPANESE STORY", "has_genre", "DRAMA" ], [ "JAPANESE STORY", "in_language", "JAPANESE" ], [ "KIKUJIRO", "has_genre", "DRAMA" ], [ "KIKUJIRO", "in_language", "JAPANESE" ], [ "LATE AUTUMN", "has_genre", "DRAMA" ], [ "LATE AUTUMN", "in_language", "JAPANESE" ], [ "LATE SPRING", "has_genre", "DRAMA" ], [ "LATE SPRING", "in_language", "JAPANESE" ], [ "LIKE FATHER, LIKE SON", "has_genre", "DRAMA" ], [ "LIKE FATHER, LIKE SON", "in_language", "JAPANESE" ], [ "LIKE SOMEONE IN LOVE", "has_genre", "DRAMA" ], [ "LIKE SOMEONE IN LOVE", "in_language", "JAPANESE" ], [ "MACHIBUSE", "has_genre", "DRAMA" ], [ "MACHIBUSE", "in_language", "JAPANESE" ], [ "MOONLIGHT SERENADE", "has_genre", "DRAMA" ], [ "MOONLIGHT SERENADE", "in_language", "JAPANESE" ], [ "NANA", "has_genre", "DRAMA" ], [ "NANA", "in_language", "JAPANESE" ], [ "NINE QUEENS", "directed_by", "FABIÁN BIELINSKY" ], [ "NINE QUEENS", "has_genre", "DRAMA" ], [ "NINE QUEENS", "written_by", "FABIÁN BIELINSKY" ], [ "NOBODY KNOWS", "has_genre", "DRAMA" ], [ "NOBODY KNOWS", "in_language", "JAPANESE" ], [ "NORWEGIAN WOOD", "has_genre", "DRAMA" ], [ "NORWEGIAN WOOD", "has_tags", "JAPANESE" ], [ "NORWEGIAN WOOD", "in_language", "JAPANESE" ], [ "ODD OBSESSION", "has_genre", "DRAMA" ], [ "ODD OBSESSION", "in_language", "JAPANESE" ], [ "ONLY YESTERDAY", "has_genre", "DRAMA" ], [ "ONLY YESTERDAY", "in_language", "JAPANESE" ], [ "PEARL HARBOR", "has_genre", "DRAMA" ], [ "PEARL HARBOR", "has_tags", "DRAMA" ], [ "PEARL HARBOR", "in_language", "JAPANESE" ], [ "POSTMAN BLUES", "has_genre", "DRAMA" ], [ "POSTMAN BLUES", "in_language", "JAPANESE" ], [ "PRINCESS MONONOKE", "has_tags", "DRAMA" ], [ "PRINCESS MONONOKE", "has_tags", "JAPANESE" ], [ "PRINCESS MONONOKE", "in_language", "JAPANESE" ], [ "RASHOMON", "has_genre", "DRAMA" ], [ "RASHOMON", "in_language", "JAPANESE" ], [ "RIDING ALONE FOR THOUSANDS OF MILES", "has_genre", "DRAMA" ], [ "RIDING ALONE FOR THOUSANDS OF MILES", "in_language", "JAPANESE" ], [ "SAMURAI BANNERS", "has_genre", "DRAMA" ], [ "SAMURAI BANNERS", "in_language", "JAPANESE" ], [ "SEVEN SAMURAI", "has_genre", "DRAMA" ], [ "SEVEN SAMURAI", "has_tags", "DRAMA" ], [ "SEVEN SAMURAI", "has_tags", "SAMURAI" ], [ "SEVEN SAMURAI", "in_language", "JAPANESE" ], [ "SHARA", "has_genre", "DRAMA" ], [ "SHARA", "in_language", "JAPANESE" ], [ "SILENCE", "has_genre", "DRAMA" ], [ "SILENCE", "in_language", "JAPANESE" ], [ "SISTERS OF THE GION", "has_genre", "DRAMA" ], [ "SISTERS OF THE GION", "in_language", "JAPANESE" ], [ "SUSPECT X", "has_genre", "DRAMA" ], [ "SUSPECT X", "in_language", "JAPANESE" ], [ "THE ADVENTURES OF MILO AND OTIS", "has_genre", "DRAMA" ], [ "THE ADVENTURES OF MILO AND OTIS", "in_language", "JAPANESE" ], [ "THE FLOATING CASTLE", "has_genre", "DRAMA" ], [ "THE FLOATING CASTLE", "in_language", "JAPANESE" ], [ "THE FLOWERS OF WAR", "has_genre", "DRAMA" ], [ "THE FLOWERS OF WAR", "in_language", "JAPANESE" ], [ "THE FUNERAL", "has_genre", "DRAMA" ], [ "THE FUNERAL", "in_language", "JAPANESE" ], [ "THE LOYAL 47 RONIN", "has_genre", "DRAMA" ], [ "THE LOYAL 47 RONIN", "in_language", "JAPANESE" ], [ "THE PILLOW BOOK", "has_genre", "DRAMA" ], [ "THE PILLOW BOOK", "in_language", "JAPANESE" ], [ "THE SUN", "has_genre", "DRAMA" ], [ "THE SUN", "in_language", "JAPANESE" ], [ "THE TALE OF ZATOICHI", "has_genre", "DRAMA" ], [ "THE TALE OF ZATOICHI", "has_tags", "SAMURAI" ], [ "THE TALE OF ZATOICHI", "in_language", "JAPANESE" ], [ "THE TALE OF ZATOICHI", "written_by", "KAN SHIMOZAWA" ], [ "THE WIND RISES", "has_genre", "DRAMA" ], [ "THE WIND RISES", "in_language", "JAPANESE" ], [ "THRONE OF BLOOD", "has_genre", "DRAMA" ], [ "THRONE OF BLOOD", "has_tags", "JAPANESE" ], [ "THRONE OF BLOOD", "in_language", "JAPANESE" ], [ "TOKYO FIST", "has_genre", "DRAMA" ], [ "TOKYO FIST", "in_language", "JAPANESE" ], [ "TORA! TORA! TORA!", "has_genre", "DRAMA" ], [ "TORA! TORA! TORA!", "has_tags", "JAPANESE" ], [ "TORA! TORA! TORA!", "in_language", "JAPANESE" ], [ "UGETSU", "has_genre", "DRAMA" ], [ "UGETSU", "in_language", "JAPANESE" ], [ "VOICES OF A DISTANT STAR", "has_genre", "DRAMA" ], [ "VOICES OF A DISTANT STAR", "in_language", "JAPANESE" ], [ "WHEN A WOMAN ASCENDS THE STAIRS", "has_genre", "DRAMA" ], [ "WHEN A WOMAN ASCENDS THE STAIRS", "in_language", "JAPANESE" ], [ "WHISPER OF THE HEART", "has_genre", "DRAMA" ], [ "WHISPER OF THE HEART", "in_language", "JAPANESE" ], [ "YOJIMBO", "has_genre", "DRAMA" ], [ "YOJIMBO", "in_language", "JAPANESE" ], [ "ZATOICHI MEETS YOJIMBO", "has_genre", "DRAMA" ], [ "ZATOICHI MEETS YOJIMBO", "in_language", "JAPANESE" ], [ "ZATOICHI MEETS YOJIMBO", "written_by", "KAN SHIMOZAWA" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 24438, 1993 1097, 2003 15174, AND STARRING PANCHO VILLA AS HIMSELF 21382, ANTONIO BANDERAS 37088, GLENN CLOSE 2957, ONCE UPON A TIME IN MEXICO 18917, SKYLARK 31584, SLIM SUSIE 34001, THE HOUSE OF THE SPIRITS 18569, ULF MALMROS src, edge_attr, dst 15174, release_year, 1097 15174, starred_actors, 21382 2957, has_tags, 21382 2957, release_year, 1097 2957, starred_actors, 21382 18917, release_year, 24438 18917, starred_actors, 37088 31584, directed_by, 18569 31584, release_year, 1097 31584, written_by, 18569 34001, has_tags, 21382 34001, has_tags, 37088 34001, release_year, 24438 Question: How are ANTONIO BANDERAS, SKYLARK, and ULF MALMROS related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ANTONIO BANDERAS", "SKYLARK", "ULF MALMROS" ], "valid_edges": [ [ "AND STARRING PANCHO VILLA AS HIMSELF", "release_year", "2003" ], [ "AND STARRING PANCHO VILLA AS HIMSELF", "starred_actors", "ANTONIO BANDERAS" ], [ "ONCE UPON A TIME IN MEXICO", "has_tags", "ANTONIO BANDERAS" ], [ "ONCE UPON A TIME IN MEXICO", "release_year", "2003" ], [ "ONCE UPON A TIME IN MEXICO", "starred_actors", "ANTONIO BANDERAS" ], [ "SKYLARK", "release_year", "1993" ], [ "SKYLARK", "starred_actors", "GLENN CLOSE" ], [ "SLIM SUSIE", "directed_by", "ULF MALMROS" ], [ "SLIM SUSIE", "release_year", "2003" ], [ "SLIM SUSIE", "written_by", "ULF MALMROS" ], [ "THE HOUSE OF THE SPIRITS", "has_tags", "ANTONIO BANDERAS" ], [ "THE HOUSE OF THE SPIRITS", "has_tags", "GLENN CLOSE" ], [ "THE HOUSE OF THE SPIRITS", "release_year", "1993" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 27261, 2009 1421, 2013 26267, CINEMATOGRAPHY 6480, GERMAN 21573, GIRL ON A BICYCLE 34974, GOLD 31326, GRAVITY 20941, HAMLET 33460, HANS ZIMMER 2722, INSIDE LLEWYN DAVIS 848, OCULUS 13471, PRISONERS 11124, STALINGRAD 19442, THE BOOK THIEF 13153, THE GERMAN DOCTOR 2872, THE GREAT GATSBY 14478, THE LION KING 3104, THE WHITE RIBBON 37916, WETLANDS src, edge_attr, dst 21573, in_language, 6480 21573, release_year, 1421 34974, in_language, 6480 34974, release_year, 1421 31326, has_tags, 26267 31326, release_year, 1421 20941, release_year, 27261 2722, has_tags, 26267 2722, release_year, 1421 848, release_year, 1421 13471, has_tags, 26267 13471, release_year, 1421 11124, has_tags, 6480 11124, in_language, 6480 11124, release_year, 1421 19442, in_language, 6480 19442, release_year, 1421 13153, in_language, 6480 13153, release_year, 1421 2872, has_tags, 26267 2872, release_year, 1421 14478, has_tags, 20941 14478, has_tags, 33460 3104, has_tags, 26267 3104, has_tags, 6480 3104, in_language, 6480 3104, release_year, 27261 37916, in_language, 6480 37916, release_year, 1421 Question: In what context are HANS ZIMMER, OCULUS, and THE WHITE RIBBON connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "HANS ZIMMER", "OCULUS", "THE WHITE RIBBON" ], "valid_edges": [ [ "GIRL ON A BICYCLE", "in_language", "GERMAN" ], [ "GIRL ON A BICYCLE", "release_year", "2013" ], [ "GOLD", "in_language", "GERMAN" ], [ "GOLD", "release_year", "2013" ], [ "GRAVITY", "has_tags", "CINEMATOGRAPHY" ], [ "GRAVITY", "release_year", "2013" ], [ "HAMLET", "release_year", "2009" ], [ "INSIDE LLEWYN DAVIS", "has_tags", "CINEMATOGRAPHY" ], [ "INSIDE LLEWYN DAVIS", "release_year", "2013" ], [ "OCULUS", "release_year", "2013" ], [ "PRISONERS", "has_tags", "CINEMATOGRAPHY" ], [ "PRISONERS", "release_year", "2013" ], [ "STALINGRAD", "has_tags", "GERMAN" ], [ "STALINGRAD", "in_language", "GERMAN" ], [ "STALINGRAD", "release_year", "2013" ], [ "THE BOOK THIEF", "in_language", "GERMAN" ], [ "THE BOOK THIEF", "release_year", "2013" ], [ "THE GERMAN DOCTOR", "in_language", "GERMAN" ], [ "THE GERMAN DOCTOR", "release_year", "2013" ], [ "THE GREAT GATSBY", "has_tags", "CINEMATOGRAPHY" ], [ "THE GREAT GATSBY", "release_year", "2013" ], [ "THE LION KING", "has_tags", "HAMLET" ], [ "THE LION KING", "has_tags", "HANS ZIMMER" ], [ "THE WHITE RIBBON", "has_tags", "CINEMATOGRAPHY" ], [ "THE WHITE RIBBON", "has_tags", "GERMAN" ], [ "THE WHITE RIBBON", "in_language", "GERMAN" ], [ "THE WHITE RIBBON", "release_year", "2009" ], [ "WETLANDS", "in_language", "GERMAN" ], [ "WETLANDS", "release_year", "2013" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15421, 100 BLOODY ACRES 24580, 2-HEADED SHARK ATTACK 658, 2012 38712, 30 DAYS OF NIGHT 18899, A BUCKET OF BLOOD 30586, A FANTASTIC FEAR OF EVERYTHING 25327, AFTERSHOCK 12146, ANNE BOBBY 34229, ANTIVIRAL 35584, ANY QUESTIONS FOR BEN? 3449, APARTMENT 1303 3D 22078, ARBITRAGE 10277, ARGO 36593, BLACK CHRISTMAS 13736, BLACK ROCK 2570, BLACK SUNDAY 31909, BLACK SWAN 5946, BLOODY BLOODY BIBLE CAMP 17899, BUG 21399, CABIN 27929, CABIN FEVER 38311, CHAOS 30191, CHERNOBYL DIARIES 6286, CHRIS HEMSWORTH 30339, CITADEL 38925, CLOUD ATLAS 38598, COME OUT AND PLAY 22349, CRAWLSPACE 30529, DARK SHADOWS 33477, DARKEST NIGHT 21942, DAWN OF THE DEAD 17466, DETENTION OF THE DEAD 31163, DIRECTORIAL DEBUT 16041, DJANGO UNCHAINED 28770, DRACULA 3D 32596, DREW GODDARD 6394, EXCISION 35384, FAT KID RULES THE WORLD 3785, FRAN KRANZ 1041, GALLOWWALKERS 24098, GRAVE ENCOUNTERS 2 2486, GRINDHOUSE 5870, HORROR 4823, HOUSE AT THE END OF THE STREET 36133, JOHN DIES AT THE END 8192, JOSS WHEDON 1000, LET THE RIGHT ONE IN 35217, LOOPER 24437, MANIAC 31377, MUCH ADO ABOUT NOTHING 11484, MUTANT CHRONICLES 33156, MY NAME IS BRUCE 22011, NIGHTBREED 25269, NINE LIVES 10573, NO ONE LIVES 36239, PARANORMAL ACTIVITY 4 35289, PARANORMAN 17916, PIRANHA 3DD 23588, PROMETHEUS 6175, QUARANTINE 25023, QUARTET 13081, R 27373, RED DAWN 38226, RESOLUTION 31339, RISE OF THE ZOMBIES 20664, ROB SITCH 38815, SADAKO 3D 33912, SANTO CILAURO 38572, SATIRE 24992, SCARY OR DIE 9373, SEVERANCE 15926, SILENT NIGHT 34584, SILVER LININGS PLAYBOOK 8359, SINISTER 13768, SLEEPY HOLLOW 26930, SLITHER 21148, SNOW WHITE AND THE HUNTSMAN 9247, STITCHES 39265, STORAGE 24 273, TED 31162, THE ABCS OF DEATH 39296, THE APPARITION 16103, THE AVENGERS 25250, THE BARRENS 19623, THE BATTERY 16753, THE BAY 19938, THE CABIN IN THE WOODS 13392, THE CASTLE 2974, THE COLLECTION 18592, THE DEVIL INSIDE 7153, THE DEVIL'S CARNIVAL 19064, THE HAUNTING OF HELENA 11861, THE LORDS OF SALEM 24652, THE MAN WHO LAUGHS 33919, THE MIST 956, THE ORPHANAGE 29525, THE PACT 2023, THE POSSESSION 22294, THE PROPHECY 18162, THE RAVEN 27427, THE STRANGERS 27003, THE THOMPSONS 3437, THE WOMAN IN BLACK 939, THIS IS 40 24359, TOM GLEISNER 31252, V/H/S 30621, VACANCY 24570, VAMPS 20105, WORLD WAR Z 39464, WOULD YOU RATHER src, edge_attr, dst 15421, has_genre, 5870 15421, release_year, 658 24580, has_genre, 5870 24580, release_year, 658 38712, has_genre, 5870 38712, has_tags, 13081 18899, has_genre, 5870 18899, has_tags, 38572 30586, has_genre, 5870 30586, has_tags, 5870 30586, release_year, 658 25327, has_genre, 5870 25327, release_year, 658 34229, has_genre, 5870 34229, release_year, 658 35584, directed_by, 20664 35584, release_year, 658 35584, written_by, 20664 35584, written_by, 33912 35584, written_by, 24359 3449, has_genre, 5870 3449, release_year, 658 22078, has_tags, 13081 22078, release_year, 658 10277, has_tags, 13081 10277, release_year, 658 36593, has_genre, 5870 36593, has_tags, 5870 36593, has_tags, 13081 13736, has_genre, 5870 13736, release_year, 658 2570, has_genre, 5870 2570, has_tags, 31163 31909, has_tags, 5870 31909, has_tags, 13081 5946, has_genre, 5870 5946, release_year, 658 17899, has_genre, 5870 17899, has_tags, 13081 27929, has_genre, 5870 27929, has_tags, 21399 27929, has_tags, 5870 38311, has_genre, 5870 38311, has_tags, 13081 30191, has_genre, 5870 30191, release_year, 658 30339, has_genre, 5870 30339, release_year, 658 38925, has_tags, 13081 38925, release_year, 658 38598, has_genre, 5870 38598, release_year, 658 22349, has_genre, 5870 22349, release_year, 658 30529, has_genre, 5870 30529, release_year, 658 33477, has_genre, 5870 33477, has_tags, 5870 33477, release_year, 658 21942, has_genre, 5870 21942, has_tags, 31163 21942, has_tags, 5870 21942, has_tags, 13081 17466, has_genre, 5870 17466, release_year, 658 16041, has_tags, 13081 16041, release_year, 658 28770, has_genre, 5870 28770, release_year, 658 6394, has_genre, 5870 6394, release_year, 658 35384, has_tags, 31163 35384, release_year, 658 1041, has_genre, 5870 1041, release_year, 658 24098, has_genre, 5870 24098, release_year, 658 2486, has_genre, 5870 2486, has_tags, 13081 4823, has_genre, 5870 4823, release_year, 658 36133, has_genre, 5870 36133, release_year, 658 1000, has_genre, 5870 1000, has_tags, 5870 1000, has_tags, 13081 35217, has_tags, 13081 35217, release_year, 658 24437, has_genre, 5870 24437, release_year, 658 31377, directed_by, 8192 31377, has_tags, 3785 31377, has_tags, 8192 31377, release_year, 658 31377, written_by, 8192 11484, has_genre, 5870 11484, has_tags, 13081 33156, has_genre, 5870 33156, has_tags, 13081 22011, has_genre, 5870 22011, starred_actors, 12146 25269, has_genre, 5870 25269, has_tags, 13081 10573, has_genre, 5870 10573, release_year, 658 36239, has_genre, 5870 36239, release_year, 658 35289, has_tags, 5870 35289, release_year, 658 17916, has_genre, 5870 17916, release_year, 658 23588, has_tags, 13081 23588, release_year, 658 6175, has_genre, 5870 6175, has_tags, 13081 25023, has_tags, 31163 25023, release_year, 658 27373, has_tags, 6286 27373, release_year, 658 27373, starred_actors, 6286 38226, has_genre, 5870 38226, release_year, 658 31339, has_genre, 5870 31339, release_year, 658 38815, has_genre, 5870 38815, release_year, 658 24992, has_genre, 5870 24992, release_year, 658 9373, has_genre, 5870 9373, has_tags, 5870 9373, has_tags, 13081 15926, has_genre, 5870 15926, release_year, 658 34584, has_tags, 13081 34584, release_year, 658 8359, has_genre, 5870 8359, has_tags, 5870 8359, release_year, 658 13768, has_genre, 5870 13768, has_tags, 5870 13768, has_tags, 13081 26930, has_genre, 5870 26930, has_tags, 5870 26930, has_tags, 13081 21148, has_tags, 6286 21148, release_year, 658 21148, starred_actors, 6286 9247, has_genre, 5870 9247, release_year, 658 39265, has_genre, 5870 39265, release_year, 658 273, has_tags, 31163 273, release_year, 658 31162, has_genre, 5870 31162, release_year, 658 39296, has_genre, 5870 39296, release_year, 658 16103, directed_by, 8192 16103, has_tags, 6286 16103, has_tags, 8192 16103, release_year, 658 16103, starred_actors, 6286 16103, written_by, 8192 25250, has_genre, 5870 25250, release_year, 658 19623, has_genre, 5870 19623, release_year, 658 16753, has_genre, 5870 16753, release_year, 658 19938, directed_by, 32596 19938, has_genre, 5870 19938, has_tags, 21399 19938, has_tags, 6286 19938, has_tags, 31163 19938, has_tags, 32596 19938, has_tags, 3785 19938, has_tags, 5870 19938, has_tags, 8192 19938, has_tags, 13081 19938, has_tags, 38572 19938, release_year, 658 19938, starred_actors, 6286 19938, starred_actors, 3785 19938, written_by, 32596 19938, written_by, 8192 13392, directed_by, 20664 13392, has_tags, 20664 13392, written_by, 20664 13392, written_by, 33912 13392, written_by, 24359 2974, has_genre, 5870 2974, release_year, 658 18592, has_genre, 5870 18592, release_year, 658 7153, has_genre, 5870 7153, release_year, 658 19064, has_genre, 5870 19064, release_year, 658 11861, has_genre, 5870 11861, release_year, 658 24652, has_genre, 5870 24652, release_year, 658 33919, has_genre, 5870 33919, has_tags, 13081 956, has_tags, 5870 956, has_tags, 13081 29525, has_genre, 5870 29525, release_year, 658 2023, has_genre, 5870 2023, has_tags, 5870 2023, release_year, 658 22294, has_genre, 5870 22294, has_tags, 13081 18162, has_genre, 5870 18162, has_tags, 5870 18162, release_year, 658 27427, has_genre, 5870 27427, has_tags, 13081 27003, has_genre, 5870 27003, release_year, 658 3437, has_genre, 5870 3437, has_tags, 5870 3437, release_year, 658 939, has_tags, 13081 939, release_year, 658 31252, has_genre, 5870 31252, has_tags, 5870 31252, release_year, 658 30621, has_genre, 5870 30621, has_tags, 13081 24570, has_genre, 5870 24570, release_year, 658 20105, has_genre, 5870 20105, written_by, 32596 39464, has_genre, 5870 39464, has_tags, 5870 39464, release_year, 658 Question: In what context are ANNE BOBBY, THE CABIN IN THE WOODS, and TOM GLEISNER connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ANNE BOBBY", "THE CABIN IN THE WOODS", "TOM GLEISNER" ], "valid_edges": [ [ "100 BLOODY ACRES", "has_genre", "HORROR" ], [ "100 BLOODY ACRES", "release_year", "2012" ], [ "2-HEADED SHARK ATTACK", "has_genre", "HORROR" ], [ "2-HEADED SHARK ATTACK", "release_year", "2012" ], [ "30 DAYS OF NIGHT", "has_genre", "HORROR" ], [ "30 DAYS OF NIGHT", "has_tags", "R" ], [ "A BUCKET OF BLOOD", "has_genre", "HORROR" ], [ "A BUCKET OF BLOOD", "has_tags", "SATIRE" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_genre", "HORROR" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_tags", "HORROR" ], [ "A FANTASTIC FEAR OF EVERYTHING", "release_year", "2012" ], [ "AFTERSHOCK", "has_genre", "HORROR" ], [ "AFTERSHOCK", "release_year", "2012" ], [ "ANTIVIRAL", "has_genre", "HORROR" ], [ "ANTIVIRAL", "release_year", "2012" ], [ "ANY QUESTIONS FOR BEN?", "directed_by", "ROB SITCH" ], [ "ANY QUESTIONS FOR BEN?", "release_year", "2012" ], [ "ANY QUESTIONS FOR BEN?", "written_by", "ROB SITCH" ], [ "ANY QUESTIONS FOR BEN?", "written_by", "SANTO CILAURO" ], [ "ANY QUESTIONS FOR BEN?", "written_by", "TOM GLEISNER" ], [ "APARTMENT 1303 3D", "has_genre", "HORROR" ], [ "APARTMENT 1303 3D", "release_year", "2012" ], [ "ARBITRAGE", "has_tags", "R" ], [ "ARBITRAGE", "release_year", "2012" ], [ "ARGO", "has_tags", "R" ], [ "ARGO", "release_year", "2012" ], [ "BLACK CHRISTMAS", "has_genre", "HORROR" ], [ "BLACK CHRISTMAS", "has_tags", "HORROR" ], [ "BLACK CHRISTMAS", "has_tags", "R" ], [ "BLACK ROCK", "has_genre", "HORROR" ], [ "BLACK ROCK", "release_year", "2012" ], [ "BLACK SUNDAY", "has_genre", "HORROR" ], [ "BLACK SUNDAY", "has_tags", "DIRECTORIAL DEBUT" ], [ "BLACK SWAN", "has_tags", "HORROR" ], [ "BLACK SWAN", "has_tags", "R" ], [ "BLOODY BLOODY BIBLE CAMP", "has_genre", "HORROR" ], [ "BLOODY BLOODY BIBLE CAMP", "release_year", "2012" ], [ "BUG", "has_genre", "HORROR" ], [ "BUG", "has_tags", "R" ], [ "CABIN FEVER", "has_genre", "HORROR" ], [ "CABIN FEVER", "has_tags", "CABIN" ], [ "CABIN FEVER", "has_tags", "HORROR" ], [ "CHAOS", "has_genre", "HORROR" ], [ "CHAOS", "has_tags", "R" ], [ "CHERNOBYL DIARIES", "has_genre", "HORROR" ], [ "CHERNOBYL DIARIES", "release_year", "2012" ], [ "CITADEL", "has_genre", "HORROR" ], [ "CITADEL", "release_year", "2012" ], [ "CLOUD ATLAS", "has_tags", "R" ], [ "CLOUD ATLAS", "release_year", "2012" ], [ "COME OUT AND PLAY", "has_genre", "HORROR" ], [ "COME OUT AND PLAY", "release_year", "2012" ], [ "CRAWLSPACE", "has_genre", "HORROR" ], [ "CRAWLSPACE", "release_year", "2012" ], [ "DARK SHADOWS", "has_genre", "HORROR" ], [ "DARK SHADOWS", "release_year", "2012" ], [ "DARKEST NIGHT", "has_genre", "HORROR" ], [ "DARKEST NIGHT", "has_tags", "HORROR" ], [ "DARKEST NIGHT", "release_year", "2012" ], [ "DAWN OF THE DEAD", "has_genre", "HORROR" ], [ "DAWN OF THE DEAD", "has_tags", "DIRECTORIAL DEBUT" ], [ "DAWN OF THE DEAD", "has_tags", "HORROR" ], [ "DAWN OF THE DEAD", "has_tags", "R" ], [ "DETENTION OF THE DEAD", "has_genre", "HORROR" ], [ "DETENTION OF THE DEAD", "release_year", "2012" ], [ "DJANGO UNCHAINED", "has_tags", "R" ], [ "DJANGO UNCHAINED", "release_year", "2012" ], [ "DRACULA 3D", "has_genre", "HORROR" ], [ "DRACULA 3D", "release_year", "2012" ], [ "EXCISION", "has_genre", "HORROR" ], [ "EXCISION", "release_year", "2012" ], [ "FAT KID RULES THE WORLD", "has_tags", "DIRECTORIAL DEBUT" ], [ "FAT KID RULES THE WORLD", "release_year", "2012" ], [ "GALLOWWALKERS", "has_genre", "HORROR" ], [ "GALLOWWALKERS", "release_year", "2012" ], [ "GRAVE ENCOUNTERS 2", "has_genre", "HORROR" ], [ "GRAVE ENCOUNTERS 2", "release_year", "2012" ], [ "GRINDHOUSE", "has_genre", "HORROR" ], [ "GRINDHOUSE", "has_tags", "R" ], [ "HOUSE AT THE END OF THE STREET", "has_genre", "HORROR" ], [ "HOUSE AT THE END OF THE STREET", "release_year", "2012" ], [ "JOHN DIES AT THE END", "has_genre", "HORROR" ], [ "JOHN DIES AT THE END", "release_year", "2012" ], [ "LET THE RIGHT ONE IN", "has_genre", "HORROR" ], [ "LET THE RIGHT ONE IN", "has_tags", "HORROR" ], [ "LET THE RIGHT ONE IN", "has_tags", "R" ], [ "LOOPER", "has_tags", "R" ], [ "LOOPER", "release_year", "2012" ], [ "MANIAC", "has_genre", "HORROR" ], [ "MANIAC", "release_year", "2012" ], [ "MUCH ADO ABOUT NOTHING", "directed_by", "JOSS WHEDON" ], [ "MUCH ADO ABOUT NOTHING", "has_tags", "FRAN KRANZ" ], [ "MUCH ADO ABOUT NOTHING", "has_tags", "JOSS WHEDON" ], [ "MUCH ADO ABOUT NOTHING", "release_year", "2012" ], [ "MUCH ADO ABOUT NOTHING", "written_by", "JOSS WHEDON" ], [ "MUTANT CHRONICLES", "has_genre", "HORROR" ], [ "MUTANT CHRONICLES", "has_tags", "R" ], [ "MY NAME IS BRUCE", "has_genre", "HORROR" ], [ "MY NAME IS BRUCE", "has_tags", "R" ], [ "NIGHTBREED", "has_genre", "HORROR" ], [ "NIGHTBREED", "starred_actors", "ANNE BOBBY" ], [ "NINE LIVES", "has_genre", "HORROR" ], [ "NINE LIVES", "has_tags", "R" ], [ "NO ONE LIVES", "has_genre", "HORROR" ], [ "NO ONE LIVES", "release_year", "2012" ], [ "PARANORMAL ACTIVITY 4", "has_genre", "HORROR" ], [ "PARANORMAL ACTIVITY 4", "release_year", "2012" ], [ "PARANORMAN", "has_tags", "HORROR" ], [ "PARANORMAN", "release_year", "2012" ], [ "PIRANHA 3DD", "has_genre", "HORROR" ], [ "PIRANHA 3DD", "release_year", "2012" ], [ "PROMETHEUS", "has_tags", "R" ], [ "PROMETHEUS", "release_year", "2012" ], [ "QUARANTINE", "has_genre", "HORROR" ], [ "QUARANTINE", "has_tags", "R" ], [ "QUARTET", "has_tags", "DIRECTORIAL DEBUT" ], [ "QUARTET", "release_year", "2012" ], [ "RED DAWN", "has_tags", "CHRIS HEMSWORTH" ], [ "RED DAWN", "release_year", "2012" ], [ "RED DAWN", "starred_actors", "CHRIS HEMSWORTH" ], [ "RESOLUTION", "has_genre", "HORROR" ], [ "RESOLUTION", "release_year", "2012" ], [ "RISE OF THE ZOMBIES", "has_genre", "HORROR" ], [ "RISE OF THE ZOMBIES", "release_year", "2012" ], [ "SADAKO 3D", "has_genre", "HORROR" ], [ "SADAKO 3D", "release_year", "2012" ], [ "SCARY OR DIE", "has_genre", "HORROR" ], [ "SCARY OR DIE", "release_year", "2012" ], [ "SEVERANCE", "has_genre", "HORROR" ], [ "SEVERANCE", "has_tags", "HORROR" ], [ "SEVERANCE", "has_tags", "R" ], [ "SILENT NIGHT", "has_genre", "HORROR" ], [ "SILENT NIGHT", "release_year", "2012" ], [ "SILVER LININGS PLAYBOOK", "has_tags", "R" ], [ "SILVER LININGS PLAYBOOK", "release_year", "2012" ], [ "SINISTER", "has_genre", "HORROR" ], [ "SINISTER", "has_tags", "HORROR" ], [ "SINISTER", "release_year", "2012" ], [ "SLEEPY HOLLOW", "has_genre", "HORROR" ], [ "SLEEPY HOLLOW", "has_tags", "HORROR" ], [ "SLEEPY HOLLOW", "has_tags", "R" ], [ "SLITHER", "has_genre", "HORROR" ], [ "SLITHER", "has_tags", "HORROR" ], [ "SLITHER", "has_tags", "R" ], [ "SNOW WHITE AND THE HUNTSMAN", "has_tags", "CHRIS HEMSWORTH" ], [ "SNOW WHITE AND THE HUNTSMAN", "release_year", "2012" ], [ "SNOW WHITE AND THE HUNTSMAN", "starred_actors", "CHRIS HEMSWORTH" ], [ "STITCHES", "has_genre", "HORROR" ], [ "STITCHES", "release_year", "2012" ], [ "STORAGE 24", "has_genre", "HORROR" ], [ "STORAGE 24", "release_year", "2012" ], [ "TED", "has_tags", "DIRECTORIAL DEBUT" ], [ "TED", "release_year", "2012" ], [ "THE ABCS OF DEATH", "has_genre", "HORROR" ], [ "THE ABCS OF DEATH", "release_year", "2012" ], [ "THE APPARITION", "has_genre", "HORROR" ], [ "THE APPARITION", "release_year", "2012" ], [ "THE AVENGERS", "directed_by", "JOSS WHEDON" ], [ "THE AVENGERS", "has_tags", "CHRIS HEMSWORTH" ], [ "THE AVENGERS", "has_tags", "JOSS WHEDON" ], [ "THE AVENGERS", "release_year", "2012" ], [ "THE AVENGERS", "starred_actors", "CHRIS HEMSWORTH" ], [ "THE AVENGERS", "written_by", "JOSS WHEDON" ], [ "THE BARRENS", "has_genre", "HORROR" ], [ "THE BARRENS", "release_year", "2012" ], [ "THE BATTERY", "has_genre", "HORROR" ], [ "THE BATTERY", "release_year", "2012" ], [ "THE BAY", "has_genre", "HORROR" ], [ "THE BAY", "release_year", "2012" ], [ "THE CABIN IN THE WOODS", "directed_by", "DREW GODDARD" ], [ "THE CABIN IN THE WOODS", "has_genre", "HORROR" ], [ "THE CABIN IN THE WOODS", "has_tags", "CABIN" ], [ "THE CABIN IN THE WOODS", "has_tags", "CHRIS HEMSWORTH" ], [ "THE CABIN IN THE WOODS", "has_tags", "DIRECTORIAL DEBUT" ], [ "THE CABIN IN THE WOODS", "has_tags", "DREW GODDARD" ], [ "THE CABIN IN THE WOODS", "has_tags", "FRAN KRANZ" ], [ "THE CABIN IN THE WOODS", "has_tags", "HORROR" ], [ "THE CABIN IN THE WOODS", "has_tags", "JOSS WHEDON" ], [ "THE CABIN IN THE WOODS", "has_tags", "R" ], [ "THE CABIN IN THE WOODS", "has_tags", "SATIRE" ], [ "THE CABIN IN THE WOODS", "release_year", "2012" ], [ "THE CABIN IN THE WOODS", "starred_actors", "CHRIS HEMSWORTH" ], [ "THE CABIN IN THE WOODS", "starred_actors", "FRAN KRANZ" ], [ "THE CABIN IN THE WOODS", "written_by", "DREW GODDARD" ], [ "THE CABIN IN THE WOODS", "written_by", "JOSS WHEDON" ], [ "THE CASTLE", "directed_by", "ROB SITCH" ], [ "THE CASTLE", "has_tags", "ROB SITCH" ], [ "THE CASTLE", "written_by", "ROB SITCH" ], [ "THE CASTLE", "written_by", "SANTO CILAURO" ], [ "THE CASTLE", "written_by", "TOM GLEISNER" ], [ "THE COLLECTION", "has_genre", "HORROR" ], [ "THE COLLECTION", "release_year", "2012" ], [ "THE DEVIL INSIDE", "has_genre", "HORROR" ], [ "THE DEVIL INSIDE", "release_year", "2012" ], [ "THE DEVIL'S CARNIVAL", "has_genre", "HORROR" ], [ "THE DEVIL'S CARNIVAL", "release_year", "2012" ], [ "THE HAUNTING OF HELENA", "has_genre", "HORROR" ], [ "THE HAUNTING OF HELENA", "release_year", "2012" ], [ "THE LORDS OF SALEM", "has_genre", "HORROR" ], [ "THE LORDS OF SALEM", "release_year", "2012" ], [ "THE MAN WHO LAUGHS", "has_genre", "HORROR" ], [ "THE MAN WHO LAUGHS", "release_year", "2012" ], [ "THE MIST", "has_genre", "HORROR" ], [ "THE MIST", "has_tags", "R" ], [ "THE ORPHANAGE", "has_tags", "HORROR" ], [ "THE ORPHANAGE", "has_tags", "R" ], [ "THE PACT", "has_genre", "HORROR" ], [ "THE PACT", "release_year", "2012" ], [ "THE POSSESSION", "has_genre", "HORROR" ], [ "THE POSSESSION", "has_tags", "HORROR" ], [ "THE POSSESSION", "release_year", "2012" ], [ "THE PROPHECY", "has_genre", "HORROR" ], [ "THE PROPHECY", "has_tags", "R" ], [ "THE RAVEN", "has_genre", "HORROR" ], [ "THE RAVEN", "has_tags", "HORROR" ], [ "THE RAVEN", "release_year", "2012" ], [ "THE STRANGERS", "has_genre", "HORROR" ], [ "THE STRANGERS", "has_tags", "R" ], [ "THE THOMPSONS", "has_genre", "HORROR" ], [ "THE THOMPSONS", "release_year", "2012" ], [ "THE WOMAN IN BLACK", "has_genre", "HORROR" ], [ "THE WOMAN IN BLACK", "has_tags", "HORROR" ], [ "THE WOMAN IN BLACK", "release_year", "2012" ], [ "THIS IS 40", "has_tags", "R" ], [ "THIS IS 40", "release_year", "2012" ], [ "V/H/S", "has_genre", "HORROR" ], [ "V/H/S", "has_tags", "HORROR" ], [ "V/H/S", "release_year", "2012" ], [ "VACANCY", "has_genre", "HORROR" ], [ "VACANCY", "has_tags", "R" ], [ "VAMPS", "has_genre", "HORROR" ], [ "VAMPS", "release_year", "2012" ], [ "WORLD WAR Z", "has_genre", "HORROR" ], [ "WORLD WAR Z", "written_by", "DREW GODDARD" ], [ "WOULD YOU RATHER", "has_genre", "HORROR" ], [ "WOULD YOU RATHER", "has_tags", "HORROR" ], [ "WOULD YOU RATHER", "release_year", "2012" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 33013, A LETTER TO MOMO 24177, ANIMATION 6294, ANIME 30907, BAREFOOT GEN 7106, BROKEN VESSELS 36212, DRAMA 21774, GUNBUSTER 8248, JAPAN 36874, JAPANESE 18529, JOHN MCMAHON 34288, ONLY YESTERDAY 18584, PRINCESS MONONOKE 38976, STUDIO GHIBLI 1277, THE COLOR OF MONEY 14956, VOICES OF A DISTANT STAR 16901, WALTER TEVIS src, edge_attr, dst 33013, has_genre, 24177 33013, has_genre, 36212 33013, has_tags, 6294 33013, in_language, 36874 30907, has_genre, 36212 30907, has_tags, 6294 30907, in_language, 36874 7106, has_genre, 36212 7106, written_by, 18529 21774, has_genre, 36212 21774, has_tags, 6294 34288, has_genre, 24177 34288, has_genre, 36212 34288, has_tags, 6294 34288, has_tags, 8248 34288, has_tags, 38976 34288, in_language, 36874 18584, has_tags, 6294 18584, has_tags, 36212 18584, has_tags, 8248 18584, has_tags, 36874 18584, has_tags, 38976 18584, in_language, 36874 1277, has_genre, 36212 1277, written_by, 16901 14956, has_genre, 24177 14956, has_genre, 36212 14956, has_tags, 6294 14956, in_language, 36874 Question: For what reason are ANIME, JOHN MCMAHON, and WALTER TEVIS associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ANIME", "JOHN MCMAHON", "WALTER TEVIS" ], "valid_edges": [ [ "A LETTER TO MOMO", "has_genre", "ANIMATION" ], [ "A LETTER TO MOMO", "has_genre", "DRAMA" ], [ "A LETTER TO MOMO", "has_tags", "ANIME" ], [ "A LETTER TO MOMO", "in_language", "JAPANESE" ], [ "BAREFOOT GEN", "has_genre", "DRAMA" ], [ "BAREFOOT GEN", "has_tags", "ANIME" ], [ "BAREFOOT GEN", "in_language", "JAPANESE" ], [ "BROKEN VESSELS", "has_genre", "DRAMA" ], [ "BROKEN VESSELS", "written_by", "JOHN MCMAHON" ], [ "GUNBUSTER", "has_genre", "DRAMA" ], [ "GUNBUSTER", "has_tags", "ANIME" ], [ "ONLY YESTERDAY", "has_genre", "ANIMATION" ], [ "ONLY YESTERDAY", "has_genre", "DRAMA" ], [ "ONLY YESTERDAY", "has_tags", "ANIME" ], [ "ONLY YESTERDAY", "has_tags", "JAPAN" ], [ "ONLY YESTERDAY", "has_tags", "STUDIO GHIBLI" ], [ "ONLY YESTERDAY", "in_language", "JAPANESE" ], [ "PRINCESS MONONOKE", "has_tags", "ANIME" ], [ "PRINCESS MONONOKE", "has_tags", "DRAMA" ], [ "PRINCESS MONONOKE", "has_tags", "JAPAN" ], [ "PRINCESS MONONOKE", "has_tags", "JAPANESE" ], [ "PRINCESS MONONOKE", "has_tags", "STUDIO GHIBLI" ], [ "PRINCESS MONONOKE", "in_language", "JAPANESE" ], [ "THE COLOR OF MONEY", "has_genre", "DRAMA" ], [ "THE COLOR OF MONEY", "written_by", "WALTER TEVIS" ], [ "VOICES OF A DISTANT STAR", "has_genre", "ANIMATION" ], [ "VOICES OF A DISTANT STAR", "has_genre", "DRAMA" ], [ "VOICES OF A DISTANT STAR", "has_tags", "ANIME" ], [ "VOICES OF A DISTANT STAR", "in_language", "JAPANESE" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 16426, BELLE DE JOUR 14724, CRIME 38279, CYBERPUNK 9542, DIARY OF A CHAMBERMAID 36212, DRAMA 11389, EDDY MORETTI 6012, FRENCH 1003, HACKERS 35505, LUIS BUÑUEL 26605, THE CRIMINAL LIFE OF ARCHIBALDO DE LA CRUZ 2049, WHITE LIGHTNIN' src, edge_attr, dst 16426, directed_by, 35505 16426, has_genre, 36212 16426, has_tags, 36212 16426, has_tags, 35505 16426, in_language, 6012 16426, written_by, 35505 9542, directed_by, 35505 9542, has_genre, 36212 9542, has_tags, 35505 9542, in_language, 6012 9542, written_by, 35505 1003, has_genre, 14724 1003, has_tags, 38279 1003, has_tags, 1003 26605, directed_by, 35505 26605, has_genre, 14724 26605, has_tags, 35505 26605, written_by, 35505 2049, has_genre, 36212 2049, written_by, 11389 Question: How are CYBERPUNK, EDDY MORETTI, and LUIS BUÑUEL related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "CYBERPUNK", "EDDY MORETTI", "LUIS BUÑUEL" ], "valid_edges": [ [ "BELLE DE JOUR", "directed_by", "LUIS BUÑUEL" ], [ "BELLE DE JOUR", "has_genre", "DRAMA" ], [ "BELLE DE JOUR", "has_tags", "DRAMA" ], [ "BELLE DE JOUR", "has_tags", "LUIS BUÑUEL" ], [ "BELLE DE JOUR", "in_language", "FRENCH" ], [ "BELLE DE JOUR", "written_by", "LUIS BUÑUEL" ], [ "DIARY OF A CHAMBERMAID", "directed_by", "LUIS BUÑUEL" ], [ "DIARY OF A CHAMBERMAID", "has_genre", "DRAMA" ], [ "DIARY OF A CHAMBERMAID", "has_tags", "LUIS BUÑUEL" ], [ "DIARY OF A CHAMBERMAID", "in_language", "FRENCH" ], [ "DIARY OF A CHAMBERMAID", "written_by", "LUIS BUÑUEL" ], [ "HACKERS", "has_genre", "CRIME" ], [ "HACKERS", "has_tags", "CYBERPUNK" ], [ "HACKERS", "has_tags", "HACKERS" ], [ "THE CRIMINAL LIFE OF ARCHIBALDO DE LA CRUZ", "directed_by", "LUIS BUÑUEL" ], [ "THE CRIMINAL LIFE OF ARCHIBALDO DE LA CRUZ", "has_genre", "CRIME" ], [ "THE CRIMINAL LIFE OF ARCHIBALDO DE LA CRUZ", "has_tags", "LUIS BUÑUEL" ], [ "THE CRIMINAL LIFE OF ARCHIBALDO DE LA CRUZ", "written_by", "LUIS BUÑUEL" ], [ "WHITE LIGHTNIN'", "has_genre", "DRAMA" ], [ "WHITE LIGHTNIN'", "written_by", "EDDY MORETTI" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 7977, 1969 658, 2012 5423, A LATE QUARTET 32632, A ROYAL AFFAIR 20562, A THOUSAND WORDS 21800, ABOUT CHERRY 29800, ACE ATTORNEY 30332, AMOUR 36963, ANNA KARENINA 24708, ANY DAY NOW 22078, ARBITRAGE 10277, ARGO 13686, ARTHUR NEWMAN 9695, AT ANY PRICE 31449, BARABBAS 38263, BARBARA 23994, BARFI! 30581, BEASTS OF THE SOUTHERN WILD 37191, BEING FLYNN 28199, BEL AMI 32770, BEST MAN DOWN 14594, BEYOND THE HILLS 890, BIG MIRACLE 36071, BLACKBIRD 39848, BLUE LIKE JAZZ 18062, BOOTH TARKINGTON 22284, BOY EATING THE BIRD'S FOOD 27030, BROKEN 33711, CAESAR MUST DIE 32831, CALL GIRL 24707, CAMILLE REWINDS 12735, CHASING MAVERICKS 32450, CHEERFUL WEATHER FOR THE WEDDING 27338, CLIP 38925, CLOUD ATLAS 21016, COMPLIANCE 24757, CONSUMING SPIRITS 37589, COSMOPOLIS 19894, CRAVE 32797, DANGEROUS LIAISONS 10643, DARLING COMPANION 31694, DEADFALL 38977, DISCONNECT 36212, DRAMA 7480, EDEN 26106, END OF WATCH 32892, ENGLISH VINGLISH 6394, EXCISION 19570, FAREWELL, MY QUEEN 39636, FILL THE VOID 5563, FLIGHT 12314, FOR ELLEN 7740, FOREIGN LETTERS 15473, FORGETTING THE GIRL 6076, FRANCES HA 24535, GAME CHANGE 2427, GIRL IN PROGRESS 5647, HANNAH ARENDT 3917, HELLO HERMAN 22793, HEROINE 27350, HITCHCOCK 3030, HOLY MOTORS 35319, HOPE SPRINGS 37844, HYDE PARK ON HUDSON 19154, I BELONG 38589, I DO 32376, IMAGINE 26911, IN THE FOG 24025, INESCAPABLE 6301, JAYNE MANSFIELD'S CAR 5418, K-11 12835, KAUWBOY 26232, KEEP THE LIGHTS ON 2354, LADISLAV FUKS 3103, LAURENCE ANYWAYS 1418, LAWLESS 14601, LES MISÉRABLES 14931, LIBERAL ARTS 39727, LIFE OF PI 36020, LIKE SOMEONE IN LOVE 47, LINCOLN 17372, LOL 34517, LUV 35871, MAGIC MIKE 13801, MARFA GIRL 34611, ME AND YOU 31735, MIDDLE OF NOWHERE 39165, MUD 19118, MUSEUM HOURS 24915, MY WAY 26089, NAKED HARBOUR 25504, NEIGHBORING SOUNDS 8107, NO 20503, NOBODY WALKS 17532, NOT FADE AWAY 17263, NOW IS GOOD 21677, ON THE ROAD 38602, OUR CHILDREN 8523, OUT IN THE DARK 31312, PEOPLE LIKE US 15815, POST TENEBRAS LUX 11253, PROMISED LAND 11237, PURGE 25023, QUARTET 35746, REALITY 33501, RENOIR 11839, REVENGE FOR JOLLY! 12597, RUBY SPARKS 30316, RUST AND BONE 35808, SAFE 17168, SEEKING A FRIEND FOR THE END OF THE WORLD 25060, SEXUAL CHRONICLES OF A FRENCH FAMILY 28583, SHADOW DANCER 34584, SILVER LININGS PLAYBOOK 7174, SISTER 39426, SMASHED 28026, SOMEBODY UP THERE LIKES ME 33759, SOMETHING IN THE AIR 17346, SPRING BREAKERS 17608, STARLET 39286, STILL MINE 40067, STOLEN 25002, STRUCK BY LIGHTNING 3038, STUCK IN LOVE 18335, TABU 19501, THANKS FOR SHARING 3885, THE ARTIST AND THE MODEL 19623, THE BATTERY 24441, THE BROKEN CIRCLE BREAKDOWN 38175, THE CITIZEN 6644, THE COMEDY 24231, THE CREMATOR 2399, THE DEEP 10295, THE END OF LOVE 27103, THE FITZGERALD FAMILY CHRISTMAS 14688, THE FLOATING CASTLE 35267, THE FORGER 16929, THE GIRL 15980, THE GUILT TRIP 9328, THE HUNT 38614, THE IMPOSSIBLE 22800, THE LESSER BLESSED 33948, THE LETTER 4984, THE LUCKY ONE 23191, THE MAGIC OF BELLE ISLE 37370, THE MAGNIFICENT AMBERSONS 24652, THE MAN WHO LAUGHS 17964, THE MASTER 15798, THE ODD LIFE OF TIMOTHY GREEN 28735, THE OTHER SON 1195, THE PATIENCE STONE 7673, THE PERKS OF BEING A WALLFLOWER 2739, THE SAPPHIRES 27064, THE SCAPEGOAT 8650, THE SESSIONS 37915, THE STORY OF LUKE 14524, THE SWEENEY 12086, THE VOW 26797, THE WALL 4614, THE WE AND THE I 3437, THE WOMAN IN BLACK 15504, THE WORDS 266, THREE WORLDS 5352, THY WOMB 9918, TO THE WONDER 17451, TROUBLE WITH THE CURVE 13359, TWO LIVES 10305, UNCONDITIONAL 30056, WAR WITCH 7030, WHAT IF... 24333, WHAT MAISIE KNEW 34118, WHAT TO EXPECT WHEN YOU'RE EXPECTING 21183, WHITE ELEPHANT 18608, WHITE FROG 36174, WINNING STREAK 26337, WON'T BACK DOWN 31040, XINGU 9052, YOSSI 16849, ZERO DARK THIRTY src, edge_attr, dst 7977, has_genre, 36212 5423, has_genre, 36212 5423, has_tags, 36212 5423, release_year, 658 32632, has_genre, 36212 32632, release_year, 658 20562, has_genre, 36212 20562, release_year, 658 21800, has_genre, 36212 21800, release_year, 658 29800, has_genre, 36212 29800, release_year, 658 30332, has_genre, 36212 30332, release_year, 658 36963, has_genre, 36212 36963, has_tags, 36212 36963, release_year, 658 24708, has_genre, 36212 24708, release_year, 658 22078, has_genre, 36212 22078, release_year, 658 10277, has_genre, 36212 10277, has_tags, 36212 10277, release_year, 658 13686, has_genre, 36212 13686, release_year, 658 9695, has_genre, 36212 9695, release_year, 658 31449, has_genre, 36212 31449, release_year, 658 38263, has_genre, 36212 38263, release_year, 658 23994, has_genre, 36212 23994, release_year, 658 30581, has_genre, 36212 30581, release_year, 658 37191, has_genre, 36212 37191, release_year, 658 28199, has_genre, 36212 28199, release_year, 658 32770, has_genre, 36212 32770, release_year, 658 14594, has_genre, 36212 14594, release_year, 658 890, has_genre, 36212 890, release_year, 658 36071, has_genre, 36212 36071, release_year, 658 39848, has_genre, 36212 39848, release_year, 658 22284, has_genre, 36212 22284, release_year, 658 27030, has_genre, 36212 27030, release_year, 658 33711, has_genre, 36212 33711, release_year, 658 32831, has_genre, 36212 32831, release_year, 658 24707, has_genre, 36212 24707, release_year, 658 12735, has_genre, 36212 12735, release_year, 658 32450, has_genre, 36212 32450, release_year, 658 27338, has_genre, 36212 27338, release_year, 658 38925, has_genre, 36212 38925, has_tags, 36212 38925, release_year, 658 21016, has_genre, 36212 21016, release_year, 658 24757, has_genre, 36212 24757, release_year, 658 37589, has_genre, 36212 37589, release_year, 658 19894, has_genre, 36212 19894, release_year, 658 32797, has_genre, 36212 32797, release_year, 658 10643, has_genre, 36212 10643, release_year, 658 31694, has_genre, 36212 31694, release_year, 658 38977, has_genre, 36212 38977, has_tags, 36212 38977, release_year, 658 7480, has_genre, 36212 7480, release_year, 658 26106, has_genre, 36212 26106, release_year, 658 32892, has_genre, 36212 32892, release_year, 658 6394, has_genre, 36212 6394, release_year, 658 19570, has_genre, 36212 19570, release_year, 658 39636, has_genre, 36212 39636, release_year, 658 5563, has_genre, 36212 5563, release_year, 658 12314, has_genre, 36212 12314, release_year, 658 7740, has_genre, 36212 7740, release_year, 658 15473, has_genre, 36212 15473, release_year, 658 6076, has_genre, 36212 6076, release_year, 658 24535, has_genre, 36212 24535, release_year, 658 2427, has_genre, 36212 2427, release_year, 658 5647, has_genre, 36212 5647, release_year, 658 3917, has_genre, 36212 3917, release_year, 658 22793, has_genre, 36212 22793, release_year, 658 27350, has_genre, 36212 27350, release_year, 658 3030, has_genre, 36212 3030, release_year, 658 35319, has_genre, 36212 35319, release_year, 658 37844, has_genre, 36212 37844, release_year, 658 19154, has_genre, 36212 19154, release_year, 658 38589, has_genre, 36212 38589, release_year, 658 32376, has_genre, 36212 32376, release_year, 658 26911, has_genre, 36212 26911, release_year, 658 24025, has_genre, 36212 24025, release_year, 658 6301, has_genre, 36212 6301, release_year, 658 5418, has_genre, 36212 5418, release_year, 658 12835, has_genre, 36212 12835, release_year, 658 26232, has_genre, 36212 26232, release_year, 658 3103, has_genre, 36212 3103, release_year, 658 1418, has_genre, 36212 1418, has_tags, 36212 1418, release_year, 658 14601, has_genre, 36212 14601, release_year, 658 14931, has_genre, 36212 14931, release_year, 658 39727, has_genre, 36212 39727, release_year, 658 36020, has_genre, 36212 36020, release_year, 658 47, has_genre, 36212 47, has_tags, 36212 47, release_year, 658 17372, has_genre, 36212 17372, release_year, 658 34517, has_genre, 36212 34517, release_year, 658 35871, has_genre, 36212 35871, release_year, 658 13801, has_genre, 36212 13801, release_year, 658 34611, has_genre, 36212 34611, release_year, 658 31735, has_genre, 36212 31735, release_year, 658 39165, has_genre, 36212 39165, release_year, 658 19118, has_genre, 36212 19118, release_year, 658 24915, has_genre, 36212 24915, release_year, 658 26089, has_genre, 36212 26089, release_year, 658 25504, has_genre, 36212 25504, release_year, 658 8107, has_genre, 36212 8107, release_year, 658 20503, has_genre, 36212 20503, release_year, 658 17532, has_genre, 36212 17532, release_year, 658 17263, has_genre, 36212 17263, release_year, 658 21677, has_genre, 36212 21677, release_year, 658 38602, has_genre, 36212 38602, release_year, 658 8523, has_genre, 36212 8523, release_year, 658 31312, has_genre, 36212 31312, has_tags, 36212 31312, release_year, 658 15815, has_genre, 36212 15815, release_year, 658 11253, has_genre, 36212 11253, release_year, 658 11237, has_genre, 36212 11237, release_year, 658 25023, has_genre, 36212 25023, release_year, 658 35746, has_genre, 36212 35746, release_year, 658 33501, has_genre, 36212 33501, release_year, 658 11839, has_genre, 36212 11839, release_year, 658 12597, has_genre, 36212 12597, release_year, 658 30316, has_genre, 36212 30316, release_year, 658 35808, has_genre, 36212 35808, release_year, 658 17168, has_genre, 36212 17168, release_year, 658 25060, has_genre, 36212 25060, release_year, 658 28583, has_genre, 36212 28583, release_year, 658 34584, has_genre, 36212 34584, has_tags, 36212 34584, release_year, 658 7174, has_genre, 36212 7174, release_year, 658 39426, has_genre, 36212 39426, release_year, 658 28026, has_genre, 36212 28026, release_year, 658 33759, has_genre, 36212 33759, release_year, 658 17346, has_genre, 36212 17346, release_year, 658 17608, has_genre, 36212 17608, release_year, 658 39286, has_genre, 36212 39286, release_year, 658 40067, has_genre, 36212 40067, release_year, 658 25002, has_genre, 36212 25002, release_year, 658 3038, has_genre, 36212 3038, release_year, 658 18335, has_genre, 36212 18335, release_year, 658 19501, has_genre, 36212 19501, release_year, 658 3885, has_genre, 36212 3885, release_year, 658 19623, has_genre, 36212 19623, release_year, 658 24441, has_genre, 36212 24441, has_tags, 36212 24441, release_year, 658 38175, has_genre, 36212 38175, release_year, 658 6644, has_genre, 36212 6644, release_year, 658 24231, has_genre, 36212 24231, release_year, 7977 24231, written_by, 2354 2399, has_genre, 36212 2399, release_year, 658 10295, has_genre, 36212 10295, release_year, 658 27103, has_genre, 36212 27103, release_year, 658 14688, has_genre, 36212 14688, release_year, 658 35267, has_genre, 36212 35267, release_year, 658 16929, has_genre, 36212 16929, release_year, 658 15980, has_genre, 36212 15980, release_year, 658 9328, has_genre, 36212 9328, has_tags, 36212 9328, release_year, 658 38614, has_genre, 36212 38614, has_tags, 36212 38614, release_year, 658 22800, has_genre, 36212 22800, release_year, 658 33948, has_genre, 36212 33948, release_year, 658 4984, has_genre, 36212 4984, release_year, 658 23191, has_genre, 36212 23191, release_year, 658 37370, has_genre, 36212 37370, written_by, 18062 24652, has_genre, 36212 24652, release_year, 658 17964, has_genre, 36212 17964, release_year, 658 15798, has_genre, 36212 15798, release_year, 658 28735, has_genre, 36212 28735, release_year, 658 1195, has_genre, 36212 1195, release_year, 658 7673, has_genre, 36212 7673, has_tags, 36212 7673, release_year, 658 2739, has_genre, 36212 2739, release_year, 658 27064, has_genre, 36212 27064, release_year, 658 8650, has_genre, 36212 8650, has_tags, 36212 8650, release_year, 658 37915, has_genre, 36212 37915, release_year, 658 14524, has_genre, 36212 14524, release_year, 658 12086, has_genre, 36212 12086, release_year, 658 26797, has_genre, 36212 26797, release_year, 658 4614, has_genre, 36212 4614, release_year, 658 3437, has_genre, 36212 3437, release_year, 658 15504, has_genre, 36212 15504, release_year, 658 266, has_genre, 36212 266, release_year, 658 5352, has_genre, 36212 5352, release_year, 658 9918, has_genre, 36212 9918, release_year, 658 17451, has_genre, 36212 17451, release_year, 658 13359, has_genre, 36212 13359, release_year, 658 10305, has_genre, 36212 10305, release_year, 658 30056, has_genre, 36212 30056, release_year, 658 7030, has_genre, 36212 7030, release_year, 658 24333, has_genre, 36212 24333, release_year, 658 34118, has_genre, 36212 34118, release_year, 658 21183, has_genre, 36212 21183, release_year, 658 18608, has_genre, 36212 18608, release_year, 658 36174, has_genre, 36212 36174, release_year, 658 26337, has_genre, 36212 26337, release_year, 658 31040, has_genre, 36212 31040, release_year, 658 9052, has_genre, 36212 9052, release_year, 658 16849, has_genre, 36212 16849, release_year, 658 Question: For what reason are BOOTH TARKINGTON, FORGETTING THE GIRL, and LADISLAV FUKS associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BOOTH TARKINGTON", "FORGETTING THE GIRL", "LADISLAV FUKS" ], "valid_edges": [ [ "1969", "has_genre", "DRAMA" ], [ "A LATE QUARTET", "has_genre", "DRAMA" ], [ "A LATE QUARTET", "has_tags", "DRAMA" ], [ "A LATE QUARTET", "release_year", "2012" ], [ "A ROYAL AFFAIR", "has_genre", "DRAMA" ], [ "A ROYAL AFFAIR", "release_year", "2012" ], [ "A THOUSAND WORDS", "has_genre", "DRAMA" ], [ "A THOUSAND WORDS", "release_year", "2012" ], [ "ABOUT CHERRY", "has_genre", "DRAMA" ], [ "ABOUT CHERRY", "release_year", "2012" ], [ "ACE ATTORNEY", "has_genre", "DRAMA" ], [ "ACE ATTORNEY", "release_year", "2012" ], [ "AMOUR", "has_genre", "DRAMA" ], [ "AMOUR", "release_year", "2012" ], [ "ANNA KARENINA", "has_genre", "DRAMA" ], [ "ANNA KARENINA", "has_tags", "DRAMA" ], [ "ANNA KARENINA", "release_year", "2012" ], [ "ANY DAY NOW", "has_genre", "DRAMA" ], [ "ANY DAY NOW", "release_year", "2012" ], [ "ARBITRAGE", "has_genre", "DRAMA" ], [ "ARBITRAGE", "release_year", "2012" ], [ "ARGO", "has_genre", "DRAMA" ], [ "ARGO", "has_tags", "DRAMA" ], [ "ARGO", "release_year", "2012" ], [ "ARTHUR NEWMAN", "has_genre", "DRAMA" ], [ "ARTHUR NEWMAN", "release_year", "2012" ], [ "AT ANY PRICE", "has_genre", "DRAMA" ], [ "AT ANY PRICE", "release_year", "2012" ], [ "BARABBAS", "has_genre", "DRAMA" ], [ "BARABBAS", "release_year", "2012" ], [ "BARBARA", "has_genre", "DRAMA" ], [ "BARBARA", "release_year", "2012" ], [ "BARFI!", "has_genre", "DRAMA" ], [ "BARFI!", "release_year", "2012" ], [ "BEASTS OF THE SOUTHERN WILD", "has_genre", "DRAMA" ], [ "BEASTS OF THE SOUTHERN WILD", "release_year", "2012" ], [ "BEING FLYNN", "has_genre", "DRAMA" ], [ "BEING FLYNN", "release_year", "2012" ], [ "BEL AMI", "has_genre", "DRAMA" ], [ "BEL AMI", "release_year", "2012" ], [ "BEST MAN DOWN", "has_genre", "DRAMA" ], [ "BEST MAN DOWN", "release_year", "2012" ], [ "BEYOND THE HILLS", "has_genre", "DRAMA" ], [ "BEYOND THE HILLS", "release_year", "2012" ], [ "BIG MIRACLE", "has_genre", "DRAMA" ], [ "BIG MIRACLE", "release_year", "2012" ], [ "BLACKBIRD", "has_genre", "DRAMA" ], [ "BLACKBIRD", "release_year", "2012" ], [ "BLUE LIKE JAZZ", "has_genre", "DRAMA" ], [ "BLUE LIKE JAZZ", "release_year", "2012" ], [ "BOY EATING THE BIRD'S FOOD", "has_genre", "DRAMA" ], [ "BOY EATING THE BIRD'S FOOD", "release_year", "2012" ], [ "BROKEN", "has_genre", "DRAMA" ], [ "BROKEN", "release_year", "2012" ], [ "CAESAR MUST DIE", "has_genre", "DRAMA" ], [ "CAESAR MUST DIE", "release_year", "2012" ], [ "CALL GIRL", "has_genre", "DRAMA" ], [ "CALL GIRL", "release_year", "2012" ], [ "CAMILLE REWINDS", "has_genre", "DRAMA" ], [ "CAMILLE REWINDS", "release_year", "2012" ], [ "CHASING MAVERICKS", "has_genre", "DRAMA" ], [ "CHASING MAVERICKS", "release_year", "2012" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "has_genre", "DRAMA" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "release_year", "2012" ], [ "CLIP", "has_genre", "DRAMA" ], [ "CLIP", "release_year", "2012" ], [ "CLOUD ATLAS", "has_genre", "DRAMA" ], [ "CLOUD ATLAS", "has_tags", "DRAMA" ], [ "CLOUD ATLAS", "release_year", "2012" ], [ "COMPLIANCE", "has_genre", "DRAMA" ], [ "COMPLIANCE", "release_year", "2012" ], [ "CONSUMING SPIRITS", "has_genre", "DRAMA" ], [ "CONSUMING SPIRITS", "release_year", "2012" ], [ "COSMOPOLIS", "has_genre", "DRAMA" ], [ "COSMOPOLIS", "release_year", "2012" ], [ "CRAVE", "has_genre", "DRAMA" ], [ "CRAVE", "release_year", "2012" ], [ "DANGEROUS LIAISONS", "has_genre", "DRAMA" ], [ "DANGEROUS LIAISONS", "release_year", "2012" ], [ "DARLING COMPANION", "has_genre", "DRAMA" ], [ "DARLING COMPANION", "release_year", "2012" ], [ "DEADFALL", "has_genre", "DRAMA" ], [ "DEADFALL", "release_year", "2012" ], [ "DISCONNECT", "has_genre", "DRAMA" ], [ "DISCONNECT", "has_tags", "DRAMA" ], [ "DISCONNECT", "release_year", "2012" ], [ "EDEN", "has_genre", "DRAMA" ], [ "EDEN", "release_year", "2012" ], [ "END OF WATCH", "has_genre", "DRAMA" ], [ "END OF WATCH", "release_year", "2012" ], [ "ENGLISH VINGLISH", "has_genre", "DRAMA" ], [ "ENGLISH VINGLISH", "release_year", "2012" ], [ "EXCISION", "has_genre", "DRAMA" ], [ "EXCISION", "release_year", "2012" ], [ "FAREWELL, MY QUEEN", "has_genre", "DRAMA" ], [ "FAREWELL, MY QUEEN", "release_year", "2012" ], [ "FILL THE VOID", "has_genre", "DRAMA" ], [ "FILL THE VOID", "release_year", "2012" ], [ "FLIGHT", "has_genre", "DRAMA" ], [ "FLIGHT", "release_year", "2012" ], [ "FOR ELLEN", "has_genre", "DRAMA" ], [ "FOR ELLEN", "release_year", "2012" ], [ "FOREIGN LETTERS", "has_genre", "DRAMA" ], [ "FOREIGN LETTERS", "release_year", "2012" ], [ "FORGETTING THE GIRL", "has_genre", "DRAMA" ], [ "FORGETTING THE GIRL", "release_year", "2012" ], [ "FRANCES HA", "has_genre", "DRAMA" ], [ "FRANCES HA", "release_year", "2012" ], [ "GAME CHANGE", "has_genre", "DRAMA" ], [ "GAME CHANGE", "release_year", "2012" ], [ "GIRL IN PROGRESS", "has_genre", "DRAMA" ], [ "GIRL IN PROGRESS", "release_year", "2012" ], [ "HANNAH ARENDT", "has_genre", "DRAMA" ], [ "HANNAH ARENDT", "release_year", "2012" ], [ "HELLO HERMAN", "has_genre", "DRAMA" ], [ "HELLO HERMAN", "release_year", "2012" ], [ "HEROINE", "has_genre", "DRAMA" ], [ "HEROINE", "release_year", "2012" ], [ "HITCHCOCK", "has_genre", "DRAMA" ], [ "HITCHCOCK", "release_year", "2012" ], [ "HOLY MOTORS", "has_genre", "DRAMA" ], [ "HOLY MOTORS", "release_year", "2012" ], [ "HOPE SPRINGS", "has_genre", "DRAMA" ], [ "HOPE SPRINGS", "release_year", "2012" ], [ "HYDE PARK ON HUDSON", "has_genre", "DRAMA" ], [ "HYDE PARK ON HUDSON", "release_year", "2012" ], [ "I BELONG", "has_genre", "DRAMA" ], [ "I BELONG", "release_year", "2012" ], [ "I DO", "has_genre", "DRAMA" ], [ "I DO", "release_year", "2012" ], [ "IMAGINE", "has_genre", "DRAMA" ], [ "IMAGINE", "release_year", "2012" ], [ "IN THE FOG", "has_genre", "DRAMA" ], [ "IN THE FOG", "release_year", "2012" ], [ "INESCAPABLE", "has_genre", "DRAMA" ], [ "INESCAPABLE", "release_year", "2012" ], [ "JAYNE MANSFIELD'S CAR", "has_genre", "DRAMA" ], [ "JAYNE MANSFIELD'S CAR", "release_year", "2012" ], [ "K-11", "has_genre", "DRAMA" ], [ "K-11", "release_year", "2012" ], [ "KAUWBOY", "has_genre", "DRAMA" ], [ "KAUWBOY", "release_year", "2012" ], [ "KEEP THE LIGHTS ON", "has_genre", "DRAMA" ], [ "KEEP THE LIGHTS ON", "release_year", "2012" ], [ "LAURENCE ANYWAYS", "has_genre", "DRAMA" ], [ "LAURENCE ANYWAYS", "release_year", "2012" ], [ "LAWLESS", "has_genre", "DRAMA" ], [ "LAWLESS", "has_tags", "DRAMA" ], [ "LAWLESS", "release_year", "2012" ], [ "LES MISÉRABLES", "has_genre", "DRAMA" ], [ "LES MISÉRABLES", "release_year", "2012" ], [ "LIBERAL ARTS", "has_genre", "DRAMA" ], [ "LIBERAL ARTS", "release_year", "2012" ], [ "LIFE OF PI", "has_genre", "DRAMA" ], [ "LIFE OF PI", "release_year", "2012" ], [ "LIKE SOMEONE IN LOVE", "has_genre", "DRAMA" ], [ "LIKE SOMEONE IN LOVE", "release_year", "2012" ], [ "LINCOLN", "has_genre", "DRAMA" ], [ "LINCOLN", "has_tags", "DRAMA" ], [ "LINCOLN", "release_year", "2012" ], [ "LOL", "has_genre", "DRAMA" ], [ "LOL", "release_year", "2012" ], [ "LUV", "has_genre", "DRAMA" ], [ "LUV", "release_year", "2012" ], [ "MAGIC MIKE", "has_genre", "DRAMA" ], [ "MAGIC MIKE", "release_year", "2012" ], [ "MARFA GIRL", "has_genre", "DRAMA" ], [ "MARFA GIRL", "release_year", "2012" ], [ "ME AND YOU", "has_genre", "DRAMA" ], [ "ME AND YOU", "release_year", "2012" ], [ "MIDDLE OF NOWHERE", "has_genre", "DRAMA" ], [ "MIDDLE OF NOWHERE", "release_year", "2012" ], [ "MUD", "has_genre", "DRAMA" ], [ "MUD", "release_year", "2012" ], [ "MUSEUM HOURS", "has_genre", "DRAMA" ], [ "MUSEUM HOURS", "release_year", "2012" ], [ "MY WAY", "has_genre", "DRAMA" ], [ "MY WAY", "release_year", "2012" ], [ "NAKED HARBOUR", "has_genre", "DRAMA" ], [ "NAKED HARBOUR", "release_year", "2012" ], [ "NEIGHBORING SOUNDS", "has_genre", "DRAMA" ], [ "NEIGHBORING SOUNDS", "release_year", "2012" ], [ "NO", "has_genre", "DRAMA" ], [ "NO", "release_year", "2012" ], [ "NOBODY WALKS", "has_genre", "DRAMA" ], [ "NOBODY WALKS", "release_year", "2012" ], [ "NOT FADE AWAY", "has_genre", "DRAMA" ], [ "NOT FADE AWAY", "release_year", "2012" ], [ "NOW IS GOOD", "has_genre", "DRAMA" ], [ "NOW IS GOOD", "release_year", "2012" ], [ "ON THE ROAD", "has_genre", "DRAMA" ], [ "ON THE ROAD", "release_year", "2012" ], [ "OUR CHILDREN", "has_genre", "DRAMA" ], [ "OUR CHILDREN", "release_year", "2012" ], [ "OUT IN THE DARK", "has_genre", "DRAMA" ], [ "OUT IN THE DARK", "release_year", "2012" ], [ "PEOPLE LIKE US", "has_genre", "DRAMA" ], [ "PEOPLE LIKE US", "has_tags", "DRAMA" ], [ "PEOPLE LIKE US", "release_year", "2012" ], [ "POST TENEBRAS LUX", "has_genre", "DRAMA" ], [ "POST TENEBRAS LUX", "release_year", "2012" ], [ "PROMISED LAND", "has_genre", "DRAMA" ], [ "PROMISED LAND", "release_year", "2012" ], [ "PURGE", "has_genre", "DRAMA" ], [ "PURGE", "release_year", "2012" ], [ "QUARTET", "has_genre", "DRAMA" ], [ "QUARTET", "release_year", "2012" ], [ "REALITY", "has_genre", "DRAMA" ], [ "REALITY", "release_year", "2012" ], [ "RENOIR", "has_genre", "DRAMA" ], [ "RENOIR", "release_year", "2012" ], [ "REVENGE FOR JOLLY!", "has_genre", "DRAMA" ], [ "REVENGE FOR JOLLY!", "release_year", "2012" ], [ "RUBY SPARKS", "has_genre", "DRAMA" ], [ "RUBY SPARKS", "release_year", "2012" ], [ "RUST AND BONE", "has_genre", "DRAMA" ], [ "RUST AND BONE", "release_year", "2012" ], [ "SAFE", "has_genre", "DRAMA" ], [ "SAFE", "release_year", "2012" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "has_genre", "DRAMA" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "release_year", "2012" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "has_genre", "DRAMA" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "release_year", "2012" ], [ "SHADOW DANCER", "has_genre", "DRAMA" ], [ "SHADOW DANCER", "release_year", "2012" ], [ "SILVER LININGS PLAYBOOK", "has_genre", "DRAMA" ], [ "SILVER LININGS PLAYBOOK", "has_tags", "DRAMA" ], [ "SILVER LININGS PLAYBOOK", "release_year", "2012" ], [ "SISTER", "has_genre", "DRAMA" ], [ "SISTER", "release_year", "2012" ], [ "SMASHED", "has_genre", "DRAMA" ], [ "SMASHED", "release_year", "2012" ], [ "SOMEBODY UP THERE LIKES ME", "has_genre", "DRAMA" ], [ "SOMEBODY UP THERE LIKES ME", "release_year", "2012" ], [ "SOMETHING IN THE AIR", "has_genre", "DRAMA" ], [ "SOMETHING IN THE AIR", "release_year", "2012" ], [ "SPRING BREAKERS", "has_genre", "DRAMA" ], [ "SPRING BREAKERS", "release_year", "2012" ], [ "STARLET", "has_genre", "DRAMA" ], [ "STARLET", "release_year", "2012" ], [ "STILL MINE", "has_genre", "DRAMA" ], [ "STILL MINE", "release_year", "2012" ], [ "STOLEN", "has_genre", "DRAMA" ], [ "STOLEN", "release_year", "2012" ], [ "STRUCK BY LIGHTNING", "has_genre", "DRAMA" ], [ "STRUCK BY LIGHTNING", "release_year", "2012" ], [ "STUCK IN LOVE", "has_genre", "DRAMA" ], [ "STUCK IN LOVE", "release_year", "2012" ], [ "TABU", "has_genre", "DRAMA" ], [ "TABU", "release_year", "2012" ], [ "THANKS FOR SHARING", "has_genre", "DRAMA" ], [ "THANKS FOR SHARING", "release_year", "2012" ], [ "THE ARTIST AND THE MODEL", "has_genre", "DRAMA" ], [ "THE ARTIST AND THE MODEL", "release_year", "2012" ], [ "THE BATTERY", "has_genre", "DRAMA" ], [ "THE BATTERY", "release_year", "2012" ], [ "THE BROKEN CIRCLE BREAKDOWN", "has_genre", "DRAMA" ], [ "THE BROKEN CIRCLE BREAKDOWN", "has_tags", "DRAMA" ], [ "THE BROKEN CIRCLE BREAKDOWN", "release_year", "2012" ], [ "THE CITIZEN", "has_genre", "DRAMA" ], [ "THE CITIZEN", "release_year", "2012" ], [ "THE COMEDY", "has_genre", "DRAMA" ], [ "THE COMEDY", "release_year", "2012" ], [ "THE CREMATOR", "has_genre", "DRAMA" ], [ "THE CREMATOR", "release_year", "1969" ], [ "THE CREMATOR", "written_by", "LADISLAV FUKS" ], [ "THE DEEP", "has_genre", "DRAMA" ], [ "THE DEEP", "release_year", "2012" ], [ "THE END OF LOVE", "has_genre", "DRAMA" ], [ "THE END OF LOVE", "release_year", "2012" ], [ "THE FITZGERALD FAMILY CHRISTMAS", "has_genre", "DRAMA" ], [ "THE FITZGERALD FAMILY CHRISTMAS", "release_year", "2012" ], [ "THE FLOATING CASTLE", "has_genre", "DRAMA" ], [ "THE FLOATING CASTLE", "release_year", "2012" ], [ "THE FORGER", "has_genre", "DRAMA" ], [ "THE FORGER", "release_year", "2012" ], [ "THE GIRL", "has_genre", "DRAMA" ], [ "THE GIRL", "release_year", "2012" ], [ "THE GUILT TRIP", "has_genre", "DRAMA" ], [ "THE GUILT TRIP", "release_year", "2012" ], [ "THE HUNT", "has_genre", "DRAMA" ], [ "THE HUNT", "has_tags", "DRAMA" ], [ "THE HUNT", "release_year", "2012" ], [ "THE IMPOSSIBLE", "has_genre", "DRAMA" ], [ "THE IMPOSSIBLE", "has_tags", "DRAMA" ], [ "THE IMPOSSIBLE", "release_year", "2012" ], [ "THE LESSER BLESSED", "has_genre", "DRAMA" ], [ "THE LESSER BLESSED", "release_year", "2012" ], [ "THE LETTER", "has_genre", "DRAMA" ], [ "THE LETTER", "release_year", "2012" ], [ "THE LUCKY ONE", "has_genre", "DRAMA" ], [ "THE LUCKY ONE", "release_year", "2012" ], [ "THE MAGIC OF BELLE ISLE", "has_genre", "DRAMA" ], [ "THE MAGIC OF BELLE ISLE", "release_year", "2012" ], [ "THE MAGNIFICENT AMBERSONS", "has_genre", "DRAMA" ], [ "THE MAGNIFICENT AMBERSONS", "written_by", "BOOTH TARKINGTON" ], [ "THE MAN WHO LAUGHS", "has_genre", "DRAMA" ], [ "THE MAN WHO LAUGHS", "release_year", "2012" ], [ "THE MASTER", "has_genre", "DRAMA" ], [ "THE MASTER", "release_year", "2012" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "has_genre", "DRAMA" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "release_year", "2012" ], [ "THE OTHER SON", "has_genre", "DRAMA" ], [ "THE OTHER SON", "release_year", "2012" ], [ "THE PATIENCE STONE", "has_genre", "DRAMA" ], [ "THE PATIENCE STONE", "release_year", "2012" ], [ "THE PERKS OF BEING A WALLFLOWER", "has_genre", "DRAMA" ], [ "THE PERKS OF BEING A WALLFLOWER", "has_tags", "DRAMA" ], [ "THE PERKS OF BEING A WALLFLOWER", "release_year", "2012" ], [ "THE SAPPHIRES", "has_genre", "DRAMA" ], [ "THE SAPPHIRES", "release_year", "2012" ], [ "THE SCAPEGOAT", "has_genre", "DRAMA" ], [ "THE SCAPEGOAT", "release_year", "2012" ], [ "THE SESSIONS", "has_genre", "DRAMA" ], [ "THE SESSIONS", "has_tags", "DRAMA" ], [ "THE SESSIONS", "release_year", "2012" ], [ "THE STORY OF LUKE", "has_genre", "DRAMA" ], [ "THE STORY OF LUKE", "release_year", "2012" ], [ "THE SWEENEY", "has_genre", "DRAMA" ], [ "THE SWEENEY", "release_year", "2012" ], [ "THE VOW", "has_genre", "DRAMA" ], [ "THE VOW", "release_year", "2012" ], [ "THE WALL", "has_genre", "DRAMA" ], [ "THE WALL", "release_year", "2012" ], [ "THE WE AND THE I", "has_genre", "DRAMA" ], [ "THE WE AND THE I", "release_year", "2012" ], [ "THE WOMAN IN BLACK", "has_genre", "DRAMA" ], [ "THE WOMAN IN BLACK", "release_year", "2012" ], [ "THE WORDS", "has_genre", "DRAMA" ], [ "THE WORDS", "release_year", "2012" ], [ "THREE WORLDS", "has_genre", "DRAMA" ], [ "THREE WORLDS", "release_year", "2012" ], [ "THY WOMB", "has_genre", "DRAMA" ], [ "THY WOMB", "release_year", "2012" ], [ "TO THE WONDER", "has_genre", "DRAMA" ], [ "TO THE WONDER", "release_year", "2012" ], [ "TROUBLE WITH THE CURVE", "has_genre", "DRAMA" ], [ "TROUBLE WITH THE CURVE", "release_year", "2012" ], [ "TWO LIVES", "has_genre", "DRAMA" ], [ "TWO LIVES", "release_year", "2012" ], [ "UNCONDITIONAL", "has_genre", "DRAMA" ], [ "UNCONDITIONAL", "release_year", "2012" ], [ "WAR WITCH", "has_genre", "DRAMA" ], [ "WAR WITCH", "release_year", "2012" ], [ "WHAT IF...", "has_genre", "DRAMA" ], [ "WHAT IF...", "release_year", "2012" ], [ "WHAT MAISIE KNEW", "has_genre", "DRAMA" ], [ "WHAT MAISIE KNEW", "release_year", "2012" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "has_genre", "DRAMA" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "release_year", "2012" ], [ "WHITE ELEPHANT", "has_genre", "DRAMA" ], [ "WHITE ELEPHANT", "release_year", "2012" ], [ "WHITE FROG", "has_genre", "DRAMA" ], [ "WHITE FROG", "release_year", "2012" ], [ "WINNING STREAK", "has_genre", "DRAMA" ], [ "WINNING STREAK", "release_year", "2012" ], [ "WON'T BACK DOWN", "has_genre", "DRAMA" ], [ "WON'T BACK DOWN", "release_year", "2012" ], [ "XINGU", "has_genre", "DRAMA" ], [ "XINGU", "release_year", "2012" ], [ "YOSSI", "has_genre", "DRAMA" ], [ "YOSSI", "release_year", "2012" ], [ "ZERO DARK THIRTY", "has_genre", "DRAMA" ], [ "ZERO DARK THIRTY", "release_year", "2012" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35845, 2006 6378, ANDREA ARNOLD 24510, CANVAS 26721, DAVID KROSS 14586, RED ROAD 39122, TOUGH ENOUGH src, edge_attr, dst 24510, release_year, 35845 14586, directed_by, 6378 14586, has_tags, 6378 14586, release_year, 35845 14586, written_by, 6378 39122, release_year, 35845 39122, starred_actors, 26721 Question: In what context are ANDREA ARNOLD, CANVAS, and DAVID KROSS connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ANDREA ARNOLD", "CANVAS", "DAVID KROSS" ], "valid_edges": [ [ "CANVAS", "release_year", "2006" ], [ "RED ROAD", "directed_by", "ANDREA ARNOLD" ], [ "RED ROAD", "has_tags", "ANDREA ARNOLD" ], [ "RED ROAD", "release_year", "2006" ], [ "RED ROAD", "written_by", "ANDREA ARNOLD" ], [ "TOUGH ENOUGH", "release_year", "2006" ], [ "TOUGH ENOUGH", "starred_actors", "DAVID KROSS" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 36522, 1934 1006, 1996 10251, ARIZONA 24896, CLAUDETTE COLBERT 19510, CLEOPATRA 15436, EPIC 4803, FEAR 31865, FINAL DESTINATION 2 29375, GENERATION X 11565, GOOD 5921, IMITATION OF LIFE 7575, INTIMATE RELATIONS 802, IT'S MY PARTY 25290, JERRY MAGUIRE 5127, MOTHER NIGHT 19341, RACE 21462, RAISING ARIZONA 20393, THE LONG KISS GOODNIGHT 1993, WARREN WILLIAM src, edge_attr, dst 10251, starred_actors, 1993 19510, has_tags, 15436 19510, release_year, 36522 19510, starred_actors, 24896 19510, starred_actors, 1993 15436, has_imdb_rating, 11565 4803, has_imdb_rating, 11565 4803, release_year, 1006 31865, has_imdb_rating, 11565 29375, release_year, 1006 11565, has_imdb_rating, 11565 5921, has_tags, 19341 5921, release_year, 36522 5921, starred_actors, 24896 5921, starred_actors, 1993 7575, has_imdb_rating, 11565 7575, release_year, 1006 802, has_imdb_rating, 11565 802, release_year, 1006 25290, has_imdb_rating, 11565 25290, release_year, 1006 5127, has_imdb_rating, 11565 5127, release_year, 1006 19341, has_imdb_rating, 11565 21462, has_imdb_rating, 11565 21462, has_tags, 10251 20393, has_imdb_rating, 11565 20393, release_year, 1006 Question: For what reason are FINAL DESTINATION 2, GENERATION X, and WARREN WILLIAM associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "FINAL DESTINATION 2", "GENERATION X", "WARREN WILLIAM" ], "valid_edges": [ [ "ARIZONA", "starred_actors", "WARREN WILLIAM" ], [ "CLEOPATRA", "has_tags", "EPIC" ], [ "CLEOPATRA", "release_year", "1934" ], [ "CLEOPATRA", "starred_actors", "CLAUDETTE COLBERT" ], [ "CLEOPATRA", "starred_actors", "WARREN WILLIAM" ], [ "EPIC", "has_imdb_rating", "GOOD" ], [ "FEAR", "has_imdb_rating", "GOOD" ], [ "FEAR", "release_year", "1996" ], [ "FINAL DESTINATION 2", "has_imdb_rating", "GOOD" ], [ "GENERATION X", "release_year", "1996" ], [ "GOOD", "has_imdb_rating", "GOOD" ], [ "IMITATION OF LIFE", "has_tags", "RACE" ], [ "IMITATION OF LIFE", "release_year", "1934" ], [ "IMITATION OF LIFE", "starred_actors", "CLAUDETTE COLBERT" ], [ "IMITATION OF LIFE", "starred_actors", "WARREN WILLIAM" ], [ "INTIMATE RELATIONS", "has_imdb_rating", "GOOD" ], [ "INTIMATE RELATIONS", "release_year", "1996" ], [ "IT'S MY PARTY", "has_imdb_rating", "GOOD" ], [ "IT'S MY PARTY", "release_year", "1996" ], [ "JERRY MAGUIRE", "has_imdb_rating", "GOOD" ], [ "JERRY MAGUIRE", "release_year", "1996" ], [ "MOTHER NIGHT", "has_imdb_rating", "GOOD" ], [ "MOTHER NIGHT", "release_year", "1996" ], [ "RACE", "has_imdb_rating", "GOOD" ], [ "RAISING ARIZONA", "has_imdb_rating", "GOOD" ], [ "RAISING ARIZONA", "has_tags", "ARIZONA" ], [ "THE LONG KISS GOODNIGHT", "has_imdb_rating", "GOOD" ], [ "THE LONG KISS GOODNIGHT", "release_year", "1996" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 724, 1979 39289, ACTION 21803, CAPTAIN AMERICA 23921, CUBA 1544, KEVIN DRONEY 28310, METEOR 39027, MORTAL KOMBAT 6183, REB BROWN 36591, SEAN CONNERY 16103, THE AVENGERS 19052, UNCOMMON VALOR src, edge_attr, dst 21803, has_tags, 21803 21803, release_year, 724 21803, starred_actors, 6183 23921, release_year, 724 23921, starred_actors, 36591 28310, release_year, 724 28310, starred_actors, 36591 39027, has_genre, 39289 39027, written_by, 1544 16103, has_tags, 21803 16103, has_tags, 36591 16103, starred_actors, 36591 19052, has_genre, 39289 19052, starred_actors, 6183 Question: In what context are KEVIN DRONEY, METEOR, and REB BROWN connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "KEVIN DRONEY", "METEOR", "REB BROWN" ], "valid_edges": [ [ "CAPTAIN AMERICA", "has_tags", "CAPTAIN AMERICA" ], [ "CAPTAIN AMERICA", "release_year", "1979" ], [ "CAPTAIN AMERICA", "starred_actors", "REB BROWN" ], [ "CUBA", "release_year", "1979" ], [ "CUBA", "starred_actors", "SEAN CONNERY" ], [ "METEOR", "release_year", "1979" ], [ "METEOR", "starred_actors", "SEAN CONNERY" ], [ "MORTAL KOMBAT", "has_genre", "ACTION" ], [ "MORTAL KOMBAT", "written_by", "KEVIN DRONEY" ], [ "THE AVENGERS", "has_tags", "CAPTAIN AMERICA" ], [ "THE AVENGERS", "has_tags", "SEAN CONNERY" ], [ "THE AVENGERS", "starred_actors", "SEAN CONNERY" ], [ "UNCOMMON VALOR", "has_genre", "ACTION" ], [ "UNCOMMON VALOR", "starred_actors", "REB BROWN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8486, 1999 27123, ANGEL BABY 36212, DRAMA 27446, IN TOO DEEP 33911, INSTINCT 22200, MAURA TIERNEY 9509, MICHAEL RYMER 24816, OXYGEN 25336, RIDE THE PINK HORSE 183, THOMAS GOMEZ src, edge_attr, dst 27123, directed_by, 9509 27123, has_genre, 36212 27123, written_by, 9509 27446, directed_by, 9509 27446, has_genre, 36212 27446, release_year, 8486 33911, release_year, 8486 33911, starred_actors, 22200 24816, release_year, 8486 24816, starred_actors, 22200 25336, has_genre, 36212 25336, starred_actors, 183 Question: For what reason are MAURA TIERNEY, MICHAEL RYMER, and THOMAS GOMEZ associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "MAURA TIERNEY", "MICHAEL RYMER", "THOMAS GOMEZ" ], "valid_edges": [ [ "ANGEL BABY", "directed_by", "MICHAEL RYMER" ], [ "ANGEL BABY", "has_genre", "DRAMA" ], [ "ANGEL BABY", "written_by", "MICHAEL RYMER" ], [ "IN TOO DEEP", "directed_by", "MICHAEL RYMER" ], [ "IN TOO DEEP", "has_genre", "DRAMA" ], [ "IN TOO DEEP", "release_year", "1999" ], [ "INSTINCT", "release_year", "1999" ], [ "INSTINCT", "starred_actors", "MAURA TIERNEY" ], [ "OXYGEN", "release_year", "1999" ], [ "OXYGEN", "starred_actors", "MAURA TIERNEY" ], [ "RIDE THE PINK HORSE", "has_genre", "DRAMA" ], [ "RIDE THE PINK HORSE", "starred_actors", "THOMAS GOMEZ" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 1006, 1996 30219, BLONDE ICE 14724, CRIME 37290, DALTON JAMES 33345, DANIEL TOPOLSKI 18084, JACK BERNHARD 4294, THE SUBSTITUTE 3432, TRUE BLUE src, edge_attr, dst 30219, directed_by, 18084 30219, has_genre, 14724 4294, has_genre, 14724 4294, release_year, 1006 4294, starred_actors, 37290 3432, release_year, 1006 3432, written_by, 33345 Question: In what context are DALTON JAMES, DANIEL TOPOLSKI, and JACK BERNHARD connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "DALTON JAMES", "DANIEL TOPOLSKI", "JACK BERNHARD" ], "valid_edges": [ [ "BLONDE ICE", "directed_by", "JACK BERNHARD" ], [ "BLONDE ICE", "has_genre", "CRIME" ], [ "THE SUBSTITUTE", "has_genre", "CRIME" ], [ "THE SUBSTITUTE", "release_year", "1996" ], [ "THE SUBSTITUTE", "starred_actors", "DALTON JAMES" ], [ "TRUE BLUE", "release_year", "1996" ], [ "TRUE BLUE", "written_by", "DANIEL TOPOLSKI" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6181, ALL THE KING'S MEN 5044, AUTUMN SONATA 10045, BD-R 19826, BLACK DEATH 14530, COLM MEANEY 23112, CON AIR 26931, COOL HAND LUKE 23205, DARLING 36212, DRAMA 3099, EVENING 10210, LAJOS KOLTAI 19809, LATE SPRING 17728, MAD LOVE 31562, MY LIFE AS A DOG 27893, POSSESSED 30919, PRISON 15475, SHOTGUN STORIES 34349, SVENGALI 33978, THE BRIBE 15198, THE HUNCHBACK OF NOTRE DAME 35764, THE PASSIONATE FRIENDS 8727, THE PICTURE OF DORIAN GRAY 164, UNDER CAPRICORN src, edge_attr, dst 6181, has_genre, 36212 6181, has_tags, 10045 5044, has_genre, 36212 5044, has_tags, 10045 19826, has_genre, 36212 19826, has_tags, 10045 23112, has_tags, 30919 23112, starred_actors, 14530 26931, has_genre, 36212 26931, has_tags, 10045 26931, has_tags, 36212 23205, has_genre, 36212 23205, has_tags, 10045 3099, directed_by, 10210 3099, has_genre, 36212 19809, has_genre, 36212 19809, has_tags, 10045 17728, has_genre, 36212 17728, has_tags, 10045 31562, has_genre, 36212 31562, has_tags, 10045 27893, has_genre, 36212 27893, has_tags, 10045 30919, has_genre, 36212 15475, has_genre, 36212 15475, has_tags, 10045 34349, has_genre, 36212 34349, has_tags, 10045 33978, has_genre, 36212 33978, has_tags, 10045 15198, has_genre, 36212 15198, has_tags, 10045 35764, has_genre, 36212 35764, has_tags, 10045 8727, has_genre, 36212 8727, has_tags, 10045 164, has_genre, 36212 164, has_tags, 10045 Question: For what reason are COLM MEANEY, LAJOS KOLTAI, and POSSESSED associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "COLM MEANEY", "LAJOS KOLTAI", "POSSESSED" ], "valid_edges": [ [ "ALL THE KING'S MEN", "has_genre", "DRAMA" ], [ "ALL THE KING'S MEN", "has_tags", "BD-R" ], [ "AUTUMN SONATA", "has_genre", "DRAMA" ], [ "AUTUMN SONATA", "has_tags", "BD-R" ], [ "BLACK DEATH", "has_genre", "DRAMA" ], [ "BLACK DEATH", "has_tags", "BD-R" ], [ "CON AIR", "has_tags", "PRISON" ], [ "CON AIR", "starred_actors", "COLM MEANEY" ], [ "COOL HAND LUKE", "has_genre", "DRAMA" ], [ "COOL HAND LUKE", "has_tags", "BD-R" ], [ "COOL HAND LUKE", "has_tags", "DRAMA" ], [ "DARLING", "has_genre", "DRAMA" ], [ "DARLING", "has_tags", "BD-R" ], [ "EVENING", "directed_by", "LAJOS KOLTAI" ], [ "EVENING", "has_genre", "DRAMA" ], [ "LATE SPRING", "has_genre", "DRAMA" ], [ "LATE SPRING", "has_tags", "BD-R" ], [ "MAD LOVE", "has_genre", "DRAMA" ], [ "MAD LOVE", "has_tags", "BD-R" ], [ "MY LIFE AS A DOG", "has_genre", "DRAMA" ], [ "MY LIFE AS A DOG", "has_tags", "BD-R" ], [ "POSSESSED", "has_genre", "DRAMA" ], [ "POSSESSED", "has_tags", "BD-R" ], [ "PRISON", "has_genre", "DRAMA" ], [ "SHOTGUN STORIES", "has_genre", "DRAMA" ], [ "SHOTGUN STORIES", "has_tags", "BD-R" ], [ "SVENGALI", "has_genre", "DRAMA" ], [ "SVENGALI", "has_tags", "BD-R" ], [ "THE BRIBE", "has_genre", "DRAMA" ], [ "THE BRIBE", "has_tags", "BD-R" ], [ "THE HUNCHBACK OF NOTRE DAME", "has_genre", "DRAMA" ], [ "THE HUNCHBACK OF NOTRE DAME", "has_tags", "BD-R" ], [ "THE PASSIONATE FRIENDS", "has_genre", "DRAMA" ], [ "THE PASSIONATE FRIENDS", "has_tags", "BD-R" ], [ "THE PICTURE OF DORIAN GRAY", "has_genre", "DRAMA" ], [ "THE PICTURE OF DORIAN GRAY", "has_tags", "BD-R" ], [ "UNDER CAPRICORN", "has_genre", "DRAMA" ], [ "UNDER CAPRICORN", "has_tags", "BD-R" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 18366, 1960 37484, 2004 12102, BUSH'S BRAIN 1551, CLUB DREAD 40091, CONTROL ROOM 17267, CRAZY SEXY CANCER 8300, CZECH DREAM 19515, DARWIN'S NIGHTMARE 24831, DIG! 12841, DOCUMENTARY 30411, FAHRENHEIT 9/11 24923, FAHRENHYPE 9/11 23477, HAWKING 28027, ISLAND 25583, JOHN PILGER 27565, KRIS CARR 4832, L'AVVENTURA 13797, MONDOVINO 12983, PALESTINE IS STILL THE ISSUE 40022, PAPER CLIPS 18482, PRIMARY 32499, RIDING GIANTS 5274, STEALING A NATION 24109, SUPER SIZE ME 8061, SWISS FAMILY ROBINSON 3734, THE 3RD VOICE 28461, THE ALAMO 4420, THE HUNTING OF THE PRESIDENT 29242, THE NEW RULERS OF THE WORLD 30565, THE NOMI SONG 30297, THE WAR ON DEMOCRACY 4773, THE WHITE DIAMOND 19891, WATERMARKS src, edge_attr, dst 12102, has_genre, 12841 12102, release_year, 37484 1551, has_tags, 28027 1551, release_year, 37484 40091, has_genre, 12841 40091, has_tags, 12841 40091, release_year, 37484 17267, directed_by, 27565 17267, has_genre, 12841 17267, starred_actors, 27565 17267, written_by, 27565 8300, has_genre, 12841 8300, release_year, 37484 19515, has_genre, 12841 19515, release_year, 37484 24831, has_genre, 12841 24831, release_year, 37484 30411, has_genre, 12841 30411, has_tags, 12841 30411, release_year, 37484 24923, has_genre, 12841 24923, release_year, 37484 23477, has_genre, 12841 23477, release_year, 37484 4832, has_tags, 28027 4832, release_year, 18366 13797, has_genre, 12841 13797, release_year, 37484 12983, has_genre, 12841 12983, starred_actors, 25583 12983, written_by, 25583 40022, has_genre, 12841 40022, release_year, 37484 18482, has_genre, 12841 18482, release_year, 18366 32499, has_genre, 12841 32499, release_year, 37484 5274, directed_by, 25583 5274, has_genre, 12841 5274, has_tags, 28027 5274, has_tags, 25583 5274, release_year, 37484 5274, starred_actors, 25583 5274, written_by, 25583 24109, has_genre, 12841 24109, has_tags, 12841 24109, release_year, 37484 8061, has_tags, 28027 8061, release_year, 18366 3734, release_year, 18366 28461, release_year, 18366 28461, release_year, 37484 4420, has_genre, 12841 4420, has_tags, 12841 4420, release_year, 37484 29242, directed_by, 25583 29242, has_genre, 12841 29242, has_tags, 25583 29242, written_by, 25583 30565, has_genre, 12841 30565, release_year, 37484 30297, directed_by, 25583 30297, has_genre, 12841 30297, has_tags, 25583 30297, starred_actors, 25583 30297, written_by, 25583 4773, has_genre, 12841 4773, has_tags, 12841 4773, release_year, 37484 19891, has_genre, 12841 19891, release_year, 37484 Question: For what reason are KRIS CARR, STEALING A NATION, and THE 3RD VOICE associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "KRIS CARR", "STEALING A NATION", "THE 3RD VOICE" ], "valid_edges": [ [ "BUSH'S BRAIN", "has_genre", "DOCUMENTARY" ], [ "BUSH'S BRAIN", "release_year", "2004" ], [ "CLUB DREAD", "has_tags", "ISLAND" ], [ "CLUB DREAD", "release_year", "2004" ], [ "CONTROL ROOM", "has_genre", "DOCUMENTARY" ], [ "CONTROL ROOM", "has_tags", "DOCUMENTARY" ], [ "CONTROL ROOM", "release_year", "2004" ], [ "CRAZY SEXY CANCER", "directed_by", "KRIS CARR" ], [ "CRAZY SEXY CANCER", "has_genre", "DOCUMENTARY" ], [ "CRAZY SEXY CANCER", "starred_actors", "KRIS CARR" ], [ "CRAZY SEXY CANCER", "written_by", "KRIS CARR" ], [ "CZECH DREAM", "has_genre", "DOCUMENTARY" ], [ "CZECH DREAM", "release_year", "2004" ], [ "DARWIN'S NIGHTMARE", "has_genre", "DOCUMENTARY" ], [ "DARWIN'S NIGHTMARE", "release_year", "2004" ], [ "DIG!", "has_genre", "DOCUMENTARY" ], [ "DIG!", "release_year", "2004" ], [ "FAHRENHEIT 9/11", "has_genre", "DOCUMENTARY" ], [ "FAHRENHEIT 9/11", "has_tags", "DOCUMENTARY" ], [ "FAHRENHEIT 9/11", "release_year", "2004" ], [ "FAHRENHYPE 9/11", "has_genre", "DOCUMENTARY" ], [ "FAHRENHYPE 9/11", "release_year", "2004" ], [ "HAWKING", "has_genre", "DOCUMENTARY" ], [ "HAWKING", "release_year", "2004" ], [ "L'AVVENTURA", "has_tags", "ISLAND" ], [ "L'AVVENTURA", "release_year", "1960" ], [ "MONDOVINO", "has_genre", "DOCUMENTARY" ], [ "MONDOVINO", "release_year", "2004" ], [ "PALESTINE IS STILL THE ISSUE", "has_genre", "DOCUMENTARY" ], [ "PALESTINE IS STILL THE ISSUE", "starred_actors", "JOHN PILGER" ], [ "PALESTINE IS STILL THE ISSUE", "written_by", "JOHN PILGER" ], [ "PAPER CLIPS", "has_genre", "DOCUMENTARY" ], [ "PAPER CLIPS", "release_year", "2004" ], [ "PRIMARY", "has_genre", "DOCUMENTARY" ], [ "PRIMARY", "release_year", "1960" ], [ "RIDING GIANTS", "has_genre", "DOCUMENTARY" ], [ "RIDING GIANTS", "release_year", "2004" ], [ "STEALING A NATION", "directed_by", "JOHN PILGER" ], [ "STEALING A NATION", "has_genre", "DOCUMENTARY" ], [ "STEALING A NATION", "has_tags", "ISLAND" ], [ "STEALING A NATION", "has_tags", "JOHN PILGER" ], [ "STEALING A NATION", "release_year", "2004" ], [ "STEALING A NATION", "starred_actors", "JOHN PILGER" ], [ "STEALING A NATION", "written_by", "JOHN PILGER" ], [ "SUPER SIZE ME", "has_genre", "DOCUMENTARY" ], [ "SUPER SIZE ME", "has_tags", "DOCUMENTARY" ], [ "SUPER SIZE ME", "release_year", "2004" ], [ "SWISS FAMILY ROBINSON", "has_tags", "ISLAND" ], [ "SWISS FAMILY ROBINSON", "release_year", "1960" ], [ "THE 3RD VOICE", "release_year", "1960" ], [ "THE ALAMO", "release_year", "1960" ], [ "THE ALAMO", "release_year", "2004" ], [ "THE HUNTING OF THE PRESIDENT", "has_genre", "DOCUMENTARY" ], [ "THE HUNTING OF THE PRESIDENT", "has_tags", "DOCUMENTARY" ], [ "THE HUNTING OF THE PRESIDENT", "release_year", "2004" ], [ "THE NEW RULERS OF THE WORLD", "directed_by", "JOHN PILGER" ], [ "THE NEW RULERS OF THE WORLD", "has_genre", "DOCUMENTARY" ], [ "THE NEW RULERS OF THE WORLD", "has_tags", "JOHN PILGER" ], [ "THE NEW RULERS OF THE WORLD", "written_by", "JOHN PILGER" ], [ "THE NOMI SONG", "has_genre", "DOCUMENTARY" ], [ "THE NOMI SONG", "release_year", "2004" ], [ "THE WAR ON DEMOCRACY", "directed_by", "JOHN PILGER" ], [ "THE WAR ON DEMOCRACY", "has_genre", "DOCUMENTARY" ], [ "THE WAR ON DEMOCRACY", "has_tags", "JOHN PILGER" ], [ "THE WAR ON DEMOCRACY", "starred_actors", "JOHN PILGER" ], [ "THE WAR ON DEMOCRACY", "written_by", "JOHN PILGER" ], [ "THE WHITE DIAMOND", "has_genre", "DOCUMENTARY" ], [ "THE WHITE DIAMOND", "has_tags", "DOCUMENTARY" ], [ "THE WHITE DIAMOND", "release_year", "2004" ], [ "WATERMARKS", "has_genre", "DOCUMENTARY" ], [ "WATERMARKS", "release_year", "2004" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 39435, 1975 8503, BETTIE PAGE REVEALS ALL 17218, BULLE OGIER 12841, DOCUMENTARY 34808, GEORGE HICKENLOOPER 1205, MAYOR OF THE SUNSET STRIP 18415, MAÎTRESSE 5835, REBECCA ROMIJN 36518, ROLLERBALL src, edge_attr, dst 8503, has_genre, 12841 8503, starred_actors, 5835 1205, directed_by, 34808 1205, has_genre, 12841 1205, has_tags, 12841 1205, written_by, 34808 18415, release_year, 39435 18415, starred_actors, 17218 36518, release_year, 39435 36518, starred_actors, 5835 Question: In what context are BULLE OGIER, GEORGE HICKENLOOPER, and REBECCA ROMIJN connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BULLE OGIER", "GEORGE HICKENLOOPER", "REBECCA ROMIJN" ], "valid_edges": [ [ "BETTIE PAGE REVEALS ALL", "has_genre", "DOCUMENTARY" ], [ "BETTIE PAGE REVEALS ALL", "starred_actors", "REBECCA ROMIJN" ], [ "MAYOR OF THE SUNSET STRIP", "directed_by", "GEORGE HICKENLOOPER" ], [ "MAYOR OF THE SUNSET STRIP", "has_genre", "DOCUMENTARY" ], [ "MAYOR OF THE SUNSET STRIP", "has_tags", "DOCUMENTARY" ], [ "MAYOR OF THE SUNSET STRIP", "written_by", "GEORGE HICKENLOOPER" ], [ "MAÎTRESSE", "release_year", "1975" ], [ "MAÎTRESSE", "starred_actors", "BULLE OGIER" ], [ "ROLLERBALL", "release_year", "1975" ], [ "ROLLERBALL", "starred_actors", "REBECCA ROMIJN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 21136, 10 MINUTES 7977, 1969 35935, 2002 10349, CHICAGO 36212, DRAMA 18394, EMPIRE 19943, HOUSE OF FOOLS 30206, KEN PARK 39755, LIVE FROM BAGHDAD 26032, MR. FREEDOM 38585, NICHOLAS NICKLEBY 11696, THE CUCKOO 12614, THE PIANIST 8949, WE WERE SOLDIERS 14051, WILLIAM KLEIN src, edge_attr, dst 21136, has_genre, 36212 21136, release_year, 35935 7977, has_genre, 36212 10349, has_genre, 36212 10349, release_year, 35935 18394, release_year, 35935 19943, has_genre, 36212 19943, release_year, 35935 30206, has_genre, 36212 30206, release_year, 35935 39755, has_genre, 36212 39755, release_year, 35935 26032, directed_by, 14051 26032, has_tags, 14051 26032, release_year, 7977 26032, written_by, 14051 38585, has_genre, 36212 38585, release_year, 35935 11696, has_genre, 36212 11696, release_year, 35935 12614, has_genre, 36212 12614, has_tags, 36212 12614, release_year, 35935 8949, has_genre, 36212 8949, release_year, 35935 Question: How are EMPIRE, NICHOLAS NICKLEBY, and WILLIAM KLEIN related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "EMPIRE", "NICHOLAS NICKLEBY", "WILLIAM KLEIN" ], "valid_edges": [ [ "10 MINUTES", "has_genre", "DRAMA" ], [ "10 MINUTES", "release_year", "2002" ], [ "1969", "has_genre", "DRAMA" ], [ "CHICAGO", "has_genre", "DRAMA" ], [ "CHICAGO", "release_year", "2002" ], [ "EMPIRE", "release_year", "2002" ], [ "HOUSE OF FOOLS", "has_genre", "DRAMA" ], [ "HOUSE OF FOOLS", "release_year", "2002" ], [ "KEN PARK", "has_genre", "DRAMA" ], [ "KEN PARK", "release_year", "2002" ], [ "LIVE FROM BAGHDAD", "has_genre", "DRAMA" ], [ "LIVE FROM BAGHDAD", "release_year", "2002" ], [ "MR. FREEDOM", "directed_by", "WILLIAM KLEIN" ], [ "MR. FREEDOM", "has_tags", "WILLIAM KLEIN" ], [ "MR. FREEDOM", "release_year", "1969" ], [ "MR. FREEDOM", "written_by", "WILLIAM KLEIN" ], [ "NICHOLAS NICKLEBY", "has_genre", "DRAMA" ], [ "NICHOLAS NICKLEBY", "release_year", "2002" ], [ "THE CUCKOO", "has_genre", "DRAMA" ], [ "THE CUCKOO", "release_year", "2002" ], [ "THE PIANIST", "has_genre", "DRAMA" ], [ "THE PIANIST", "has_tags", "DRAMA" ], [ "THE PIANIST", "release_year", "2002" ], [ "WE WERE SOLDIERS", "has_genre", "DRAMA" ], [ "WE WERE SOLDIERS", "release_year", "2002" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35187, 1948 35063, 1976 6718, A FAREWELL TO ARMS 40009, A FOREIGN AFFAIR 33215, A NIGHT TO REMEMBER 24319, A PASSAGE TO INDIA 9152, A STAR IS BORN 10341, ADVENTURES OF DON JUAN 19904, ADVENTURES OF KITTY O'DAY 28463, AFTER THE FOX 36358, ALEC GUINNESS 29638, ALICE IN WONDERLAND 19293, ALL THE PRESIDENT'S MEN 6793, AND THEN THERE WERE NONE 13573, ANNA CHRISTIE 24104, ARSÈNE LUPIN 694, ASSAULT ON PRECINCT 13 30204, BAFFLED! 10045, BD-R 38657, BEAT THE DEVIL 34744, BEING THERE 25490, BERLIN EXPRESS 8139, BICYCLE THIEVES 18599, BILOXI BLUES 23878, BOUND FOR GLORY 12718, BREAKFAST AT TIFFANY'S 16659, CASINO ROYALE 13302, CHAPTER TWO 6903, CHARIOTS OF FIRE 14858, CHINATOWN 16536, CYRANO DE BERGERAC 981, DEATH ON THE NILE 2362, DENNIS PALUMBO 23396, DIAL M FOR MURDER 16041, DJANGO UNCHAINED 18025, DÉDÉE D'ANVERS 39812, EILEEN BRENNAN 31783, ENGLISH 819, FIST OF THE NORTH STAR 9003, FUNNY GAMES 19221, GASLIGHT 5527, GIGI 8046, GOSFORD PARK 4709, GREAT EXPECTATIONS 20941, HAMLET 36495, I REMEMBER MAMA 30468, I'M ALL RIGHT JACK 30184, IN COLD BLOOD 28169, IN THE CUT 847, JACK THE GIANT KILLER 37227, JAMES COCO 21922, JANE EYRE 38579, JOAN OF ARC 4929, KEY LARGO 19098, KING KONG 26649, KISMET 17954, LETTER FROM AN UNKNOWN WOMAN 33160, LITTLE DORRIT 34533, LOGAN'S RUN 32275, LORD OF THE FLIES 20992, MACBETH 27050, MAN OF LA MANCHA 20579, MARAT/SADE 22678, MARLOWE 16925, MAYERLING 20138, MIDWAY 36083, MIRANDA 6543, MODEL SHOP 7186, MR. BLANDINGS BUILDS HIS DREAM HOUSE 35137, MURDER BY DEATH 31661, MY FAIR LADY 10680, MY FAVORITE YEAR 15145, MYSTERY 35498, MYSTERY OF THE 13TH GUEST 16462, MYSTERY OF THE WAX MUSEUM 37159, NEIL SIMON 31566, NETWORK 30629, NIGHT NURSE 37989, OBSESSION 15644, OLIVER TWIST 5794, ONLY WHEN I LAUGH 36164, OUR MAN IN HAVANA 23755, PETER FALK 38529, PETER SELLERS 39459, RED RIVER 5897, REPULSION 8389, ROBERT MOORE 15873, ROBIN AND MARIAN 2738, ROMEO AND JULIET 32487, SAPPHIRE 6119, SLEUTH 31624, SPELLBOUND 8436, SPIRITS OF THE DEAD 39288, SUDDENLY, LAST SUMMER 29067, SUMMER HOLIDAY 24926, SWEET CHARITY 31138, THAT'S ENTERTAINMENT, PART II 18998, THE BAT 30757, THE BOY WITH GREEN HAIR 36857, THE BRIDGE ON THE RIVER KWAI 14222, THE CANTERVILLE GHOST 36282, THE CHEAP DETECTIVE 18651, THE CORSICAN BROTHERS 24990, THE CURSE OF FRANKENSTEIN 6442, THE FALLEN IDOL 22869, THE GHOST SHIP 6597, THE GOLDEN EYE 19595, THE HEARTBREAK KID 31283, THE HOUND OF THE BASKERVILLES 15198, THE HUNCHBACK OF NOTRE DAME 19104, THE IN-LAWS 4091, THE LADY VANISHES 30690, THE LADYKILLERS 5508, THE LAVENDER HILL MOB 29109, THE LODGER 5834, THE MOUSE THAT ROARED 7341, THE NAKED CITY 11902, THE OUTLAW JOSEY WALES 929, THE PARTY 31851, THE PRISONER OF ZENDA 18162, THE RAVEN 36665, THE RED SHOES 18274, THE ROSE TATTOO 8477, THE SCARLET PIMPERNEL 11265, THE SEA HAWK 30746, THE SECRET LIFE OF WALTER MITTY 8304, THE SILVER CHALICE 4784, THE SNAKE PIT 34579, THE SPY WHO CAME IN FROM THE COLD 26081, THE SUNSHINE BOYS 3516, THE SWAN 7816, THE THREE MUSKETEERS 16504, THE TOWN THAT DREADED SUNDOWN 983, THE TREASURE OF THE SIERRA MADRE 19255, THE VALACHI PAPERS 17568, THE VANISHING 14962, THE WATCHER IN THE WOODS 22751, THE WICKER MAN 14983, THE WINSLOW BOY 34254, THEY ONLY KILL THEIR MASTERS 14471, THIS HAPPY BREED 190, TRUMAN CAPOTE 36262, UNFAITHFULLY YOURS 4378, VICTIM 11659, VIVA MARIA! 22844, WENT THE DAY WELL? 8390, WHISTLING IN DIXIE 10142, WHISTLING IN THE DARK 35212, WHO'S AFRAID OF VIRGINIA WOOLF? 39738, WILLIAM BEAUDINE 6037, WITCHFINDER GENERAL src, edge_attr, dst 6718, has_tags, 10045 6718, in_language, 31783 40009, has_tags, 10045 40009, release_year, 35187 33215, has_genre, 15145 33215, has_tags, 10045 24319, has_tags, 10045 24319, in_language, 31783 9152, has_tags, 10045 9152, release_year, 35063 10341, has_tags, 10045 10341, release_year, 35187 19904, directed_by, 39738 19904, has_genre, 15145 28463, has_tags, 10045 28463, has_tags, 37159 28463, in_language, 31783 28463, starred_actors, 38529 28463, written_by, 37159 29638, has_tags, 10045 29638, in_language, 31783 19293, has_tags, 10045 19293, release_year, 35063 6793, has_genre, 15145 6793, has_tags, 10045 6793, has_tags, 15145 13573, has_tags, 10045 13573, in_language, 31783 24104, has_genre, 15145 24104, has_tags, 10045 694, has_tags, 10045 694, release_year, 35063 30204, has_genre, 15145 30204, in_language, 31783 38657, has_tags, 10045 38657, written_by, 190 34744, has_tags, 10045 34744, has_tags, 38529 34744, starred_actors, 38529 25490, has_tags, 10045 25490, release_year, 35187 8139, has_tags, 10045 8139, release_year, 35187 18599, has_tags, 10045 18599, has_tags, 37159 18599, written_by, 37159 23878, has_tags, 10045 23878, release_year, 35063 12718, has_tags, 10045 12718, has_tags, 190 12718, written_by, 190 16659, has_tags, 10045 16659, has_tags, 38529 16659, starred_actors, 38529 13302, directed_by, 8389 13302, has_tags, 10045 13302, written_by, 37159 6903, has_tags, 10045 6903, in_language, 31783 14858, has_genre, 15145 14858, has_tags, 10045 14858, has_tags, 15145 16536, has_tags, 10045 16536, in_language, 31783 981, has_genre, 15145 981, has_tags, 10045 981, has_tags, 15145 23396, has_tags, 10045 23396, in_language, 31783 16041, has_tags, 10045 16041, in_language, 31783 18025, in_language, 31783 18025, release_year, 35187 819, has_tags, 10045 819, in_language, 31783 9003, has_tags, 10045 9003, in_language, 31783 19221, has_genre, 15145 19221, has_tags, 10045 5527, has_tags, 10045 5527, in_language, 31783 8046, has_genre, 15145 8046, in_language, 31783 4709, has_tags, 36358 4709, has_tags, 10045 20941, has_tags, 10045 20941, in_language, 31783 20941, release_year, 35187 36495, has_tags, 10045 36495, release_year, 35187 30468, has_tags, 10045 30468, has_tags, 38529 30468, starred_actors, 38529 30184, has_tags, 10045 30184, has_tags, 190 30184, written_by, 190 28169, has_genre, 15145 28169, in_language, 31783 847, has_tags, 10045 847, in_language, 31783 21922, has_tags, 10045 21922, in_language, 31783 38579, has_tags, 10045 38579, release_year, 35187 4929, has_tags, 10045 4929, release_year, 35187 19098, has_tags, 10045 19098, release_year, 35063 26649, has_tags, 10045 26649, in_language, 31783 17954, has_tags, 10045 17954, release_year, 35187 33160, has_tags, 36358 33160, has_tags, 10045 34533, has_tags, 10045 34533, release_year, 35063 32275, has_tags, 10045 32275, in_language, 31783 20992, in_language, 31783 20992, release_year, 35187 27050, has_tags, 10045 27050, in_language, 31783 27050, starred_actors, 37227 20579, has_tags, 10045 20579, in_language, 31783 22678, has_genre, 15145 22678, has_tags, 10045 16925, has_tags, 10045 16925, in_language, 31783 20138, has_tags, 10045 20138, release_year, 35063 36083, has_tags, 10045 36083, release_year, 35187 6543, has_tags, 10045 6543, in_language, 31783 7186, has_tags, 10045 7186, release_year, 35187 35137, directed_by, 8389 35137, has_genre, 15145 35137, has_tags, 36358 35137, has_tags, 10045 35137, has_tags, 15145 35137, has_tags, 37159 35137, has_tags, 38529 35137, has_tags, 190 35137, release_year, 35063 35137, starred_actors, 39812 35137, starred_actors, 37227 35137, starred_actors, 23755 35137, starred_actors, 190 35137, written_by, 37159 31661, has_tags, 10045 31661, in_language, 31783 10680, has_tags, 10045 10680, written_by, 2362 35498, directed_by, 39738 35498, has_genre, 15145 16462, has_genre, 15145 16462, has_tags, 10045 31566, has_tags, 10045 31566, release_year, 35063 30629, has_genre, 15145 30629, has_tags, 10045 37989, has_genre, 15145 37989, has_tags, 10045 37989, release_year, 35063 15644, has_tags, 36358 15644, has_tags, 10045 15644, release_year, 35187 15644, starred_actors, 36358 5794, has_tags, 10045 5794, has_tags, 37159 5794, starred_actors, 37227 5794, written_by, 37159 36164, has_tags, 36358 36164, has_tags, 10045 36164, starred_actors, 36358 39459, has_tags, 10045 39459, release_year, 35187 5897, has_tags, 10045 5897, in_language, 31783 15873, has_tags, 10045 15873, release_year, 35063 2738, has_tags, 10045 2738, in_language, 31783 32487, has_genre, 15145 32487, in_language, 31783 6119, has_genre, 15145 6119, has_tags, 10045 6119, has_tags, 15145 31624, has_genre, 15145 31624, has_tags, 10045 31624, has_tags, 15145 8436, has_genre, 15145 8436, has_tags, 10045 8436, in_language, 31783 39288, has_genre, 15145 39288, has_tags, 10045 29067, has_tags, 10045 29067, release_year, 35187 24926, has_tags, 10045 24926, written_by, 37159 31138, has_tags, 10045 31138, release_year, 35063 18998, has_genre, 15145 18998, has_tags, 10045 30757, has_tags, 10045 30757, release_year, 35187 36857, has_tags, 36358 36857, has_tags, 10045 36857, starred_actors, 36358 14222, has_tags, 10045 14222, in_language, 31783 36282, directed_by, 8389 36282, has_tags, 10045 36282, has_tags, 37159 36282, starred_actors, 39812 36282, starred_actors, 23755 36282, written_by, 37159 18651, has_tags, 10045 18651, in_language, 31783 24990, has_tags, 10045 24990, in_language, 31783 6442, has_tags, 10045 6442, release_year, 35187 22869, has_genre, 15145 22869, has_tags, 10045 6597, directed_by, 39738 6597, in_language, 31783 6597, release_year, 35187 19595, has_tags, 10045 19595, has_tags, 37159 19595, written_by, 37159 31283, has_genre, 15145 31283, has_tags, 10045 15198, has_tags, 10045 15198, in_language, 31783 19104, has_tags, 10045 19104, starred_actors, 23755 4091, has_genre, 15145 4091, has_tags, 10045 4091, in_language, 31783 30690, has_tags, 36358 30690, has_tags, 10045 30690, has_tags, 38529 30690, starred_actors, 36358 30690, starred_actors, 38529 5508, has_tags, 36358 5508, has_tags, 10045 5508, starred_actors, 36358 29109, has_genre, 15145 29109, has_tags, 10045 5834, has_tags, 10045 5834, has_tags, 38529 5834, starred_actors, 38529 7341, has_tags, 10045 7341, release_year, 35187 11902, has_tags, 10045 11902, release_year, 35063 929, has_tags, 10045 929, has_tags, 38529 929, starred_actors, 38529 31851, has_tags, 10045 31851, starred_actors, 38529 18162, has_genre, 15145 18162, has_tags, 10045 36665, has_tags, 10045 36665, release_year, 35187 18274, has_tags, 10045 18274, in_language, 31783 8477, has_tags, 10045 8477, in_language, 31783 11265, has_tags, 10045 11265, in_language, 31783 30746, has_tags, 10045 30746, in_language, 31783 8304, has_tags, 10045 8304, in_language, 31783 4784, has_tags, 10045 4784, release_year, 35187 34579, has_tags, 10045 34579, in_language, 31783 26081, has_tags, 10045 26081, has_tags, 37159 26081, written_by, 37159 3516, has_tags, 10045 3516, starred_actors, 36358 7816, has_tags, 10045 7816, release_year, 35187 16504, has_tags, 10045 16504, release_year, 35063 983, has_tags, 10045 983, in_language, 31783 983, release_year, 35187 19255, has_tags, 10045 19255, in_language, 31783 17568, has_tags, 10045 17568, in_language, 31783 14962, has_genre, 15145 14962, in_language, 31783 22751, has_genre, 15145 22751, has_tags, 10045 14983, in_language, 31783 14983, release_year, 35187 34254, has_genre, 15145 34254, has_tags, 10045 14471, has_tags, 10045 14471, in_language, 31783 36262, has_tags, 10045 36262, release_year, 35187 4378, has_tags, 10045 4378, in_language, 31783 11659, has_tags, 10045 11659, in_language, 31783 22844, has_tags, 10045 22844, in_language, 31783 8390, has_genre, 15145 8390, has_tags, 10045 10142, has_genre, 15145 10142, has_tags, 10045 35212, has_tags, 10045 35212, in_language, 31783 6037, has_tags, 10045 6037, in_language, 31783 Question: For what reason are DENNIS PALUMBO, MURDER BY DEATH, and THE GOLDEN EYE associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "DENNIS PALUMBO", "MURDER BY DEATH", "THE GOLDEN EYE" ], "valid_edges": [ [ "A FAREWELL TO ARMS", "has_tags", "BD-R" ], [ "A FAREWELL TO ARMS", "in_language", "ENGLISH" ], [ "A FOREIGN AFFAIR", "has_tags", "BD-R" ], [ "A FOREIGN AFFAIR", "release_year", "1948" ], [ "A NIGHT TO REMEMBER", "has_genre", "MYSTERY" ], [ "A NIGHT TO REMEMBER", "has_tags", "BD-R" ], [ "A PASSAGE TO INDIA", "has_tags", "BD-R" ], [ "A PASSAGE TO INDIA", "in_language", "ENGLISH" ], [ "A STAR IS BORN", "has_tags", "BD-R" ], [ "A STAR IS BORN", "release_year", "1976" ], [ "ADVENTURES OF DON JUAN", "has_tags", "BD-R" ], [ "ADVENTURES OF DON JUAN", "release_year", "1948" ], [ "ADVENTURES OF KITTY O'DAY", "directed_by", "WILLIAM BEAUDINE" ], [ "ADVENTURES OF KITTY O'DAY", "has_genre", "MYSTERY" ], [ "AFTER THE FOX", "has_tags", "BD-R" ], [ "AFTER THE FOX", "has_tags", "NEIL SIMON" ], [ "AFTER THE FOX", "in_language", "ENGLISH" ], [ "AFTER THE FOX", "starred_actors", "PETER SELLERS" ], [ "AFTER THE FOX", "written_by", "NEIL SIMON" ], [ "ALICE IN WONDERLAND", "has_tags", "BD-R" ], [ "ALICE IN WONDERLAND", "in_language", "ENGLISH" ], [ "ALL THE PRESIDENT'S MEN", "has_tags", "BD-R" ], [ "ALL THE PRESIDENT'S MEN", "release_year", "1976" ], [ "AND THEN THERE WERE NONE", "has_genre", "MYSTERY" ], [ "AND THEN THERE WERE NONE", "has_tags", "BD-R" ], [ "AND THEN THERE WERE NONE", "has_tags", "MYSTERY" ], [ "ANNA CHRISTIE", "has_tags", "BD-R" ], [ "ANNA CHRISTIE", "in_language", "ENGLISH" ], [ "ARSÈNE LUPIN", "has_genre", "MYSTERY" ], [ "ARSÈNE LUPIN", "has_tags", "BD-R" ], [ "ASSAULT ON PRECINCT 13", "has_tags", "BD-R" ], [ "ASSAULT ON PRECINCT 13", "release_year", "1976" ], [ "BAFFLED!", "has_genre", "MYSTERY" ], [ "BAFFLED!", "in_language", "ENGLISH" ], [ "BEAT THE DEVIL", "has_tags", "BD-R" ], [ "BEAT THE DEVIL", "written_by", "TRUMAN CAPOTE" ], [ "BEING THERE", "has_tags", "BD-R" ], [ "BEING THERE", "has_tags", "PETER SELLERS" ], [ "BEING THERE", "starred_actors", "PETER SELLERS" ], [ "BERLIN EXPRESS", "has_tags", "BD-R" ], [ "BERLIN EXPRESS", "release_year", "1948" ], [ "BICYCLE THIEVES", "has_tags", "BD-R" ], [ "BICYCLE THIEVES", "release_year", "1948" ], [ "BILOXI BLUES", "has_tags", "BD-R" ], [ "BILOXI BLUES", "has_tags", "NEIL SIMON" ], [ "BILOXI BLUES", "written_by", "NEIL SIMON" ], [ "BOUND FOR GLORY", "has_tags", "BD-R" ], [ "BOUND FOR GLORY", "release_year", "1976" ], [ "BREAKFAST AT TIFFANY'S", "has_tags", "BD-R" ], [ "BREAKFAST AT TIFFANY'S", "has_tags", "TRUMAN CAPOTE" ], [ "BREAKFAST AT TIFFANY'S", "written_by", "TRUMAN CAPOTE" ], [ "CASINO ROYALE", "has_tags", "BD-R" ], [ "CASINO ROYALE", "has_tags", "PETER SELLERS" ], [ "CASINO ROYALE", "starred_actors", "PETER SELLERS" ], [ "CHAPTER TWO", "directed_by", "ROBERT MOORE" ], [ "CHAPTER TWO", "has_tags", "BD-R" ], [ "CHAPTER TWO", "written_by", "NEIL SIMON" ], [ "CHARIOTS OF FIRE", "has_tags", "BD-R" ], [ "CHARIOTS OF FIRE", "in_language", "ENGLISH" ], [ "CHINATOWN", "has_genre", "MYSTERY" ], [ "CHINATOWN", "has_tags", "BD-R" ], [ "CHINATOWN", "has_tags", "MYSTERY" ], [ "CYRANO DE BERGERAC", "has_tags", "BD-R" ], [ "CYRANO DE BERGERAC", "in_language", "ENGLISH" ], [ "DEATH ON THE NILE", "has_genre", "MYSTERY" ], [ "DEATH ON THE NILE", "has_tags", "BD-R" ], [ "DEATH ON THE NILE", "has_tags", "MYSTERY" ], [ "DIAL M FOR MURDER", "has_tags", "BD-R" ], [ "DIAL M FOR MURDER", "in_language", "ENGLISH" ], [ "DJANGO UNCHAINED", "has_tags", "BD-R" ], [ "DJANGO UNCHAINED", "in_language", "ENGLISH" ], [ "DÉDÉE D'ANVERS", "in_language", "ENGLISH" ], [ "DÉDÉE D'ANVERS", "release_year", "1948" ], [ "FIST OF THE NORTH STAR", "has_tags", "BD-R" ], [ "FIST OF THE NORTH STAR", "in_language", "ENGLISH" ], [ "FUNNY GAMES", "has_tags", "BD-R" ], [ "FUNNY GAMES", "in_language", "ENGLISH" ], [ "GASLIGHT", "has_genre", "MYSTERY" ], [ "GASLIGHT", "has_tags", "BD-R" ], [ "GIGI", "has_tags", "BD-R" ], [ "GIGI", "in_language", "ENGLISH" ], [ "GOSFORD PARK", "has_genre", "MYSTERY" ], [ "GOSFORD PARK", "in_language", "ENGLISH" ], [ "GREAT EXPECTATIONS", "has_tags", "ALEC GUINNESS" ], [ "GREAT EXPECTATIONS", "has_tags", "BD-R" ], [ "HAMLET", "has_tags", "BD-R" ], [ "HAMLET", "in_language", "ENGLISH" ], [ "HAMLET", "release_year", "1948" ], [ "I REMEMBER MAMA", "has_tags", "BD-R" ], [ "I REMEMBER MAMA", "release_year", "1948" ], [ "I'M ALL RIGHT JACK", "has_tags", "BD-R" ], [ "I'M ALL RIGHT JACK", "has_tags", "PETER SELLERS" ], [ "I'M ALL RIGHT JACK", "starred_actors", "PETER SELLERS" ], [ "IN COLD BLOOD", "has_tags", "BD-R" ], [ "IN COLD BLOOD", "has_tags", "TRUMAN CAPOTE" ], [ "IN COLD BLOOD", "written_by", "TRUMAN CAPOTE" ], [ "IN THE CUT", "has_genre", "MYSTERY" ], [ "IN THE CUT", "in_language", "ENGLISH" ], [ "JACK THE GIANT KILLER", "has_tags", "BD-R" ], [ "JACK THE GIANT KILLER", "in_language", "ENGLISH" ], [ "JANE EYRE", "has_tags", "BD-R" ], [ "JANE EYRE", "in_language", "ENGLISH" ], [ "JOAN OF ARC", "has_tags", "BD-R" ], [ "JOAN OF ARC", "release_year", "1948" ], [ "KEY LARGO", "has_tags", "BD-R" ], [ "KEY LARGO", "release_year", "1948" ], [ "KING KONG", "has_tags", "BD-R" ], [ "KING KONG", "release_year", "1976" ], [ "KISMET", "has_tags", "BD-R" ], [ "KISMET", "in_language", "ENGLISH" ], [ "LETTER FROM AN UNKNOWN WOMAN", "has_tags", "BD-R" ], [ "LETTER FROM AN UNKNOWN WOMAN", "release_year", "1948" ], [ "LITTLE DORRIT", "has_tags", "ALEC GUINNESS" ], [ "LITTLE DORRIT", "has_tags", "BD-R" ], [ "LOGAN'S RUN", "has_tags", "BD-R" ], [ "LOGAN'S RUN", "release_year", "1976" ], [ "LORD OF THE FLIES", "has_tags", "BD-R" ], [ "LORD OF THE FLIES", "in_language", "ENGLISH" ], [ "MACBETH", "in_language", "ENGLISH" ], [ "MACBETH", "release_year", "1948" ], [ "MAN OF LA MANCHA", "has_tags", "BD-R" ], [ "MAN OF LA MANCHA", "in_language", "ENGLISH" ], [ "MAN OF LA MANCHA", "starred_actors", "JAMES COCO" ], [ "MARAT/SADE", "has_tags", "BD-R" ], [ "MARAT/SADE", "in_language", "ENGLISH" ], [ "MARLOWE", "has_genre", "MYSTERY" ], [ "MARLOWE", "has_tags", "BD-R" ], [ "MAYERLING", "has_tags", "BD-R" ], [ "MAYERLING", "in_language", "ENGLISH" ], [ "MIDWAY", "has_tags", "BD-R" ], [ "MIDWAY", "release_year", "1976" ], [ "MIRANDA", "has_tags", "BD-R" ], [ "MIRANDA", "release_year", "1948" ], [ "MODEL SHOP", "has_tags", "BD-R" ], [ "MODEL SHOP", "in_language", "ENGLISH" ], [ "MR. BLANDINGS BUILDS HIS DREAM HOUSE", "has_tags", "BD-R" ], [ "MR. BLANDINGS BUILDS HIS DREAM HOUSE", "release_year", "1948" ], [ "MURDER BY DEATH", "directed_by", "ROBERT MOORE" ], [ "MURDER BY DEATH", "has_genre", "MYSTERY" ], [ "MURDER BY DEATH", "has_tags", "ALEC GUINNESS" ], [ "MURDER BY DEATH", "has_tags", "BD-R" ], [ "MURDER BY DEATH", "has_tags", "MYSTERY" ], [ "MURDER BY DEATH", "has_tags", "NEIL SIMON" ], [ "MURDER BY DEATH", "has_tags", "PETER SELLERS" ], [ "MURDER BY DEATH", "has_tags", "TRUMAN CAPOTE" ], [ "MURDER BY DEATH", "release_year", "1976" ], [ "MURDER BY DEATH", "starred_actors", "EILEEN BRENNAN" ], [ "MURDER BY DEATH", "starred_actors", "JAMES COCO" ], [ "MURDER BY DEATH", "starred_actors", "PETER FALK" ], [ "MURDER BY DEATH", "starred_actors", "TRUMAN CAPOTE" ], [ "MURDER BY DEATH", "written_by", "NEIL SIMON" ], [ "MY FAIR LADY", "has_tags", "BD-R" ], [ "MY FAIR LADY", "in_language", "ENGLISH" ], [ "MY FAVORITE YEAR", "has_tags", "BD-R" ], [ "MY FAVORITE YEAR", "written_by", "DENNIS PALUMBO" ], [ "MYSTERY OF THE 13TH GUEST", "directed_by", "WILLIAM BEAUDINE" ], [ "MYSTERY OF THE 13TH GUEST", "has_genre", "MYSTERY" ], [ "MYSTERY OF THE WAX MUSEUM", "has_genre", "MYSTERY" ], [ "MYSTERY OF THE WAX MUSEUM", "has_tags", "BD-R" ], [ "NETWORK", "has_tags", "BD-R" ], [ "NETWORK", "release_year", "1976" ], [ "NIGHT NURSE", "has_genre", "MYSTERY" ], [ "NIGHT NURSE", "has_tags", "BD-R" ], [ "OBSESSION", "has_genre", "MYSTERY" ], [ "OBSESSION", "has_tags", "BD-R" ], [ "OBSESSION", "release_year", "1976" ], [ "OLIVER TWIST", "has_tags", "ALEC GUINNESS" ], [ "OLIVER TWIST", "has_tags", "BD-R" ], [ "OLIVER TWIST", "release_year", "1948" ], [ "OLIVER TWIST", "starred_actors", "ALEC GUINNESS" ], [ "ONLY WHEN I LAUGH", "has_tags", "BD-R" ], [ "ONLY WHEN I LAUGH", "has_tags", "NEIL SIMON" ], [ "ONLY WHEN I LAUGH", "starred_actors", "JAMES COCO" ], [ "ONLY WHEN I LAUGH", "written_by", "NEIL SIMON" ], [ "OUR MAN IN HAVANA", "has_tags", "ALEC GUINNESS" ], [ "OUR MAN IN HAVANA", "has_tags", "BD-R" ], [ "OUR MAN IN HAVANA", "starred_actors", "ALEC GUINNESS" ], [ "RED RIVER", "has_tags", "BD-R" ], [ "RED RIVER", "release_year", "1948" ], [ "REPULSION", "has_tags", "BD-R" ], [ "REPULSION", "in_language", "ENGLISH" ], [ "ROBIN AND MARIAN", "has_tags", "BD-R" ], [ "ROBIN AND MARIAN", "release_year", "1976" ], [ "ROMEO AND JULIET", "has_tags", "BD-R" ], [ "ROMEO AND JULIET", "in_language", "ENGLISH" ], [ "SAPPHIRE", "has_genre", "MYSTERY" ], [ "SAPPHIRE", "in_language", "ENGLISH" ], [ "SLEUTH", "has_genre", "MYSTERY" ], [ "SLEUTH", "has_tags", "BD-R" ], [ "SLEUTH", "has_tags", "MYSTERY" ], [ "SPELLBOUND", "has_genre", "MYSTERY" ], [ "SPELLBOUND", "has_tags", "BD-R" ], [ "SPELLBOUND", "has_tags", "MYSTERY" ], [ "SPIRITS OF THE DEAD", "has_genre", "MYSTERY" ], [ "SPIRITS OF THE DEAD", "has_tags", "BD-R" ], [ "SPIRITS OF THE DEAD", "in_language", "ENGLISH" ], [ "SUDDENLY, LAST SUMMER", "has_genre", "MYSTERY" ], [ "SUDDENLY, LAST SUMMER", "has_tags", "BD-R" ], [ "SUMMER HOLIDAY", "has_tags", "BD-R" ], [ "SUMMER HOLIDAY", "release_year", "1948" ], [ "SWEET CHARITY", "has_tags", "BD-R" ], [ "SWEET CHARITY", "written_by", "NEIL SIMON" ], [ "THAT'S ENTERTAINMENT, PART II", "has_tags", "BD-R" ], [ "THAT'S ENTERTAINMENT, PART II", "release_year", "1976" ], [ "THE BAT", "has_genre", "MYSTERY" ], [ "THE BAT", "has_tags", "BD-R" ], [ "THE BOY WITH GREEN HAIR", "has_tags", "BD-R" ], [ "THE BOY WITH GREEN HAIR", "release_year", "1948" ], [ "THE BRIDGE ON THE RIVER KWAI", "has_tags", "ALEC GUINNESS" ], [ "THE BRIDGE ON THE RIVER KWAI", "has_tags", "BD-R" ], [ "THE BRIDGE ON THE RIVER KWAI", "starred_actors", "ALEC GUINNESS" ], [ "THE CANTERVILLE GHOST", "has_tags", "BD-R" ], [ "THE CANTERVILLE GHOST", "in_language", "ENGLISH" ], [ "THE CHEAP DETECTIVE", "directed_by", "ROBERT MOORE" ], [ "THE CHEAP DETECTIVE", "has_tags", "BD-R" ], [ "THE CHEAP DETECTIVE", "has_tags", "NEIL SIMON" ], [ "THE CHEAP DETECTIVE", "starred_actors", "EILEEN BRENNAN" ], [ "THE CHEAP DETECTIVE", "starred_actors", "PETER FALK" ], [ "THE CHEAP DETECTIVE", "written_by", "NEIL SIMON" ], [ "THE CORSICAN BROTHERS", "has_tags", "BD-R" ], [ "THE CORSICAN BROTHERS", "in_language", "ENGLISH" ], [ "THE CURSE OF FRANKENSTEIN", "has_tags", "BD-R" ], [ "THE CURSE OF FRANKENSTEIN", "in_language", "ENGLISH" ], [ "THE FALLEN IDOL", "has_tags", "BD-R" ], [ "THE FALLEN IDOL", "release_year", "1948" ], [ "THE GHOST SHIP", "has_genre", "MYSTERY" ], [ "THE GHOST SHIP", "has_tags", "BD-R" ], [ "THE GOLDEN EYE", "directed_by", "WILLIAM BEAUDINE" ], [ "THE GOLDEN EYE", "in_language", "ENGLISH" ], [ "THE GOLDEN EYE", "release_year", "1948" ], [ "THE HEARTBREAK KID", "has_tags", "BD-R" ], [ "THE HEARTBREAK KID", "has_tags", "NEIL SIMON" ], [ "THE HEARTBREAK KID", "written_by", "NEIL SIMON" ], [ "THE HOUND OF THE BASKERVILLES", "has_genre", "MYSTERY" ], [ "THE HOUND OF THE BASKERVILLES", "has_tags", "BD-R" ], [ "THE HUNCHBACK OF NOTRE DAME", "has_tags", "BD-R" ], [ "THE HUNCHBACK OF NOTRE DAME", "in_language", "ENGLISH" ], [ "THE IN-LAWS", "has_tags", "BD-R" ], [ "THE IN-LAWS", "starred_actors", "PETER FALK" ], [ "THE LADY VANISHES", "has_genre", "MYSTERY" ], [ "THE LADY VANISHES", "has_tags", "BD-R" ], [ "THE LADY VANISHES", "in_language", "ENGLISH" ], [ "THE LADYKILLERS", "has_tags", "ALEC GUINNESS" ], [ "THE LADYKILLERS", "has_tags", "BD-R" ], [ "THE LADYKILLERS", "has_tags", "PETER SELLERS" ], [ "THE LADYKILLERS", "starred_actors", "ALEC GUINNESS" ], [ "THE LADYKILLERS", "starred_actors", "PETER SELLERS" ], [ "THE LAVENDER HILL MOB", "has_tags", "ALEC GUINNESS" ], [ "THE LAVENDER HILL MOB", "has_tags", "BD-R" ], [ "THE LAVENDER HILL MOB", "starred_actors", "ALEC GUINNESS" ], [ "THE LODGER", "has_genre", "MYSTERY" ], [ "THE LODGER", "has_tags", "BD-R" ], [ "THE MOUSE THAT ROARED", "has_tags", "BD-R" ], [ "THE MOUSE THAT ROARED", "has_tags", "PETER SELLERS" ], [ "THE MOUSE THAT ROARED", "starred_actors", "PETER SELLERS" ], [ "THE NAKED CITY", "has_tags", "BD-R" ], [ "THE NAKED CITY", "release_year", "1948" ], [ "THE OUTLAW JOSEY WALES", "has_tags", "BD-R" ], [ "THE OUTLAW JOSEY WALES", "release_year", "1976" ], [ "THE PARTY", "has_tags", "BD-R" ], [ "THE PARTY", "has_tags", "PETER SELLERS" ], [ "THE PARTY", "starred_actors", "PETER SELLERS" ], [ "THE PRISONER OF ZENDA", "has_tags", "BD-R" ], [ "THE PRISONER OF ZENDA", "starred_actors", "PETER SELLERS" ], [ "THE RAVEN", "has_genre", "MYSTERY" ], [ "THE RAVEN", "has_tags", "BD-R" ], [ "THE RED SHOES", "has_tags", "BD-R" ], [ "THE RED SHOES", "release_year", "1948" ], [ "THE ROSE TATTOO", "has_tags", "BD-R" ], [ "THE ROSE TATTOO", "in_language", "ENGLISH" ], [ "THE SCARLET PIMPERNEL", "has_tags", "BD-R" ], [ "THE SCARLET PIMPERNEL", "in_language", "ENGLISH" ], [ "THE SEA HAWK", "has_tags", "BD-R" ], [ "THE SEA HAWK", "in_language", "ENGLISH" ], [ "THE SECRET LIFE OF WALTER MITTY", "has_tags", "BD-R" ], [ "THE SECRET LIFE OF WALTER MITTY", "in_language", "ENGLISH" ], [ "THE SILVER CHALICE", "has_tags", "BD-R" ], [ "THE SILVER CHALICE", "in_language", "ENGLISH" ], [ "THE SNAKE PIT", "has_tags", "BD-R" ], [ "THE SNAKE PIT", "release_year", "1948" ], [ "THE SPY WHO CAME IN FROM THE COLD", "has_tags", "BD-R" ], [ "THE SPY WHO CAME IN FROM THE COLD", "in_language", "ENGLISH" ], [ "THE SUNSHINE BOYS", "has_tags", "BD-R" ], [ "THE SUNSHINE BOYS", "has_tags", "NEIL SIMON" ], [ "THE SUNSHINE BOYS", "written_by", "NEIL SIMON" ], [ "THE SWAN", "has_tags", "BD-R" ], [ "THE SWAN", "starred_actors", "ALEC GUINNESS" ], [ "THE THREE MUSKETEERS", "has_tags", "BD-R" ], [ "THE THREE MUSKETEERS", "release_year", "1948" ], [ "THE TOWN THAT DREADED SUNDOWN", "has_tags", "BD-R" ], [ "THE TOWN THAT DREADED SUNDOWN", "release_year", "1976" ], [ "THE TREASURE OF THE SIERRA MADRE", "has_tags", "BD-R" ], [ "THE TREASURE OF THE SIERRA MADRE", "in_language", "ENGLISH" ], [ "THE TREASURE OF THE SIERRA MADRE", "release_year", "1948" ], [ "THE VALACHI PAPERS", "has_tags", "BD-R" ], [ "THE VALACHI PAPERS", "in_language", "ENGLISH" ], [ "THE VANISHING", "has_tags", "BD-R" ], [ "THE VANISHING", "in_language", "ENGLISH" ], [ "THE WATCHER IN THE WOODS", "has_genre", "MYSTERY" ], [ "THE WATCHER IN THE WOODS", "in_language", "ENGLISH" ], [ "THE WICKER MAN", "has_genre", "MYSTERY" ], [ "THE WICKER MAN", "has_tags", "BD-R" ], [ "THE WINSLOW BOY", "in_language", "ENGLISH" ], [ "THE WINSLOW BOY", "release_year", "1948" ], [ "THEY ONLY KILL THEIR MASTERS", "has_genre", "MYSTERY" ], [ "THEY ONLY KILL THEIR MASTERS", "has_tags", "BD-R" ], [ "THIS HAPPY BREED", "has_tags", "BD-R" ], [ "THIS HAPPY BREED", "in_language", "ENGLISH" ], [ "UNFAITHFULLY YOURS", "has_tags", "BD-R" ], [ "UNFAITHFULLY YOURS", "release_year", "1948" ], [ "VICTIM", "has_tags", "BD-R" ], [ "VICTIM", "in_language", "ENGLISH" ], [ "VIVA MARIA!", "has_tags", "BD-R" ], [ "VIVA MARIA!", "in_language", "ENGLISH" ], [ "WENT THE DAY WELL?", "has_tags", "BD-R" ], [ "WENT THE DAY WELL?", "in_language", "ENGLISH" ], [ "WHISTLING IN DIXIE", "has_genre", "MYSTERY" ], [ "WHISTLING IN DIXIE", "has_tags", "BD-R" ], [ "WHISTLING IN THE DARK", "has_genre", "MYSTERY" ], [ "WHISTLING IN THE DARK", "has_tags", "BD-R" ], [ "WHO'S AFRAID OF VIRGINIA WOOLF?", "has_tags", "BD-R" ], [ "WHO'S AFRAID OF VIRGINIA WOOLF?", "in_language", "ENGLISH" ], [ "WITCHFINDER GENERAL", "has_tags", "BD-R" ], [ "WITCHFINDER GENERAL", "in_language", "ENGLISH" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 28964, A PLACE OF ONE'S OWN 15359, ANTOINE BERTRAND 36388, BOURVIL 30463, COMEDY 36212, DRAMA 17468, LOUIS CYR 34047, STARBUCK 15930, THE SUCKER src, edge_attr, dst 28964, has_genre, 36212 17468, has_genre, 36212 17468, starred_actors, 15359 34047, has_genre, 30463 34047, starred_actors, 15359 15930, has_genre, 30463 15930, starred_actors, 36388 Question: In what context are A PLACE OF ONE'S OWN, ANTOINE BERTRAND, and BOURVIL connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "A PLACE OF ONE'S OWN", "ANTOINE BERTRAND", "BOURVIL" ], "valid_edges": [ [ "A PLACE OF ONE'S OWN", "has_genre", "DRAMA" ], [ "LOUIS CYR", "has_genre", "DRAMA" ], [ "LOUIS CYR", "starred_actors", "ANTOINE BERTRAND" ], [ "STARBUCK", "has_genre", "COMEDY" ], [ "STARBUCK", "starred_actors", "ANTOINE BERTRAND" ], [ "THE SUCKER", "has_genre", "COMEDY" ], [ "THE SUCKER", "starred_actors", "BOURVIL" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 37224, 1990 15374, 2005 1856, EUROPA EUROPA 7498, FLIGHTPLAN 36633, FOUR EYED MONSTERS 6480, GERMAN 34073, HELL 38312, MOUTH TO MOUTH 33684, SIBLING RIVALRY 3008, SUMMER IN BERLIN 3354, THE BROTHERS GRIMM 25829, THE CABINET OF DR. CALIGARI 39544, WE FEED THE WORLD src, edge_attr, dst 1856, has_tags, 6480 1856, in_language, 6480 1856, release_year, 37224 7498, in_language, 6480 7498, release_year, 15374 36633, release_year, 15374 34073, in_language, 6480 34073, release_year, 15374 38312, in_language, 6480 38312, release_year, 15374 33684, release_year, 37224 3008, in_language, 6480 3008, release_year, 15374 3354, in_language, 6480 3354, release_year, 15374 25829, in_language, 6480 39544, in_language, 6480 39544, release_year, 15374 Question: For what reason are FOUR EYED MONSTERS, SIBLING RIVALRY, and THE CABINET OF DR. CALIGARI associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "FOUR EYED MONSTERS", "SIBLING RIVALRY", "THE CABINET OF DR. CALIGARI" ], "valid_edges": [ [ "EUROPA EUROPA", "has_tags", "GERMAN" ], [ "EUROPA EUROPA", "in_language", "GERMAN" ], [ "EUROPA EUROPA", "release_year", "1990" ], [ "FLIGHTPLAN", "in_language", "GERMAN" ], [ "FLIGHTPLAN", "release_year", "2005" ], [ "FOUR EYED MONSTERS", "release_year", "2005" ], [ "HELL", "in_language", "GERMAN" ], [ "HELL", "release_year", "2005" ], [ "MOUTH TO MOUTH", "in_language", "GERMAN" ], [ "MOUTH TO MOUTH", "release_year", "2005" ], [ "SIBLING RIVALRY", "release_year", "1990" ], [ "SUMMER IN BERLIN", "in_language", "GERMAN" ], [ "SUMMER IN BERLIN", "release_year", "2005" ], [ "THE BROTHERS GRIMM", "in_language", "GERMAN" ], [ "THE BROTHERS GRIMM", "release_year", "2005" ], [ "THE CABINET OF DR. CALIGARI", "in_language", "GERMAN" ], [ "WE FEED THE WORLD", "in_language", "GERMAN" ], [ "WE FEED THE WORLD", "release_year", "2005" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 1006, 1996 26762, 2008 11565, GOOD 24425, HARRIET THE SPY 7575, INTIMATE RELATIONS 19855, PEYTON REED 1945, YES MAN src, edge_attr, dst 11565, has_imdb_rating, 11565 11565, release_year, 26762 24425, release_year, 1006 7575, has_imdb_rating, 11565 7575, release_year, 1006 1945, directed_by, 19855 1945, has_tags, 19855 1945, release_year, 26762 Question: In what context are HARRIET THE SPY, INTIMATE RELATIONS, and PEYTON REED connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "HARRIET THE SPY", "INTIMATE RELATIONS", "PEYTON REED" ], "valid_edges": [ [ "GOOD", "has_imdb_rating", "GOOD" ], [ "GOOD", "release_year", "2008" ], [ "HARRIET THE SPY", "release_year", "1996" ], [ "INTIMATE RELATIONS", "has_imdb_rating", "GOOD" ], [ "INTIMATE RELATIONS", "release_year", "1996" ], [ "YES MAN", "directed_by", "PEYTON REED" ], [ "YES MAN", "has_tags", "PEYTON REED" ], [ "YES MAN", "release_year", "2008" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 14259, 1997 14724, CRIME 13964, JOHN RIDLEY 15546, THE TUNNEL OF LOVE 19965, THIS WORLD, THEN THE FIREWORKS 21589, U TURN 10133, UNKNOWN src, edge_attr, dst 15546, has_imdb_votes, 10133 19965, release_year, 14259 21589, has_genre, 14724 21589, release_year, 14259 21589, written_by, 13964 10133, has_genre, 14724 Question: For what reason are JOHN RIDLEY, THE TUNNEL OF LOVE, and THIS WORLD, THEN THE FIREWORKS associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "JOHN RIDLEY", "THE TUNNEL OF LOVE", "THIS WORLD, THEN THE FIREWORKS" ], "valid_edges": [ [ "THE TUNNEL OF LOVE", "has_imdb_votes", "UNKNOWN" ], [ "THIS WORLD, THEN THE FIREWORKS", "release_year", "1997" ], [ "U TURN", "has_genre", "CRIME" ], [ "U TURN", "release_year", "1997" ], [ "U TURN", "written_by", "JOHN RIDLEY" ], [ "UNKNOWN", "has_genre", "CRIME" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 29424, 2011 658, 2012 20562, A THOUSAND WORDS 29800, ACE ATTORNEY 21413, AMARCORD 23994, BARFI! 32770, BEST MAN DOWN 39848, BLUE LIKE JAZZ 35916, BREAD AND CHOCOLATE 33711, CAESAR MUST DIE 26848, CELEBRITY 32450, CHEERFUL WEATHER FOR THE WEDDING 38745, CITY LIGHTS 30463, COMEDY 36212, DRAMA 32892, ENGLISH VINGLISH 6076, FRANCES HA 15135, GINGER AND FRED 1937, HANNAH AND HER SISTERS 39349, HONEYSUCKLE ROSE 35319, HOPE SPRINGS 25277, HUSBANDS AND WIVES 37844, HYDE PARK ON HUDSON 38589, I DO 17344, I VITELLONI 16200, ITALIAN 34634, JASON BUTLER HARNER 18699, JULIET OF THE SPIRITS 25104, LA DOLCE VITA 14931, LIBERAL ARTS 24419, LIFE IS BEAUTIFUL 17372, LOL 19802, LOVE 13672, LOVE ACTUALLY 34517, LUV 35871, MAGIC MIKE 33790, MANHATTAN 34611, ME AND YOU 831, MELINDA AND MELINDA 19216, MID-AUGUST LUNCH 31735, MIDDLE OF NOWHERE 32186, ONE MAN UP 34602, PUNCH-DRUNK LOVE 25023, QUARTET 35746, REALITY 11839, REVENGE FOR JOLLY! 12597, RUBY SPARKS 12153, RUSHMORE 35586, SAHARA 17168, SEEKING A FRIEND FOR THE END OF THE WORLD 25060, SEXUAL CHRONICLES OF A FRENCH FAMILY 34584, SILVER LININGS PLAYBOOK 28026, SOMEBODY UP THERE LIKES ME 7288, STARDUST MEMORIES 25002, STRUCK BY LIGHTNING 3038, STUCK IN LOVE 32984, SWEET AND LOWDOWN 26790, SWEPT AWAY 19501, THANKS FOR SHARING 4157, THE BEST MAN 36932, THE CAIMAN 6644, THE COMEDY 15373, THE FRONT 34673, THE GREEN 15980, THE GUILT TRIP 28107, THE LAST KISS 28217, THE LIFE AQUATIC WITH STEVE ZISSOU 15798, THE ODD LIFE OF TIMOTHY GREEN 20728, THE ROYAL TENENBAUMS 2739, THE SAPPHIRES 37915, THE STORY OF LUKE 6598, TO ROME WITH LOVE 4325, WE ALL LOVED EACH OTHER SO MUCH 35511, WE HAVE A POPE 34118, WHAT TO EXPECT WHEN YOU'RE EXPECTING 35681, WILLIE NELSON 24451, WONDER BOYS 21552, WOODY ALLEN 6279, YOU WILL MEET A TALL DARK STRANGER src, edge_attr, dst 20562, has_genre, 30463 20562, release_year, 658 29800, has_genre, 30463 29800, release_year, 658 21413, has_genre, 30463 21413, in_language, 16200 23994, has_genre, 30463 23994, release_year, 658 32770, has_genre, 30463 32770, release_year, 658 39848, has_genre, 30463 39848, release_year, 658 35916, has_genre, 30463 35916, in_language, 16200 33711, in_language, 16200 33711, release_year, 658 26848, directed_by, 21552 26848, has_genre, 30463 26848, has_tags, 21552 26848, written_by, 21552 32450, has_genre, 30463 32450, release_year, 658 38745, has_genre, 30463 38745, has_tags, 30463 38745, has_tags, 19802 32892, has_genre, 30463 32892, release_year, 658 6076, has_genre, 30463 6076, release_year, 658 15135, has_genre, 30463 15135, in_language, 16200 1937, directed_by, 21552 1937, has_genre, 30463 1937, has_tags, 30463 1937, has_tags, 21552 1937, written_by, 21552 39349, has_genre, 36212 39349, starred_actors, 35681 35319, has_genre, 30463 35319, release_year, 658 25277, directed_by, 21552 25277, has_genre, 30463 25277, has_tags, 21552 25277, starred_actors, 21552 25277, written_by, 21552 37844, has_genre, 30463 37844, release_year, 658 38589, has_genre, 30463 38589, release_year, 658 17344, has_genre, 30463 17344, has_tags, 16200 17344, in_language, 16200 18699, has_genre, 30463 18699, has_tags, 16200 18699, in_language, 16200 25104, has_genre, 30463 25104, has_tags, 16200 25104, in_language, 16200 14931, has_genre, 30463 14931, release_year, 658 24419, has_genre, 30463 24419, has_tags, 30463 24419, has_tags, 16200 24419, in_language, 16200 17372, has_genre, 30463 17372, release_year, 658 19802, has_genre, 36212 19802, release_year, 29424 13672, has_genre, 30463 13672, has_tags, 30463 13672, has_tags, 19802 34517, has_genre, 30463 34517, release_year, 658 35871, has_genre, 30463 35871, release_year, 658 33790, directed_by, 21552 33790, has_genre, 30463 33790, has_tags, 30463 33790, has_tags, 19802 33790, has_tags, 21552 33790, starred_actors, 21552 33790, written_by, 21552 34611, in_language, 16200 34611, release_year, 658 831, directed_by, 21552 831, has_genre, 30463 831, has_tags, 21552 831, written_by, 21552 19216, has_genre, 30463 19216, in_language, 16200 31735, has_genre, 30463 31735, release_year, 658 32186, has_genre, 30463 32186, in_language, 16200 34602, has_genre, 30463 34602, has_tags, 30463 34602, has_tags, 19802 25023, has_genre, 30463 25023, has_tags, 30463 25023, release_year, 658 35746, in_language, 16200 35746, release_year, 658 11839, has_genre, 30463 11839, release_year, 658 12597, has_genre, 30463 12597, release_year, 658 12153, has_genre, 30463 12153, has_tags, 30463 12153, has_tags, 19802 35586, has_genre, 30463 35586, in_language, 16200 17168, has_genre, 30463 17168, release_year, 658 25060, has_genre, 30463 25060, release_year, 658 34584, has_genre, 30463 34584, release_year, 658 28026, has_genre, 30463 28026, release_year, 658 7288, directed_by, 21552 7288, has_genre, 30463 7288, has_tags, 21552 7288, starred_actors, 21552 7288, written_by, 21552 25002, has_genre, 30463 25002, release_year, 658 3038, has_genre, 30463 3038, has_tags, 19802 3038, release_year, 658 32984, directed_by, 21552 32984, has_genre, 30463 32984, has_tags, 21552 32984, starred_actors, 21552 32984, written_by, 21552 26790, has_genre, 30463 26790, in_language, 16200 19501, has_genre, 30463 19501, release_year, 658 4157, has_genre, 30463 4157, in_language, 16200 36932, has_genre, 30463 36932, in_language, 16200 6644, has_tags, 30463 6644, release_year, 658 15373, has_genre, 30463 15373, has_tags, 21552 15373, starred_actors, 21552 34673, has_genre, 36212 34673, release_year, 29424 34673, starred_actors, 34634 15980, has_genre, 30463 15980, release_year, 658 28107, has_genre, 30463 28107, in_language, 16200 28217, has_genre, 30463 28217, has_tags, 30463 28217, in_language, 16200 15798, has_genre, 30463 15798, release_year, 658 20728, has_genre, 30463 20728, has_tags, 30463 20728, has_tags, 19802 2739, has_genre, 30463 2739, release_year, 658 37915, has_genre, 30463 37915, release_year, 658 6598, directed_by, 21552 6598, has_genre, 30463 6598, has_tags, 30463 6598, has_tags, 19802 6598, has_tags, 21552 6598, in_language, 16200 6598, release_year, 658 6598, written_by, 21552 4325, has_genre, 30463 4325, in_language, 16200 35511, has_genre, 30463 35511, has_tags, 30463 35511, has_tags, 16200 35511, in_language, 16200 34118, has_genre, 30463 34118, release_year, 658 24451, has_genre, 30463 24451, has_tags, 19802 6279, directed_by, 21552 6279, has_genre, 30463 6279, has_tags, 30463 6279, has_tags, 21552 6279, written_by, 21552 Question: How are JASON BUTLER HARNER, TO ROME WITH LOVE, and WILLIE NELSON related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "JASON BUTLER HARNER", "TO ROME WITH LOVE", "WILLIE NELSON" ], "valid_edges": [ [ "A THOUSAND WORDS", "has_genre", "COMEDY" ], [ "A THOUSAND WORDS", "release_year", "2012" ], [ "ACE ATTORNEY", "has_genre", "COMEDY" ], [ "ACE ATTORNEY", "release_year", "2012" ], [ "AMARCORD", "has_genre", "COMEDY" ], [ "AMARCORD", "in_language", "ITALIAN" ], [ "BARFI!", "has_genre", "COMEDY" ], [ "BARFI!", "release_year", "2012" ], [ "BEST MAN DOWN", "has_genre", "COMEDY" ], [ "BEST MAN DOWN", "release_year", "2012" ], [ "BLUE LIKE JAZZ", "has_genre", "COMEDY" ], [ "BLUE LIKE JAZZ", "release_year", "2012" ], [ "BREAD AND CHOCOLATE", "has_genre", "COMEDY" ], [ "BREAD AND CHOCOLATE", "in_language", "ITALIAN" ], [ "CAESAR MUST DIE", "in_language", "ITALIAN" ], [ "CAESAR MUST DIE", "release_year", "2012" ], [ "CELEBRITY", "directed_by", "WOODY ALLEN" ], [ "CELEBRITY", "has_genre", "COMEDY" ], [ "CELEBRITY", "has_tags", "WOODY ALLEN" ], [ "CELEBRITY", "written_by", "WOODY ALLEN" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "has_genre", "COMEDY" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "release_year", "2012" ], [ "CITY LIGHTS", "has_genre", "COMEDY" ], [ "CITY LIGHTS", "has_tags", "COMEDY" ], [ "CITY LIGHTS", "has_tags", "LOVE" ], [ "ENGLISH VINGLISH", "has_genre", "COMEDY" ], [ "ENGLISH VINGLISH", "release_year", "2012" ], [ "FRANCES HA", "has_genre", "COMEDY" ], [ "FRANCES HA", "release_year", "2012" ], [ "GINGER AND FRED", "has_genre", "COMEDY" ], [ "GINGER AND FRED", "in_language", "ITALIAN" ], [ "HANNAH AND HER SISTERS", "directed_by", "WOODY ALLEN" ], [ "HANNAH AND HER SISTERS", "has_genre", "COMEDY" ], [ "HANNAH AND HER SISTERS", "has_tags", "COMEDY" ], [ "HANNAH AND HER SISTERS", "has_tags", "WOODY ALLEN" ], [ "HANNAH AND HER SISTERS", "written_by", "WOODY ALLEN" ], [ "HONEYSUCKLE ROSE", "has_genre", "DRAMA" ], [ "HONEYSUCKLE ROSE", "starred_actors", "WILLIE NELSON" ], [ "HOPE SPRINGS", "has_genre", "COMEDY" ], [ "HOPE SPRINGS", "release_year", "2012" ], [ "HUSBANDS AND WIVES", "directed_by", "WOODY ALLEN" ], [ "HUSBANDS AND WIVES", "has_genre", "COMEDY" ], [ "HUSBANDS AND WIVES", "has_tags", "WOODY ALLEN" ], [ "HUSBANDS AND WIVES", "starred_actors", "WOODY ALLEN" ], [ "HUSBANDS AND WIVES", "written_by", "WOODY ALLEN" ], [ "HYDE PARK ON HUDSON", "has_genre", "COMEDY" ], [ "HYDE PARK ON HUDSON", "release_year", "2012" ], [ "I DO", "has_genre", "COMEDY" ], [ "I DO", "release_year", "2012" ], [ "I VITELLONI", "has_genre", "COMEDY" ], [ "I VITELLONI", "has_tags", "ITALIAN" ], [ "I VITELLONI", "in_language", "ITALIAN" ], [ "JULIET OF THE SPIRITS", "has_genre", "COMEDY" ], [ "JULIET OF THE SPIRITS", "has_tags", "ITALIAN" ], [ "JULIET OF THE SPIRITS", "in_language", "ITALIAN" ], [ "LA DOLCE VITA", "has_genre", "COMEDY" ], [ "LA DOLCE VITA", "has_tags", "ITALIAN" ], [ "LA DOLCE VITA", "in_language", "ITALIAN" ], [ "LIBERAL ARTS", "has_genre", "COMEDY" ], [ "LIBERAL ARTS", "release_year", "2012" ], [ "LIFE IS BEAUTIFUL", "has_genre", "COMEDY" ], [ "LIFE IS BEAUTIFUL", "has_tags", "COMEDY" ], [ "LIFE IS BEAUTIFUL", "has_tags", "ITALIAN" ], [ "LIFE IS BEAUTIFUL", "in_language", "ITALIAN" ], [ "LOL", "has_genre", "COMEDY" ], [ "LOL", "release_year", "2012" ], [ "LOVE", "has_genre", "DRAMA" ], [ "LOVE", "release_year", "2011" ], [ "LOVE ACTUALLY", "has_genre", "COMEDY" ], [ "LOVE ACTUALLY", "has_tags", "COMEDY" ], [ "LOVE ACTUALLY", "has_tags", "LOVE" ], [ "LUV", "has_genre", "COMEDY" ], [ "LUV", "release_year", "2012" ], [ "MAGIC MIKE", "has_genre", "COMEDY" ], [ "MAGIC MIKE", "release_year", "2012" ], [ "MANHATTAN", "directed_by", "WOODY ALLEN" ], [ "MANHATTAN", "has_genre", "COMEDY" ], [ "MANHATTAN", "has_tags", "COMEDY" ], [ "MANHATTAN", "has_tags", "LOVE" ], [ "MANHATTAN", "has_tags", "WOODY ALLEN" ], [ "MANHATTAN", "starred_actors", "WOODY ALLEN" ], [ "MANHATTAN", "written_by", "WOODY ALLEN" ], [ "ME AND YOU", "in_language", "ITALIAN" ], [ "ME AND YOU", "release_year", "2012" ], [ "MELINDA AND MELINDA", "directed_by", "WOODY ALLEN" ], [ "MELINDA AND MELINDA", "has_genre", "COMEDY" ], [ "MELINDA AND MELINDA", "has_tags", "WOODY ALLEN" ], [ "MELINDA AND MELINDA", "written_by", "WOODY ALLEN" ], [ "MID-AUGUST LUNCH", "has_genre", "COMEDY" ], [ "MID-AUGUST LUNCH", "in_language", "ITALIAN" ], [ "MIDDLE OF NOWHERE", "has_genre", "COMEDY" ], [ "MIDDLE OF NOWHERE", "release_year", "2012" ], [ "ONE MAN UP", "has_genre", "COMEDY" ], [ "ONE MAN UP", "in_language", "ITALIAN" ], [ "PUNCH-DRUNK LOVE", "has_genre", "COMEDY" ], [ "PUNCH-DRUNK LOVE", "has_tags", "COMEDY" ], [ "PUNCH-DRUNK LOVE", "has_tags", "LOVE" ], [ "QUARTET", "has_genre", "COMEDY" ], [ "QUARTET", "has_tags", "COMEDY" ], [ "QUARTET", "release_year", "2012" ], [ "REALITY", "in_language", "ITALIAN" ], [ "REALITY", "release_year", "2012" ], [ "REVENGE FOR JOLLY!", "has_genre", "COMEDY" ], [ "REVENGE FOR JOLLY!", "release_year", "2012" ], [ "RUBY SPARKS", "has_genre", "COMEDY" ], [ "RUBY SPARKS", "release_year", "2012" ], [ "RUSHMORE", "has_genre", "COMEDY" ], [ "RUSHMORE", "has_tags", "COMEDY" ], [ "RUSHMORE", "has_tags", "LOVE" ], [ "SAHARA", "has_genre", "COMEDY" ], [ "SAHARA", "in_language", "ITALIAN" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "has_genre", "COMEDY" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "release_year", "2012" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "has_genre", "COMEDY" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "release_year", "2012" ], [ "SILVER LININGS PLAYBOOK", "has_genre", "COMEDY" ], [ "SILVER LININGS PLAYBOOK", "release_year", "2012" ], [ "SOMEBODY UP THERE LIKES ME", "has_genre", "COMEDY" ], [ "SOMEBODY UP THERE LIKES ME", "release_year", "2012" ], [ "STARDUST MEMORIES", "directed_by", "WOODY ALLEN" ], [ "STARDUST MEMORIES", "has_genre", "COMEDY" ], [ "STARDUST MEMORIES", "has_tags", "WOODY ALLEN" ], [ "STARDUST MEMORIES", "starred_actors", "WOODY ALLEN" ], [ "STARDUST MEMORIES", "written_by", "WOODY ALLEN" ], [ "STRUCK BY LIGHTNING", "has_genre", "COMEDY" ], [ "STRUCK BY LIGHTNING", "release_year", "2012" ], [ "STUCK IN LOVE", "has_genre", "COMEDY" ], [ "STUCK IN LOVE", "has_tags", "LOVE" ], [ "STUCK IN LOVE", "release_year", "2012" ], [ "SWEET AND LOWDOWN", "directed_by", "WOODY ALLEN" ], [ "SWEET AND LOWDOWN", "has_genre", "COMEDY" ], [ "SWEET AND LOWDOWN", "has_tags", "WOODY ALLEN" ], [ "SWEET AND LOWDOWN", "starred_actors", "WOODY ALLEN" ], [ "SWEET AND LOWDOWN", "written_by", "WOODY ALLEN" ], [ "SWEPT AWAY", "has_genre", "COMEDY" ], [ "SWEPT AWAY", "in_language", "ITALIAN" ], [ "THANKS FOR SHARING", "has_genre", "COMEDY" ], [ "THANKS FOR SHARING", "release_year", "2012" ], [ "THE BEST MAN", "has_genre", "COMEDY" ], [ "THE BEST MAN", "in_language", "ITALIAN" ], [ "THE CAIMAN", "has_genre", "COMEDY" ], [ "THE CAIMAN", "in_language", "ITALIAN" ], [ "THE COMEDY", "has_tags", "COMEDY" ], [ "THE COMEDY", "release_year", "2012" ], [ "THE FRONT", "has_genre", "COMEDY" ], [ "THE FRONT", "has_tags", "WOODY ALLEN" ], [ "THE FRONT", "starred_actors", "WOODY ALLEN" ], [ "THE GREEN", "has_genre", "DRAMA" ], [ "THE GREEN", "release_year", "2011" ], [ "THE GREEN", "starred_actors", "JASON BUTLER HARNER" ], [ "THE GUILT TRIP", "has_genre", "COMEDY" ], [ "THE GUILT TRIP", "release_year", "2012" ], [ "THE LAST KISS", "has_genre", "COMEDY" ], [ "THE LAST KISS", "in_language", "ITALIAN" ], [ "THE LIFE AQUATIC WITH STEVE ZISSOU", "has_genre", "COMEDY" ], [ "THE LIFE AQUATIC WITH STEVE ZISSOU", "has_tags", "COMEDY" ], [ "THE LIFE AQUATIC WITH STEVE ZISSOU", "in_language", "ITALIAN" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "has_genre", "COMEDY" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "release_year", "2012" ], [ "THE ROYAL TENENBAUMS", "has_genre", "COMEDY" ], [ "THE ROYAL TENENBAUMS", "has_tags", "COMEDY" ], [ "THE ROYAL TENENBAUMS", "has_tags", "LOVE" ], [ "THE SAPPHIRES", "has_genre", "COMEDY" ], [ "THE SAPPHIRES", "release_year", "2012" ], [ "THE STORY OF LUKE", "has_genre", "COMEDY" ], [ "THE STORY OF LUKE", "release_year", "2012" ], [ "TO ROME WITH LOVE", "directed_by", "WOODY ALLEN" ], [ "TO ROME WITH LOVE", "has_genre", "COMEDY" ], [ "TO ROME WITH LOVE", "has_tags", "COMEDY" ], [ "TO ROME WITH LOVE", "has_tags", "LOVE" ], [ "TO ROME WITH LOVE", "has_tags", "WOODY ALLEN" ], [ "TO ROME WITH LOVE", "in_language", "ITALIAN" ], [ "TO ROME WITH LOVE", "release_year", "2012" ], [ "TO ROME WITH LOVE", "written_by", "WOODY ALLEN" ], [ "WE ALL LOVED EACH OTHER SO MUCH", "has_genre", "COMEDY" ], [ "WE ALL LOVED EACH OTHER SO MUCH", "in_language", "ITALIAN" ], [ "WE HAVE A POPE", "has_genre", "COMEDY" ], [ "WE HAVE A POPE", "has_tags", "COMEDY" ], [ "WE HAVE A POPE", "has_tags", "ITALIAN" ], [ "WE HAVE A POPE", "in_language", "ITALIAN" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "has_genre", "COMEDY" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "release_year", "2012" ], [ "WONDER BOYS", "has_genre", "COMEDY" ], [ "WONDER BOYS", "has_tags", "LOVE" ], [ "YOU WILL MEET A TALL DARK STRANGER", "directed_by", "WOODY ALLEN" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_genre", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_tags", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_tags", "WOODY ALLEN" ], [ "YOU WILL MEET A TALL DARK STRANGER", "written_by", "WOODY ALLEN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6925, 1966 13408, 2001 658, 2012 166, A MAN AND A WOMAN 20786, CLAUDE LELOUCH 24124, GAMBIT 32305, GEORGE W. GEORGE 31828, JAMES DEAN 14601, LES MISÉRABLES 5365, THE BODY 13676, THE JAMES DEAN STORY src, edge_attr, dst 166, directed_by, 20786 166, has_tags, 20786 166, release_year, 6925 24124, release_year, 6925 24124, release_year, 658 31828, release_year, 13408 14601, directed_by, 20786 14601, has_tags, 20786 14601, release_year, 658 14601, written_by, 20786 5365, release_year, 13408 5365, release_year, 658 13676, directed_by, 32305 13676, starred_actors, 31828 Question: For what reason are A MAN AND A WOMAN, GEORGE W. GEORGE, and THE BODY associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "A MAN AND A WOMAN", "GEORGE W. GEORGE", "THE BODY" ], "valid_edges": [ [ "A MAN AND A WOMAN", "directed_by", "CLAUDE LELOUCH" ], [ "A MAN AND A WOMAN", "has_tags", "CLAUDE LELOUCH" ], [ "A MAN AND A WOMAN", "release_year", "1966" ], [ "GAMBIT", "release_year", "1966" ], [ "GAMBIT", "release_year", "2012" ], [ "JAMES DEAN", "release_year", "2001" ], [ "LES MISÉRABLES", "directed_by", "CLAUDE LELOUCH" ], [ "LES MISÉRABLES", "has_tags", "CLAUDE LELOUCH" ], [ "LES MISÉRABLES", "release_year", "2012" ], [ "LES MISÉRABLES", "written_by", "CLAUDE LELOUCH" ], [ "THE BODY", "release_year", "2001" ], [ "THE BODY", "release_year", "2012" ], [ "THE JAMES DEAN STORY", "directed_by", "GEORGE W. GEORGE" ], [ "THE JAMES DEAN STORY", "starred_actors", "JAMES DEAN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 39396, BLACK DYNAMITE 30463, COMEDY 1884, FUN 358, HOWARD THE DUCK 31646, KNIGHT AND DAY 17559, LEGALLY BLONDE 36771, MYRA BRECKINRIDGE 22572, RAFAL ZIELINSKI 6255, THE MAGNET 29641, THE PRINCESS BRIDE 27619, THE WRONG TROUSERS 13738, THERE'S SOMETHING ABOUT MARY 22157, WHEN HARRY MET SALLY... src, edge_attr, dst 39396, has_genre, 30463 39396, has_tags, 1884 1884, directed_by, 22572 358, has_genre, 30463 358, has_tags, 1884 31646, has_genre, 30463 31646, has_tags, 30463 31646, has_tags, 1884 17559, has_genre, 30463 17559, has_tags, 30463 17559, has_tags, 1884 36771, has_genre, 30463 6255, has_genre, 30463 29641, has_genre, 30463 29641, has_tags, 30463 29641, has_tags, 1884 27619, has_genre, 30463 27619, has_tags, 30463 27619, has_tags, 1884 13738, has_genre, 30463 13738, has_tags, 30463 13738, has_tags, 1884 22157, has_genre, 30463 22157, has_tags, 1884 Question: In what context are MYRA BRECKINRIDGE, RAFAL ZIELINSKI, and THE MAGNET connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "MYRA BRECKINRIDGE", "RAFAL ZIELINSKI", "THE MAGNET" ], "valid_edges": [ [ "BLACK DYNAMITE", "has_genre", "COMEDY" ], [ "BLACK DYNAMITE", "has_tags", "FUN" ], [ "FUN", "directed_by", "RAFAL ZIELINSKI" ], [ "HOWARD THE DUCK", "has_genre", "COMEDY" ], [ "HOWARD THE DUCK", "has_tags", "FUN" ], [ "KNIGHT AND DAY", "has_genre", "COMEDY" ], [ "KNIGHT AND DAY", "has_tags", "COMEDY" ], [ "KNIGHT AND DAY", "has_tags", "FUN" ], [ "LEGALLY BLONDE", "has_genre", "COMEDY" ], [ "LEGALLY BLONDE", "has_tags", "COMEDY" ], [ "LEGALLY BLONDE", "has_tags", "FUN" ], [ "MYRA BRECKINRIDGE", "has_genre", "COMEDY" ], [ "THE MAGNET", "has_genre", "COMEDY" ], [ "THE PRINCESS BRIDE", "has_genre", "COMEDY" ], [ "THE PRINCESS BRIDE", "has_tags", "COMEDY" ], [ "THE PRINCESS BRIDE", "has_tags", "FUN" ], [ "THE WRONG TROUSERS", "has_genre", "COMEDY" ], [ "THE WRONG TROUSERS", "has_tags", "COMEDY" ], [ "THE WRONG TROUSERS", "has_tags", "FUN" ], [ "THERE'S SOMETHING ABOUT MARY", "has_genre", "COMEDY" ], [ "THERE'S SOMETHING ABOUT MARY", "has_tags", "COMEDY" ], [ "THERE'S SOMETHING ABOUT MARY", "has_tags", "FUN" ], [ "WHEN HARRY MET SALLY...", "has_genre", "COMEDY" ], [ "WHEN HARRY MET SALLY...", "has_tags", "FUN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 18366, 1960 8486, 1999 30284, ALL ABOUT MY MOTHER 2067, BETWEEN YOUR LEGS 27477, BIUTIFUL 12217, DON'T TEMPT ME 35657, GLORIA 10457, GOYA IN BORDEAUX 10242, INHERIT THE WIND 36, JAVIER BARDEM 233, LIVE FLESH 33721, LOVE CAN SERIOUSLY DAMAGE YOUR HEALTH 30409, MANUEL GÓMEZ PEREIRA 31871, MARIANO COHN 37995, MONDAYS IN THE SUN 37006, NO ONE WRITES TO THE COLONEL 23816, NOBODY KNOWS ANYBODY 11432, SECOND SKIN 21147, SOLAS 7556, SPANISH 32597, THE DANCER UPSTAIRS 475, THE MAN NEXT DOOR 29990, THE NAMELESS 37903, THE NINTH GATE 5969, THE SEA INSIDE 15139, THE WARPED ONES 30250, TIE ME UP! TIE ME DOWN! 39026, TWO WOMEN 21343, VICTORIA ABRIL src, edge_attr, dst 30284, has_tags, 7556 30284, in_language, 7556 30284, release_year, 8486 2067, directed_by, 30409 2067, in_language, 7556 2067, release_year, 8486 2067, starred_actors, 36 2067, starred_actors, 21343 2067, written_by, 30409 27477, has_tags, 36 27477, in_language, 7556 27477, starred_actors, 36 12217, in_language, 7556 12217, starred_actors, 21343 35657, in_language, 7556 35657, release_year, 8486 10457, in_language, 7556 10457, release_year, 8486 10242, release_year, 18366 10242, release_year, 8486 233, in_language, 7556 233, starred_actors, 36 33721, directed_by, 30409 33721, in_language, 7556 33721, written_by, 30409 37995, in_language, 7556 37995, starred_actors, 36 37006, in_language, 7556 37006, release_year, 8486 23816, in_language, 7556 23816, release_year, 8486 11432, in_language, 7556 11432, release_year, 8486 11432, starred_actors, 36 21147, in_language, 7556 21147, release_year, 8486 32597, in_language, 7556 32597, starred_actors, 36 475, directed_by, 31871 475, in_language, 7556 29990, in_language, 7556 29990, release_year, 8486 37903, in_language, 7556 37903, release_year, 8486 5969, has_tags, 36 5969, in_language, 7556 5969, starred_actors, 36 15139, release_year, 18366 30250, has_tags, 7556 30250, in_language, 7556 30250, starred_actors, 21343 39026, release_year, 18366 39026, release_year, 8486 Question: In what context are BETWEEN YOUR LEGS, MARIANO COHN, and THE WARPED ONES connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BETWEEN YOUR LEGS", "MARIANO COHN", "THE WARPED ONES" ], "valid_edges": [ [ "ALL ABOUT MY MOTHER", "has_tags", "SPANISH" ], [ "ALL ABOUT MY MOTHER", "in_language", "SPANISH" ], [ "ALL ABOUT MY MOTHER", "release_year", "1999" ], [ "BETWEEN YOUR LEGS", "directed_by", "MANUEL GÓMEZ PEREIRA" ], [ "BETWEEN YOUR LEGS", "in_language", "SPANISH" ], [ "BETWEEN YOUR LEGS", "release_year", "1999" ], [ "BETWEEN YOUR LEGS", "starred_actors", "JAVIER BARDEM" ], [ "BETWEEN YOUR LEGS", "starred_actors", "VICTORIA ABRIL" ], [ "BETWEEN YOUR LEGS", "written_by", "MANUEL GÓMEZ PEREIRA" ], [ "BIUTIFUL", "has_tags", "JAVIER BARDEM" ], [ "BIUTIFUL", "in_language", "SPANISH" ], [ "BIUTIFUL", "starred_actors", "JAVIER BARDEM" ], [ "DON'T TEMPT ME", "in_language", "SPANISH" ], [ "DON'T TEMPT ME", "starred_actors", "VICTORIA ABRIL" ], [ "GLORIA", "in_language", "SPANISH" ], [ "GLORIA", "release_year", "1999" ], [ "GOYA IN BORDEAUX", "in_language", "SPANISH" ], [ "GOYA IN BORDEAUX", "release_year", "1999" ], [ "INHERIT THE WIND", "release_year", "1960" ], [ "INHERIT THE WIND", "release_year", "1999" ], [ "LIVE FLESH", "in_language", "SPANISH" ], [ "LIVE FLESH", "starred_actors", "JAVIER BARDEM" ], [ "LOVE CAN SERIOUSLY DAMAGE YOUR HEALTH", "directed_by", "MANUEL GÓMEZ PEREIRA" ], [ "LOVE CAN SERIOUSLY DAMAGE YOUR HEALTH", "in_language", "SPANISH" ], [ "LOVE CAN SERIOUSLY DAMAGE YOUR HEALTH", "written_by", "MANUEL GÓMEZ PEREIRA" ], [ "MONDAYS IN THE SUN", "in_language", "SPANISH" ], [ "MONDAYS IN THE SUN", "starred_actors", "JAVIER BARDEM" ], [ "NO ONE WRITES TO THE COLONEL", "in_language", "SPANISH" ], [ "NO ONE WRITES TO THE COLONEL", "release_year", "1999" ], [ "NOBODY KNOWS ANYBODY", "in_language", "SPANISH" ], [ "NOBODY KNOWS ANYBODY", "release_year", "1999" ], [ "SECOND SKIN", "in_language", "SPANISH" ], [ "SECOND SKIN", "release_year", "1999" ], [ "SECOND SKIN", "starred_actors", "JAVIER BARDEM" ], [ "SOLAS", "in_language", "SPANISH" ], [ "SOLAS", "release_year", "1999" ], [ "THE DANCER UPSTAIRS", "in_language", "SPANISH" ], [ "THE DANCER UPSTAIRS", "starred_actors", "JAVIER BARDEM" ], [ "THE MAN NEXT DOOR", "directed_by", "MARIANO COHN" ], [ "THE MAN NEXT DOOR", "in_language", "SPANISH" ], [ "THE NAMELESS", "in_language", "SPANISH" ], [ "THE NAMELESS", "release_year", "1999" ], [ "THE NINTH GATE", "in_language", "SPANISH" ], [ "THE NINTH GATE", "release_year", "1999" ], [ "THE SEA INSIDE", "has_tags", "JAVIER BARDEM" ], [ "THE SEA INSIDE", "in_language", "SPANISH" ], [ "THE SEA INSIDE", "starred_actors", "JAVIER BARDEM" ], [ "THE WARPED ONES", "release_year", "1960" ], [ "TIE ME UP! TIE ME DOWN!", "has_tags", "SPANISH" ], [ "TIE ME UP! TIE ME DOWN!", "in_language", "SPANISH" ], [ "TIE ME UP! TIE ME DOWN!", "starred_actors", "VICTORIA ABRIL" ], [ "TWO WOMEN", "release_year", "1960" ], [ "TWO WOMEN", "release_year", "1999" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 22041, A BULLET FOR THE GENERAL 12694, A PISTOL FOR RINGO 14053, ACE HIGH 4763, ADVENTURE 26786, APARTMENT 143 17157, ARABIAN NIGHTS 19219, BACK TO THE FUTURE PART III 38657, BEAT THE DEVIL 14981, BREAKHEART PASS 29632, CHINA 9, LIBERTY 37 13888, CITY OF THE LIVING DEAD 8438, CUT AND RUN 20620, DJANGO 17645, DJANGO THE BASTARD 7704, FOR A FEW DOLLARS MORE 22710, GOD'S GUN 2600, HELL OF THE LIVING DEAD 5870, HORROR 24167, INFERNO 16200, ITALIAN 22136, JESSE JAMES MEETS FRANKENSTEIN'S DAUGHTER 20149, LAST OF THE DOGMEN 18287, MAN OF THE EAST 1909, MANUEL POIRIER 25283, MAVERICK 14237, NEAR DARK 147, ONCE UPON A TIME IN THE WEST 38962, OPERA 16671, RUN, MAN, RUN 31235, SABATA 35586, SAHARA 4109, SEVEN DOLLARS ON THE RED 8436, SPIRITS OF THE DEAD 29427, STARCRASH 26790, SWEPT AWAY 12891, TENTACLES 10334, THE CHURCH 23804, THE COMANCHEROS 36158, THE FIVE MAN ARMY 21435, THE GOOD, THE BAD AND THE UGLY 3711, THE GREAT SILENCE 32270, THE HORSEMAN ON THE ROOF 37677, THE HUNTING PARTY 28599, THE MOUNTAIN MEN 26820, THE MUMMY 7342, THEY CALL ME TRINITY 32079, ULYSSES 919, VAMPIRE IN VENICE 10352, WARLOCK 36026, WESTERN 29631, WHITE FANG src, edge_attr, dst 22041, has_genre, 36026 22041, in_language, 16200 12694, has_genre, 36026 12694, in_language, 16200 14053, has_genre, 36026 14053, in_language, 16200 26786, has_genre, 5870 17157, has_genre, 4763 17157, in_language, 16200 19219, has_genre, 4763 19219, has_tags, 4763 19219, has_tags, 36026 38657, has_genre, 4763 38657, in_language, 16200 14981, has_genre, 36026 14981, has_tags, 4763 14981, has_tags, 36026 29632, has_genre, 36026 29632, in_language, 16200 13888, has_genre, 5870 13888, has_tags, 5870 13888, has_tags, 16200 13888, in_language, 16200 8438, has_genre, 4763 8438, in_language, 16200 20620, has_genre, 36026 20620, in_language, 16200 17645, has_genre, 36026 17645, in_language, 16200 7704, has_genre, 36026 7704, has_tags, 16200 7704, has_tags, 36026 7704, in_language, 16200 22710, has_genre, 36026 22710, in_language, 16200 2600, has_genre, 5870 2600, in_language, 16200 24167, has_genre, 5870 24167, in_language, 16200 22136, has_genre, 5870 22136, has_genre, 36026 20149, has_genre, 4763 20149, has_genre, 36026 18287, has_genre, 36026 18287, in_language, 16200 25283, has_genre, 4763 25283, has_tags, 36026 14237, has_genre, 5870 14237, has_tags, 36026 147, has_genre, 36026 147, has_tags, 36026 147, in_language, 16200 38962, has_genre, 5870 38962, in_language, 16200 16671, has_genre, 36026 16671, in_language, 16200 31235, has_genre, 36026 31235, in_language, 16200 35586, has_genre, 4763 35586, in_language, 16200 4109, has_genre, 36026 4109, in_language, 16200 8436, has_genre, 5870 8436, in_language, 16200 29427, has_genre, 4763 29427, in_language, 16200 26790, has_genre, 4763 26790, in_language, 16200 12891, has_genre, 5870 12891, has_tags, 5870 12891, has_tags, 16200 10334, has_genre, 5870 10334, in_language, 16200 23804, has_genre, 4763 23804, has_genre, 36026 36158, has_genre, 36026 36158, in_language, 16200 21435, has_genre, 36026 21435, has_tags, 16200 21435, has_tags, 36026 21435, in_language, 16200 3711, has_genre, 36026 3711, in_language, 16200 32270, has_genre, 4763 32270, in_language, 16200 37677, has_genre, 4763 37677, has_genre, 36026 28599, has_genre, 4763 28599, has_genre, 36026 26820, has_genre, 4763 26820, has_genre, 5870 26820, has_tags, 4763 26820, has_tags, 5870 7342, has_genre, 36026 7342, in_language, 16200 32079, has_genre, 4763 32079, in_language, 16200 919, has_genre, 5870 919, in_language, 16200 10352, has_genre, 5870 10352, has_genre, 36026 36026, directed_by, 1909 36026, written_by, 1909 29631, has_genre, 4763 29631, in_language, 16200 Question: In what context are APARTMENT 143, MANUEL POIRIER, and SAHARA connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "APARTMENT 143", "MANUEL POIRIER", "SAHARA" ], "valid_edges": [ [ "A BULLET FOR THE GENERAL", "has_genre", "WESTERN" ], [ "A BULLET FOR THE GENERAL", "in_language", "ITALIAN" ], [ "A PISTOL FOR RINGO", "has_genre", "WESTERN" ], [ "A PISTOL FOR RINGO", "in_language", "ITALIAN" ], [ "ACE HIGH", "has_genre", "WESTERN" ], [ "ACE HIGH", "in_language", "ITALIAN" ], [ "APARTMENT 143", "has_genre", "HORROR" ], [ "ARABIAN NIGHTS", "has_genre", "ADVENTURE" ], [ "ARABIAN NIGHTS", "in_language", "ITALIAN" ], [ "BACK TO THE FUTURE PART III", "has_genre", "ADVENTURE" ], [ "BACK TO THE FUTURE PART III", "has_tags", "ADVENTURE" ], [ "BACK TO THE FUTURE PART III", "has_tags", "WESTERN" ], [ "BEAT THE DEVIL", "has_genre", "ADVENTURE" ], [ "BEAT THE DEVIL", "in_language", "ITALIAN" ], [ "BREAKHEART PASS", "has_genre", "WESTERN" ], [ "BREAKHEART PASS", "has_tags", "ADVENTURE" ], [ "BREAKHEART PASS", "has_tags", "WESTERN" ], [ "CHINA 9, LIBERTY 37", "has_genre", "WESTERN" ], [ "CHINA 9, LIBERTY 37", "in_language", "ITALIAN" ], [ "CITY OF THE LIVING DEAD", "has_genre", "HORROR" ], [ "CITY OF THE LIVING DEAD", "has_tags", "HORROR" ], [ "CITY OF THE LIVING DEAD", "has_tags", "ITALIAN" ], [ "CITY OF THE LIVING DEAD", "in_language", "ITALIAN" ], [ "CUT AND RUN", "has_genre", "ADVENTURE" ], [ "CUT AND RUN", "in_language", "ITALIAN" ], [ "DJANGO", "has_genre", "WESTERN" ], [ "DJANGO", "in_language", "ITALIAN" ], [ "DJANGO THE BASTARD", "has_genre", "WESTERN" ], [ "DJANGO THE BASTARD", "in_language", "ITALIAN" ], [ "FOR A FEW DOLLARS MORE", "has_genre", "WESTERN" ], [ "FOR A FEW DOLLARS MORE", "has_tags", "ITALIAN" ], [ "FOR A FEW DOLLARS MORE", "has_tags", "WESTERN" ], [ "FOR A FEW DOLLARS MORE", "in_language", "ITALIAN" ], [ "GOD'S GUN", "has_genre", "WESTERN" ], [ "GOD'S GUN", "in_language", "ITALIAN" ], [ "HELL OF THE LIVING DEAD", "has_genre", "HORROR" ], [ "HELL OF THE LIVING DEAD", "in_language", "ITALIAN" ], [ "INFERNO", "has_genre", "HORROR" ], [ "INFERNO", "in_language", "ITALIAN" ], [ "JESSE JAMES MEETS FRANKENSTEIN'S DAUGHTER", "has_genre", "HORROR" ], [ "JESSE JAMES MEETS FRANKENSTEIN'S DAUGHTER", "has_genre", "WESTERN" ], [ "LAST OF THE DOGMEN", "has_genre", "ADVENTURE" ], [ "LAST OF THE DOGMEN", "has_genre", "WESTERN" ], [ "MAN OF THE EAST", "has_genre", "WESTERN" ], [ "MAN OF THE EAST", "in_language", "ITALIAN" ], [ "MAVERICK", "has_genre", "ADVENTURE" ], [ "MAVERICK", "has_tags", "WESTERN" ], [ "NEAR DARK", "has_genre", "HORROR" ], [ "NEAR DARK", "has_tags", "WESTERN" ], [ "ONCE UPON A TIME IN THE WEST", "has_genre", "WESTERN" ], [ "ONCE UPON A TIME IN THE WEST", "has_tags", "WESTERN" ], [ "ONCE UPON A TIME IN THE WEST", "in_language", "ITALIAN" ], [ "OPERA", "has_genre", "HORROR" ], [ "OPERA", "in_language", "ITALIAN" ], [ "RUN, MAN, RUN", "has_genre", "WESTERN" ], [ "RUN, MAN, RUN", "in_language", "ITALIAN" ], [ "SABATA", "has_genre", "WESTERN" ], [ "SABATA", "in_language", "ITALIAN" ], [ "SAHARA", "has_genre", "ADVENTURE" ], [ "SAHARA", "in_language", "ITALIAN" ], [ "SEVEN DOLLARS ON THE RED", "has_genre", "WESTERN" ], [ "SEVEN DOLLARS ON THE RED", "in_language", "ITALIAN" ], [ "SPIRITS OF THE DEAD", "has_genre", "HORROR" ], [ "SPIRITS OF THE DEAD", "in_language", "ITALIAN" ], [ "STARCRASH", "has_genre", "ADVENTURE" ], [ "STARCRASH", "in_language", "ITALIAN" ], [ "SWEPT AWAY", "has_genre", "ADVENTURE" ], [ "SWEPT AWAY", "in_language", "ITALIAN" ], [ "TENTACLES", "has_genre", "HORROR" ], [ "TENTACLES", "has_tags", "HORROR" ], [ "TENTACLES", "has_tags", "ITALIAN" ], [ "THE CHURCH", "has_genre", "HORROR" ], [ "THE CHURCH", "in_language", "ITALIAN" ], [ "THE COMANCHEROS", "has_genre", "ADVENTURE" ], [ "THE COMANCHEROS", "has_genre", "WESTERN" ], [ "THE FIVE MAN ARMY", "has_genre", "WESTERN" ], [ "THE FIVE MAN ARMY", "in_language", "ITALIAN" ], [ "THE GOOD, THE BAD AND THE UGLY", "has_genre", "WESTERN" ], [ "THE GOOD, THE BAD AND THE UGLY", "has_tags", "ITALIAN" ], [ "THE GOOD, THE BAD AND THE UGLY", "has_tags", "WESTERN" ], [ "THE GOOD, THE BAD AND THE UGLY", "in_language", "ITALIAN" ], [ "THE GREAT SILENCE", "has_genre", "WESTERN" ], [ "THE GREAT SILENCE", "in_language", "ITALIAN" ], [ "THE HORSEMAN ON THE ROOF", "has_genre", "ADVENTURE" ], [ "THE HORSEMAN ON THE ROOF", "in_language", "ITALIAN" ], [ "THE HUNTING PARTY", "has_genre", "ADVENTURE" ], [ "THE HUNTING PARTY", "has_genre", "WESTERN" ], [ "THE MOUNTAIN MEN", "has_genre", "ADVENTURE" ], [ "THE MOUNTAIN MEN", "has_genre", "WESTERN" ], [ "THE MUMMY", "has_genre", "ADVENTURE" ], [ "THE MUMMY", "has_genre", "HORROR" ], [ "THE MUMMY", "has_tags", "ADVENTURE" ], [ "THE MUMMY", "has_tags", "HORROR" ], [ "THEY CALL ME TRINITY", "has_genre", "WESTERN" ], [ "THEY CALL ME TRINITY", "in_language", "ITALIAN" ], [ "ULYSSES", "has_genre", "ADVENTURE" ], [ "ULYSSES", "in_language", "ITALIAN" ], [ "VAMPIRE IN VENICE", "has_genre", "HORROR" ], [ "VAMPIRE IN VENICE", "in_language", "ITALIAN" ], [ "WARLOCK", "has_genre", "HORROR" ], [ "WARLOCK", "has_genre", "WESTERN" ], [ "WESTERN", "directed_by", "MANUEL POIRIER" ], [ "WESTERN", "written_by", "MANUEL POIRIER" ], [ "WHITE FANG", "has_genre", "ADVENTURE" ], [ "WHITE FANG", "in_language", "ITALIAN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 38097, 1985 31672, A.K. 9989, ALICE AND MARTIN 35549, ANDRÉ TÉCHINÉ 24003, BAROCCO 20489, CHANGING TIMES 6012, FRENCH 31496, MAD MAX BEYOND THUNDERDOME 12358, MY FAVORITE SEASON 29620, POLICE 27191, RENDEZ-VOUS 11723, SHOAH 6323, STRAYED 39270, SUBWAY 32637, THE EARRINGS OF MADAME DE... 36893, THE GIRL ON THE TRAIN 35826, THE PEANUT BUTTER SOLUTION 5537, THE WITNESSES 3617, THIEVES 12294, VAGABOND 23141, WILD REEDS src, edge_attr, dst 31672, in_language, 6012 31672, release_year, 38097 9989, directed_by, 35549 9989, in_language, 6012 9989, written_by, 35549 24003, directed_by, 35549 24003, in_language, 6012 24003, written_by, 35549 20489, directed_by, 35549 20489, in_language, 6012 20489, written_by, 35549 31496, release_year, 38097 12358, directed_by, 35549 12358, has_tags, 35549 12358, in_language, 6012 12358, written_by, 35549 29620, in_language, 6012 29620, release_year, 38097 27191, directed_by, 35549 27191, in_language, 6012 27191, release_year, 38097 27191, written_by, 35549 11723, in_language, 6012 11723, release_year, 38097 6323, directed_by, 35549 6323, has_tags, 35549 6323, in_language, 6012 6323, written_by, 35549 39270, in_language, 6012 39270, release_year, 38097 32637, in_language, 6012 36893, directed_by, 35549 36893, in_language, 6012 36893, written_by, 35549 35826, in_language, 6012 35826, release_year, 38097 5537, directed_by, 35549 5537, has_tags, 35549 5537, in_language, 6012 5537, written_by, 35549 3617, directed_by, 35549 3617, has_tags, 35549 3617, in_language, 6012 3617, written_by, 35549 12294, in_language, 6012 12294, release_year, 38097 23141, directed_by, 35549 23141, has_tags, 35549 23141, in_language, 6012 23141, written_by, 35549 Question: For what reason are MAD MAX BEYOND THUNDERDOME, THE EARRINGS OF MADAME DE..., and WILD REEDS associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "MAD MAX BEYOND THUNDERDOME", "THE EARRINGS OF MADAME DE...", "WILD REEDS" ], "valid_edges": [ [ "A.K.", "in_language", "FRENCH" ], [ "A.K.", "release_year", "1985" ], [ "ALICE AND MARTIN", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "ALICE AND MARTIN", "in_language", "FRENCH" ], [ "ALICE AND MARTIN", "written_by", "ANDRÉ TÉCHINÉ" ], [ "BAROCCO", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "BAROCCO", "in_language", "FRENCH" ], [ "BAROCCO", "written_by", "ANDRÉ TÉCHINÉ" ], [ "CHANGING TIMES", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "CHANGING TIMES", "in_language", "FRENCH" ], [ "CHANGING TIMES", "written_by", "ANDRÉ TÉCHINÉ" ], [ "MAD MAX BEYOND THUNDERDOME", "release_year", "1985" ], [ "MY FAVORITE SEASON", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "MY FAVORITE SEASON", "has_tags", "ANDRÉ TÉCHINÉ" ], [ "MY FAVORITE SEASON", "in_language", "FRENCH" ], [ "MY FAVORITE SEASON", "written_by", "ANDRÉ TÉCHINÉ" ], [ "POLICE", "in_language", "FRENCH" ], [ "POLICE", "release_year", "1985" ], [ "RENDEZ-VOUS", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "RENDEZ-VOUS", "in_language", "FRENCH" ], [ "RENDEZ-VOUS", "release_year", "1985" ], [ "RENDEZ-VOUS", "written_by", "ANDRÉ TÉCHINÉ" ], [ "SHOAH", "in_language", "FRENCH" ], [ "SHOAH", "release_year", "1985" ], [ "STRAYED", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "STRAYED", "has_tags", "ANDRÉ TÉCHINÉ" ], [ "STRAYED", "in_language", "FRENCH" ], [ "STRAYED", "written_by", "ANDRÉ TÉCHINÉ" ], [ "SUBWAY", "in_language", "FRENCH" ], [ "SUBWAY", "release_year", "1985" ], [ "THE EARRINGS OF MADAME DE...", "in_language", "FRENCH" ], [ "THE GIRL ON THE TRAIN", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "THE GIRL ON THE TRAIN", "in_language", "FRENCH" ], [ "THE GIRL ON THE TRAIN", "written_by", "ANDRÉ TÉCHINÉ" ], [ "THE PEANUT BUTTER SOLUTION", "in_language", "FRENCH" ], [ "THE PEANUT BUTTER SOLUTION", "release_year", "1985" ], [ "THE WITNESSES", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "THE WITNESSES", "has_tags", "ANDRÉ TÉCHINÉ" ], [ "THE WITNESSES", "in_language", "FRENCH" ], [ "THE WITNESSES", "written_by", "ANDRÉ TÉCHINÉ" ], [ "THIEVES", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "THIEVES", "has_tags", "ANDRÉ TÉCHINÉ" ], [ "THIEVES", "in_language", "FRENCH" ], [ "THIEVES", "written_by", "ANDRÉ TÉCHINÉ" ], [ "VAGABOND", "in_language", "FRENCH" ], [ "VAGABOND", "release_year", "1985" ], [ "WILD REEDS", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "WILD REEDS", "has_tags", "ANDRÉ TÉCHINÉ" ], [ "WILD REEDS", "in_language", "FRENCH" ], [ "WILD REEDS", "written_by", "ANDRÉ TÉCHINÉ" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15177, ANDY BELLIN 30463, COMEDY 36212, DRAMA 24002, LOVELACE 26738, OSAMA BIN LADEN 584, THREE SMART GIRLS GROW UP 11987, TRUST 16849, ZERO DARK THIRTY src, edge_attr, dst 24002, has_genre, 36212 24002, written_by, 15177 584, has_genre, 30463 11987, has_genre, 30463 11987, has_genre, 36212 11987, written_by, 15177 16849, has_genre, 36212 16849, has_tags, 26738 Question: In what context are ANDY BELLIN, OSAMA BIN LADEN, and THREE SMART GIRLS GROW UP connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ANDY BELLIN", "OSAMA BIN LADEN", "THREE SMART GIRLS GROW UP" ], "valid_edges": [ [ "LOVELACE", "has_genre", "DRAMA" ], [ "LOVELACE", "written_by", "ANDY BELLIN" ], [ "THREE SMART GIRLS GROW UP", "has_genre", "COMEDY" ], [ "TRUST", "has_genre", "COMEDY" ], [ "TRUST", "has_genre", "DRAMA" ], [ "TRUST", "written_by", "ANDY BELLIN" ], [ "ZERO DARK THIRTY", "has_genre", "DRAMA" ], [ "ZERO DARK THIRTY", "has_tags", "OSAMA BIN LADEN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 7841, 1987 13634, ARTHUR PENN 2325, BILLY CRUDUP 19131, BONNIE AND CLYDE 13176, DEAD OF WINTER 5429, FAYE DUNAWAY 34231, GENE HACKMAN 11565, GOOD 28397, KEVIN COSTNER 33575, LITTLE BIG MAN 8253, MICKEY ONE 6446, NIGHT MOVES 32910, NO WAY OUT 6742, TARGET 7, THE LEFT HANDED GUN 18782, THE QUICK AND THE DEAD 37585, WARREN BEATTY 20465, WATCHMEN 19845, WITHOUT LIMITS 29933, WYATT EARP src, edge_attr, dst 19131, directed_by, 13634 19131, has_imdb_rating, 11565 19131, has_tags, 13634 19131, has_tags, 5429 19131, has_tags, 34231 19131, has_tags, 37585 19131, starred_actors, 5429 19131, starred_actors, 34231 19131, starred_actors, 37585 13176, directed_by, 13634 13176, release_year, 7841 33575, directed_by, 13634 33575, has_tags, 13634 33575, starred_actors, 5429 8253, directed_by, 13634 8253, starred_actors, 37585 6446, directed_by, 13634 6446, has_tags, 13634 6446, has_tags, 34231 6446, starred_actors, 34231 32910, has_tags, 34231 32910, has_tags, 28397 32910, release_year, 7841 32910, starred_actors, 34231 32910, starred_actors, 28397 6742, directed_by, 13634 6742, starred_actors, 34231 7, directed_by, 13634 7, has_imdb_rating, 11565 18782, has_tags, 34231 18782, release_year, 7841 18782, starred_actors, 34231 20465, has_imdb_rating, 11565 20465, has_tags, 2325 20465, starred_actors, 2325 19845, has_imdb_rating, 11565 19845, has_tags, 2325 19845, starred_actors, 2325 29933, has_tags, 34231 29933, has_tags, 28397 29933, starred_actors, 34231 29933, starred_actors, 28397 Question: How are ARTHUR PENN, BILLY CRUDUP, and NO WAY OUT related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ARTHUR PENN", "BILLY CRUDUP", "NO WAY OUT" ], "valid_edges": [ [ "BONNIE AND CLYDE", "directed_by", "ARTHUR PENN" ], [ "BONNIE AND CLYDE", "has_imdb_rating", "GOOD" ], [ "BONNIE AND CLYDE", "has_tags", "ARTHUR PENN" ], [ "BONNIE AND CLYDE", "has_tags", "FAYE DUNAWAY" ], [ "BONNIE AND CLYDE", "has_tags", "GENE HACKMAN" ], [ "BONNIE AND CLYDE", "has_tags", "WARREN BEATTY" ], [ "BONNIE AND CLYDE", "starred_actors", "FAYE DUNAWAY" ], [ "BONNIE AND CLYDE", "starred_actors", "GENE HACKMAN" ], [ "BONNIE AND CLYDE", "starred_actors", "WARREN BEATTY" ], [ "DEAD OF WINTER", "directed_by", "ARTHUR PENN" ], [ "DEAD OF WINTER", "release_year", "1987" ], [ "LITTLE BIG MAN", "directed_by", "ARTHUR PENN" ], [ "LITTLE BIG MAN", "has_tags", "ARTHUR PENN" ], [ "LITTLE BIG MAN", "starred_actors", "FAYE DUNAWAY" ], [ "MICKEY ONE", "directed_by", "ARTHUR PENN" ], [ "MICKEY ONE", "starred_actors", "WARREN BEATTY" ], [ "NIGHT MOVES", "directed_by", "ARTHUR PENN" ], [ "NIGHT MOVES", "has_tags", "ARTHUR PENN" ], [ "NIGHT MOVES", "has_tags", "GENE HACKMAN" ], [ "NIGHT MOVES", "starred_actors", "GENE HACKMAN" ], [ "NO WAY OUT", "has_tags", "GENE HACKMAN" ], [ "NO WAY OUT", "has_tags", "KEVIN COSTNER" ], [ "NO WAY OUT", "release_year", "1987" ], [ "NO WAY OUT", "starred_actors", "GENE HACKMAN" ], [ "NO WAY OUT", "starred_actors", "KEVIN COSTNER" ], [ "TARGET", "directed_by", "ARTHUR PENN" ], [ "TARGET", "starred_actors", "GENE HACKMAN" ], [ "THE LEFT HANDED GUN", "directed_by", "ARTHUR PENN" ], [ "THE LEFT HANDED GUN", "has_imdb_rating", "GOOD" ], [ "THE QUICK AND THE DEAD", "has_tags", "GENE HACKMAN" ], [ "THE QUICK AND THE DEAD", "release_year", "1987" ], [ "THE QUICK AND THE DEAD", "starred_actors", "GENE HACKMAN" ], [ "WATCHMEN", "has_imdb_rating", "GOOD" ], [ "WATCHMEN", "has_tags", "BILLY CRUDUP" ], [ "WATCHMEN", "starred_actors", "BILLY CRUDUP" ], [ "WITHOUT LIMITS", "has_imdb_rating", "GOOD" ], [ "WITHOUT LIMITS", "has_tags", "BILLY CRUDUP" ], [ "WITHOUT LIMITS", "starred_actors", "BILLY CRUDUP" ], [ "WYATT EARP", "has_tags", "GENE HACKMAN" ], [ "WYATT EARP", "has_tags", "KEVIN COSTNER" ], [ "WYATT EARP", "starred_actors", "GENE HACKMAN" ], [ "WYATT EARP", "starred_actors", "KEVIN COSTNER" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 19407, 8½ 17698, ALBERTO SORDI 30463, COMEDY 10941, DRIVING MISS DAISY 29506, ENNIO FLAIANO 3353, FEDERICO FELLINI 11227, GIULIETTA MASINA 17344, I VITELLONI 16200, ITALIAN 8923, JOHNNY ENGLISH REBORN 20913, LA STRADA 24926, SWEET CHARITY 34529, THE WHITE SHEIK 25136, TULLIO PINELLI src, edge_attr, dst 19407, directed_by, 3353 19407, has_tags, 3353 19407, in_language, 16200 19407, written_by, 29506 19407, written_by, 3353 19407, written_by, 25136 10941, has_genre, 30463 17344, directed_by, 3353 17344, has_genre, 30463 17344, has_tags, 3353 17344, has_tags, 16200 17344, in_language, 16200 17344, starred_actors, 17698 17344, written_by, 29506 17344, written_by, 3353 17344, written_by, 25136 8923, has_genre, 30463 8923, has_tags, 30463 20913, directed_by, 3353 20913, has_tags, 3353 20913, has_tags, 11227 20913, has_tags, 16200 20913, in_language, 16200 20913, starred_actors, 11227 20913, written_by, 29506 20913, written_by, 3353 20913, written_by, 25136 24926, written_by, 29506 24926, written_by, 3353 24926, written_by, 25136 34529, directed_by, 3353 34529, has_genre, 30463 34529, has_tags, 3353 34529, in_language, 16200 34529, starred_actors, 17698 34529, starred_actors, 11227 34529, written_by, 29506 34529, written_by, 3353 34529, written_by, 25136 Question: For what reason are DRIVING MISS DAISY, JOHNNY ENGLISH REBORN, and TULLIO PINELLI associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "DRIVING MISS DAISY", "JOHNNY ENGLISH REBORN", "TULLIO PINELLI" ], "valid_edges": [ [ "8½", "directed_by", "FEDERICO FELLINI" ], [ "8½", "has_tags", "FEDERICO FELLINI" ], [ "8½", "in_language", "ITALIAN" ], [ "8½", "written_by", "ENNIO FLAIANO" ], [ "8½", "written_by", "FEDERICO FELLINI" ], [ "8½", "written_by", "TULLIO PINELLI" ], [ "DRIVING MISS DAISY", "has_genre", "COMEDY" ], [ "I VITELLONI", "directed_by", "FEDERICO FELLINI" ], [ "I VITELLONI", "has_genre", "COMEDY" ], [ "I VITELLONI", "has_tags", "FEDERICO FELLINI" ], [ "I VITELLONI", "has_tags", "ITALIAN" ], [ "I VITELLONI", "in_language", "ITALIAN" ], [ "I VITELLONI", "starred_actors", "ALBERTO SORDI" ], [ "I VITELLONI", "written_by", "ENNIO FLAIANO" ], [ "I VITELLONI", "written_by", "FEDERICO FELLINI" ], [ "I VITELLONI", "written_by", "TULLIO PINELLI" ], [ "JOHNNY ENGLISH REBORN", "has_genre", "COMEDY" ], [ "JOHNNY ENGLISH REBORN", "has_tags", "COMEDY" ], [ "LA STRADA", "directed_by", "FEDERICO FELLINI" ], [ "LA STRADA", "has_tags", "FEDERICO FELLINI" ], [ "LA STRADA", "has_tags", "GIULIETTA MASINA" ], [ "LA STRADA", "has_tags", "ITALIAN" ], [ "LA STRADA", "in_language", "ITALIAN" ], [ "LA STRADA", "starred_actors", "GIULIETTA MASINA" ], [ "LA STRADA", "written_by", "ENNIO FLAIANO" ], [ "LA STRADA", "written_by", "FEDERICO FELLINI" ], [ "LA STRADA", "written_by", "TULLIO PINELLI" ], [ "SWEET CHARITY", "written_by", "ENNIO FLAIANO" ], [ "SWEET CHARITY", "written_by", "FEDERICO FELLINI" ], [ "SWEET CHARITY", "written_by", "TULLIO PINELLI" ], [ "THE WHITE SHEIK", "directed_by", "FEDERICO FELLINI" ], [ "THE WHITE SHEIK", "has_genre", "COMEDY" ], [ "THE WHITE SHEIK", "has_tags", "FEDERICO FELLINI" ], [ "THE WHITE SHEIK", "in_language", "ITALIAN" ], [ "THE WHITE SHEIK", "starred_actors", "ALBERTO SORDI" ], [ "THE WHITE SHEIK", "starred_actors", "GIULIETTA MASINA" ], [ "THE WHITE SHEIK", "written_by", "ENNIO FLAIANO" ], [ "THE WHITE SHEIK", "written_by", "FEDERICO FELLINI" ], [ "THE WHITE SHEIK", "written_by", "TULLIO PINELLI" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 13241, 2 39289, ACTION 31367, ED O'ROSS 5870, HORROR 11016, MARTITA HUNT 38677, MIAMI VICE 24020, NADJA 13081, R 9555, RED HEAT 29005, SIN CITY 2709, THE BRIDES OF DRACULA src, edge_attr, dst 38677, has_genre, 39289 38677, has_tags, 13241 38677, has_tags, 13081 24020, has_genre, 5870 24020, has_tags, 13241 9555, has_genre, 39289 9555, has_tags, 39289 9555, starred_actors, 31367 29005, has_tags, 13241 29005, has_tags, 39289 29005, has_tags, 13081 2709, has_genre, 5870 2709, starred_actors, 11016 Question: How are 2, ED O'ROSS, and MARTITA HUNT related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "2", "ED O'ROSS", "MARTITA HUNT" ], "valid_edges": [ [ "MIAMI VICE", "has_genre", "ACTION" ], [ "MIAMI VICE", "has_tags", "2" ], [ "MIAMI VICE", "has_tags", "R" ], [ "NADJA", "has_genre", "HORROR" ], [ "NADJA", "has_tags", "2" ], [ "RED HEAT", "has_genre", "ACTION" ], [ "RED HEAT", "has_tags", "ACTION" ], [ "RED HEAT", "starred_actors", "ED O'ROSS" ], [ "SIN CITY", "has_tags", "2" ], [ "SIN CITY", "has_tags", "ACTION" ], [ "SIN CITY", "has_tags", "R" ], [ "THE BRIDES OF DRACULA", "has_genre", "HORROR" ], [ "THE BRIDES OF DRACULA", "starred_actors", "MARTITA HUNT" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 13408, 2001 30463, COMEDY 4107, DIRECTOR'S CUT 22255, DONNIE DARKO 36212, DRAMA 21955, JACINDA BARRETT 21401, KIM DARBY 32018, TEEN WOLF TOO 28107, THE LAST KISS src, edge_attr, dst 22255, has_genre, 36212 22255, has_tags, 4107 22255, release_year, 13408 32018, has_genre, 30463 32018, starred_actors, 21401 28107, has_genre, 30463 28107, has_genre, 36212 28107, release_year, 13408 28107, starred_actors, 21955 Question: In what context are DIRECTOR'S CUT, JACINDA BARRETT, and KIM DARBY connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "DIRECTOR'S CUT", "JACINDA BARRETT", "KIM DARBY" ], "valid_edges": [ [ "DONNIE DARKO", "has_genre", "DRAMA" ], [ "DONNIE DARKO", "has_tags", "DIRECTOR'S CUT" ], [ "DONNIE DARKO", "release_year", "2001" ], [ "TEEN WOLF TOO", "has_genre", "COMEDY" ], [ "TEEN WOLF TOO", "starred_actors", "KIM DARBY" ], [ "THE LAST KISS", "has_genre", "COMEDY" ], [ "THE LAST KISS", "has_genre", "DRAMA" ], [ "THE LAST KISS", "release_year", "2001" ], [ "THE LAST KISS", "starred_actors", "JACINDA BARRETT" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8486, 1999 27261, 2009 30146, A CHRISTMAS CAROL 10045, BD-R 20946, BIG PUN 722, CHARACTERS 22958, FAMOUS 3179, NANETTE NEWMAN 21512, SHERLOCK HOLMES 16219, STORY 23847, THE STEPFORD WIVES 14499, TOY STORY 2 7105, URBAN MENACE src, edge_attr, dst 30146, has_imdb_votes, 22958 30146, has_tags, 10045 30146, has_tags, 722 30146, release_year, 8486 30146, release_year, 27261 21512, has_imdb_votes, 22958 21512, has_tags, 722 21512, has_tags, 21512 21512, has_tags, 16219 21512, release_year, 27261 23847, has_tags, 10045 23847, starred_actors, 3179 14499, has_tags, 722 14499, has_tags, 16219 14499, release_year, 8486 7105, release_year, 8486 7105, starred_actors, 20946 Question: In what context are BIG PUN, CHARACTERS, and NANETTE NEWMAN connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BIG PUN", "CHARACTERS", "NANETTE NEWMAN" ], "valid_edges": [ [ "A CHRISTMAS CAROL", "has_imdb_votes", "FAMOUS" ], [ "A CHRISTMAS CAROL", "has_tags", "BD-R" ], [ "A CHRISTMAS CAROL", "has_tags", "CHARACTERS" ], [ "A CHRISTMAS CAROL", "release_year", "1999" ], [ "A CHRISTMAS CAROL", "release_year", "2009" ], [ "SHERLOCK HOLMES", "has_imdb_votes", "FAMOUS" ], [ "SHERLOCK HOLMES", "has_tags", "CHARACTERS" ], [ "SHERLOCK HOLMES", "has_tags", "SHERLOCK HOLMES" ], [ "SHERLOCK HOLMES", "has_tags", "STORY" ], [ "SHERLOCK HOLMES", "release_year", "2009" ], [ "THE STEPFORD WIVES", "has_tags", "BD-R" ], [ "THE STEPFORD WIVES", "starred_actors", "NANETTE NEWMAN" ], [ "TOY STORY 2", "has_tags", "CHARACTERS" ], [ "TOY STORY 2", "has_tags", "STORY" ], [ "TOY STORY 2", "release_year", "1999" ], [ "URBAN MENACE", "release_year", "1999" ], [ "URBAN MENACE", "starred_actors", "BIG PUN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 12998, 1 29424, 2011 2105, ANAND TUCKER 30976, DOUGLAS BUCK 7139, HILARY AND JACKIE 19247, ITALIAN FOR BEGINNERS 17950, LONE SCHERFIG 29488, ONE DAY 25301, THE THEATRE BIZARRE src, edge_attr, dst 7139, directed_by, 2105 7139, has_tags, 12998 19247, directed_by, 17950 19247, has_tags, 12998 19247, has_tags, 17950 19247, written_by, 17950 29488, directed_by, 17950 29488, release_year, 29424 25301, directed_by, 30976 25301, release_year, 29424 25301, written_by, 30976 Question: How are ANAND TUCKER, DOUGLAS BUCK, and LONE SCHERFIG related? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "ANAND TUCKER", "DOUGLAS BUCK", "LONE SCHERFIG" ], "valid_edges": [ [ "HILARY AND JACKIE", "directed_by", "ANAND TUCKER" ], [ "HILARY AND JACKIE", "has_tags", "1" ], [ "ITALIAN FOR BEGINNERS", "directed_by", "LONE SCHERFIG" ], [ "ITALIAN FOR BEGINNERS", "has_tags", "1" ], [ "ITALIAN FOR BEGINNERS", "has_tags", "LONE SCHERFIG" ], [ "ITALIAN FOR BEGINNERS", "written_by", "LONE SCHERFIG" ], [ "ONE DAY", "directed_by", "LONE SCHERFIG" ], [ "ONE DAY", "release_year", "2011" ], [ "THE THEATRE BIZARRE", "directed_by", "DOUGLAS BUCK" ], [ "THE THEATRE BIZARRE", "release_year", "2011" ], [ "THE THEATRE BIZARRE", "written_by", "DOUGLAS BUCK" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35063, 1976 29424, 2011 1421, 2013 11835, 21 HOURS AT MUNICH 39501, 30 MINUTES OR LESS 24454, CARRIE 21922, JANE EYRE 14847, JESSE EISENBERG 15254, JORDANA BEATTY 208, JUDY MOODY AND THE NOT BUMMER SUMMER 4136, MIA WASIKOWSKA 35664, RESTLESS 33994, TAKE ME HOME TONIGHT 3680, THE DOUBLE 18520, TOPHER GRACE src, edge_attr, dst 11835, release_year, 35063 39501, release_year, 29424 39501, starred_actors, 14847 24454, release_year, 35063 24454, release_year, 1421 21922, has_tags, 4136 21922, release_year, 29424 21922, starred_actors, 4136 208, release_year, 29424 208, starred_actors, 15254 35664, release_year, 29424 35664, starred_actors, 4136 33994, has_tags, 18520 33994, release_year, 29424 33994, starred_actors, 18520 33994, written_by, 18520 3680, has_tags, 4136 3680, release_year, 29424 3680, release_year, 1421 3680, starred_actors, 14847 3680, starred_actors, 4136 3680, starred_actors, 18520 Question: In what context are 21 HOURS AT MUNICH, JORDANA BEATTY, and THE DOUBLE connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "21 HOURS AT MUNICH", "JORDANA BEATTY", "THE DOUBLE" ], "valid_edges": [ [ "21 HOURS AT MUNICH", "release_year", "1976" ], [ "30 MINUTES OR LESS", "release_year", "2011" ], [ "30 MINUTES OR LESS", "starred_actors", "JESSE EISENBERG" ], [ "CARRIE", "release_year", "1976" ], [ "CARRIE", "release_year", "2013" ], [ "JANE EYRE", "has_tags", "MIA WASIKOWSKA" ], [ "JANE EYRE", "release_year", "2011" ], [ "JANE EYRE", "starred_actors", "MIA WASIKOWSKA" ], [ "JUDY MOODY AND THE NOT BUMMER SUMMER", "release_year", "2011" ], [ "JUDY MOODY AND THE NOT BUMMER SUMMER", "starred_actors", "JORDANA BEATTY" ], [ "RESTLESS", "release_year", "2011" ], [ "RESTLESS", "starred_actors", "MIA WASIKOWSKA" ], [ "TAKE ME HOME TONIGHT", "has_tags", "TOPHER GRACE" ], [ "TAKE ME HOME TONIGHT", "release_year", "2011" ], [ "TAKE ME HOME TONIGHT", "starred_actors", "TOPHER GRACE" ], [ "TAKE ME HOME TONIGHT", "written_by", "TOPHER GRACE" ], [ "THE DOUBLE", "has_tags", "MIA WASIKOWSKA" ], [ "THE DOUBLE", "release_year", "2011" ], [ "THE DOUBLE", "release_year", "2013" ], [ "THE DOUBLE", "starred_actors", "JESSE EISENBERG" ], [ "THE DOUBLE", "starred_actors", "MIA WASIKOWSKA" ], [ "THE DOUBLE", "starred_actors", "TOPHER GRACE" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 20247, 44 INCH CHEST 36212, DRAMA 31783, ENGLISH 9931, JANE GOLDMAN 5650, LOUIS MELLIS 16986, PAVEL LUNGIN 11556, TAXI BLUES 25509, THE DEBT 3437, THE WOMAN IN BLACK 34970, TOM WILKINSON src, edge_attr, dst 20247, has_genre, 36212 20247, starred_actors, 34970 20247, written_by, 5650 11556, directed_by, 16986 11556, has_genre, 36212 11556, written_by, 16986 25509, has_genre, 36212 25509, has_tags, 34970 25509, in_language, 31783 25509, starred_actors, 34970 25509, written_by, 9931 3437, has_genre, 36212 3437, in_language, 31783 3437, written_by, 9931 Question: For what reason are JANE GOLDMAN, LOUIS MELLIS, and PAVEL LUNGIN associated? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "JANE GOLDMAN", "LOUIS MELLIS", "PAVEL LUNGIN" ], "valid_edges": [ [ "44 INCH CHEST", "has_genre", "DRAMA" ], [ "44 INCH CHEST", "starred_actors", "TOM WILKINSON" ], [ "44 INCH CHEST", "written_by", "LOUIS MELLIS" ], [ "TAXI BLUES", "directed_by", "PAVEL LUNGIN" ], [ "TAXI BLUES", "has_genre", "DRAMA" ], [ "TAXI BLUES", "written_by", "PAVEL LUNGIN" ], [ "THE DEBT", "has_genre", "DRAMA" ], [ "THE DEBT", "has_tags", "TOM WILKINSON" ], [ "THE DEBT", "in_language", "ENGLISH" ], [ "THE DEBT", "starred_actors", "TOM WILKINSON" ], [ "THE DEBT", "written_by", "JANE GOLDMAN" ], [ "THE WOMAN IN BLACK", "has_genre", "DRAMA" ], [ "THE WOMAN IN BLACK", "in_language", "ENGLISH" ], [ "THE WOMAN IN BLACK", "written_by", "JANE GOLDMAN" ] ] }
metaqa	You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"\|predicate\|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15421, 100 BLOODY ACRES 28153, 2 DAYS IN NEW YORK 658, 2012 37304, 21 JUMP STREET 31304, 30 BEATS 30586, A FANTASTIC FEAR OF EVERYTHING 24585, A GLIMPSE INSIDE THE MIND OF CHARLES SWAN III 20562, A THOUSAND WORDS 29800, ACE ATTORNEY 35724, ALTER EGOS 36099, AMERICAN REUNION 35584, ANY QUESTIONS FOR BEN? 26059, BACHELORETTE 23994, BARFI! 26923, BATTLESHIP 2284, BENDING THE RULES 32770, BEST MAN DOWN 10002, BIO-DOME 5946, BLOODY BLOODY BIBLE CAMP 39848, BLUE LIKE JAZZ 14480, BUDDIES 32450, CHEERFUL WEATHER FOR THE WEDDING 274, COCKNEYS VS ZOMBIES 30463, COMEDY 29161, DABANGG 2 30529, DARK SHADOWS 17466, DETENTION OF THE DEAD 32892, ENGLISH VINGLISH 10151, FAMILY WAY 35384, FAT KID RULES THE WORLD 21972, FOODFIGHT! 20213, FOR A GOOD TIME, CALL... 6076, FRANCES HA 34131, FREE SAMPLES 3550, FUN SIZE 24124, GAMBIT 22623, GIMME THE LOOT 39859, GIRL MOST LIKELY 13971, GOATS 13477, GUNS, GIRLS AND GAMBLING 34345, HAPPINESS NEVER COMES ALONE 32513, HERE COMES THE BOOM 6518, HIGH SOCIETY 34440, HIT AND RUN 35319, HOPE SPRINGS 34869, HOTEL TRANSYLVANIA 6886, HOUSEFULL 2 37844, HYDE PARK ON HUDSON 38589, I DO 15403, IT'S A DISASTER 6685, JASON BLOOM 33283, JESUS HENRY CHRIST 36133, JOHN DIES AT THE END 35632, JOKER 16510, JOYFUL NOISE 21883, LAY THE FAVORITE 14931, LIBERAL ARTS 17372, LOL 22226, LOLA VERSUS 38894, LOVE AND OTHER TROUBLES 4564, LOVE IS ALL YOU NEED 34517, LUV 6450, MADEA'S WITNESS PROTECTION 35871, MAGIC MIKE 902, MEN IN BLACK 3 35361, MENTAL 31735, MIDDLE OF NOWHERE 424, MIRROR MIRROR 15981, MOONRISE KINGDOM 31377, MUCH ADO ABOUT NOTHING 8368, MUSHROOMING 34821, MY AWKWARD SEXUAL ADVENTURE 33972, NOT SUITABLE FOR CHILDREN 29217, ONE FOR THE MONEY 1300, OVERNIGHT DELIVERY 19562, PAPERMAN 35289, PARANORMAN 29610, PARENTAL GUIDANCE 11056, PAULETTE 17916, PIRANHA 3DD 6864, PITCH PERFECT 2388, PLAYING FOR KEEPS 10493, POPULAIRE 29303, PRICE CHECK 12939, PROJECT X 25023, QUARTET 11839, REVENGE FOR JOLLY! 14305, ROCK OF AGES 12597, RUBY SPARKS 17321, SAFETY NOT GUARANTEED 17168, SEEKING A FRIEND FOR THE END OF THE WORLD 30190, SEVEN PSYCHOPATHS 25060, SEXUAL CHRONICLES OF A FRENCH FAMILY 10227, SHANGHAI CALLING 37459, SIGHTSEERS 34584, SILVER LININGS PLAYBOOK 31434, SLEEPWALK WITH ME 28361, SMALL APARTMENTS 28026, SOMEBODY UP THERE LIKES ME 36369, STAND UP GUYS 9247, STITCHES 25002, STRUCK BY LIGHTNING 3038, STUCK IN LOVE 10856, STUDENT OF THE YEAR 273, TED 19501, THANKS FOR SHARING 16163, THAT'S MY BOY 31162, THE ABCS OF DEATH 22040, THE BABYMAKERS 10138, THE BAYTOWN OUTLAWS 33050, THE CAMPAIGN 6644, THE COMEDY 12300, THE DICTATOR 16732, THE END 31019, THE FIRST TIME 13130, THE FIVE-YEAR ENGAGEMENT 15980, THE GUILT TRIP 34429, THE LORAX 27052, THE NEWEST PLEDGE 15798, THE ODD LIFE OF TIMOTHY GREEN 24519, THE PLAYERS 18162, THE RAVEN 2739, THE SAPPHIRES 37915, THE STORY OF LUKE 1249, THE THREE STOOGES 31679, THE WATCH 24380, THINK LIKE A MAN 939, THIS IS 40 20169, THIS MEANS WAR 6357, TIM AND ERIC'S BILLION DOLLAR MOVIE 6598, TO ROME WITH LOVE 21694, TOOTH FAIRY 2 24570, VAMPS 21268, VICKY DONOR 26675, WANDERLUST 34118, WHAT TO EXPECT WHEN YOU'RE EXPECTING 38905, WHAT'S IN A NAME? 21236, WRECK-IT RALPH 10235, WRONG src, edge_attr, dst 15421, has_genre, 30463 15421, release_year, 658 28153, has_genre, 30463 28153, release_year, 658 37304, has_genre, 30463 37304, has_tags, 30463 37304, release_year, 658 31304, has_genre, 30463 31304, release_year, 658 30586, has_genre, 30463 30586, has_tags, 30463 30586, release_year, 658 24585, has_genre, 30463 24585, release_year, 658 20562, has_genre, 30463 20562, release_year, 658 29800, has_genre, 30463 29800, release_year, 658 35724, has_genre, 30463 35724, release_year, 658 36099, has_genre, 30463 36099, release_year, 658 35584, has_genre, 30463 35584, release_year, 658 26059, has_genre, 30463 26059, release_year, 658 23994, has_genre, 30463 23994, release_year, 658 26923, release_year, 658 2284, has_genre, 30463 2284, release_year, 658 32770, has_genre, 30463 32770, release_year, 658 10002, directed_by, 6685 10002, has_genre, 30463 5946, has_genre, 30463 5946, release_year, 658 39848, has_genre, 30463 39848, release_year, 658 14480, has_genre, 30463 14480, release_year, 658 32450, has_genre, 30463 32450, release_year, 658 274, has_genre, 30463 274, release_year, 658 29161, has_genre, 30463 29161, release_year, 658 30529, has_genre, 30463 30529, release_year, 658 17466, has_genre, 30463 17466, release_year, 658 32892, has_genre, 30463 32892, release_year, 658 10151, has_genre, 30463 10151, release_year, 658 35384, has_genre, 30463 35384, release_year, 658 21972, has_genre, 30463 21972, release_year, 658 20213, has_genre, 30463 20213, release_year, 658 6076, has_genre, 30463 6076, release_year, 658 34131, has_genre, 30463 34131, release_year, 658 3550, has_genre, 30463 3550, release_year, 658 24124, has_genre, 30463 24124, release_year, 658 22623, has_genre, 30463 22623, release_year, 658 39859, has_genre, 30463 39859, release_year, 658 13971, has_genre, 30463 13971, release_year, 658 13477, has_genre, 30463 13477, release_year, 658 34345, has_genre, 30463 34345, release_year, 658 32513, has_genre, 30463 32513, release_year, 658 6518, has_genre, 30463 34440, has_genre, 30463 34440, release_year, 658 35319, has_genre, 30463 35319, release_year, 658 34869, has_genre, 30463 34869, release_year, 658 6886, has_genre, 30463 6886, release_year, 658 37844, has_genre, 30463 37844, release_year, 658 38589, has_genre, 30463 38589, release_year, 658 15403, has_genre, 30463 15403, has_tags, 30463 15403, release_year, 658 33283, has_genre, 30463 33283, release_year, 658 36133, has_genre, 30463 36133, has_tags, 30463 36133, release_year, 658 35632, has_genre, 30463 35632, release_year, 658 16510, has_genre, 30463 16510, release_year, 658 21883, has_genre, 30463 21883, release_year, 658 14931, has_genre, 30463 14931, release_year, 658 17372, has_genre, 30463 17372, release_year, 658 22226, has_genre, 30463 22226, release_year, 658 38894, has_genre, 30463 38894, release_year, 658 4564, has_genre, 30463 4564, release_year, 658 34517, has_genre, 30463 34517, release_year, 658 6450, has_genre, 30463 6450, release_year, 658 35871, has_genre, 30463 35871, release_year, 658 902, has_genre, 30463 902, release_year, 658 35361, has_genre, 30463 35361, release_year, 658 31735, has_genre, 30463 31735, release_year, 658 424, has_genre, 30463 424, release_year, 658 15981, has_genre, 30463 15981, has_tags, 30463 15981, release_year, 658 31377, has_genre, 30463 31377, has_tags, 30463 31377, release_year, 658 8368, has_genre, 30463 8368, release_year, 658 34821, has_genre, 30463 34821, release_year, 658 33972, has_genre, 30463 33972, has_tags, 30463 33972, release_year, 658 29217, has_genre, 30463 29217, release_year, 658 1300, directed_by, 6685 1300, has_genre, 30463 19562, has_genre, 30463 19562, release_year, 658 35289, has_genre, 30463 35289, release_year, 658 29610, has_genre, 30463 29610, release_year, 658 11056, has_genre, 30463 11056, release_year, 658 17916, has_genre, 30463 17916, release_year, 658 6864, has_genre, 30463 6864, release_year, 658 2388, has_genre, 30463 2388, release_year, 658 10493, has_genre, 30463 10493, release_year, 658 29303, has_genre, 30463 29303, release_year, 658 12939, has_genre, 30463 12939, release_year, 658 25023, has_genre, 30463 25023, has_tags, 30463 25023, release_year, 658 11839, has_genre, 30463 11839, release_year, 658 14305, has_genre, 30463 14305, release_year, 658 12597, has_genre, 30463 12597, release_year, 658 17321, has_genre, 30463 17321, release_year, 658 17168, has_genre, 30463 17168, release_year, 658 30190, has_genre, 30463 30190, release_year, 658 25060, has_genre, 30463 25060, release_year, 658 10227, has_genre, 30463 10227, release_year, 658 37459, has_genre, 30463 37459, release_year, 658 34584, has_genre, 30463 34584, release_year, 658 31434, has_genre, 30463 31434, release_year, 658 28361, has_genre, 30463 28361, release_year, 658 28026, has_genre, 30463 28026, release_year, 658 36369, has_genre, 30463 36369, release_year, 658 9247, has_genre, 30463 9247, release_year, 658 25002, has_genre, 30463 25002, release_year, 658 3038, has_genre, 30463 3038, release_year, 658 10856, has_genre, 30463 10856, release_year, 658 273, has_genre, 30463 273, has_tags, 30463 273, release_year, 658 19501, has_genre, 30463 19501, release_year, 658 16163, has_genre, 30463 16163, release_year, 658 31162, has_genre, 30463 31162, release_year, 658 22040, has_genre, 30463 22040, release_year, 658 10138, has_genre, 30463 10138, release_year, 658 33050, has_genre, 30463 33050, release_year, 658 6644, has_tags, 30463 6644, release_year, 658 12300, has_genre, 30463 12300, release_year, 658 16732, has_genre, 30463 16732, release_year, 658 31019, has_genre, 30463 31019, release_year, 658 13130, has_genre, 30463 13130, release_year, 658 15980, has_genre, 30463 15980, release_year, 658 34429, has_genre, 30463 34429, release_year, 658 27052, has_genre, 30463 27052, release_year, 658 15798, has_genre, 30463 15798, release_year, 658 24519, has_genre, 30463 24519, release_year, 658 18162, has_genre, 30463 18162, release_year, 658 2739, has_genre, 30463 2739, release_year, 658 37915, has_genre, 30463 37915, release_year, 658 1249, has_genre, 30463 1249, release_year, 658 31679, has_genre, 30463 31679, release_year, 658 24380, has_genre, 30463 24380, release_year, 658 939, has_genre, 30463 939, release_year, 658 20169, has_genre, 30463 20169, release_year, 658 6357, has_genre, 30463 6357, release_year, 658 6598, has_genre, 30463 6598, has_tags, 30463 6598, release_year, 658 21694, has_genre, 30463 21694, release_year, 658 24570, has_genre, 30463 24570, release_year, 658 21268, has_genre, 30463 21268, release_year, 658 26675, has_genre, 30463 26675, release_year, 658 34118, has_genre, 30463 34118, release_year, 658 38905, has_genre, 30463 38905, release_year, 658 21236, has_genre, 30463 21236, release_year, 658 10235, has_genre, 30463 10235, release_year, 658 Question: In what context are BATTLESHIP, HIGH SOCIETY, and JASON BLOOM connected? Your output must be ONLY the PATH block.	graph_path	{ "style": "rule" }	{ "entities": [ "BATTLESHIP", "HIGH SOCIETY", "JASON BLOOM" ], "valid_edges": [ [ "100 BLOODY ACRES", "has_genre", "COMEDY" ], [ "100 BLOODY ACRES", "release_year", "2012" ], [ "2 DAYS IN NEW YORK", "has_genre", "COMEDY" ], [ "2 DAYS IN NEW YORK", "release_year", "2012" ], [ "21 JUMP STREET", "has_genre", "COMEDY" ], [ "21 JUMP STREET", "has_tags", "COMEDY" ], [ "21 JUMP STREET", "release_year", "2012" ], [ "30 BEATS", "has_genre", "COMEDY" ], [ "30 BEATS", "release_year", "2012" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_genre", "COMEDY" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_tags", "COMEDY" ], [ "A FANTASTIC FEAR OF EVERYTHING", "release_year", "2012" ], [ "A GLIMPSE INSIDE THE MIND OF CHARLES SWAN III", "has_genre", "COMEDY" ], [ "A GLIMPSE INSIDE THE MIND OF CHARLES SWAN III", "release_year", "2012" ], [ "A THOUSAND WORDS", "has_genre", "COMEDY" ], [ "A THOUSAND WORDS", "release_year", "2012" ], [ "ACE ATTORNEY", "has_genre", "COMEDY" ], [ "ACE ATTORNEY", "release_year", "2012" ], [ "ALTER EGOS", "has_genre", "COMEDY" ], [ "ALTER EGOS", "release_year", "2012" ], [ "AMERICAN REUNION", "has_genre", "COMEDY" ], [ "AMERICAN REUNION", "release_year", "2012" ], [ "ANY QUESTIONS FOR BEN?", "has_genre", "COMEDY" ], [ "ANY QUESTIONS FOR BEN?", "release_year", "2012" ], [ "BACHELORETTE", "has_genre", "COMEDY" ], [ "BACHELORETTE", "release_year", "2012" ], [ "BARFI!", "has_genre", "COMEDY" ], [ "BARFI!", "release_year", "2012" ], [ "BATTLESHIP", "release_year", "2012" ], [ "BENDING THE RULES", "has_genre", "COMEDY" ], [ "BENDING THE RULES", "release_year", "2012" ], [ "BEST MAN DOWN", "has_genre", "COMEDY" ], [ "BEST MAN DOWN", "release_year", "2012" ], [ "BIO-DOME", "directed_by", "JASON BLOOM" ], [ "BIO-DOME", "has_genre", "COMEDY" ], [ "BLOODY BLOODY BIBLE CAMP", "has_genre", "COMEDY" ], [ "BLOODY BLOODY BIBLE CAMP", "release_year", "2012" ], [ "BLUE LIKE JAZZ", "has_genre", "COMEDY" ], [ "BLUE LIKE JAZZ", "release_year", "2012" ], [ "BUDDIES", "has_genre", "COMEDY" ], [ "BUDDIES", "release_year", "2012" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "has_genre", "COMEDY" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "release_year", "2012" ], [ "COCKNEYS VS ZOMBIES", "has_genre", "COMEDY" ], [ "COCKNEYS VS ZOMBIES", "release_year", "2012" ], [ "DABANGG 2", "has_genre", "COMEDY" ], [ "DABANGG 2", "release_year", "2012" ], [ "DARK SHADOWS", "has_genre", "COMEDY" ], [ "DARK SHADOWS", "release_year", "2012" ], [ "DETENTION OF THE DEAD", "has_genre", "COMEDY" ], [ "DETENTION OF THE DEAD", "release_year", "2012" ], [ "ENGLISH VINGLISH", "has_genre", "COMEDY" ], [ "ENGLISH VINGLISH", "release_year", "2012" ], [ "FAMILY WAY", "has_genre", "COMEDY" ], [ "FAMILY WAY", "release_year", "2012" ], [ "FAT KID RULES THE WORLD", "has_genre", "COMEDY" ], [ "FAT KID RULES THE WORLD", "release_year", "2012" ], [ "FOODFIGHT!", "has_genre", "COMEDY" ], [ "FOODFIGHT!", "release_year", "2012" ], [ "FOR A GOOD TIME, CALL...", "has_genre", "COMEDY" ], [ "FOR A GOOD TIME, CALL...", "release_year", "2012" ], [ "FRANCES HA", "has_genre", "COMEDY" ], [ "FRANCES HA", "release_year", "2012" ], [ "FREE SAMPLES", "has_genre", "COMEDY" ], [ "FREE SAMPLES", "release_year", "2012" ], [ "FUN SIZE", "has_genre", "COMEDY" ], [ "FUN SIZE", "release_year", "2012" ], [ "GAMBIT", "has_genre", "COMEDY" ], [ "GAMBIT", "release_year", "2012" ], [ "GIMME THE LOOT", "has_genre", "COMEDY" ], [ "GIMME THE LOOT", "release_year", "2012" ], [ "GIRL MOST LIKELY", "has_genre", "COMEDY" ], [ "GIRL MOST LIKELY", "release_year", "2012" ], [ "GOATS", "has_genre", "COMEDY" ], [ "GOATS", "release_year", "2012" ], [ "GUNS, GIRLS AND GAMBLING", "has_genre", "COMEDY" ], [ "GUNS, GIRLS AND GAMBLING", "release_year", "2012" ], [ "HAPPINESS NEVER COMES ALONE", "has_genre", "COMEDY" ], [ "HAPPINESS NEVER COMES ALONE", "release_year", "2012" ], [ "HERE COMES THE BOOM", "has_genre", "COMEDY" ], [ "HERE COMES THE BOOM", "release_year", "2012" ], [ "HIGH SOCIETY", "has_genre", "COMEDY" ], [ "HIT AND RUN", "has_genre", "COMEDY" ], [ "HIT AND RUN", "release_year", "2012" ], [ "HOPE SPRINGS", "has_genre", "COMEDY" ], [ "HOPE SPRINGS", "release_year", "2012" ], [ "HOTEL TRANSYLVANIA", "has_genre", "COMEDY" ], [ "HOTEL TRANSYLVANIA", "release_year", "2012" ], [ "HOUSEFULL 2", "has_genre", "COMEDY" ], [ "HOUSEFULL 2", "release_year", "2012" ], [ "HYDE PARK ON HUDSON", "has_genre", "COMEDY" ], [ "HYDE PARK ON HUDSON", "release_year", "2012" ], [ "I DO", "has_genre", "COMEDY" ], [ "I DO", "release_year", "2012" ], [ "IT'S A DISASTER", "has_genre", "COMEDY" ], [ "IT'S A DISASTER", "has_tags", "COMEDY" ], [ "IT'S A DISASTER", "release_year", "2012" ], [ "JESUS HENRY CHRIST", "has_genre", "COMEDY" ], [ "JESUS HENRY CHRIST", "release_year", "2012" ], [ "JOHN DIES AT THE END", "has_genre", "COMEDY" ], [ "JOHN DIES AT THE END", "has_tags", "COMEDY" ], [ "JOHN DIES AT THE END", "release_year", "2012" ], [ "JOKER", "has_genre", "COMEDY" ], [ "JOKER", "release_year", "2012" ], [ "JOYFUL NOISE", "has_genre", "COMEDY" ], [ "JOYFUL NOISE", "release_year", "2012" ], [ "LAY THE FAVORITE", "has_genre", "COMEDY" ], [ "LAY THE FAVORITE", "release_year", "2012" ], [ "LIBERAL ARTS", "has_genre", "COMEDY" ], [ "LIBERAL ARTS", "release_year", "2012" ], [ "LOL", "has_genre", "COMEDY" ], [ "LOL", "release_year", "2012" ], [ "LOLA VERSUS", "has_genre", "COMEDY" ], [ "LOLA VERSUS", "release_year", "2012" ], [ "LOVE AND OTHER TROUBLES", "has_genre", "COMEDY" ], [ "LOVE AND OTHER TROUBLES", "release_year", "2012" ], [ "LOVE IS ALL YOU NEED", "has_genre", "COMEDY" ], [ "LOVE IS ALL YOU NEED", "release_year", "2012" ], [ "LUV", "has_genre", "COMEDY" ], [ "LUV", "release_year", "2012" ], [ "MADEA'S WITNESS PROTECTION", "has_genre", "COMEDY" ], [ "MADEA'S WITNESS PROTECTION", "release_year", "2012" ], [ "MAGIC MIKE", "has_genre", "COMEDY" ], [ "MAGIC MIKE", "release_year", "2012" ], [ "MEN IN BLACK 3", "has_genre", "COMEDY" ], [ "MEN IN BLACK 3", "release_year", "2012" ], [ "MENTAL", "has_genre", "COMEDY" ], [ "MENTAL", "release_year", "2012" ], [ "MIDDLE OF NOWHERE", "has_genre", "COMEDY" ], [ "MIDDLE OF NOWHERE", "release_year", "2012" ], [ "MIRROR MIRROR", "has_genre", "COMEDY" ], [ "MIRROR MIRROR", "release_year", "2012" ], [ "MOONRISE KINGDOM", "has_genre", "COMEDY" ], [ "MOONRISE KINGDOM", "has_tags", "COMEDY" ], [ "MOONRISE KINGDOM", "release_year", "2012" ], [ "MUCH ADO ABOUT NOTHING", "has_genre", "COMEDY" ], [ "MUCH ADO ABOUT NOTHING", "has_tags", "COMEDY" ], [ "MUCH ADO ABOUT NOTHING", "release_year", "2012" ], [ "MUSHROOMING", "has_genre", "COMEDY" ], [ "MUSHROOMING", "release_year", "2012" ], [ "MY AWKWARD SEXUAL ADVENTURE", "has_genre", "COMEDY" ], [ "MY AWKWARD SEXUAL ADVENTURE", "release_year", "2012" ], [ "NOT SUITABLE FOR CHILDREN", "has_genre", "COMEDY" ], [ "NOT SUITABLE FOR CHILDREN", "has_tags", "COMEDY" ], [ "NOT SUITABLE FOR CHILDREN", "release_year", "2012" ], [ "ONE FOR THE MONEY", "has_genre", "COMEDY" ], [ "ONE FOR THE MONEY", "release_year", "2012" ], [ "OVERNIGHT DELIVERY", "directed_by", "JASON BLOOM" ], [ "OVERNIGHT DELIVERY", "has_genre", "COMEDY" ], [ "PAPERMAN", "has_genre", "COMEDY" ], [ "PAPERMAN", "release_year", "2012" ], [ "PARANORMAN", "has_genre", "COMEDY" ], [ "PARANORMAN", "release_year", "2012" ], [ "PARENTAL GUIDANCE", "has_genre", "COMEDY" ], [ "PARENTAL GUIDANCE", "release_year", "2012" ], [ "PAULETTE", "has_genre", "COMEDY" ], [ "PAULETTE", "release_year", "2012" ], [ "PIRANHA 3DD", "has_genre", "COMEDY" ], [ "PIRANHA 3DD", "release_year", "2012" ], [ "PITCH PERFECT", "has_genre", "COMEDY" ], [ "PITCH PERFECT", "release_year", "2012" ], [ "PLAYING FOR KEEPS", "has_genre", "COMEDY" ], [ "PLAYING FOR KEEPS", "release_year", "2012" ], [ "POPULAIRE", "has_genre", "COMEDY" ], [ "POPULAIRE", "release_year", "2012" ], [ "PRICE CHECK", "has_genre", "COMEDY" ], [ "PRICE CHECK", "release_year", "2012" ], [ "PROJECT X", "has_genre", "COMEDY" ], [ "PROJECT X", "release_year", "2012" ], [ "QUARTET", "has_genre", "COMEDY" ], [ "QUARTET", "has_tags", "COMEDY" ], [ "QUARTET", "release_year", "2012" ], [ "REVENGE FOR JOLLY!", "has_genre", "COMEDY" ], [ "REVENGE FOR JOLLY!", "release_year", "2012" ], [ "ROCK OF AGES", "has_genre", "COMEDY" ], [ "ROCK OF AGES", "release_year", "2012" ], [ "RUBY SPARKS", "has_genre", "COMEDY" ], [ "RUBY SPARKS", "release_year", "2012" ], [ "SAFETY NOT GUARANTEED", "has_genre", "COMEDY" ], [ "SAFETY NOT GUARANTEED", "release_year", "2012" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "has_genre", "COMEDY" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "release_year", "2012" ], [ "SEVEN PSYCHOPATHS", "has_genre", "COMEDY" ], [ "SEVEN PSYCHOPATHS", "release_year", "2012" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "has_genre", "COMEDY" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "release_year", "2012" ], [ "SHANGHAI CALLING", "has_genre", "COMEDY" ], [ "SHANGHAI CALLING", "release_year", "2012" ], [ "SIGHTSEERS", "has_genre", "COMEDY" ], [ "SIGHTSEERS", "release_year", "2012" ], [ "SILVER LININGS PLAYBOOK", "has_genre", "COMEDY" ], [ "SILVER LININGS PLAYBOOK", "release_year", "2012" ], [ "SLEEPWALK WITH ME", "has_genre", "COMEDY" ], [ "SLEEPWALK WITH ME", "release_year", "2012" ], [ "SMALL APARTMENTS", "has_genre", "COMEDY" ], [ "SMALL APARTMENTS", "release_year", "2012" ], [ "SOMEBODY UP THERE LIKES ME", "has_genre", "COMEDY" ], [ "SOMEBODY UP THERE LIKES ME", "release_year", "2012" ], [ "STAND UP GUYS", "has_genre", "COMEDY" ], [ "STAND UP GUYS", "release_year", "2012" ], [ "STITCHES", "has_genre", "COMEDY" ], [ "STITCHES", "release_year", "2012" ], [ "STRUCK BY LIGHTNING", "has_genre", "COMEDY" ], [ "STRUCK BY LIGHTNING", "release_year", "2012" ], [ "STUCK IN LOVE", "has_genre", "COMEDY" ], [ "STUCK IN LOVE", "release_year", "2012" ], [ "STUDENT OF THE YEAR", "has_genre", "COMEDY" ], [ "STUDENT OF THE YEAR", "release_year", "2012" ], [ "TED", "has_genre", "COMEDY" ], [ "TED", "has_tags", "COMEDY" ], [ "TED", "release_year", "2012" ], [ "THANKS FOR SHARING", "has_genre", "COMEDY" ], [ "THANKS FOR SHARING", "release_year", "2012" ], [ "THAT'S MY BOY", "has_genre", "COMEDY" ], [ "THAT'S MY BOY", "release_year", "2012" ], [ "THE ABCS OF DEATH", "has_genre", "COMEDY" ], [ "THE ABCS OF DEATH", "release_year", "2012" ], [ "THE BABYMAKERS", "has_genre", "COMEDY" ], [ "THE BABYMAKERS", "release_year", "2012" ], [ "THE BAYTOWN OUTLAWS", "has_genre", "COMEDY" ], [ "THE BAYTOWN OUTLAWS", "release_year", "2012" ], [ "THE CAMPAIGN", "has_genre", "COMEDY" ], [ "THE CAMPAIGN", "release_year", "2012" ], [ "THE COMEDY", "has_tags", "COMEDY" ], [ "THE COMEDY", "release_year", "2012" ], [ "THE DICTATOR", "has_genre", "COMEDY" ], [ "THE DICTATOR", "release_year", "2012" ], [ "THE END", "has_genre", "COMEDY" ], [ "THE END", "release_year", "2012" ], [ "THE FIRST TIME", "has_genre", "COMEDY" ], [ "THE FIRST TIME", "release_year", "2012" ], [ "THE FIVE-YEAR ENGAGEMENT", "has_genre", "COMEDY" ], [ "THE FIVE-YEAR ENGAGEMENT", "release_year", "2012" ], [ "THE GUILT TRIP", "has_genre", "COMEDY" ], [ "THE GUILT TRIP", "release_year", "2012" ], [ "THE LORAX", "has_genre", "COMEDY" ], [ "THE LORAX", "release_year", "2012" ], [ "THE NEWEST PLEDGE", "has_genre", "COMEDY" ], [ "THE NEWEST PLEDGE", "release_year", "2012" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "has_genre", "COMEDY" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "release_year", "2012" ], [ "THE PLAYERS", "has_genre", "COMEDY" ], [ "THE PLAYERS", "release_year", "2012" ], [ "THE RAVEN", "has_genre", "COMEDY" ], [ "THE RAVEN", "release_year", "2012" ], [ "THE SAPPHIRES", "has_genre", "COMEDY" ], [ "THE SAPPHIRES", "release_year", "2012" ], [ "THE STORY OF LUKE", "has_genre", "COMEDY" ], [ "THE STORY OF LUKE", "release_year", "2012" ], [ "THE THREE STOOGES", "has_genre", "COMEDY" ], [ "THE THREE STOOGES", "release_year", "2012" ], [ "THE WATCH", "has_genre", "COMEDY" ], [ "THE WATCH", "release_year", "2012" ], [ "THINK LIKE A MAN", "has_genre", "COMEDY" ], [ "THINK LIKE A MAN", "release_year", "2012" ], [ "THIS IS 40", "has_genre", "COMEDY" ], [ "THIS IS 40", "release_year", "2012" ], [ "THIS MEANS WAR", "has_genre", "COMEDY" ], [ "THIS MEANS WAR", "release_year", "2012" ], [ "TIM AND ERIC'S BILLION DOLLAR MOVIE", "has_genre", "COMEDY" ], [ "TIM AND ERIC'S BILLION DOLLAR MOVIE", "release_year", "2012" ], [ "TO ROME WITH LOVE", "has_genre", "COMEDY" ], [ "TO ROME WITH LOVE", "has_tags", "COMEDY" ], [ "TO ROME WITH LOVE", "release_year", "2012" ], [ "TOOTH FAIRY 2", "has_genre", "COMEDY" ], [ "TOOTH FAIRY 2", "release_year", "2012" ], [ "VAMPS", "has_genre", "COMEDY" ], [ "VAMPS", "release_year", "2012" ], [ "VICKY DONOR", "has_genre", "COMEDY" ], [ "VICKY DONOR", "release_year", "2012" ], [ "WANDERLUST", "has_genre", "COMEDY" ], [ "WANDERLUST", "release_year", "2012" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "has_genre", "COMEDY" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "release_year", "2012" ], [ "WHAT'S IN A NAME?", "has_genre", "COMEDY" ], [ "WHAT'S IN A NAME?", "release_year", "2012" ], [ "WRECK-IT RALPH", "has_genre", "COMEDY" ], [ "WRECK-IT RALPH", "release_year", "2012" ], [ "WRONG", "has_genre", "COMEDY" ], [ "WRONG", "release_year", "2012" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35798, 2010 37090, A LITTLE HELP 29713, A SOMEWHAT GENTLE MAN 11665, ABIGAIL CRUTTENDEN 19743, ALPHA AND OMEGA 5054, AN ALLIGATOR NAMED DAISY 29304, ANIMALS UNITED 9116, ARMLESS 12144, BAD BOYS 27714, BARNEY'S VERSION 22023, BARRY MUNDAY 28846, BEGINNERS 31957, BOY 8003, CALAMARI UNION 18996, CEMETERY JUNCTION 29794, CHARLOTTE GRAY 10091, CHERRY 30463, COMEDY 1439, COP OUT 6133, CRAZY ON THE OUTSIDE 16600, CYRUS 13575, DATE NIGHT 37059, DEATH AT A FUNERAL 11161, DESPICABLE ME 16564, DIARY OF A WIMPY KID 38915, DINNER FOR SCHMUCKS 3172, DIRTY GIRL 18136, DOCTOR AT LARGE 25258, DONALD SINDEN 36212, DRAMA 19072, DRONES 33512, DUE DATE 10417, EASY A 25445, EASY MONEY 1069, ELEKTRA LUXX 14240, EVERYTHING MUST GO 2969, FATHER OF INVENTION 6666, FINNISH 24880, FLIPPED 32830, FOUR LIONS 5287, FROZEN 7656, FURRY VENGEANCE 21888, GET HIM TO THE GREEK 24815, GOING THE DISTANCE 27638, GREENBERG 19912, GRIFF THE INVISIBLE 18974, GROWN UPS 17440, GULLIVER'S TRAVELS 28838, GUNLESS 32384, HAMLET GOES BUSINESS 20140, HAPPY, HAPPY 33701, HAPPYTHANKYOUMOREPLEASE 11783, HEARTBREAKER 19039, HESHER 37827, HIGH SCHOOL 5617, HOT TUB TIME MACHINE 4931, HOW DO YOU KNOW 3485, HOW TO MAKE LOVE TO A WOMAN 883, I LOVE YOU TOO 37419, IT'S A WONDERFUL AFTERLIFE 21788, IT'S KIND OF A FUNNY STORY 15800, JACK GOES BOATING 17668, JACKASS 3D 5271, JAMES ROBERTSON JUSTICE 35402, JUST WRIGHT 12359, KICK-ASS 26839, KILLERS 5227, KLOWN 31646, KNIGHT AND DAY 1261, KNUCKLEHEAD 27072, LAPLAND ODYSSEY 22957, LEAP YEAR 6624, LET THE BULLETS FLY 9452, LIFE AS WE KNOW IT 27960, LITTLE BIG SOLDIER 251, LITTLE FOCKERS 16814, LITTLE WHITE LIES 35654, LOOSE CANNONS 971, LOTTERY TICKET 38894, LOVE AND OTHER TROUBLES 17855, MACGRUBER 29319, MAN EXPOSED 22924, MEET MONICA VELOUR 15238, MEGAMIND 5180, MISSION LONDON 1954, MOGAMBO 16578, MOOMINS ON THE RIVIERA 16662, MORNING GLORY 19597, MY GIRLFRIEND'S BOYFRIEND 33865, NICE GUY JOHNNY 4003, NORWEGIAN NINJA 12152, NOTHING TO DECLARE 32901, ON TOUR 30781, OPEN SEASON 3 12743, OUR FAMILY WEDDING 11129, PEEP WORLD 21830, PIRANHA 3D 34247, PLEASE GIVE 31445, PONTEROSA 11213, POTICHE 35110, PYAAR IMPOSSIBLE! 23764, RIO SEX COMEDY 34137, RUBBER 30519, SCOTT PILGRIM VS. THE WORLD 9094, SEX AND THE CITY 2 37803, SHADOWS IN PARADISE 34093, SHE'S OUT OF MY LEAGUE 21512, SHERLOCK HOLMES 30155, SIMPLE SIMON 37133, SOMEWHERE 13437, SOUND OF NOISE 30733, SUBMARINE 5700, SUPER 12457, TAMARA DREWE 12755, TANGLED 35302, THE A-TEAM 9091, THE ADVENTURES OF PICASSO 37777, THE BACK-UP PLAN 32454, THE BIG PICTURE 871, THE BOUNTY HUNTER 32654, THE CHOSEN ONE 39773, THE CLINK OF ICE 11696, THE CUCKOO 16488, THE EXTRA MAN 36722, THE FOUR-FACED LIAR 32459, THE HAMMER 5080, THE HOUSE OF BRANCHING LOVE 9510, THE INFIDEL 2507, THE KIDS ARE ALL RIGHT 20669, THE LOSERS 2147, THE MAN WITHOUT A PAST 25623, THE MATRIARCH 27824, THE OTHER GUYS 38086, THE PERFECT HOST 13614, THE ROMANTICS 18537, THE SPY NEXT DOOR 20830, THE STORAGE 37431, THE SWITCH 26036, THE TEMPEST 20739, THE VIRGINITY HIT 1903, TINY FURNITURE 16818, TOOTH FAIRY 22777, TOY STORY 3 11987, TRUST 26275, TRUST ME 16636, UUNO TURHAPURO 31620, UUNO TURHAPURO ARMEIJAN LEIVISSÄ 30682, VALENTINE'S DAY 27019, VIOLET TENDENCIES 33258, WEST IS WEST 39802, WHEN IN ROME 29999, WHY DID I GET MARRIED TOO? 18499, WILD TARGET 367, WITH LOVE... FROM THE AGE OF REASON 30255, YOGI BEAR 1826, YOU AGAIN 6279, YOU WILL MEET A TALL DARK STRANGER src, edge_attr, dst 37090, has_genre, 30463 37090, release_year, 35798 29713, has_genre, 30463 29713, release_year, 35798 19743, has_genre, 30463 19743, release_year, 35798 5054, has_genre, 30463 5054, starred_actors, 25258 5054, starred_actors, 5271 29304, has_genre, 30463 29304, release_year, 35798 9116, has_genre, 30463 9116, release_year, 35798 12144, has_genre, 30463 12144, has_tags, 30463 12144, in_language, 6666 27714, has_genre, 30463 27714, release_year, 35798 22023, has_genre, 30463 22023, release_year, 35798 28846, has_genre, 30463 28846, release_year, 35798 31957, has_genre, 30463 31957, release_year, 35798 8003, has_genre, 30463 8003, has_tags, 30463 8003, in_language, 6666 18996, has_genre, 30463 18996, has_tags, 30463 18996, release_year, 35798 29794, has_genre, 36212 29794, starred_actors, 11665 10091, has_genre, 30463 10091, release_year, 35798 1439, has_genre, 30463 1439, release_year, 35798 6133, has_genre, 30463 6133, release_year, 35798 16600, has_genre, 30463 16600, release_year, 35798 13575, has_genre, 30463 13575, has_tags, 30463 13575, release_year, 35798 37059, has_genre, 30463 37059, has_tags, 30463 37059, release_year, 35798 11161, has_genre, 30463 11161, release_year, 35798 16564, has_genre, 30463 16564, release_year, 35798 38915, has_genre, 30463 38915, release_year, 35798 3172, has_genre, 30463 3172, release_year, 35798 18136, has_genre, 30463 18136, starred_actors, 25258 18136, starred_actors, 5271 19072, has_genre, 30463 19072, release_year, 35798 33512, has_genre, 30463 33512, has_tags, 30463 33512, release_year, 35798 10417, has_genre, 30463 10417, has_tags, 30463 10417, release_year, 35798 25445, has_genre, 30463 25445, release_year, 35798 1069, has_genre, 30463 1069, release_year, 35798 14240, has_genre, 30463 14240, release_year, 35798 2969, has_genre, 30463 2969, release_year, 35798 24880, has_genre, 30463 24880, release_year, 35798 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31620, in_language, 6666 30682, has_genre, 30463 30682, has_tags, 30463 30682, release_year, 35798 27019, has_genre, 30463 27019, release_year, 35798 33258, has_genre, 30463 33258, release_year, 35798 39802, has_genre, 30463 39802, release_year, 35798 29999, has_genre, 30463 29999, release_year, 35798 18499, has_genre, 30463 18499, has_tags, 30463 18499, release_year, 35798 367, has_genre, 30463 367, release_year, 35798 30255, has_genre, 30463 30255, release_year, 35798 1826, has_genre, 30463 1826, release_year, 35798 6279, has_genre, 30463 6279, has_tags, 30463 6279, release_year, 35798 Question: How are ABIGAIL CRUTTENDEN, DONALD SINDEN, and LAPLAND ODYSSEY related? Your output must be ONLY the PATH block.

graph_path

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THE WORLD", "has_genre", "COMEDY" ], [ "SCOTT PILGRIM VS. THE WORLD", "has_tags", "COMEDY" ], [ "SCOTT PILGRIM VS. THE WORLD", "release_year", "2010" ], [ "SEX AND THE CITY 2", "has_genre", "COMEDY" ], [ "SEX AND THE CITY 2", "release_year", "2010" ], [ "SHADOWS IN PARADISE", "has_genre", "COMEDY" ], [ "SHADOWS IN PARADISE", "in_language", "FINNISH" ], [ "SHE'S OUT OF MY LEAGUE", "has_genre", "COMEDY" ], [ "SHE'S OUT OF MY LEAGUE", "release_year", "2010" ], [ "SHERLOCK HOLMES", "has_tags", "COMEDY" ], [ "SHERLOCK HOLMES", "release_year", "2010" ], [ "SIMPLE SIMON", "has_genre", "COMEDY" ], [ "SIMPLE SIMON", "release_year", "2010" ], [ "SOMEWHERE", "has_genre", "COMEDY" ], [ "SOMEWHERE", "release_year", "2010" ], [ "SOUND OF NOISE", "has_genre", "COMEDY" ], [ "SOUND OF NOISE", "release_year", "2010" ], [ "SUBMARINE", "has_genre", "COMEDY" ], [ "SUBMARINE", "release_year", "2010" ], [ "SUPER", "has_genre", "COMEDY" ], [ "SUPER", "release_year", "2010" ], [ "TAMARA DREWE", "has_genre", "COMEDY" ], [ "TAMARA DREWE", "release_year", "2010" ], [ "TANGLED", "has_genre", "COMEDY" ], [ "TANGLED", "release_year", "2010" ], [ "THE A-TEAM", "has_genre", "COMEDY" ], [ "THE A-TEAM", "release_year", "2010" ], [ "THE ADVENTURES OF PICASSO", "has_genre", "COMEDY" ], [ "THE ADVENTURES OF PICASSO", "in_language", "FINNISH" ], [ "THE BACK-UP PLAN", "has_genre", "COMEDY" ], [ "THE BACK-UP PLAN", "release_year", "2010" ], [ "THE BIG PICTURE", "has_genre", "COMEDY" ], [ "THE BIG PICTURE", "release_year", "2010" ], [ "THE BOUNTY HUNTER", "has_genre", "COMEDY" ], [ "THE BOUNTY HUNTER", "has_tags", "COMEDY" ], [ "THE BOUNTY HUNTER", "release_year", "2010" ], [ "THE CHOSEN ONE", "has_genre", "COMEDY" ], [ "THE CHOSEN ONE", "release_year", "2010" ], [ "THE CLINK OF ICE", "has_genre", "COMEDY" ], [ "THE CLINK OF ICE", "release_year", "2010" ], [ "THE CUCKOO", "has_genre", "COMEDY" ], [ "THE CUCKOO", "has_tags", "FINNISH" ], [ "THE CUCKOO", "in_language", "FINNISH" ], [ "THE EXTRA MAN", "has_genre", "COMEDY" ], [ "THE EXTRA MAN", "release_year", "2010" ], [ "THE FOUR-FACED LIAR", "has_genre", "COMEDY" ], [ "THE FOUR-FACED LIAR", "release_year", "2010" ], [ "THE HAMMER", "has_genre", "COMEDY" ], [ "THE HAMMER", "release_year", "2010" ], [ "THE HOUSE OF BRANCHING LOVE", "has_genre", "COMEDY" ], [ "THE HOUSE OF BRANCHING LOVE", "in_language", "FINNISH" ], [ "THE INFIDEL", "has_genre", "COMEDY" ], [ "THE INFIDEL", "release_year", "2010" ], [ "THE KIDS ARE ALL RIGHT", "has_genre", "COMEDY" ], [ "THE KIDS ARE ALL RIGHT", "has_tags", "COMEDY" ], [ "THE KIDS ARE ALL RIGHT", "release_year", "2010" ], [ "THE LOSERS", "has_tags", "COMEDY" ], [ "THE LOSERS", "release_year", "2010" ], [ "THE MAN WITHOUT A PAST", "has_genre", "COMEDY" ], [ "THE MAN WITHOUT A PAST", "has_tags", "FINNISH" ], [ "THE MAN WITHOUT A PAST", "in_language", "FINNISH" ], [ "THE MATRIARCH", "has_genre", "COMEDY" ], [ "THE MATRIARCH", "has_tags", "FINNISH" ], [ "THE MATRIARCH", "in_language", "FINNISH" ], [ "THE OTHER GUYS", "has_genre", "COMEDY" ], [ "THE OTHER GUYS", "has_tags", "COMEDY" ], [ "THE OTHER GUYS", "release_year", "2010" ], [ "THE PERFECT HOST", "has_genre", "COMEDY" ], [ "THE PERFECT HOST", "release_year", "2010" ], [ "THE ROMANTICS", "has_genre", "COMEDY" ], [ "THE ROMANTICS", "release_year", "2010" ], [ "THE SPY NEXT DOOR", "has_genre", "COMEDY" ], [ "THE SPY NEXT DOOR", "release_year", "2010" ], [ "THE STORAGE", "has_genre", "COMEDY" ], [ "THE STORAGE", "in_language", "FINNISH" ], [ "THE SWITCH", "has_genre", "COMEDY" ], [ "THE SWITCH", "has_tags", "COMEDY" ], [ "THE SWITCH", "release_year", "2010" ], [ "THE TEMPEST", "has_genre", "COMEDY" ], [ "THE TEMPEST", "release_year", "2010" ], [ "THE VIRGINITY HIT", "has_genre", "COMEDY" ], [ "THE VIRGINITY HIT", "release_year", "2010" ], [ "TINY FURNITURE", "has_genre", "COMEDY" ], [ "TINY FURNITURE", "release_year", "2010" ], [ "TOOTH FAIRY", "has_genre", "COMEDY" ], [ "TOOTH FAIRY", "release_year", "2010" ], [ "TOY STORY 3", "has_genre", "COMEDY" ], [ "TOY STORY 3", "has_tags", "COMEDY" ], [ "TOY STORY 3", "release_year", "2010" ], [ "TRUST", "has_genre", "COMEDY" ], [ "TRUST", "release_year", "2010" ], [ "TRUST ME", "has_genre", "COMEDY" ], [ "TRUST ME", "release_year", "2010" ], [ "UUNO TURHAPURO", "has_genre", "COMEDY" ], [ "UUNO TURHAPURO", "in_language", "FINNISH" ], [ "UUNO TURHAPURO ARMEIJAN LEIVISSÄ", "has_genre", "COMEDY" ], [ "UUNO TURHAPURO ARMEIJAN LEIVISSÄ", "in_language", "FINNISH" ], [ "VALENTINE'S DAY", "has_genre", "COMEDY" ], [ "VALENTINE'S DAY", "has_tags", "COMEDY" ], [ "VALENTINE'S DAY", "release_year", "2010" ], [ "VIOLET TENDENCIES", "has_genre", "COMEDY" ], [ "VIOLET TENDENCIES", "release_year", "2010" ], [ "WEST IS WEST", "has_genre", "COMEDY" ], [ "WEST IS WEST", "release_year", "2010" ], [ "WHEN IN ROME", "has_genre", "COMEDY" ], [ "WHEN IN ROME", "release_year", "2010" ], [ "WHY DID I GET MARRIED TOO?", "has_genre", "COMEDY" ], [ "WHY DID I GET MARRIED TOO?", "release_year", "2010" ], [ "WILD TARGET", "has_genre", "COMEDY" ], [ "WILD TARGET", "has_tags", "COMEDY" ], [ "WILD TARGET", "release_year", "2010" ], [ "WITH LOVE... FROM THE AGE OF REASON", "has_genre", "COMEDY" ], [ "WITH LOVE... FROM THE AGE OF REASON", "release_year", "2010" ], [ "YOGI BEAR", "has_genre", "COMEDY" ], [ "YOGI BEAR", "release_year", "2010" ], [ "YOU AGAIN", "has_genre", "COMEDY" ], [ "YOU AGAIN", "release_year", "2010" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_genre", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_tags", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "release_year", "2010" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 24438, 1993 2133, 1998 319, A BUG'S LIFE 5076, EXTREME JUSTICE 483, FARAWAY, SO CLOSE! 35493, FIRESTORM 1313, JOHN LASSETER 19394, OTTO SANDER 9281, SCOTT GLENN src, edge_attr, dst 319, directed_by, 1313 319, has_tags, 1313 319, release_year, 2133 319, written_by, 1313 5076, release_year, 24438 5076, starred_actors, 9281 483, release_year, 24438 483, starred_actors, 19394 35493, release_year, 2133 35493, starred_actors, 9281 Question: How are JOHN LASSETER, OTTO SANDER, and SCOTT GLENN related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "JOHN LASSETER", "OTTO SANDER", "SCOTT GLENN" ], "valid_edges": [ [ "A BUG'S LIFE", "directed_by", "JOHN LASSETER" ], [ "A BUG'S LIFE", "has_tags", "JOHN LASSETER" ], [ "A BUG'S LIFE", "release_year", "1998" ], [ "A BUG'S LIFE", "written_by", "JOHN LASSETER" ], [ "EXTREME JUSTICE", "release_year", "1993" ], [ "EXTREME JUSTICE", "starred_actors", "SCOTT GLENN" ], [ "FARAWAY, SO CLOSE!", "release_year", "1993" ], [ "FARAWAY, SO CLOSE!", "starred_actors", "OTTO SANDER" ], [ "FIRESTORM", "release_year", "1998" ], [ "FIRESTORM", "starred_actors", "SCOTT GLENN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8636, 1930 6718, A FAREWELL TO ARMS 7463, ALL QUIET ON THE WESTERN FRONT 6966, FORBIDDEN GAMES 3306, FRANÇOIS BOYER 39145, FURY 29757, GARY COOPER 20693, GONE WITH THE WIND 1422, MOROCCO 37497, NATIONAL FILM REGISTRY 8379, ROMANCE 3719, SERGEANT YORK 35043, SYLVIA SIDNEY 38627, TEN TALL MEN 22214, WAR src, edge_attr, dst 6718, has_genre, 8379 6718, starred_actors, 29757 7463, has_tags, 37497 7463, release_year, 8636 6966, has_genre, 22214 6966, written_by, 3306 39145, has_genre, 22214 39145, has_tags, 22214 39145, starred_actors, 35043 20693, has_genre, 8379 20693, has_tags, 37497 20693, has_tags, 8379 1422, has_genre, 8379 1422, has_tags, 29757 1422, has_tags, 37497 1422, release_year, 8636 1422, starred_actors, 29757 3719, has_tags, 29757 3719, has_tags, 37497 3719, starred_actors, 29757 38627, has_genre, 22214 38627, has_tags, 1422 Question: For what reason are FRANÇOIS BOYER, MOROCCO, and SYLVIA SIDNEY associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "FRANÇOIS BOYER", "MOROCCO", "SYLVIA SIDNEY" ], "valid_edges": [ [ "A FAREWELL TO ARMS", "has_genre", "ROMANCE" ], [ "A FAREWELL TO ARMS", "starred_actors", "GARY COOPER" ], [ "ALL QUIET ON THE WESTERN FRONT", "has_tags", "NATIONAL FILM REGISTRY" ], [ "ALL QUIET ON THE WESTERN FRONT", "release_year", "1930" ], [ "FORBIDDEN GAMES", "has_genre", "WAR" ], [ "FORBIDDEN GAMES", "written_by", "FRANÇOIS BOYER" ], [ "FURY", "has_genre", "WAR" ], [ "FURY", "has_tags", "WAR" ], [ "FURY", "starred_actors", "SYLVIA SIDNEY" ], [ "GONE WITH THE WIND", "has_genre", "ROMANCE" ], [ "GONE WITH THE WIND", "has_tags", "NATIONAL FILM REGISTRY" ], [ "GONE WITH THE WIND", "has_tags", "ROMANCE" ], [ "MOROCCO", "has_genre", "ROMANCE" ], [ "MOROCCO", "has_tags", "GARY COOPER" ], [ "MOROCCO", "has_tags", "NATIONAL FILM REGISTRY" ], [ "MOROCCO", "release_year", "1930" ], [ "MOROCCO", "starred_actors", "GARY COOPER" ], [ "SERGEANT YORK", "has_tags", "GARY COOPER" ], [ "SERGEANT YORK", "has_tags", "NATIONAL FILM REGISTRY" ], [ "SERGEANT YORK", "starred_actors", "GARY COOPER" ], [ "TEN TALL MEN", "has_genre", "WAR" ], [ "TEN TALL MEN", "has_tags", "MOROCCO" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8486, 1999 17315, 2007 17159, A MIGHTY HEART 3197, ANGELINA JOLIE 30501, BEOWULF 34555, FLAWLESS 3697, FLIGHT OF FURY 22532, GIRL, INTERRUPTED 8068, MALEFICENT 35249, RING 2 26125, SUNSHINE 451, THE BONE COLLECTOR 25509, THE DEBT src, edge_attr, dst 17159, has_tags, 3197 17159, release_year, 17315 17159, starred_actors, 3197 30501, has_tags, 3197 30501, release_year, 8486 30501, release_year, 17315 34555, release_year, 8486 34555, release_year, 17315 3697, release_year, 17315 22532, has_tags, 3197 22532, release_year, 8486 22532, starred_actors, 3197 8068, has_tags, 3197 8068, starred_actors, 3197 35249, release_year, 8486 26125, release_year, 8486 26125, release_year, 17315 451, has_tags, 3197 451, release_year, 8486 451, starred_actors, 3197 25509, release_year, 8486 25509, release_year, 17315 Question: For what reason are FLIGHT OF FURY, MALEFICENT, and RING 2 associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "FLIGHT OF FURY", "MALEFICENT", "RING 2" ], "valid_edges": [ [ "A MIGHTY HEART", "has_tags", "ANGELINA JOLIE" ], [ "A MIGHTY HEART", "release_year", "2007" ], [ "A MIGHTY HEART", "starred_actors", "ANGELINA JOLIE" ], [ "BEOWULF", "has_tags", "ANGELINA JOLIE" ], [ "BEOWULF", "release_year", "1999" ], [ "BEOWULF", "release_year", "2007" ], [ "FLAWLESS", "release_year", "1999" ], [ "FLAWLESS", "release_year", "2007" ], [ "FLIGHT OF FURY", "release_year", "2007" ], [ "GIRL, INTERRUPTED", "has_tags", "ANGELINA JOLIE" ], [ "GIRL, INTERRUPTED", "release_year", "1999" ], [ "GIRL, INTERRUPTED", "starred_actors", "ANGELINA JOLIE" ], [ "MALEFICENT", "has_tags", "ANGELINA JOLIE" ], [ "MALEFICENT", "starred_actors", "ANGELINA JOLIE" ], [ "RING 2", "release_year", "1999" ], [ "SUNSHINE", "release_year", "1999" ], [ "SUNSHINE", "release_year", "2007" ], [ "THE BONE COLLECTOR", "has_tags", "ANGELINA JOLIE" ], [ "THE BONE COLLECTOR", "release_year", "1999" ], [ "THE BONE COLLECTOR", "starred_actors", "ANGELINA JOLIE" ], [ "THE DEBT", "release_year", "1999" ], [ "THE DEBT", "release_year", "2007" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 14004, 1955 12094, A LAWLESS STREET 2216, ALONG THE GREAT DIVIDE 20106, BATTLE CRY 30230, COLORADO TERRITORY 34347, COUNT THREE AND PRAY 10663, DARK COMMAND 28252, DISTANT DRUMS 12841, DOCUMENTARY 7649, MAN WITH THE GUN 12039, NIGHT AND FOG 10835, RAGE AT DAWN 33941, RAOUL WALSH 38516, REGENERATION 20243, RUN FOR COVER 31613, SAN ANTONIO 32417, SINS OF MY FATHER 7585, TEXAS 33304, THE FAR HORIZONS 34953, THE GUN THAT WON THE WEST 20896, THE LAST FRONTIER 15059, THE LAWLESS BREED 17394, THE MAN FROM LARAMIE 9409, THE TALL MEN 30942, THE VIOLENT MEN 36026, WESTERN 18860, WICHITA src, edge_attr, dst 12094, has_genre, 36026 12094, release_year, 14004 2216, directed_by, 33941 2216, has_genre, 36026 20106, directed_by, 33941 20106, release_year, 14004 30230, directed_by, 33941 30230, has_genre, 36026 30230, has_tags, 33941 34347, has_genre, 36026 34347, release_year, 14004 10663, directed_by, 33941 10663, has_genre, 36026 28252, directed_by, 33941 28252, has_genre, 36026 7649, has_genre, 36026 7649, release_year, 14004 12039, has_genre, 12841 12039, has_tags, 12841 12039, release_year, 14004 10835, has_genre, 36026 10835, release_year, 14004 38516, directed_by, 33941 38516, has_genre, 12841 38516, written_by, 33941 20243, has_genre, 36026 20243, release_year, 14004 31613, directed_by, 33941 31613, has_genre, 36026 32417, has_genre, 12841 7585, has_genre, 36026 33304, has_genre, 36026 33304, release_year, 14004 34953, has_genre, 36026 34953, release_year, 14004 20896, has_genre, 36026 20896, release_year, 14004 15059, directed_by, 33941 15059, has_genre, 36026 17394, has_genre, 36026 17394, release_year, 14004 9409, directed_by, 33941 9409, release_year, 14004 30942, has_genre, 36026 30942, release_year, 14004 18860, has_genre, 36026 18860, release_year, 14004 Question: How are SINS OF MY FATHER, TEXAS, and THE TALL MEN related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "SINS OF MY FATHER", "TEXAS", "THE TALL MEN" ], "valid_edges": [ [ "A LAWLESS STREET", "has_genre", "WESTERN" ], [ "A LAWLESS STREET", "release_year", "1955" ], [ "ALONG THE GREAT DIVIDE", "directed_by", "RAOUL WALSH" ], [ "ALONG THE GREAT DIVIDE", "has_genre", "WESTERN" ], [ "BATTLE CRY", "directed_by", "RAOUL WALSH" ], [ "BATTLE CRY", "release_year", "1955" ], [ "COLORADO TERRITORY", "directed_by", "RAOUL WALSH" ], [ "COLORADO TERRITORY", "has_genre", "WESTERN" ], [ "COLORADO TERRITORY", "has_tags", "RAOUL WALSH" ], [ "COUNT THREE AND PRAY", "has_genre", "WESTERN" ], [ "COUNT THREE AND PRAY", "release_year", "1955" ], [ "DARK COMMAND", "directed_by", "RAOUL WALSH" ], [ "DARK COMMAND", "has_genre", "WESTERN" ], [ "DISTANT DRUMS", "directed_by", "RAOUL WALSH" ], [ "DISTANT DRUMS", "has_genre", "WESTERN" ], [ "MAN WITH THE GUN", "has_genre", "WESTERN" ], [ "MAN WITH THE GUN", "release_year", "1955" ], [ "NIGHT AND FOG", "has_genre", "DOCUMENTARY" ], [ "NIGHT AND FOG", "has_tags", "DOCUMENTARY" ], [ "NIGHT AND FOG", "release_year", "1955" ], [ "RAGE AT DAWN", "has_genre", "WESTERN" ], [ "RAGE AT DAWN", "release_year", "1955" ], [ "REGENERATION", "directed_by", "RAOUL WALSH" ], [ "REGENERATION", "has_genre", "DOCUMENTARY" ], [ "REGENERATION", "written_by", "RAOUL WALSH" ], [ "RUN FOR COVER", "has_genre", "WESTERN" ], [ "RUN FOR COVER", "release_year", "1955" ], [ "SAN ANTONIO", "directed_by", "RAOUL WALSH" ], [ "SAN ANTONIO", "has_genre", "WESTERN" ], [ "SINS OF MY FATHER", "has_genre", "DOCUMENTARY" ], [ "TEXAS", "has_genre", "WESTERN" ], [ "THE FAR HORIZONS", "has_genre", "WESTERN" ], [ "THE FAR HORIZONS", "release_year", "1955" ], [ "THE GUN THAT WON THE WEST", "has_genre", "WESTERN" ], [ "THE GUN THAT WON THE WEST", "release_year", "1955" ], [ "THE LAST FRONTIER", "has_genre", "WESTERN" ], [ "THE LAST FRONTIER", "release_year", "1955" ], [ "THE LAWLESS BREED", "directed_by", "RAOUL WALSH" ], [ "THE LAWLESS BREED", "has_genre", "WESTERN" ], [ "THE MAN FROM LARAMIE", "has_genre", "WESTERN" ], [ "THE MAN FROM LARAMIE", "release_year", "1955" ], [ "THE TALL MEN", "directed_by", "RAOUL WALSH" ], [ "THE TALL MEN", "release_year", "1955" ], [ "THE VIOLENT MEN", "has_genre", "WESTERN" ], [ "THE VIOLENT MEN", "release_year", "1955" ], [ "WICHITA", "has_genre", "WESTERN" ], [ "WICHITA", "release_year", "1955" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15506, 1933 7421, APART FROM YOU 27426, AS I LAY DYING 26475, BABY FACE 4731, BARBARY COAST 34812, BECKY SHARP 16273, BOMBSHELL 18018, CAVALCADE 30463, COMEDY 15047, COUNSELLOR AT LAW 15791, DINNER AT EIGHT 36212, DRAMA 17986, GABRIEL OVER THE WHITE HOUSE 33241, GUILLERMO DÍAZ 3745, INTRUDER IN THE DUST 36639, LADIES THEY TALK ABOUT 13476, LADY KILLER 19475, LAURA SAN GIACOMO 26784, LITTLE WOMEN 31195, LOOKING FORWARD 31323, MIRIAM HOPKINS 16662, MORNING GLORY 20443, NIGHT FLIGHT 18256, OLD ACQUAINTANCE 40059, ONCE AROUND 28686, S.O.S. EISBERG 39856, SHE DONE HIM WRONG 18733, STONEWALL 39071, THE BITTER TEA OF GENERAL YEN 15207, THE EMPEROR JONES 30083, THE HOUSE ON 56TH STREET 4700, THE STORY OF TEMPLE DRAKE 29621, THE STRANGER'S RETURN 27022, THE TARNISHED ANGELS 4442, WHERE THE DAY TAKES YOU 33943, WILLIAM FAULKNER src, edge_attr, dst 7421, has_genre, 36212 7421, release_year, 15506 27426, has_genre, 36212 27426, has_tags, 33943 27426, written_by, 33943 26475, has_genre, 36212 26475, release_year, 15506 4731, has_genre, 36212 4731, starred_actors, 31323 34812, has_genre, 36212 34812, starred_actors, 31323 16273, has_genre, 36212 16273, release_year, 15506 18018, has_genre, 36212 18018, release_year, 15506 15047, has_genre, 36212 15047, release_year, 15506 15791, has_genre, 36212 15791, release_year, 15506 17986, has_genre, 36212 17986, release_year, 15506 3745, has_genre, 36212 3745, has_tags, 33943 3745, written_by, 33943 36639, has_genre, 36212 36639, release_year, 15506 13476, has_genre, 36212 13476, release_year, 15506 26784, has_genre, 36212 26784, has_tags, 36212 26784, release_year, 15506 31195, has_genre, 36212 31195, release_year, 15506 16662, has_genre, 36212 16662, release_year, 15506 20443, has_genre, 36212 20443, release_year, 15506 18256, has_genre, 36212 18256, starred_actors, 31323 40059, has_genre, 30463 40059, has_genre, 36212 40059, has_tags, 36212 40059, starred_actors, 19475 28686, has_genre, 36212 28686, release_year, 15506 39856, has_genre, 36212 39856, release_year, 15506 18733, has_genre, 30463 18733, has_genre, 36212 18733, starred_actors, 33241 39071, has_genre, 36212 39071, release_year, 15506 15207, has_genre, 36212 15207, release_year, 15506 30083, has_genre, 36212 30083, release_year, 15506 4700, has_genre, 36212 4700, has_tags, 33943 4700, release_year, 15506 4700, starred_actors, 31323 4700, written_by, 33943 29621, has_genre, 36212 29621, release_year, 15506 29621, starred_actors, 31323 27022, has_genre, 36212 27022, written_by, 33943 4442, has_genre, 36212 4442, starred_actors, 19475 Question: In what context are GUILLERMO DÍAZ, LAURA SAN GIACOMO, and THE STORY OF TEMPLE DRAKE connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "GUILLERMO DÍAZ", "LAURA SAN GIACOMO", "THE STORY OF TEMPLE DRAKE" ], "valid_edges": [ [ "APART FROM YOU", "has_genre", "DRAMA" ], [ "APART FROM YOU", "release_year", "1933" ], [ "AS I LAY DYING", "has_genre", "DRAMA" ], [ "AS I LAY DYING", "has_tags", "WILLIAM FAULKNER" ], [ "AS I LAY DYING", "written_by", "WILLIAM FAULKNER" ], [ "BABY FACE", "has_genre", "DRAMA" ], [ "BABY FACE", "release_year", "1933" ], [ "BARBARY COAST", "has_genre", "DRAMA" ], [ "BARBARY COAST", "starred_actors", "MIRIAM HOPKINS" ], [ "BECKY SHARP", "has_genre", "DRAMA" ], [ "BECKY SHARP", "starred_actors", "MIRIAM HOPKINS" ], [ "BOMBSHELL", "has_genre", "DRAMA" ], [ "BOMBSHELL", "release_year", "1933" ], [ "CAVALCADE", "has_genre", "DRAMA" ], [ "CAVALCADE", "release_year", "1933" ], [ "COUNSELLOR AT LAW", "has_genre", "DRAMA" ], [ "COUNSELLOR AT LAW", "release_year", "1933" ], [ "DINNER AT EIGHT", "has_genre", "DRAMA" ], [ "DINNER AT EIGHT", "release_year", "1933" ], [ "GABRIEL OVER THE WHITE HOUSE", "has_genre", "DRAMA" ], [ "GABRIEL OVER THE WHITE HOUSE", "release_year", "1933" ], [ "INTRUDER IN THE DUST", "has_genre", "DRAMA" ], [ "INTRUDER IN THE DUST", "has_tags", "WILLIAM FAULKNER" ], [ "INTRUDER IN THE DUST", "written_by", "WILLIAM FAULKNER" ], [ "LADIES THEY TALK ABOUT", "has_genre", "DRAMA" ], [ "LADIES THEY TALK ABOUT", "release_year", "1933" ], [ "LADY KILLER", "has_genre", "DRAMA" ], [ "LADY KILLER", "release_year", "1933" ], [ "LITTLE WOMEN", "has_genre", "DRAMA" ], [ "LITTLE WOMEN", "has_tags", "DRAMA" ], [ "LITTLE WOMEN", "release_year", "1933" ], [ "LOOKING FORWARD", "has_genre", "DRAMA" ], [ "LOOKING FORWARD", "release_year", "1933" ], [ "MORNING GLORY", "has_genre", "DRAMA" ], [ "MORNING GLORY", "release_year", "1933" ], [ "NIGHT FLIGHT", "has_genre", "DRAMA" ], [ "NIGHT FLIGHT", "release_year", "1933" ], [ "OLD ACQUAINTANCE", "has_genre", "DRAMA" ], [ "OLD ACQUAINTANCE", "starred_actors", "MIRIAM HOPKINS" ], [ "ONCE AROUND", "has_genre", "COMEDY" ], [ "ONCE AROUND", "has_genre", "DRAMA" ], [ "ONCE AROUND", "has_tags", "DRAMA" ], [ "ONCE AROUND", "starred_actors", "LAURA SAN GIACOMO" ], [ "S.O.S. EISBERG", "has_genre", "DRAMA" ], [ "S.O.S. EISBERG", "release_year", "1933" ], [ "SHE DONE HIM WRONG", "has_genre", "DRAMA" ], [ "SHE DONE HIM WRONG", "release_year", "1933" ], [ "STONEWALL", "has_genre", "COMEDY" ], [ "STONEWALL", "has_genre", "DRAMA" ], [ "STONEWALL", "starred_actors", "GUILLERMO DÍAZ" ], [ "THE BITTER TEA OF GENERAL YEN", "has_genre", "DRAMA" ], [ "THE BITTER TEA OF GENERAL YEN", "release_year", "1933" ], [ "THE EMPEROR JONES", "has_genre", "DRAMA" ], [ "THE EMPEROR JONES", "release_year", "1933" ], [ "THE HOUSE ON 56TH STREET", "has_genre", "DRAMA" ], [ "THE HOUSE ON 56TH STREET", "release_year", "1933" ], [ "THE STORY OF TEMPLE DRAKE", "has_genre", "DRAMA" ], [ "THE STORY OF TEMPLE DRAKE", "has_tags", "WILLIAM FAULKNER" ], [ "THE STORY OF TEMPLE DRAKE", "release_year", "1933" ], [ "THE STORY OF TEMPLE DRAKE", "starred_actors", "MIRIAM HOPKINS" ], [ "THE STORY OF TEMPLE DRAKE", "written_by", "WILLIAM FAULKNER" ], [ "THE STRANGER'S RETURN", "has_genre", "DRAMA" ], [ "THE STRANGER'S RETURN", "release_year", "1933" ], [ "THE STRANGER'S RETURN", "starred_actors", "MIRIAM HOPKINS" ], [ "THE TARNISHED ANGELS", "has_genre", "DRAMA" ], [ "THE TARNISHED ANGELS", "written_by", "WILLIAM FAULKNER" ], [ "WHERE THE DAY TAKES YOU", "has_genre", "DRAMA" ], [ "WHERE THE DAY TAKES YOU", "starred_actors", "LAURA SAN GIACOMO" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 11112, 1939 25717, 1953 21802, ABBOTT AND COSTELLO GO TO MARS 15922, ABBOTT AND COSTELLO MEET DR. JEKYLL AND MR. HYDE 21132, ANOTHER THIN MAN 5192, AT THE CIRCUS 38849, BACHELOR MOTHER 38657, BEAT THE DEVIL 29294, BREAD, LOVE AND DREAMS 24967, BROADWAY BILL 8430, CALL ME MADAM 30523, CHEAPER BY THE DOZEN 30463, COMEDY 27563, DOUBLE WEDDING 1480, DREAM WIFE 36249, GENEVIEVE 17440, GULLIVER'S TRAVELS 12557, HOW TO MARRY A MILLIONAIRE 12591, I LOVE YOU AGAIN 17344, I VITELLONI 38176, IDIOT'S DELIGHT 26280, IT'S A WONDERFUL WORLD 2563, LIBELED LADY 728, LOVE CRAZY 22032, LUCKY NIGHT 23421, MEET ME AT THE FAIR 38276, MIDNIGHT 7186, MR. BLANDINGS BUILDS HIS DREAM HOUSE 19619, MYRNA LOY 5338, NINOTCHKA 13096, ROMAN HOLIDAY 35507, SONG OF THE THIN MAN 22407, THE ACTRESS 23870, THE ANIMAL KINGDOM 8318, THE BACHELOR AND THE BOBBY-SOXER 32851, THE BAND WAGON 18855, THE CADDY 18055, THE CAPTAIN'S PARADISE 31239, THE CAT AND THE CANARY 31283, THE HOUND OF THE BASKERVILLES 17571, THE INTERNSHIP 33153, THE MIKADO 12672, THE MOON IS BLUE 9027, THE RAINS CAME 3848, THE RETURN OF DON CAMILLO 37461, THE RULES OF THE GAME 10991, THE SUN SHINES BRIGHT 15833, THE THIN MAN 7816, THE THREE MUSKETEERS 8289, THE TITFIELD THUNDERBOLT 15263, THE TWONKY 20210, THE WOMEN 584, THREE SMART GIRLS GROW UP 36409, WELCOME MR. MARSHALL! 14809, WIFE VS. SECRETARY src, edge_attr, dst 21802, has_genre, 30463 21802, release_year, 25717 15922, has_genre, 30463 15922, release_year, 25717 21132, has_tags, 19619 21132, release_year, 11112 21132, starred_actors, 19619 5192, has_genre, 30463 5192, release_year, 11112 38849, has_genre, 30463 38849, release_year, 11112 38657, has_genre, 30463 38657, release_year, 25717 29294, has_genre, 30463 29294, release_year, 25717 24967, has_genre, 30463 24967, starred_actors, 19619 8430, has_genre, 30463 8430, release_year, 25717 30523, has_genre, 30463 30523, starred_actors, 19619 27563, has_genre, 30463 27563, has_tags, 19619 27563, starred_actors, 19619 1480, has_genre, 30463 1480, release_year, 25717 36249, has_genre, 30463 36249, release_year, 25717 17440, has_genre, 30463 17440, has_tags, 30463 17440, release_year, 11112 12557, has_genre, 30463 12557, release_year, 25717 12591, has_genre, 30463 12591, has_tags, 19619 12591, starred_actors, 19619 17344, has_genre, 30463 17344, release_year, 25717 38176, has_genre, 30463 38176, release_year, 11112 26280, has_genre, 30463 26280, release_year, 11112 2563, has_genre, 30463 2563, starred_actors, 19619 728, has_genre, 30463 728, has_tags, 19619 728, starred_actors, 19619 22032, has_genre, 30463 22032, release_year, 11112 22032, starred_actors, 19619 23421, release_year, 25717 38276, has_genre, 30463 38276, release_year, 11112 7186, has_genre, 30463 7186, starred_actors, 19619 5338, has_genre, 30463 5338, release_year, 11112 13096, has_genre, 30463 13096, has_tags, 30463 13096, release_year, 25717 35507, has_genre, 30463 35507, starred_actors, 19619 22407, has_genre, 30463 22407, release_year, 25717 23870, has_genre, 30463 23870, starred_actors, 19619 8318, has_genre, 30463 8318, starred_actors, 19619 32851, has_genre, 30463 32851, release_year, 25717 18855, has_genre, 30463 18855, release_year, 25717 18055, has_genre, 30463 18055, release_year, 25717 31239, has_genre, 30463 31239, release_year, 11112 31283, has_genre, 30463 31283, release_year, 11112 17571, has_genre, 30463 33153, has_genre, 30463 33153, release_year, 11112 12672, has_genre, 30463 12672, release_year, 25717 9027, release_year, 11112 9027, starred_actors, 19619 3848, has_genre, 30463 3848, release_year, 25717 37461, has_genre, 30463 37461, has_tags, 30463 37461, release_year, 11112 10991, has_genre, 30463 10991, release_year, 25717 15833, has_genre, 30463 15833, has_tags, 30463 15833, has_tags, 19619 15833, starred_actors, 19619 7816, has_genre, 30463 7816, release_year, 11112 8289, has_genre, 30463 8289, release_year, 25717 15263, has_genre, 30463 15263, release_year, 25717 20210, has_genre, 30463 20210, release_year, 11112 584, has_genre, 30463 584, release_year, 11112 36409, has_genre, 30463 36409, release_year, 25717 14809, has_genre, 30463 14809, has_tags, 19619 14809, starred_actors, 19619 Question: For what reason are MEET ME AT THE FAIR, THE INTERNSHIP, and THE RAINS CAME associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "MEET ME AT THE FAIR", "THE INTERNSHIP", "THE RAINS CAME" ], "valid_edges": [ [ "ABBOTT AND COSTELLO GO TO MARS", "has_genre", "COMEDY" ], [ "ABBOTT AND COSTELLO GO TO MARS", "release_year", "1953" ], [ "ABBOTT AND COSTELLO MEET DR. JEKYLL AND MR. HYDE", "has_genre", "COMEDY" ], [ "ABBOTT AND COSTELLO MEET DR. JEKYLL AND MR. HYDE", "release_year", "1953" ], [ "ANOTHER THIN MAN", "has_tags", "MYRNA LOY" ], [ "ANOTHER THIN MAN", "release_year", "1939" ], [ "ANOTHER THIN MAN", "starred_actors", "MYRNA LOY" ], [ "AT THE CIRCUS", "has_genre", "COMEDY" ], [ "AT THE CIRCUS", "release_year", "1939" ], [ "BACHELOR MOTHER", "has_genre", "COMEDY" ], [ "BACHELOR MOTHER", "release_year", "1939" ], [ "BEAT THE DEVIL", "has_genre", "COMEDY" ], [ "BEAT THE DEVIL", "release_year", "1953" ], [ "BREAD, LOVE AND DREAMS", "has_genre", "COMEDY" ], [ "BREAD, LOVE AND DREAMS", "release_year", "1953" ], [ "BROADWAY BILL", "has_genre", "COMEDY" ], [ "BROADWAY BILL", "starred_actors", "MYRNA LOY" ], [ "CALL ME MADAM", "has_genre", "COMEDY" ], [ "CALL ME MADAM", "release_year", "1953" ], [ "CHEAPER BY THE DOZEN", "has_genre", "COMEDY" ], [ "CHEAPER BY THE DOZEN", "starred_actors", "MYRNA LOY" ], [ "DOUBLE WEDDING", "has_genre", "COMEDY" ], [ "DOUBLE WEDDING", "has_tags", "MYRNA LOY" ], [ "DOUBLE WEDDING", "starred_actors", "MYRNA LOY" ], [ "DREAM WIFE", "has_genre", "COMEDY" ], [ "DREAM WIFE", "release_year", "1953" ], [ "GENEVIEVE", "has_genre", "COMEDY" ], [ "GENEVIEVE", "release_year", "1953" ], [ "GULLIVER'S TRAVELS", "has_genre", "COMEDY" ], [ "GULLIVER'S TRAVELS", "has_tags", "COMEDY" ], [ "GULLIVER'S TRAVELS", "release_year", "1939" ], [ "HOW TO MARRY A MILLIONAIRE", "has_genre", "COMEDY" ], [ "HOW TO MARRY A MILLIONAIRE", "release_year", "1953" ], [ "I LOVE YOU AGAIN", "has_genre", "COMEDY" ], [ "I LOVE YOU AGAIN", "has_tags", "MYRNA LOY" ], [ "I LOVE YOU AGAIN", "starred_actors", "MYRNA LOY" ], [ "I VITELLONI", "has_genre", "COMEDY" ], [ "I VITELLONI", "release_year", "1953" ], [ "IDIOT'S DELIGHT", "has_genre", "COMEDY" ], [ "IDIOT'S DELIGHT", "release_year", "1939" ], [ "IT'S A WONDERFUL WORLD", "has_genre", "COMEDY" ], [ "IT'S A WONDERFUL WORLD", "release_year", "1939" ], [ "LIBELED LADY", "has_genre", "COMEDY" ], [ "LIBELED LADY", "starred_actors", "MYRNA LOY" ], [ "LOVE CRAZY", "has_genre", "COMEDY" ], [ "LOVE CRAZY", "has_tags", "MYRNA LOY" ], [ "LOVE CRAZY", "starred_actors", "MYRNA LOY" ], [ "LUCKY NIGHT", "has_genre", "COMEDY" ], [ "LUCKY NIGHT", "release_year", "1939" ], [ "LUCKY NIGHT", "starred_actors", "MYRNA LOY" ], [ "MEET ME AT THE FAIR", "release_year", "1953" ], [ "MIDNIGHT", "has_genre", "COMEDY" ], [ "MIDNIGHT", "release_year", "1939" ], [ "MR. BLANDINGS BUILDS HIS DREAM HOUSE", "has_genre", "COMEDY" ], [ "MR. BLANDINGS BUILDS HIS DREAM HOUSE", "starred_actors", "MYRNA LOY" ], [ "NINOTCHKA", "has_genre", "COMEDY" ], [ "NINOTCHKA", "release_year", "1939" ], [ "ROMAN HOLIDAY", "has_genre", "COMEDY" ], [ "ROMAN HOLIDAY", "has_tags", "COMEDY" ], [ "ROMAN HOLIDAY", "release_year", "1953" ], [ "SONG OF THE THIN MAN", "has_genre", "COMEDY" ], [ "SONG OF THE THIN MAN", "starred_actors", "MYRNA LOY" ], [ "THE ACTRESS", "has_genre", "COMEDY" ], [ "THE ACTRESS", "release_year", "1953" ], [ "THE ANIMAL KINGDOM", "has_genre", "COMEDY" ], [ "THE ANIMAL KINGDOM", "starred_actors", "MYRNA LOY" ], [ "THE BACHELOR AND THE BOBBY-SOXER", "has_genre", "COMEDY" ], [ "THE BACHELOR AND THE BOBBY-SOXER", "starred_actors", "MYRNA LOY" ], [ "THE BAND WAGON", "has_genre", "COMEDY" ], [ "THE BAND WAGON", "release_year", "1953" ], [ "THE CADDY", "has_genre", "COMEDY" ], [ "THE CADDY", "release_year", "1953" ], [ "THE CAPTAIN'S PARADISE", "has_genre", "COMEDY" ], [ "THE CAPTAIN'S PARADISE", "release_year", "1953" ], [ "THE CAT AND THE CANARY", "has_genre", "COMEDY" ], [ "THE CAT AND THE CANARY", "release_year", "1939" ], [ "THE HOUND OF THE BASKERVILLES", "has_genre", "COMEDY" ], [ "THE HOUND OF THE BASKERVILLES", "release_year", "1939" ], [ "THE INTERNSHIP", "has_genre", "COMEDY" ], [ "THE MIKADO", "has_genre", "COMEDY" ], [ "THE MIKADO", "release_year", "1939" ], [ "THE MOON IS BLUE", "has_genre", "COMEDY" ], [ "THE MOON IS BLUE", "release_year", "1953" ], [ "THE RAINS CAME", "release_year", "1939" ], [ "THE RAINS CAME", "starred_actors", "MYRNA LOY" ], [ "THE RETURN OF DON CAMILLO", "has_genre", "COMEDY" ], [ "THE RETURN OF DON CAMILLO", "release_year", "1953" ], [ "THE RULES OF THE GAME", "has_genre", "COMEDY" ], [ "THE RULES OF THE GAME", "has_tags", "COMEDY" ], [ "THE RULES OF THE GAME", "release_year", "1939" ], [ "THE SUN SHINES BRIGHT", "has_genre", "COMEDY" ], [ "THE SUN SHINES BRIGHT", "release_year", "1953" ], [ "THE THIN MAN", "has_genre", "COMEDY" ], [ "THE THIN MAN", "has_tags", "COMEDY" ], [ "THE THIN MAN", "has_tags", "MYRNA LOY" ], [ "THE THIN MAN", "starred_actors", "MYRNA LOY" ], [ "THE THREE MUSKETEERS", "has_genre", "COMEDY" ], [ "THE THREE MUSKETEERS", "release_year", "1939" ], [ "THE TITFIELD THUNDERBOLT", "has_genre", "COMEDY" ], [ "THE TITFIELD THUNDERBOLT", "release_year", "1953" ], [ "THE TWONKY", "has_genre", "COMEDY" ], [ "THE TWONKY", "release_year", "1953" ], [ "THE WOMEN", "has_genre", "COMEDY" ], [ "THE WOMEN", "release_year", "1939" ], [ "THREE SMART GIRLS GROW UP", "has_genre", "COMEDY" ], [ "THREE SMART GIRLS GROW UP", "release_year", "1939" ], [ "WELCOME MR. MARSHALL!", "has_genre", "COMEDY" ], [ "WELCOME MR. MARSHALL!", "release_year", "1953" ], [ "WIFE VS. SECRETARY", "has_genre", "COMEDY" ], [ "WIFE VS. SECRETARY", "has_tags", "MYRNA LOY" ], [ "WIFE VS. SECRETARY", "starred_actors", "MYRNA LOY" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 16681, ALAN PAO 9644, ANNE MEARA 904, ANOTHER HARVEST MOON 36212, DRAMA 10688, HENRY BEAN 16480, LOADED 33677, NOISE 267, THE BELIEVER src, edge_attr, dst 904, has_genre, 36212 904, starred_actors, 9644 16480, directed_by, 16681 16480, has_genre, 36212 16480, written_by, 16681 33677, directed_by, 10688 33677, has_genre, 36212 33677, written_by, 10688 267, directed_by, 10688 267, has_genre, 36212 267, written_by, 10688 Question: In what context are ALAN PAO, ANNE MEARA, and HENRY BEAN connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ALAN PAO", "ANNE MEARA", "HENRY BEAN" ], "valid_edges": [ [ "ANOTHER HARVEST MOON", "has_genre", "DRAMA" ], [ "ANOTHER HARVEST MOON", "starred_actors", "ANNE MEARA" ], [ "LOADED", "directed_by", "ALAN PAO" ], [ "LOADED", "has_genre", "DRAMA" ], [ "LOADED", "written_by", "ALAN PAO" ], [ "NOISE", "directed_by", "HENRY BEAN" ], [ "NOISE", "has_genre", "DRAMA" ], [ "NOISE", "written_by", "HENRY BEAN" ], [ "THE BELIEVER", "directed_by", "HENRY BEAN" ], [ "THE BELIEVER", "has_genre", "DRAMA" ], [ "THE BELIEVER", "written_by", "HENRY BEAN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 19293, ALL THE PRESIDENT'S MEN 6725, BLACK RAINBOW 30463, COMEDY 36212, DRAMA 25387, JOAN ALLEN 10985, NIXON 14096, NORMA RAE 14282, PLEASANTVILLE 15974, SEARCHING FOR BOBBY FISCHER 10041, SILKWOOD 15323, THE CONTENDER 5278, THE CRUCIBLE 15840, THE ICE STORM 7168, THE UPSIDE OF ANGER 24811, THRILLER 37678, TOBEY MAGUIRE 2078, UNION src, edge_attr, dst 19293, has_genre, 24811 19293, has_tags, 10985 19293, has_tags, 24811 6725, has_genre, 24811 10985, starred_actors, 25387 14096, has_genre, 36212 14096, has_tags, 2078 14282, has_genre, 30463 14282, has_genre, 36212 14282, has_tags, 25387 14282, has_tags, 37678 14282, starred_actors, 25387 14282, starred_actors, 37678 15974, has_genre, 36212 15974, starred_actors, 25387 10041, has_genre, 36212 10041, has_tags, 2078 15323, has_genre, 36212 15323, has_tags, 25387 15323, starred_actors, 25387 5278, has_genre, 36212 5278, starred_actors, 25387 15840, has_genre, 36212 15840, has_tags, 36212 15840, has_tags, 37678 15840, starred_actors, 25387 7168, has_genre, 30463 7168, has_genre, 36212 7168, starred_actors, 25387 Question: How are BLACK RAINBOW, JOAN ALLEN, and UNION related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BLACK RAINBOW", "JOAN ALLEN", "UNION" ], "valid_edges": [ [ "ALL THE PRESIDENT'S MEN", "has_genre", "THRILLER" ], [ "ALL THE PRESIDENT'S MEN", "has_tags", "NIXON" ], [ "ALL THE PRESIDENT'S MEN", "has_tags", "THRILLER" ], [ "BLACK RAINBOW", "has_genre", "THRILLER" ], [ "NIXON", "starred_actors", "JOAN ALLEN" ], [ "NORMA RAE", "has_genre", "DRAMA" ], [ "NORMA RAE", "has_tags", "UNION" ], [ "PLEASANTVILLE", "has_genre", "COMEDY" ], [ "PLEASANTVILLE", "has_genre", "DRAMA" ], [ "PLEASANTVILLE", "has_tags", "JOAN ALLEN" ], [ "PLEASANTVILLE", "has_tags", "TOBEY MAGUIRE" ], [ "PLEASANTVILLE", "starred_actors", "JOAN ALLEN" ], [ "PLEASANTVILLE", "starred_actors", "TOBEY MAGUIRE" ], [ "SEARCHING FOR BOBBY FISCHER", "has_genre", "DRAMA" ], [ "SEARCHING FOR BOBBY FISCHER", "starred_actors", "JOAN ALLEN" ], [ "SILKWOOD", "has_genre", "DRAMA" ], [ "SILKWOOD", "has_tags", "UNION" ], [ "THE CONTENDER", "has_genre", "DRAMA" ], [ "THE CONTENDER", "has_tags", "JOAN ALLEN" ], [ "THE CONTENDER", "starred_actors", "JOAN ALLEN" ], [ "THE CRUCIBLE", "has_genre", "DRAMA" ], [ "THE CRUCIBLE", "starred_actors", "JOAN ALLEN" ], [ "THE ICE STORM", "has_genre", "DRAMA" ], [ "THE ICE STORM", "has_tags", "DRAMA" ], [ "THE ICE STORM", "has_tags", "TOBEY MAGUIRE" ], [ "THE ICE STORM", "starred_actors", "JOAN ALLEN" ], [ "THE UPSIDE OF ANGER", "has_genre", "COMEDY" ], [ "THE UPSIDE OF ANGER", "has_genre", "DRAMA" ], [ "THE UPSIDE OF ANGER", "starred_actors", "JOAN ALLEN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 31486, 1970 10045, BD-R 10712, MICHAEL DORMAN 15262, NO WAY TO TREAT A LADY 34389, NORTON JUSTER 35865, ON THE WATERFRONT 5286, POOLHALL JUNKIES 14216, ROD STEIGER 18821, RUN OF THE ARROW 25850, THE PAWNBROKER 24512, THE PHANTOM TOLLBOOTH 24811, THRILLER 37099, TRIANGLE 558, WATERLOO src, edge_attr, dst 15262, has_genre, 24811 15262, starred_actors, 14216 35865, has_tags, 10045 35865, starred_actors, 14216 5286, has_genre, 24811 5286, starred_actors, 14216 18821, has_tags, 10045 18821, starred_actors, 14216 25850, has_tags, 10045 25850, starred_actors, 14216 24512, has_tags, 10045 24512, release_year, 31486 24512, written_by, 34389 37099, has_genre, 24811 37099, starred_actors, 10712 558, has_tags, 14216 558, release_year, 31486 558, starred_actors, 14216 Question: How are MICHAEL DORMAN, NORTON JUSTER, and ROD STEIGER related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "MICHAEL DORMAN", "NORTON JUSTER", "ROD STEIGER" ], "valid_edges": [ [ "NO WAY TO TREAT A LADY", "has_genre", "THRILLER" ], [ "NO WAY TO TREAT A LADY", "starred_actors", "ROD STEIGER" ], [ "ON THE WATERFRONT", "has_tags", "BD-R" ], [ "ON THE WATERFRONT", "starred_actors", "ROD STEIGER" ], [ "POOLHALL JUNKIES", "has_genre", "THRILLER" ], [ "POOLHALL JUNKIES", "starred_actors", "ROD STEIGER" ], [ "RUN OF THE ARROW", "has_tags", "BD-R" ], [ "RUN OF THE ARROW", "starred_actors", "ROD STEIGER" ], [ "THE PAWNBROKER", "has_tags", "BD-R" ], [ "THE PAWNBROKER", "starred_actors", "ROD STEIGER" ], [ "THE PHANTOM TOLLBOOTH", "has_tags", "BD-R" ], [ "THE PHANTOM TOLLBOOTH", "release_year", "1970" ], [ "THE PHANTOM TOLLBOOTH", "written_by", "NORTON JUSTER" ], [ "TRIANGLE", "has_genre", "THRILLER" ], [ "TRIANGLE", "starred_actors", "MICHAEL DORMAN" ], [ "WATERLOO", "has_tags", "ROD STEIGER" ], [ "WATERLOO", "release_year", "1970" ], [ "WATERLOO", "starred_actors", "ROD STEIGER" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 36163, 1967 26762, 2008 33217, 35 SHOTS OF RUM 9698, A CHRISTMAS TALE 17521, ACES 'N' EIGHTS 36359, AFTERWARDS 36494, ANYTHING FOR HER 4131, ASTERIX AT THE OLYMPIC GAMES 31322, ASTERIX THE GAUL 16426, BELLE DE JOUR 10081, BOTTLE SHOCK 18400, CASE DÉPART 2246, CRITERION 38134, DANTE 01 9709, DE L'AUTRE CÔTÉ DU LIT 30696, DIARY OF A NYMPHOMANIAC 21405, DRAGON HUNTERS 34422, FEMALE AGENTS 6012, FRENCH 14669, FRONTIER OF THE DAWN 3586, I'VE LOVED YOU SO LONG 8150, JACQUES TATI 24305, JCVD 23661, LE SAMOURAÏ 8807, LET'S TALK ABOUT THE RAIN 14659, LOVE ME NO MORE 24257, MARK OF AN ANGEL 24399, MARTYRS 31657, MON ONCLE 12100, MOUCHETTE 5937, ORPHEUS 31134, PARIS 8740, PARIS 36 5311, PLAYTIME 21835, POINT BLANK 8764, STELLA 32587, SUMMER HOURS 5467, SÉRAPHINE 30003, TAKEN 38592, THE BEACHES OF AGNÈS 21712, THE CLASS 37721, THE FIRST DAY OF THE REST OF YOUR LIFE 25529, THE ILLUSIONIST 34283, THE LAST DEADLY MISSION 22166, THE LOWER DEPTHS 11654, THE THIEF OF PARIS 30481, THE TWO OF US 34585, THE YOUNG GIRLS OF ROCHEFORT 7251, THOMAS N'GIJOL 17536, TRAFIC 33483, WEEKEND src, edge_attr, dst 33217, in_language, 6012 33217, release_year, 26762 9698, in_language, 6012 9698, release_year, 26762 17521, release_year, 26762 36359, in_language, 6012 36359, release_year, 26762 36494, in_language, 6012 36494, release_year, 26762 4131, has_tags, 6012 4131, in_language, 6012 4131, release_year, 26762 31322, in_language, 6012 31322, release_year, 36163 16426, in_language, 6012 16426, release_year, 36163 10081, in_language, 6012 10081, release_year, 26762 18400, directed_by, 7251 18400, has_tags, 6012 18400, in_language, 6012 18400, starred_actors, 7251 18400, written_by, 7251 38134, has_tags, 6012 38134, in_language, 6012 38134, release_year, 26762 9709, in_language, 6012 9709, release_year, 26762 30696, in_language, 6012 30696, release_year, 26762 21405, in_language, 6012 21405, release_year, 26762 34422, in_language, 6012 34422, release_year, 26762 14669, in_language, 6012 14669, release_year, 26762 3586, in_language, 6012 3586, release_year, 26762 24305, in_language, 6012 24305, release_year, 26762 23661, in_language, 6012 23661, release_year, 36163 8807, in_language, 6012 8807, release_year, 26762 14659, has_tags, 6012 14659, in_language, 6012 14659, release_year, 26762 24257, in_language, 6012 24257, release_year, 26762 24399, has_tags, 6012 24399, in_language, 6012 24399, release_year, 26762 31657, directed_by, 8150 31657, has_tags, 8150 31657, in_language, 6012 31657, written_by, 8150 12100, has_tags, 2246 12100, in_language, 6012 12100, release_year, 36163 5937, has_tags, 2246 5937, in_language, 6012 31134, in_language, 6012 31134, release_year, 26762 8740, in_language, 6012 8740, release_year, 26762 5311, directed_by, 8150 5311, has_tags, 2246 5311, has_tags, 8150 5311, in_language, 6012 5311, release_year, 36163 5311, starred_actors, 8150 5311, written_by, 8150 21835, in_language, 6012 21835, release_year, 36163 8764, in_language, 6012 8764, release_year, 26762 32587, has_tags, 2246 32587, in_language, 6012 32587, release_year, 26762 5467, in_language, 6012 5467, release_year, 26762 30003, in_language, 6012 30003, release_year, 26762 38592, in_language, 6012 38592, release_year, 26762 21712, has_tags, 6012 21712, in_language, 6012 21712, release_year, 26762 37721, in_language, 6012 37721, release_year, 26762 25529, in_language, 6012 25529, written_by, 8150 34283, in_language, 6012 34283, release_year, 26762 22166, has_tags, 2246 22166, in_language, 6012 11654, in_language, 6012 11654, release_year, 36163 30481, in_language, 6012 30481, release_year, 36163 34585, in_language, 6012 34585, release_year, 36163 17536, directed_by, 8150 17536, has_tags, 8150 17536, in_language, 6012 17536, starred_actors, 8150 17536, written_by, 8150 33483, has_tags, 6012 33483, in_language, 6012 33483, release_year, 36163 Question: For what reason are ACES 'N' EIGHTS, PLAYTIME, and THOMAS N'GIJOL associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ACES 'N' EIGHTS", "PLAYTIME", "THOMAS N'GIJOL" ], "valid_edges": [ [ "35 SHOTS OF RUM", "in_language", "FRENCH" ], [ "35 SHOTS OF RUM", "release_year", "2008" ], [ "A CHRISTMAS TALE", "in_language", "FRENCH" ], [ "A CHRISTMAS TALE", "release_year", "2008" ], [ "ACES 'N' EIGHTS", "release_year", "2008" ], [ "AFTERWARDS", "in_language", "FRENCH" ], [ "AFTERWARDS", "release_year", "2008" ], [ "ANYTHING FOR HER", "in_language", "FRENCH" ], [ "ANYTHING FOR HER", "release_year", "2008" ], [ "ASTERIX AT THE OLYMPIC GAMES", "has_tags", "FRENCH" ], [ "ASTERIX AT THE OLYMPIC GAMES", "in_language", "FRENCH" ], [ "ASTERIX AT THE OLYMPIC GAMES", "release_year", "2008" ], [ "ASTERIX THE GAUL", "in_language", "FRENCH" ], [ "ASTERIX THE GAUL", "release_year", "1967" ], [ "BELLE DE JOUR", "in_language", "FRENCH" ], [ "BELLE DE JOUR", "release_year", "1967" ], [ "BOTTLE SHOCK", "in_language", "FRENCH" ], [ "BOTTLE SHOCK", "release_year", "2008" ], [ "CASE DÉPART", "directed_by", "THOMAS N'GIJOL" ], [ "CASE DÉPART", "has_tags", "FRENCH" ], [ "CASE DÉPART", "in_language", "FRENCH" ], [ "CASE DÉPART", "starred_actors", "THOMAS N'GIJOL" ], [ "CASE DÉPART", "written_by", "THOMAS N'GIJOL" ], [ "DANTE 01", "has_tags", "FRENCH" ], [ "DANTE 01", "in_language", "FRENCH" ], [ "DANTE 01", "release_year", "2008" ], [ "DE L'AUTRE CÔTÉ DU LIT", "in_language", "FRENCH" ], [ "DE L'AUTRE CÔTÉ DU LIT", "release_year", "2008" ], [ "DIARY OF A NYMPHOMANIAC", "in_language", "FRENCH" ], [ "DIARY OF A NYMPHOMANIAC", "release_year", "2008" ], [ "DRAGON HUNTERS", "in_language", "FRENCH" ], [ "DRAGON HUNTERS", "release_year", "2008" ], [ "FEMALE AGENTS", "in_language", "FRENCH" ], [ "FEMALE AGENTS", "release_year", "2008" ], [ "FRONTIER OF THE DAWN", "in_language", "FRENCH" ], [ "FRONTIER OF THE DAWN", "release_year", "2008" ], [ "I'VE LOVED YOU SO LONG", "in_language", "FRENCH" ], [ "I'VE LOVED YOU SO LONG", "release_year", "2008" ], [ "JCVD", "in_language", "FRENCH" ], [ "JCVD", "release_year", "2008" ], [ "LE SAMOURAÏ", "in_language", "FRENCH" ], [ "LE SAMOURAÏ", "release_year", "1967" ], [ "LET'S TALK ABOUT THE RAIN", "in_language", "FRENCH" ], [ "LET'S TALK ABOUT THE RAIN", "release_year", "2008" ], [ "LOVE ME NO MORE", "has_tags", "FRENCH" ], [ "LOVE ME NO MORE", "in_language", "FRENCH" ], [ "LOVE ME NO MORE", "release_year", "2008" ], [ "MARK OF AN ANGEL", "in_language", "FRENCH" ], [ "MARK OF AN ANGEL", "release_year", "2008" ], [ "MARTYRS", "has_tags", "FRENCH" ], [ "MARTYRS", "in_language", "FRENCH" ], [ "MARTYRS", "release_year", "2008" ], [ "MON ONCLE", "directed_by", "JACQUES TATI" ], [ "MON ONCLE", "has_tags", "JACQUES TATI" ], [ "MON ONCLE", "in_language", "FRENCH" ], [ "MON ONCLE", "written_by", "JACQUES TATI" ], [ "MOUCHETTE", "has_tags", "CRITERION" ], [ "MOUCHETTE", "in_language", "FRENCH" ], [ "MOUCHETTE", "release_year", "1967" ], [ "ORPHEUS", "has_tags", "CRITERION" ], [ "ORPHEUS", "in_language", "FRENCH" ], [ "PARIS", "in_language", "FRENCH" ], [ "PARIS", "release_year", "2008" ], [ "PARIS 36", "in_language", "FRENCH" ], [ "PARIS 36", "release_year", "2008" ], [ "PLAYTIME", "directed_by", "JACQUES TATI" ], [ "PLAYTIME", "has_tags", "CRITERION" ], [ "PLAYTIME", "has_tags", "JACQUES TATI" ], [ "PLAYTIME", "in_language", "FRENCH" ], [ "PLAYTIME", "release_year", "1967" ], [ "PLAYTIME", "starred_actors", "JACQUES TATI" ], [ "PLAYTIME", "written_by", "JACQUES TATI" ], [ "POINT BLANK", "in_language", "FRENCH" ], [ "POINT BLANK", "release_year", "1967" ], [ "STELLA", "in_language", "FRENCH" ], [ "STELLA", "release_year", "2008" ], [ "SUMMER HOURS", "has_tags", "CRITERION" ], [ "SUMMER HOURS", "in_language", "FRENCH" ], [ "SUMMER HOURS", "release_year", "2008" ], [ "SÉRAPHINE", "in_language", "FRENCH" ], [ "SÉRAPHINE", "release_year", "2008" ], [ "TAKEN", "in_language", "FRENCH" ], [ "TAKEN", "release_year", "2008" ], [ "THE BEACHES OF AGNÈS", "in_language", "FRENCH" ], [ "THE BEACHES OF AGNÈS", "release_year", "2008" ], [ "THE CLASS", "has_tags", "FRENCH" ], [ "THE CLASS", "in_language", "FRENCH" ], [ "THE CLASS", "release_year", "2008" ], [ "THE FIRST DAY OF THE REST OF YOUR LIFE", "in_language", "FRENCH" ], [ "THE FIRST DAY OF THE REST OF YOUR LIFE", "release_year", "2008" ], [ "THE ILLUSIONIST", "in_language", "FRENCH" ], [ "THE ILLUSIONIST", "written_by", "JACQUES TATI" ], [ "THE LAST DEADLY MISSION", "in_language", "FRENCH" ], [ "THE LAST DEADLY MISSION", "release_year", "2008" ], [ "THE LOWER DEPTHS", "has_tags", "CRITERION" ], [ "THE LOWER DEPTHS", "in_language", "FRENCH" ], [ "THE THIEF OF PARIS", "in_language", "FRENCH" ], [ "THE THIEF OF PARIS", "release_year", "1967" ], [ "THE TWO OF US", "in_language", "FRENCH" ], [ "THE TWO OF US", "release_year", "1967" ], [ "THE YOUNG GIRLS OF ROCHEFORT", "in_language", "FRENCH" ], [ "THE YOUNG GIRLS OF ROCHEFORT", "release_year", "1967" ], [ "TRAFIC", "directed_by", "JACQUES TATI" ], [ "TRAFIC", "has_tags", "JACQUES TATI" ], [ "TRAFIC", "in_language", "FRENCH" ], [ "TRAFIC", "starred_actors", "JACQUES TATI" ], [ "TRAFIC", "written_by", "JACQUES TATI" ], [ "WEEKEND", "has_tags", "FRENCH" ], [ "WEEKEND", "in_language", "FRENCH" ], [ "WEEKEND", "release_year", "1967" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 24525, 1984 35935, 2002 19509, A BREED APART 30146, A CHRISTMAS CAROL 37797, A CRUEL ROMANCE 24319, A PASSAGE TO INDIA 35695, A SOLDIER'S STORY 38344, A STREETCAR NAMED DESIRE 27196, ALPHABET CITY 20033, ANGEL 6091, ANOTHER COUNTRY 37608, AUSTRALIA 4765, BEAT STREET 33154, BIRDY 8655, BOY MEETS GIRL 9935, CAMILA 33088, CARMEN 31906, CATCH A FIRE 207, CHOOSE ME 36212, DRAMA 25651, DUMMY 28612, ELECTRIC DREAMS 1055, FALLING IN LOVE 36477, FIRSTBORN 17958, FLASHPOINT 2670, FOOTLOOSE 13111, GIVE MY REGARDS TO BROAD STREET 19773, GRANDVIEW, U.S.A. 6004, HARD TO HOLD 2941, ILLEANA DOUGLAS 37251, IRRECONCILABLE DIFFERENCES 38694, MARIA'S LOVERS 39880, MICHAEL REDGRAVE 38118, MOSCOW ON THE HUDSON 31250, MRS. SOFFEL 3910, NEWSFRONT 18680, NO SMALL AFFAIR 17528, ONCE UPON A TIME IN AMERICA 38335, PARIS, TEXAS 19877, PHILLIP NOYCE 13961, PLACES IN THE HEART 4005, PURPLE RAIN 24073, RABBIT-PROOF FENCE 26830, RACING WITH THE MOON 38232, STORIES OF LOST SOULS 11759, STREETS OF FIRE 10952, TANK 34308, TEACHERS 2212, THE BOUNTY 18176, THE COTTON CLUB 24493, THE FUNERAL 34673, THE GREEN 959, THE HIT 38956, THE HOTEL NEW HAMPSHIRE 13329, THE KARATE KID 35027, THE KILLING FIELDS 1306, THE PUBLIC WOMAN 20850, THE QUIET AMERICAN 19283, THE RAZOR'S EDGE 18649, THE RIVER 14415, THE WILD LIFE 28482, THIEF OF HEARTS 13100, THREADS 16292, TRUE STORY 21396, UNTIL SEPTEMBER 31772, WHAT HAVE I DONE TO DESERVE THIS? src, edge_attr, dst 24525, starred_actors, 39880 19509, has_genre, 36212 19509, release_year, 24525 30146, has_genre, 36212 30146, release_year, 24525 37797, has_genre, 36212 37797, release_year, 24525 24319, has_genre, 36212 24319, release_year, 24525 35695, has_genre, 36212 35695, release_year, 24525 38344, has_genre, 36212 38344, release_year, 24525 27196, has_genre, 36212 27196, release_year, 24525 20033, has_genre, 36212 20033, release_year, 24525 6091, has_genre, 36212 6091, release_year, 24525 37608, has_genre, 36212 4765, has_genre, 36212 4765, release_year, 24525 33154, has_genre, 36212 33154, has_tags, 36212 33154, release_year, 24525 8655, has_genre, 36212 8655, release_year, 24525 9935, has_genre, 36212 9935, release_year, 24525 33088, has_genre, 36212 33088, release_year, 24525 31906, directed_by, 19877 31906, has_genre, 36212 207, has_genre, 36212 207, release_year, 24525 25651, has_genre, 36212 25651, starred_actors, 2941 28612, has_genre, 36212 28612, release_year, 24525 1055, has_genre, 36212 1055, release_year, 24525 36477, has_genre, 36212 36477, release_year, 24525 17958, has_genre, 36212 17958, release_year, 24525 2670, has_genre, 36212 2670, release_year, 24525 13111, has_genre, 36212 13111, release_year, 24525 19773, has_genre, 36212 19773, release_year, 24525 6004, has_genre, 36212 6004, release_year, 24525 37251, has_genre, 36212 37251, release_year, 24525 38694, has_genre, 36212 38694, release_year, 24525 38118, has_genre, 36212 38118, release_year, 24525 31250, has_genre, 36212 31250, release_year, 24525 3910, directed_by, 19877 3910, has_genre, 36212 3910, has_tags, 37608 3910, has_tags, 19877 3910, written_by, 19877 18680, has_genre, 36212 18680, release_year, 24525 17528, has_genre, 36212 17528, release_year, 24525 38335, has_genre, 36212 38335, release_year, 24525 13961, has_genre, 36212 13961, release_year, 24525 4005, has_genre, 36212 4005, release_year, 24525 24073, directed_by, 19877 24073, has_genre, 36212 24073, has_tags, 37608 24073, has_tags, 19877 24073, has_tags, 16292 24073, release_year, 35935 26830, has_genre, 36212 26830, release_year, 24525 38232, directed_by, 2941 38232, written_by, 2941 11759, has_genre, 36212 11759, release_year, 24525 10952, has_genre, 36212 10952, release_year, 24525 34308, has_genre, 36212 34308, release_year, 24525 2212, has_genre, 36212 2212, release_year, 24525 18176, has_genre, 36212 18176, release_year, 24525 24493, has_genre, 36212 24493, release_year, 24525 34673, has_genre, 36212 34673, starred_actors, 2941 959, has_genre, 36212 959, release_year, 24525 38956, has_genre, 36212 38956, release_year, 24525 13329, has_genre, 36212 13329, has_tags, 36212 13329, release_year, 24525 35027, has_genre, 36212 35027, release_year, 24525 1306, has_genre, 36212 1306, has_tags, 36212 1306, release_year, 24525 20850, directed_by, 19877 20850, has_tags, 19877 20850, release_year, 35935 20850, starred_actors, 39880 19283, has_genre, 36212 19283, release_year, 24525 18649, has_genre, 36212 18649, release_year, 24525 14415, has_genre, 36212 14415, release_year, 24525 28482, has_genre, 36212 28482, release_year, 24525 13100, has_genre, 36212 13100, release_year, 24525 16292, has_genre, 36212 16292, has_tags, 36212 21396, has_genre, 36212 21396, release_year, 24525 31772, has_genre, 36212 31772, release_year, 24525 Question: For what reason are PHILLIP NOYCE, STORIES OF LOST SOULS, and THIEF OF HEARTS associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "PHILLIP NOYCE", "STORIES OF LOST SOULS", "THIEF OF HEARTS" ], "valid_edges": [ [ "1984", "starred_actors", "MICHAEL REDGRAVE" ], [ "A BREED APART", "has_genre", "DRAMA" ], [ "A BREED APART", "release_year", "1984" ], [ "A CHRISTMAS CAROL", "has_genre", "DRAMA" ], [ "A CHRISTMAS CAROL", "release_year", "1984" ], [ "A CRUEL ROMANCE", "has_genre", "DRAMA" ], [ "A CRUEL ROMANCE", "release_year", "1984" ], [ "A PASSAGE TO INDIA", "has_genre", "DRAMA" ], [ "A PASSAGE TO INDIA", "release_year", "1984" ], [ "A SOLDIER'S STORY", "has_genre", "DRAMA" ], [ "A SOLDIER'S STORY", "release_year", "1984" ], [ "A STREETCAR NAMED DESIRE", "has_genre", "DRAMA" ], [ "A STREETCAR NAMED DESIRE", "release_year", "1984" ], [ "ALPHABET CITY", "has_genre", "DRAMA" ], [ "ALPHABET CITY", "release_year", "1984" ], [ "ANGEL", "has_genre", "DRAMA" ], [ "ANGEL", "release_year", "1984" ], [ "ANOTHER COUNTRY", "has_genre", "DRAMA" ], [ "ANOTHER COUNTRY", "release_year", "1984" ], [ "AUSTRALIA", "has_genre", "DRAMA" ], [ "BEAT STREET", "has_genre", "DRAMA" ], [ "BEAT STREET", "release_year", "1984" ], [ "BIRDY", "has_genre", "DRAMA" ], [ "BIRDY", "has_tags", "DRAMA" ], [ "BIRDY", "release_year", "1984" ], [ "BOY MEETS GIRL", "has_genre", "DRAMA" ], [ "BOY MEETS GIRL", "release_year", "1984" ], [ "CAMILA", "has_genre", "DRAMA" ], [ "CAMILA", "release_year", "1984" ], [ "CARMEN", "has_genre", "DRAMA" ], [ "CARMEN", "release_year", "1984" ], [ "CATCH A FIRE", "directed_by", "PHILLIP NOYCE" ], [ "CATCH A FIRE", "has_genre", "DRAMA" ], [ "CHOOSE ME", "has_genre", "DRAMA" ], [ "CHOOSE ME", "release_year", "1984" ], [ "DUMMY", "has_genre", "DRAMA" ], [ "DUMMY", "starred_actors", "ILLEANA DOUGLAS" ], [ "ELECTRIC DREAMS", "has_genre", "DRAMA" ], [ "ELECTRIC DREAMS", "release_year", "1984" ], [ "FALLING IN LOVE", "has_genre", "DRAMA" ], [ "FALLING IN LOVE", "release_year", "1984" ], [ "FIRSTBORN", "has_genre", "DRAMA" ], [ "FIRSTBORN", "release_year", "1984" ], [ "FLASHPOINT", "has_genre", "DRAMA" ], [ "FLASHPOINT", "release_year", "1984" ], [ "FOOTLOOSE", "has_genre", "DRAMA" ], [ "FOOTLOOSE", "release_year", "1984" ], [ "GIVE MY REGARDS TO BROAD STREET", "has_genre", "DRAMA" ], [ "GIVE MY REGARDS TO BROAD STREET", "release_year", "1984" ], [ "GRANDVIEW, U.S.A.", "has_genre", "DRAMA" ], [ "GRANDVIEW, U.S.A.", "release_year", "1984" ], [ "HARD TO HOLD", "has_genre", "DRAMA" ], [ "HARD TO HOLD", "release_year", "1984" ], [ "IRRECONCILABLE DIFFERENCES", "has_genre", "DRAMA" ], [ "IRRECONCILABLE DIFFERENCES", "release_year", "1984" ], [ "MARIA'S LOVERS", "has_genre", "DRAMA" ], [ "MARIA'S LOVERS", "release_year", "1984" ], [ "MOSCOW ON THE HUDSON", "has_genre", "DRAMA" ], [ "MOSCOW ON THE HUDSON", "release_year", "1984" ], [ "MRS. SOFFEL", "has_genre", "DRAMA" ], [ "MRS. SOFFEL", "release_year", "1984" ], [ "NEWSFRONT", "directed_by", "PHILLIP NOYCE" ], [ "NEWSFRONT", "has_genre", "DRAMA" ], [ "NEWSFRONT", "has_tags", "AUSTRALIA" ], [ "NEWSFRONT", "has_tags", "PHILLIP NOYCE" ], [ "NEWSFRONT", "written_by", "PHILLIP NOYCE" ], [ "NO SMALL AFFAIR", "has_genre", "DRAMA" ], [ "NO SMALL AFFAIR", "release_year", "1984" ], [ "ONCE UPON A TIME IN AMERICA", "has_genre", "DRAMA" ], [ "ONCE UPON A TIME IN AMERICA", "release_year", "1984" ], [ "PARIS, TEXAS", "has_genre", "DRAMA" ], [ "PARIS, TEXAS", "release_year", "1984" ], [ "PLACES IN THE HEART", "has_genre", "DRAMA" ], [ "PLACES IN THE HEART", "release_year", "1984" ], [ "PURPLE RAIN", "has_genre", "DRAMA" ], [ "PURPLE RAIN", "release_year", "1984" ], [ "RABBIT-PROOF FENCE", "directed_by", "PHILLIP NOYCE" ], [ "RABBIT-PROOF FENCE", "has_genre", "DRAMA" ], [ "RABBIT-PROOF FENCE", "has_tags", "AUSTRALIA" ], [ "RABBIT-PROOF FENCE", "has_tags", "PHILLIP NOYCE" ], [ "RABBIT-PROOF FENCE", "has_tags", "TRUE STORY" ], [ "RABBIT-PROOF FENCE", "release_year", "2002" ], [ "RACING WITH THE MOON", "has_genre", "DRAMA" ], [ "RACING WITH THE MOON", "release_year", "1984" ], [ "STORIES OF LOST SOULS", "directed_by", "ILLEANA DOUGLAS" ], [ "STORIES OF LOST SOULS", "written_by", "ILLEANA DOUGLAS" ], [ "STREETS OF FIRE", "has_genre", "DRAMA" ], [ "STREETS OF FIRE", "release_year", "1984" ], [ "TANK", "has_genre", "DRAMA" ], [ "TANK", "release_year", "1984" ], [ "TEACHERS", "has_genre", "DRAMA" ], [ "TEACHERS", "release_year", "1984" ], [ "THE BOUNTY", "has_genre", "DRAMA" ], [ "THE BOUNTY", "release_year", "1984" ], [ "THE COTTON CLUB", "has_genre", "DRAMA" ], [ "THE COTTON CLUB", "release_year", "1984" ], [ "THE FUNERAL", "has_genre", "DRAMA" ], [ "THE FUNERAL", "release_year", "1984" ], [ "THE GREEN", "has_genre", "DRAMA" ], [ "THE GREEN", "starred_actors", "ILLEANA DOUGLAS" ], [ "THE HIT", "has_genre", "DRAMA" ], [ "THE HIT", "release_year", "1984" ], [ "THE HOTEL NEW HAMPSHIRE", "has_genre", "DRAMA" ], [ "THE HOTEL NEW HAMPSHIRE", "release_year", "1984" ], [ "THE KARATE KID", "has_genre", "DRAMA" ], [ "THE KARATE KID", "has_tags", "DRAMA" ], [ "THE KARATE KID", "release_year", "1984" ], [ "THE KILLING FIELDS", "has_genre", "DRAMA" ], [ "THE KILLING FIELDS", "release_year", "1984" ], [ "THE PUBLIC WOMAN", "has_genre", "DRAMA" ], [ "THE PUBLIC WOMAN", "has_tags", "DRAMA" ], [ "THE PUBLIC WOMAN", "release_year", "1984" ], [ "THE QUIET AMERICAN", "directed_by", "PHILLIP NOYCE" ], [ "THE QUIET AMERICAN", "has_tags", "PHILLIP NOYCE" ], [ "THE QUIET AMERICAN", "release_year", "2002" ], [ "THE QUIET AMERICAN", "starred_actors", "MICHAEL REDGRAVE" ], [ "THE RAZOR'S EDGE", "has_genre", "DRAMA" ], [ "THE RAZOR'S EDGE", "release_year", "1984" ], [ "THE RIVER", "has_genre", "DRAMA" ], [ "THE RIVER", "release_year", "1984" ], [ "THE WILD LIFE", "has_genre", "DRAMA" ], [ "THE WILD LIFE", "release_year", "1984" ], [ "THIEF OF HEARTS", "has_genre", "DRAMA" ], [ "THIEF OF HEARTS", "release_year", "1984" ], [ "THREADS", "has_genre", "DRAMA" ], [ "THREADS", "release_year", "1984" ], [ "TRUE STORY", "has_genre", "DRAMA" ], [ "TRUE STORY", "has_tags", "DRAMA" ], [ "UNTIL SEPTEMBER", "has_genre", "DRAMA" ], [ "UNTIL SEPTEMBER", "release_year", "1984" ], [ "WHAT HAVE I DONE TO DESERVE THIS?", "has_genre", "DRAMA" ], [ "WHAT HAVE I DONE TO DESERVE THIS?", "release_year", "1984" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 24818, 1992 17588, AX 'EM 16654, BRITISH 25435, CANDYMAN 6743, CRITTERS 11807, CRITTERS 4 28873, DAVID J. SCHOW 37059, DEATH AT A FUNERAL 2228, DON KEITH OPPER 15064, EQUINOX 31630, EVIL TOONS 6215, FOUR WEDDINGS AND A FUNERAL 38663, FUNERAL 5870, HORROR 7315, HOUSE IV 38385, INNOCENT BLOOD 28729, REMAKE 37397, RICHARD JOHNSON 16247, SLEEPWALKERS 11787, THE HAUNTING 30271, THE HILLS RUN RED 10379, THE LAWNMOWER MAN src, edge_attr, dst 17588, has_genre, 5870 17588, release_year, 24818 25435, has_genre, 5870 25435, has_tags, 5870 25435, release_year, 24818 6743, has_genre, 5870 6743, written_by, 2228 11807, has_genre, 5870 11807, release_year, 24818 11807, starred_actors, 2228 11807, written_by, 28873 37059, has_tags, 16654 37059, has_tags, 38663 37059, has_tags, 28729 15064, has_genre, 5870 15064, release_year, 24818 31630, has_genre, 5870 31630, release_year, 24818 6215, has_tags, 16654 6215, has_tags, 38663 7315, has_genre, 5870 7315, release_year, 24818 38385, has_genre, 5870 38385, release_year, 24818 16247, has_genre, 5870 16247, has_tags, 5870 16247, release_year, 24818 11787, has_genre, 5870 11787, has_tags, 16654 11787, has_tags, 5870 11787, has_tags, 28729 11787, starred_actors, 37397 30271, has_genre, 5870 30271, written_by, 28873 10379, has_genre, 5870 10379, release_year, 24818 Question: For what reason are CRITTERS 4, FUNERAL, and RICHARD JOHNSON associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "CRITTERS 4", "FUNERAL", "RICHARD JOHNSON" ], "valid_edges": [ [ "AX 'EM", "has_genre", "HORROR" ], [ "AX 'EM", "release_year", "1992" ], [ "CANDYMAN", "has_genre", "HORROR" ], [ "CANDYMAN", "has_tags", "HORROR" ], [ "CANDYMAN", "release_year", "1992" ], [ "CRITTERS", "has_genre", "HORROR" ], [ "CRITTERS", "written_by", "DON KEITH OPPER" ], [ "CRITTERS 4", "has_genre", "HORROR" ], [ "CRITTERS 4", "release_year", "1992" ], [ "CRITTERS 4", "starred_actors", "DON KEITH OPPER" ], [ "CRITTERS 4", "written_by", "DAVID J. SCHOW" ], [ "DEATH AT A FUNERAL", "has_tags", "BRITISH" ], [ "DEATH AT A FUNERAL", "has_tags", "FUNERAL" ], [ "DEATH AT A FUNERAL", "has_tags", "REMAKE" ], [ "EQUINOX", "has_genre", "HORROR" ], [ "EQUINOX", "release_year", "1992" ], [ "EVIL TOONS", "has_genre", "HORROR" ], [ "EVIL TOONS", "release_year", "1992" ], [ "FOUR WEDDINGS AND A FUNERAL", "has_tags", "BRITISH" ], [ "FOUR WEDDINGS AND A FUNERAL", "has_tags", "FUNERAL" ], [ "HOUSE IV", "has_genre", "HORROR" ], [ "HOUSE IV", "release_year", "1992" ], [ "INNOCENT BLOOD", "has_genre", "HORROR" ], [ "INNOCENT BLOOD", "release_year", "1992" ], [ "SLEEPWALKERS", "has_genre", "HORROR" ], [ "SLEEPWALKERS", "has_tags", "HORROR" ], [ "SLEEPWALKERS", "release_year", "1992" ], [ "THE HAUNTING", "has_genre", "HORROR" ], [ "THE HAUNTING", "has_tags", "BRITISH" ], [ "THE HAUNTING", "has_tags", "HORROR" ], [ "THE HAUNTING", "has_tags", "REMAKE" ], [ "THE HAUNTING", "starred_actors", "RICHARD JOHNSON" ], [ "THE HILLS RUN RED", "has_genre", "HORROR" ], [ "THE HILLS RUN RED", "written_by", "DAVID J. SCHOW" ], [ "THE LAWNMOWER MAN", "has_genre", "HORROR" ], [ "THE LAWNMOWER MAN", "release_year", "1992" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 13192, ABBY MANN 10045, BD-R 24416, CEREBRAL PALSY 6480, GERMAN 22600, JUDGMENT AT NUREMBERG 30, KURT VONNEGUT 5127, MOTHER NIGHT 17669, MY LEFT FOOT 22214, WAR src, edge_attr, dst 22600, has_genre, 22214 22600, has_tags, 10045 22600, has_tags, 22214 22600, in_language, 6480 22600, written_by, 13192 5127, has_genre, 22214 5127, has_tags, 30 5127, in_language, 6480 17669, has_tags, 10045 17669, has_tags, 24416 Question: For what reason are ABBY MANN, CEREBRAL PALSY, and KURT VONNEGUT associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ABBY MANN", "CEREBRAL PALSY", "KURT VONNEGUT" ], "valid_edges": [ [ "JUDGMENT AT NUREMBERG", "has_genre", "WAR" ], [ "JUDGMENT AT NUREMBERG", "has_tags", "BD-R" ], [ "JUDGMENT AT NUREMBERG", "has_tags", "WAR" ], [ "JUDGMENT AT NUREMBERG", "in_language", "GERMAN" ], [ "JUDGMENT AT NUREMBERG", "written_by", "ABBY MANN" ], [ "MOTHER NIGHT", "has_genre", "WAR" ], [ "MOTHER NIGHT", "has_tags", "KURT VONNEGUT" ], [ "MOTHER NIGHT", "in_language", "GERMAN" ], [ "MY LEFT FOOT", "has_tags", "BD-R" ], [ "MY LEFT FOOT", "has_tags", "CEREBRAL PALSY" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6216, 1952 35935, 2002 4310, 24 HOUR PARTY PEOPLE 9408, 40 DAYS AND 40 NIGHTS 36647, 8 WOMEN 11957, 9 DEAD GAY GUYS 2847, ABBOTT AND COSTELLO MEET CAPTAIN KIDD 6008, ABOUT A BOY 37599, ABOUT SCHMIDT 34883, ADAM'S RIB 17111, ALI G INDAHOUSE 22569, ALL ABOUT THE BENJAMINS 16113, ANALYZE THAT 35314, AUSTIN POWERS IN GOLDMEMBER 29535, AVENGING ANGELO 38849, BACHELOR MOTHER 23899, BAD COMPANY 36819, BARBERSHOP 30771, BECAUSE YOU'RE MINE 15433, BEND IT LIKE BECKHAM 39619, BIG FAT LIAR 8732, BIG TROUBLE 27128, BOAT TRIP 37616, BORN YESTERDAY 235, BRINGING UP BABY 32620, BUBBA HO-TEP 14572, BUYING THE COW 27929, CABIN FEVER 23286, CARNAGE 20073, CHERISH 10349, CHICAGO 32644, CHINESE ODYSSEY 2002 30463, COMEDY 21391, CRACKERJACK 30019, CROSSROADS 36382, DANTE'S INFERNO 21730, DAY OF THE WACKO 19179, DEATH TO SMOOCHY 34250, DESK SET 15791, DINNER AT EIGHT 21079, DIRTY DEEDS 14249, DREAMBOAT 25651, DUMMY 26815, EIGHT CRAZY NIGHTS 38543, EIGHT LEGGED FREAKS 38250, FATHER OF THE BRIDE 8745, FATHER'S LITTLE DIVIDEND 27539, FRANK MCKLUSKY, C.I. 30712, FREEBIE AND THE BEAN 29241, FRIDAY AFTER NEXT 14466, GARSON KANIN 19932, GEORGE CUKOR 20995, GIRLS ABOUT TOWN 29563, GUESS WHO'S COMING TO DINNER 6388, HAROLD AND MAUDE 10990, HEARTLANDS 3829, HERO 7289, HOLIDAY 15629, HOME ALONE 4 22069, I SPY 27919, I'M WITH LUCY 16371, ICE AGE 17197, IGBY GOES DOWN 30470, IT SHOULD HAPPEN TO YOU 11295, IT'S A MAD, MAD, MAD, MAD WORLD 33001, JUST A KISS 2204, JUWANNA MANN 26838, KATHARINE HEPBURN 32010, KISS THE BRIDE 19966, LE PLAISIR 20863, LES GIRLS 10267, LET'S MAKE LOVE 2563, LIBELED LADY 12137, LIFE OR SOMETHING LIKE IT 28832, LIFE WITHOUT DICK 16155, LOVE LIZA 9812, MAID IN MANHATTAN 32481, MANNEQUIN 4270, MEN WITH BROOMS 36083, MIRANDA 13717, MONKEY BUSINESS 16662, MORNING GLORY 20803, MR. DEEDS 22064, MY BIG FAT GREEK WEDDING 32122, MY FAVORITE WIFE 4887, MY LEFT EYE SEES GHOSTS 35262, MY MOTHER LIKES WOMEN 25269, NINE LIVES 35794, NOVO 1493, NOW YOU KNOW 2721, OCCIDENT 4582, ONCE UPON A TIME IN THE MIDLANDS 24375, ONE HOUR WITH YOU 37178, ORANGE COUNTY 27117, PASSIONADA 36277, PAT AND MIKE 9976, PIPE DREAM 3688, PUMPKIN 34602, PUNCH-DRUNK LOVE 21890, R.S.V.P. 30422, RICHARD RUSH 3525, ROAD TO BALI 25769, ROGER DODGER 37522, ROOM FOR ONE MORE 10638, RUTH GORDON 12987, SCOOBY-DOO 19244, SERVING SARA 6603, SEX IS COMEDY 26261, SHOWTIME 28298, SINGIN' IN THE RAIN 15043, SLACKERS 25151, SNOW DOGS 16907, SON OF PALEFACE 24961, SORORITY BOYS 31563, SPENCER TRACY 35843, SPUN 5170, STEALING HARVARD 15100, SUPER SUCKER 9276, SWEET HOME ALABAMA 26790, SWEPT AWAY 11247, SYLVIA SCARLETT 22407, THE ACTRESS 36316, THE ADVENTURES OF PLUTO NASH 2316, THE ANARCHIST COOKBOOK 304, THE BANGER SISTERS 21330, THE CRIMSON PIRATE 11696, THE CUCKOO 3324, THE DANGEROUS LIVES OF ALTAR BOYS 914, THE GOOD GIRL 26531, THE GURU 24302, THE HOT CHICK 13534, THE IMPORTANCE OF BEING EARNEST 2147, THE MAN WITHOUT A PAST 6908, THE MASTER OF DISGUISE 32297, THE MERRY WIDOW 295, THE NEW GUY 1192, THE ONE AND ONLY 28172, THE PHILADELPHIA STORY 31851, THE PRISONER OF ZENDA 16199, THE QUIET MAN 31401, THE RULES OF ATTRACTION 7052, THE SWEETEST THING 34172, THE TUXEDO 34529, THE WHITE SHEIK 20210, THE WOMEN 488, TRAVELS WITH MY AUNT 10720, TRIGGERMEN 9612, TWO WEEKS NOTICE 13214, UNCONDITIONAL LOVE 16991, UNDERCOVER BROTHER 14438, UP THE RIVER 21576, WAKING UP IN RENO 31364, WE'RE NOT MARRIED! 37568, WELCOME TO COLLINWOOD 39802, WHEN IN ROME 28231, WHERE'S POPPA? 8249, WITHOUT LOVE 2659, WOMAN OF THE YEAR src, edge_attr, dst 4310, has_genre, 30463 4310, release_year, 35935 9408, has_genre, 30463 9408, has_tags, 30463 9408, release_year, 35935 36647, has_genre, 30463 36647, release_year, 35935 11957, has_genre, 30463 11957, release_year, 35935 2847, has_genre, 30463 2847, release_year, 6216 6008, has_genre, 30463 6008, has_tags, 30463 6008, release_year, 35935 37599, has_genre, 30463 37599, has_tags, 30463 37599, release_year, 35935 34883, directed_by, 19932 34883, has_genre, 30463 34883, has_tags, 19932 34883, has_tags, 26838 34883, has_tags, 31563 34883, starred_actors, 26838 34883, starred_actors, 31563 34883, written_by, 14466 34883, written_by, 10638 17111, has_genre, 30463 17111, release_year, 35935 22569, has_genre, 30463 22569, has_tags, 30463 22569, release_year, 35935 16113, has_genre, 30463 16113, has_tags, 30463 16113, release_year, 35935 35314, has_genre, 30463 35314, has_tags, 30463 35314, release_year, 35935 29535, has_genre, 30463 29535, release_year, 35935 38849, directed_by, 14466 38849, has_genre, 30463 23899, has_genre, 30463 23899, release_year, 35935 36819, has_genre, 30463 36819, release_year, 35935 30771, has_genre, 30463 30771, release_year, 6216 15433, has_genre, 30463 15433, release_year, 35935 39619, has_genre, 30463 39619, release_year, 35935 8732, has_genre, 30463 8732, release_year, 35935 27128, has_genre, 30463 27128, release_year, 35935 37616, directed_by, 19932 37616, has_genre, 30463 37616, has_tags, 19932 37616, written_by, 14466 235, has_genre, 30463 235, has_tags, 30463 235, has_tags, 26838 235, starred_actors, 26838 32620, has_genre, 30463 32620, has_tags, 30463 32620, release_year, 35935 14572, has_genre, 30463 14572, release_year, 35935 27929, has_genre, 30463 27929, release_year, 35935 23286, has_genre, 30463 23286, release_year, 35935 20073, has_genre, 30463 20073, release_year, 35935 10349, has_genre, 30463 10349, release_year, 35935 32644, has_genre, 30463 32644, release_year, 35935 21391, has_genre, 30463 21391, release_year, 35935 30019, has_genre, 30463 30019, release_year, 35935 36382, has_genre, 30463 36382, starred_actors, 31563 21730, has_genre, 30463 21730, release_year, 35935 19179, has_genre, 30463 19179, release_year, 35935 34250, has_genre, 30463 34250, has_tags, 26838 34250, starred_actors, 26838 34250, starred_actors, 31563 15791, directed_by, 19932 15791, has_genre, 30463 15791, has_tags, 19932 21079, has_genre, 30463 21079, has_tags, 30463 21079, release_year, 35935 14249, has_genre, 30463 14249, release_year, 6216 25651, has_genre, 30463 25651, release_year, 35935 26815, has_genre, 30463 26815, release_year, 35935 38543, has_genre, 30463 38543, release_year, 35935 38250, has_genre, 30463 38250, has_tags, 30463 38250, has_tags, 31563 38250, starred_actors, 31563 8745, has_genre, 30463 8745, starred_actors, 31563 27539, has_genre, 30463 27539, release_year, 35935 30712, directed_by, 30422 30712, has_genre, 30463 29241, has_genre, 30463 29241, release_year, 35935 20995, directed_by, 19932 20995, has_genre, 30463 20995, has_tags, 19932 29563, has_genre, 30463 29563, has_tags, 31563 29563, starred_actors, 26838 29563, starred_actors, 31563 6388, has_genre, 30463 6388, has_tags, 10638 6388, starred_actors, 10638 10990, has_genre, 30463 10990, release_year, 35935 3829, has_genre, 30463 3829, release_year, 35935 7289, directed_by, 19932 7289, has_genre, 30463 7289, has_tags, 30463 7289, has_tags, 19932 7289, has_tags, 26838 7289, starred_actors, 26838 15629, has_genre, 30463 15629, release_year, 35935 22069, has_genre, 30463 22069, has_tags, 30463 22069, release_year, 35935 27919, has_genre, 30463 27919, release_year, 35935 16371, has_genre, 30463 16371, has_tags, 30463 16371, release_year, 35935 17197, has_genre, 30463 17197, release_year, 35935 30470, directed_by, 19932 30470, has_genre, 30463 30470, has_tags, 19932 30470, written_by, 14466 11295, has_genre, 30463 11295, has_tags, 30463 11295, starred_actors, 31563 33001, has_genre, 30463 33001, release_year, 35935 2204, has_genre, 30463 2204, release_year, 35935 32010, has_genre, 30463 32010, release_year, 35935 19966, has_genre, 30463 19966, release_year, 6216 20863, directed_by, 19932 20863, has_genre, 30463 10267, directed_by, 19932 10267, has_genre, 30463 2563, has_genre, 30463 2563, starred_actors, 31563 12137, has_genre, 30463 12137, release_year, 35935 28832, has_genre, 30463 28832, release_year, 35935 16155, has_genre, 30463 16155, release_year, 35935 9812, has_genre, 30463 9812, release_year, 35935 32481, has_genre, 30463 32481, starred_actors, 31563 4270, has_genre, 30463 4270, release_year, 35935 36083, has_genre, 30463 36083, release_year, 35935 13717, has_genre, 30463 13717, has_tags, 30463 13717, release_year, 6216 16662, has_genre, 30463 16662, starred_actors, 26838 20803, has_genre, 30463 20803, release_year, 35935 22064, has_genre, 30463 22064, has_tags, 30463 22064, release_year, 35935 32122, directed_by, 14466 32122, has_genre, 30463 4887, has_genre, 30463 4887, release_year, 35935 35262, has_genre, 30463 35262, release_year, 35935 25269, release_year, 35935 35794, has_genre, 30463 35794, release_year, 35935 1493, has_genre, 30463 1493, release_year, 35935 2721, has_genre, 30463 2721, release_year, 35935 4582, has_genre, 30463 4582, release_year, 35935 24375, directed_by, 19932 24375, has_genre, 30463 37178, has_genre, 30463 37178, release_year, 35935 27117, has_genre, 30463 27117, release_year, 35935 36277, directed_by, 19932 36277, has_genre, 30463 36277, has_tags, 19932 36277, release_year, 6216 36277, starred_actors, 26838 36277, starred_actors, 31563 36277, written_by, 14466 36277, written_by, 10638 9976, has_genre, 30463 9976, release_year, 35935 3688, has_genre, 30463 3688, release_year, 35935 34602, has_genre, 30463 34602, has_tags, 30463 34602, release_year, 35935 21890, has_genre, 30463 21890, release_year, 35935 3525, has_genre, 30463 3525, release_year, 6216 25769, has_genre, 30463 25769, release_year, 35935 37522, has_genre, 30463 37522, release_year, 6216 12987, has_genre, 30463 12987, release_year, 35935 19244, has_genre, 30463 19244, release_year, 35935 6603, has_genre, 30463 6603, release_year, 35935 26261, has_genre, 30463 26261, release_year, 35935 28298, has_genre, 30463 28298, has_tags, 30463 28298, release_year, 6216 15043, has_genre, 30463 15043, release_year, 35935 25151, has_genre, 30463 25151, release_year, 35935 16907, has_genre, 30463 16907, release_year, 6216 24961, has_genre, 30463 24961, release_year, 35935 35843, has_genre, 30463 35843, release_year, 35935 5170, has_genre, 30463 5170, release_year, 35935 15100, has_genre, 30463 15100, release_year, 35935 9276, has_genre, 30463 9276, release_year, 35935 26790, has_genre, 30463 26790, release_year, 35935 11247, directed_by, 19932 11247, has_genre, 30463 11247, starred_actors, 26838 22407, directed_by, 19932 22407, has_genre, 30463 22407, starred_actors, 31563 22407, written_by, 10638 36316, has_genre, 30463 36316, release_year, 35935 2316, has_genre, 30463 2316, release_year, 35935 304, has_genre, 30463 304, release_year, 35935 21330, has_genre, 30463 21330, release_year, 6216 11696, has_genre, 30463 11696, release_year, 35935 3324, has_genre, 30463 3324, release_year, 35935 914, has_genre, 30463 914, release_year, 35935 26531, has_genre, 30463 26531, release_year, 35935 24302, has_genre, 30463 24302, has_tags, 30463 24302, release_year, 35935 13534, has_genre, 30463 13534, release_year, 35935 2147, has_genre, 30463 2147, release_year, 35935 6908, has_genre, 30463 6908, release_year, 35935 32297, has_genre, 30463 32297, release_year, 6216 295, has_genre, 30463 295, release_year, 35935 1192, has_genre, 30463 1192, release_year, 35935 28172, directed_by, 19932 28172, has_genre, 30463 28172, has_tags, 19932 28172, has_tags, 26838 28172, starred_actors, 26838 31851, has_genre, 30463 31851, release_year, 6216 16199, has_genre, 30463 16199, release_year, 6216 31401, has_genre, 30463 31401, release_year, 35935 7052, has_genre, 30463 7052, has_tags, 30463 7052, release_year, 35935 34172, has_genre, 30463 34172, has_tags, 30463 34172, release_year, 35935 34529, has_genre, 30463 34529, release_year, 6216 20210, directed_by, 19932 20210, has_genre, 30463 20210, has_tags, 19932 488, directed_by, 19932 488, has_genre, 30463 10720, has_genre, 30463 10720, release_year, 35935 9612, has_genre, 30463 9612, release_year, 35935 13214, has_genre, 30463 13214, release_year, 35935 16991, has_genre, 30463 16991, release_year, 35935 14438, has_genre, 30463 14438, starred_actors, 31563 21576, has_genre, 30463 21576, release_year, 35935 31364, has_genre, 30463 31364, release_year, 6216 37568, has_genre, 30463 37568, has_tags, 30463 37568, release_year, 35935 39802, has_genre, 30463 39802, release_year, 35935 28231, has_genre, 30463 28231, has_tags, 10638 28231, starred_actors, 10638 8249, has_genre, 30463 8249, has_tags, 26838 8249, has_tags, 31563 8249, starred_actors, 26838 8249, starred_actors, 31563 2659, has_genre, 30463 2659, starred_actors, 26838 2659, starred_actors, 31563 Question: In what context are NINE LIVES, PAT AND MIKE, and RICHARD RUSH connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "NINE LIVES", "PAT AND MIKE", "RICHARD RUSH" ], "valid_edges": [ [ "24 HOUR PARTY PEOPLE", "has_genre", "COMEDY" ], [ "24 HOUR PARTY PEOPLE", "release_year", "2002" ], [ "40 DAYS AND 40 NIGHTS", "has_genre", "COMEDY" ], [ "40 DAYS AND 40 NIGHTS", "has_tags", "COMEDY" ], [ "40 DAYS AND 40 NIGHTS", "release_year", "2002" ], [ "8 WOMEN", "has_genre", "COMEDY" ], [ "8 WOMEN", "release_year", "2002" ], [ "9 DEAD GAY GUYS", "has_genre", "COMEDY" ], [ "9 DEAD GAY GUYS", "release_year", "2002" ], [ "ABBOTT AND COSTELLO MEET CAPTAIN KIDD", "has_genre", "COMEDY" ], [ "ABBOTT AND COSTELLO MEET CAPTAIN KIDD", "release_year", "1952" ], [ "ABOUT A BOY", "has_genre", "COMEDY" ], [ "ABOUT A BOY", "has_tags", "COMEDY" ], [ "ABOUT A BOY", "release_year", "2002" ], [ "ABOUT SCHMIDT", "has_genre", "COMEDY" ], [ "ABOUT SCHMIDT", "has_tags", "COMEDY" ], [ "ABOUT SCHMIDT", "release_year", "2002" ], [ "ADAM'S RIB", "directed_by", "GEORGE CUKOR" ], [ "ADAM'S RIB", "has_genre", "COMEDY" ], [ "ADAM'S RIB", "has_tags", "GEORGE CUKOR" ], [ "ADAM'S RIB", "has_tags", "KATHARINE HEPBURN" ], [ "ADAM'S RIB", "has_tags", "SPENCER TRACY" ], [ "ADAM'S RIB", "starred_actors", "KATHARINE HEPBURN" ], [ "ADAM'S RIB", "starred_actors", "SPENCER TRACY" ], [ "ADAM'S RIB", "written_by", "GARSON KANIN" ], [ "ADAM'S RIB", "written_by", "RUTH GORDON" ], [ "ALI G INDAHOUSE", "has_genre", "COMEDY" ], [ "ALI G INDAHOUSE", "release_year", "2002" ], [ "ALL ABOUT THE BENJAMINS", "has_genre", "COMEDY" ], [ "ALL ABOUT THE BENJAMINS", "has_tags", "COMEDY" ], [ "ALL ABOUT THE BENJAMINS", "release_year", "2002" ], [ "ANALYZE THAT", "has_genre", "COMEDY" ], [ "ANALYZE THAT", "has_tags", "COMEDY" ], [ "ANALYZE THAT", "release_year", "2002" ], [ "AUSTIN POWERS IN GOLDMEMBER", "has_genre", "COMEDY" ], [ "AUSTIN POWERS IN GOLDMEMBER", "has_tags", "COMEDY" ], [ "AUSTIN POWERS IN GOLDMEMBER", "release_year", "2002" ], [ "AVENGING ANGELO", "has_genre", "COMEDY" ], [ "AVENGING ANGELO", "release_year", "2002" ], [ "BACHELOR MOTHER", "directed_by", "GARSON KANIN" ], [ "BACHELOR MOTHER", "has_genre", "COMEDY" ], [ "BAD COMPANY", "has_genre", "COMEDY" ], [ "BAD COMPANY", "release_year", "2002" ], [ "BARBERSHOP", "has_genre", "COMEDY" ], [ "BARBERSHOP", "release_year", "2002" ], [ "BECAUSE YOU'RE MINE", "has_genre", "COMEDY" ], [ "BECAUSE YOU'RE MINE", "release_year", "1952" ], [ "BEND IT LIKE BECKHAM", "has_genre", "COMEDY" ], [ "BEND IT LIKE BECKHAM", "release_year", "2002" ], [ "BIG FAT LIAR", "has_genre", "COMEDY" ], [ "BIG FAT LIAR", "release_year", "2002" ], [ "BIG TROUBLE", "has_genre", "COMEDY" ], [ "BIG TROUBLE", "release_year", "2002" ], [ "BOAT TRIP", "has_genre", "COMEDY" ], [ "BOAT TRIP", "release_year", "2002" ], [ "BORN YESTERDAY", "directed_by", "GEORGE CUKOR" ], [ "BORN YESTERDAY", "has_genre", "COMEDY" ], [ "BORN YESTERDAY", "has_tags", "GEORGE CUKOR" ], [ "BORN YESTERDAY", "written_by", "GARSON KANIN" ], [ "BRINGING UP BABY", "has_genre", "COMEDY" ], [ "BRINGING UP BABY", "has_tags", "COMEDY" ], [ "BRINGING UP BABY", "has_tags", "KATHARINE HEPBURN" ], [ "BRINGING UP BABY", "starred_actors", "KATHARINE HEPBURN" ], [ "BUBBA HO-TEP", "has_genre", "COMEDY" ], [ "BUBBA HO-TEP", "has_tags", "COMEDY" ], [ "BUBBA HO-TEP", "release_year", "2002" ], [ "BUYING THE COW", "has_genre", "COMEDY" ], [ "BUYING THE COW", "release_year", "2002" ], [ "CABIN FEVER", "has_genre", "COMEDY" ], [ "CABIN FEVER", "release_year", "2002" ], [ "CARNAGE", "has_genre", "COMEDY" ], [ "CARNAGE", "release_year", "2002" ], [ "CHERISH", "has_genre", "COMEDY" ], [ "CHERISH", "release_year", "2002" ], [ "CHICAGO", "has_genre", "COMEDY" ], [ "CHICAGO", "release_year", "2002" ], [ "CHINESE ODYSSEY 2002", "has_genre", "COMEDY" ], [ "CHINESE ODYSSEY 2002", "release_year", "2002" ], [ "CRACKERJACK", "has_genre", "COMEDY" ], [ "CRACKERJACK", "release_year", "2002" ], [ "CROSSROADS", "has_genre", "COMEDY" ], [ "CROSSROADS", "release_year", "2002" ], [ "DANTE'S INFERNO", "has_genre", "COMEDY" ], [ "DANTE'S INFERNO", "starred_actors", "SPENCER TRACY" ], [ "DAY OF THE WACKO", "has_genre", "COMEDY" ], [ "DAY OF THE WACKO", "release_year", "2002" ], [ "DEATH TO SMOOCHY", "has_genre", "COMEDY" ], [ "DEATH TO SMOOCHY", "release_year", "2002" ], [ "DESK SET", "has_genre", "COMEDY" ], [ "DESK SET", "has_tags", "KATHARINE HEPBURN" ], [ "DESK SET", "starred_actors", "KATHARINE HEPBURN" ], [ "DESK SET", "starred_actors", "SPENCER TRACY" ], [ "DINNER AT EIGHT", "directed_by", "GEORGE CUKOR" ], [ "DINNER AT EIGHT", "has_genre", "COMEDY" ], [ "DINNER AT EIGHT", "has_tags", "GEORGE CUKOR" ], [ "DIRTY DEEDS", "has_genre", "COMEDY" ], [ "DIRTY DEEDS", "has_tags", "COMEDY" ], [ "DIRTY DEEDS", "release_year", "2002" ], [ "DREAMBOAT", "has_genre", "COMEDY" ], [ "DREAMBOAT", "release_year", "1952" ], [ "DUMMY", "has_genre", "COMEDY" ], [ "DUMMY", "release_year", "2002" ], [ "EIGHT CRAZY NIGHTS", "has_genre", "COMEDY" ], [ "EIGHT CRAZY NIGHTS", "release_year", "2002" ], [ "EIGHT LEGGED FREAKS", "has_genre", "COMEDY" ], [ "EIGHT LEGGED FREAKS", "release_year", "2002" ], [ "FATHER OF THE BRIDE", "has_genre", "COMEDY" ], [ "FATHER OF THE BRIDE", "has_tags", "COMEDY" ], [ "FATHER OF THE BRIDE", "has_tags", "SPENCER TRACY" ], [ "FATHER OF THE BRIDE", "starred_actors", "SPENCER TRACY" ], [ "FATHER'S LITTLE DIVIDEND", "has_genre", "COMEDY" ], [ "FATHER'S LITTLE DIVIDEND", "starred_actors", "SPENCER TRACY" ], [ "FRANK MCKLUSKY, C.I.", "has_genre", "COMEDY" ], [ "FRANK MCKLUSKY, C.I.", "release_year", "2002" ], [ "FREEBIE AND THE BEAN", "directed_by", "RICHARD RUSH" ], [ "FREEBIE AND THE BEAN", "has_genre", "COMEDY" ], [ "FRIDAY AFTER NEXT", "has_genre", "COMEDY" ], [ "FRIDAY AFTER NEXT", "release_year", "2002" ], [ "GIRLS ABOUT TOWN", "directed_by", "GEORGE CUKOR" ], [ "GIRLS ABOUT TOWN", "has_genre", "COMEDY" ], [ "GIRLS ABOUT TOWN", "has_tags", "GEORGE CUKOR" ], [ "GUESS WHO'S COMING TO DINNER", "has_genre", "COMEDY" ], [ "GUESS WHO'S COMING TO DINNER", "has_tags", "SPENCER TRACY" ], [ "GUESS WHO'S COMING TO DINNER", "starred_actors", "KATHARINE HEPBURN" ], [ "GUESS WHO'S COMING TO DINNER", "starred_actors", "SPENCER TRACY" ], [ "HAROLD AND MAUDE", "has_genre", "COMEDY" ], [ "HAROLD AND MAUDE", "has_tags", "RUTH GORDON" ], [ "HAROLD AND MAUDE", "starred_actors", "RUTH GORDON" ], [ "HEARTLANDS", "has_genre", "COMEDY" ], [ "HEARTLANDS", "release_year", "2002" ], [ "HERO", "has_genre", "COMEDY" ], [ "HERO", "release_year", "2002" ], [ "HOLIDAY", "directed_by", "GEORGE CUKOR" ], [ "HOLIDAY", "has_genre", "COMEDY" ], [ "HOLIDAY", "has_tags", "COMEDY" ], [ "HOLIDAY", "has_tags", "GEORGE CUKOR" ], [ "HOLIDAY", "has_tags", "KATHARINE HEPBURN" ], [ "HOLIDAY", "starred_actors", "KATHARINE HEPBURN" ], [ "HOME ALONE 4", "has_genre", "COMEDY" ], [ "HOME ALONE 4", "release_year", "2002" ], [ "I SPY", "has_genre", "COMEDY" ], [ "I SPY", "has_tags", "COMEDY" ], [ "I SPY", "release_year", "2002" ], [ "I'M WITH LUCY", "has_genre", "COMEDY" ], [ "I'M WITH LUCY", "release_year", "2002" ], [ "ICE AGE", "has_genre", "COMEDY" ], [ "ICE AGE", "has_tags", "COMEDY" ], [ "ICE AGE", "release_year", "2002" ], [ "IGBY GOES DOWN", "has_genre", "COMEDY" ], [ "IGBY GOES DOWN", "release_year", "2002" ], [ "IT SHOULD HAPPEN TO YOU", "directed_by", "GEORGE CUKOR" ], [ "IT SHOULD HAPPEN TO YOU", "has_genre", "COMEDY" ], [ "IT SHOULD HAPPEN TO YOU", "has_tags", "GEORGE CUKOR" ], [ "IT SHOULD HAPPEN TO YOU", "written_by", "GARSON KANIN" ], [ "IT'S A MAD, MAD, MAD, MAD WORLD", "has_genre", "COMEDY" ], [ "IT'S A MAD, MAD, MAD, MAD WORLD", "has_tags", "COMEDY" ], [ "IT'S A MAD, MAD, MAD, MAD WORLD", "starred_actors", "SPENCER TRACY" ], [ "JUST A KISS", "has_genre", "COMEDY" ], [ "JUST A KISS", "release_year", "2002" ], [ "JUWANNA MANN", "has_genre", "COMEDY" ], [ "JUWANNA MANN", "release_year", "2002" ], [ "KISS THE BRIDE", "has_genre", "COMEDY" ], [ "KISS THE BRIDE", "release_year", "2002" ], [ "LE PLAISIR", "has_genre", "COMEDY" ], [ "LE PLAISIR", "release_year", "1952" ], [ "LES GIRLS", "directed_by", "GEORGE CUKOR" ], [ "LES GIRLS", "has_genre", "COMEDY" ], [ "LET'S MAKE LOVE", "directed_by", "GEORGE CUKOR" ], [ "LET'S MAKE LOVE", "has_genre", "COMEDY" ], [ "LIBELED LADY", "has_genre", "COMEDY" ], [ "LIBELED LADY", "starred_actors", "SPENCER TRACY" ], [ "LIFE OR SOMETHING LIKE IT", "has_genre", "COMEDY" ], [ "LIFE OR SOMETHING LIKE IT", "release_year", "2002" ], [ "LIFE WITHOUT DICK", "has_genre", "COMEDY" ], [ "LIFE WITHOUT DICK", "release_year", "2002" ], [ "LOVE LIZA", "has_genre", "COMEDY" ], [ "LOVE LIZA", "release_year", "2002" ], [ "MAID IN MANHATTAN", "has_genre", "COMEDY" ], [ "MAID IN MANHATTAN", "release_year", "2002" ], [ "MANNEQUIN", "has_genre", "COMEDY" ], [ "MANNEQUIN", "starred_actors", "SPENCER TRACY" ], [ "MEN WITH BROOMS", "has_genre", "COMEDY" ], [ "MEN WITH BROOMS", "release_year", "2002" ], [ "MIRANDA", "has_genre", "COMEDY" ], [ "MIRANDA", "release_year", "2002" ], [ "MONKEY BUSINESS", "has_genre", "COMEDY" ], [ "MONKEY BUSINESS", "has_tags", "COMEDY" ], [ "MONKEY BUSINESS", "release_year", "1952" ], [ "MORNING GLORY", "has_genre", "COMEDY" ], [ "MORNING GLORY", "starred_actors", "KATHARINE HEPBURN" ], [ "MR. DEEDS", "has_genre", "COMEDY" ], [ "MR. DEEDS", "release_year", "2002" ], [ "MY BIG FAT GREEK WEDDING", "has_genre", "COMEDY" ], [ "MY BIG FAT GREEK WEDDING", "has_tags", "COMEDY" ], [ "MY BIG FAT GREEK WEDDING", "release_year", "2002" ], [ "MY FAVORITE WIFE", "directed_by", "GARSON KANIN" ], [ "MY FAVORITE WIFE", "has_genre", "COMEDY" ], [ "MY LEFT EYE SEES GHOSTS", "has_genre", "COMEDY" ], [ "MY LEFT EYE SEES GHOSTS", "release_year", "2002" ], [ "MY MOTHER LIKES WOMEN", "has_genre", "COMEDY" ], [ "MY MOTHER LIKES WOMEN", "release_year", "2002" ], [ "NINE LIVES", "release_year", "2002" ], [ "NOVO", "has_genre", "COMEDY" ], [ "NOVO", "release_year", "2002" ], [ "NOW YOU KNOW", "has_genre", "COMEDY" ], [ "NOW YOU KNOW", "release_year", "2002" ], [ "OCCIDENT", "has_genre", "COMEDY" ], [ "OCCIDENT", "release_year", "2002" ], [ "ONCE UPON A TIME IN THE MIDLANDS", "has_genre", "COMEDY" ], [ "ONCE UPON A TIME IN THE MIDLANDS", "release_year", "2002" ], [ "ONE HOUR WITH YOU", "directed_by", "GEORGE CUKOR" ], [ "ONE HOUR WITH YOU", "has_genre", "COMEDY" ], [ "ORANGE COUNTY", "has_genre", "COMEDY" ], [ "ORANGE COUNTY", "release_year", "2002" ], [ "PASSIONADA", "has_genre", "COMEDY" ], [ "PASSIONADA", "release_year", "2002" ], [ "PAT AND MIKE", "directed_by", "GEORGE CUKOR" ], [ "PAT AND MIKE", "has_genre", "COMEDY" ], [ "PAT AND MIKE", "has_tags", "GEORGE CUKOR" ], [ "PAT AND MIKE", "release_year", "1952" ], [ "PAT AND MIKE", "starred_actors", "KATHARINE HEPBURN" ], [ "PAT AND MIKE", "starred_actors", "SPENCER TRACY" ], [ "PAT AND MIKE", "written_by", "GARSON KANIN" ], [ "PAT AND MIKE", "written_by", "RUTH GORDON" ], [ "PIPE DREAM", "has_genre", "COMEDY" ], [ "PIPE DREAM", "release_year", "2002" ], [ "PUMPKIN", "has_genre", "COMEDY" ], [ "PUMPKIN", "release_year", "2002" ], [ "PUNCH-DRUNK LOVE", "has_genre", "COMEDY" ], [ "PUNCH-DRUNK LOVE", "has_tags", "COMEDY" ], [ "PUNCH-DRUNK LOVE", "release_year", "2002" ], [ "R.S.V.P.", "has_genre", "COMEDY" ], [ "R.S.V.P.", "release_year", "2002" ], [ "ROAD TO BALI", "has_genre", "COMEDY" ], [ "ROAD TO BALI", "release_year", "1952" ], [ "ROGER DODGER", "has_genre", "COMEDY" ], [ "ROGER DODGER", "release_year", "2002" ], [ "ROOM FOR ONE MORE", "has_genre", "COMEDY" ], [ "ROOM FOR ONE MORE", "release_year", "1952" ], [ "SCOOBY-DOO", "has_genre", "COMEDY" ], [ "SCOOBY-DOO", "release_year", "2002" ], [ "SERVING SARA", "has_genre", "COMEDY" ], [ "SERVING SARA", "release_year", "2002" ], [ "SEX IS COMEDY", "has_genre", "COMEDY" ], [ "SEX IS COMEDY", "release_year", "2002" ], [ "SHOWTIME", "has_genre", "COMEDY" ], [ "SHOWTIME", "release_year", "2002" ], [ "SINGIN' IN THE RAIN", "has_genre", "COMEDY" ], [ "SINGIN' IN THE RAIN", "has_tags", "COMEDY" ], [ "SINGIN' IN THE RAIN", "release_year", "1952" ], [ "SLACKERS", "has_genre", "COMEDY" ], [ "SLACKERS", "release_year", "2002" ], [ "SNOW DOGS", "has_genre", "COMEDY" ], [ "SNOW DOGS", "release_year", "2002" ], [ "SON OF PALEFACE", "has_genre", "COMEDY" ], [ "SON OF PALEFACE", "release_year", "1952" ], [ "SORORITY BOYS", "has_genre", "COMEDY" ], [ "SORORITY BOYS", "release_year", "2002" ], [ "SPUN", "has_genre", "COMEDY" ], [ "SPUN", "release_year", "2002" ], [ "STEALING HARVARD", "has_genre", "COMEDY" ], [ "STEALING HARVARD", "release_year", "2002" ], [ "SUPER SUCKER", "has_genre", "COMEDY" ], [ "SUPER SUCKER", "release_year", "2002" ], [ "SWEET HOME ALABAMA", "has_genre", "COMEDY" ], [ "SWEET HOME ALABAMA", "release_year", "2002" ], [ "SWEPT AWAY", "has_genre", "COMEDY" ], [ "SWEPT AWAY", "release_year", "2002" ], [ "SYLVIA SCARLETT", "directed_by", "GEORGE CUKOR" ], [ "SYLVIA SCARLETT", "has_genre", "COMEDY" ], [ "SYLVIA SCARLETT", "starred_actors", "KATHARINE HEPBURN" ], [ "THE ACTRESS", "directed_by", "GEORGE CUKOR" ], [ "THE ACTRESS", "has_genre", "COMEDY" ], [ "THE ACTRESS", "starred_actors", "SPENCER TRACY" ], [ "THE ACTRESS", "written_by", "RUTH GORDON" ], [ "THE ADVENTURES OF PLUTO NASH", "has_genre", "COMEDY" ], [ "THE ADVENTURES OF PLUTO NASH", "release_year", "2002" ], [ "THE ANARCHIST COOKBOOK", "has_genre", "COMEDY" ], [ "THE ANARCHIST COOKBOOK", "release_year", "2002" ], [ "THE BANGER SISTERS", "has_genre", "COMEDY" ], [ "THE BANGER SISTERS", "release_year", "2002" ], [ "THE CRIMSON PIRATE", "has_genre", "COMEDY" ], [ "THE CRIMSON PIRATE", "release_year", "1952" ], [ "THE CUCKOO", "has_genre", "COMEDY" ], [ "THE CUCKOO", "release_year", "2002" ], [ "THE DANGEROUS LIVES OF ALTAR BOYS", "has_genre", "COMEDY" ], [ "THE DANGEROUS LIVES OF ALTAR BOYS", "release_year", "2002" ], [ "THE GOOD GIRL", "has_genre", "COMEDY" ], [ "THE GOOD GIRL", "release_year", "2002" ], [ "THE GURU", "has_genre", "COMEDY" ], [ "THE GURU", "release_year", "2002" ], [ "THE HOT CHICK", "has_genre", "COMEDY" ], [ "THE HOT CHICK", "has_tags", "COMEDY" ], [ "THE HOT CHICK", "release_year", "2002" ], [ "THE IMPORTANCE OF BEING EARNEST", "has_genre", "COMEDY" ], [ "THE IMPORTANCE OF BEING EARNEST", "release_year", "2002" ], [ "THE MAN WITHOUT A PAST", "has_genre", "COMEDY" ], [ "THE MAN WITHOUT A PAST", "release_year", "2002" ], [ "THE MASTER OF DISGUISE", "has_genre", "COMEDY" ], [ "THE MASTER OF DISGUISE", "release_year", "2002" ], [ "THE MERRY WIDOW", "has_genre", "COMEDY" ], [ "THE MERRY WIDOW", "release_year", "1952" ], [ "THE NEW GUY", "has_genre", "COMEDY" ], [ "THE NEW GUY", "release_year", "2002" ], [ "THE ONE AND ONLY", "has_genre", "COMEDY" ], [ "THE ONE AND ONLY", "release_year", "2002" ], [ "THE PHILADELPHIA STORY", "directed_by", "GEORGE CUKOR" ], [ "THE PHILADELPHIA STORY", "has_genre", "COMEDY" ], [ "THE PHILADELPHIA STORY", "has_tags", "GEORGE CUKOR" ], [ "THE PHILADELPHIA STORY", "has_tags", "KATHARINE HEPBURN" ], [ "THE PHILADELPHIA STORY", "starred_actors", "KATHARINE HEPBURN" ], [ "THE PRISONER OF ZENDA", "has_genre", "COMEDY" ], [ "THE PRISONER OF ZENDA", "release_year", "1952" ], [ "THE QUIET MAN", "has_genre", "COMEDY" ], [ "THE QUIET MAN", "release_year", "1952" ], [ "THE RULES OF ATTRACTION", "has_genre", "COMEDY" ], [ "THE RULES OF ATTRACTION", "release_year", "2002" ], [ "THE SWEETEST THING", "has_genre", "COMEDY" ], [ "THE SWEETEST THING", "has_tags", "COMEDY" ], [ "THE SWEETEST THING", "release_year", "2002" ], [ "THE TUXEDO", "has_genre", "COMEDY" ], [ "THE TUXEDO", "has_tags", "COMEDY" ], [ "THE TUXEDO", "release_year", "2002" ], [ "THE WHITE SHEIK", "has_genre", "COMEDY" ], [ "THE WHITE SHEIK", "release_year", "1952" ], [ "THE WOMEN", "directed_by", "GEORGE CUKOR" ], [ "THE WOMEN", "has_genre", "COMEDY" ], [ "THE WOMEN", "has_tags", "GEORGE CUKOR" ], [ "TRAVELS WITH MY AUNT", "directed_by", "GEORGE CUKOR" ], [ "TRAVELS WITH MY AUNT", "has_genre", "COMEDY" ], [ "TRIGGERMEN", "has_genre", "COMEDY" ], [ "TRIGGERMEN", "release_year", "2002" ], [ "TWO WEEKS NOTICE", "has_genre", "COMEDY" ], [ "TWO WEEKS NOTICE", "release_year", "2002" ], [ "UNCONDITIONAL LOVE", "has_genre", "COMEDY" ], [ "UNCONDITIONAL LOVE", "release_year", "2002" ], [ "UNDERCOVER BROTHER", "has_genre", "COMEDY" ], [ "UNDERCOVER BROTHER", "release_year", "2002" ], [ "UP THE RIVER", "has_genre", "COMEDY" ], [ "UP THE RIVER", "starred_actors", "SPENCER TRACY" ], [ "WAKING UP IN RENO", "has_genre", "COMEDY" ], [ "WAKING UP IN RENO", "release_year", "2002" ], [ "WE'RE NOT MARRIED!", "has_genre", "COMEDY" ], [ "WE'RE NOT MARRIED!", "release_year", "1952" ], [ "WELCOME TO COLLINWOOD", "has_genre", "COMEDY" ], [ "WELCOME TO COLLINWOOD", "has_tags", "COMEDY" ], [ "WELCOME TO COLLINWOOD", "release_year", "2002" ], [ "WHEN IN ROME", "has_genre", "COMEDY" ], [ "WHEN IN ROME", "release_year", "2002" ], [ "WHERE'S POPPA?", "has_genre", "COMEDY" ], [ "WHERE'S POPPA?", "has_tags", "RUTH GORDON" ], [ "WHERE'S POPPA?", "starred_actors", "RUTH GORDON" ], [ "WITHOUT LOVE", "has_genre", "COMEDY" ], [ "WITHOUT LOVE", "has_tags", "KATHARINE HEPBURN" ], [ "WITHOUT LOVE", "has_tags", "SPENCER TRACY" ], [ "WITHOUT LOVE", "starred_actors", "KATHARINE HEPBURN" ], [ "WITHOUT LOVE", "starred_actors", "SPENCER TRACY" ], [ "WOMAN OF THE YEAR", "has_genre", "COMEDY" ], [ "WOMAN OF THE YEAR", "starred_actors", "KATHARINE HEPBURN" ], [ "WOMAN OF THE YEAR", "starred_actors", "SPENCER TRACY" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 36163, 1967 17315, 2007 26123, 5 CENTIMETERS PER SECOND 14771, A CAT IN PARIS 10244, A COLT IS MY PASSPORT 13496, A SECRET 31672, A.K. 21128, ACTRICES 31322, ASTERIX THE GAUL 30795, ATLANTIC CITY 23893, BEACH RED 16426, BELLE DE JOUR 22690, BIG MAN JAPAN 23951, BIRDS 26908, BLACK MOON 33881, BOARDING GATE 12252, BRANDED TO KILL 38311, CHAOS 13627, CONVERSATIONS WITH MY GARDENER 14724, CRIME 38495, CRIME SPREE 26635, CROWS ZERO 18405, DAMAGE 22667, DAYS OF DARKNESS 24410, DEMONLOVER 962, DORORO 8333, ELEVATOR TO THE GALLOWS 6012, FRENCH 37699, FRENCH CONNECTION II 1677, GENEVIÈVE BUJOLD 28864, GLORY TO THE FILMMAKER! 3553, GODZILLA 31911, HEARTBEAT DETECTOR 9613, HUNTING AND GATHERING 24208, INSIDE 36874, JAPANESE 24305, JCVD 2685, JUDEX 14772, KING KONG ESCAPES 22352, LA VIE EN ROSE 24812, LACOMBE, LUCIEN 23661, LE SAMOURAÏ 14433, LOUIS MALLE 9792, LOVE CRIME 11735, LOVE SONGS 22804, LUST, CAUTION 10560, MAN BITES DOG 29660, MAY FOOLS 11170, MOLIÈRE 12100, MOUCHETTE 7745, MURMUR OF THE HEART 11056, PAULETTE 16348, PERSEPOLIS 5311, PLAYTIME 21835, POINT BLANK 29620, POLICE 9019, RATATOUILLE 15938, RIFIFI 18478, RUSH HOUR 3 25217, SAMURAI REBELLION 39915, SHALL WE KISS? 15772, SILK 10280, SON OF GODZILLA 17447, SON OF RAMBOW 8436, SPIRITS OF THE DEAD 36983, SUMMER DAYS WITH COO 35926, SÉRIE NOIRE 24351, TAXI 4 14557, THE ADVENTURES OF ARSÈNE LUPIN 21712, THE CLASS 26236, THE CRIME OF MONSIEUR LANGE 21280, THE DUCHESS OF LANGEAIS 38918, THE FAMILY 28928, THE FIRE WITHIN 29103, THE LAST MISTRESS 9799, THE LOST SON 17428, THE LOVERS 21696, THE MAN FROM LONDON 21821, THE MOURNING FOREST 27909, THE SECRET OF THE GRAIN 26678, THE SWINDLE 11654, THE THIEF OF PARIS 30481, THE TWO OF US 31589, THE WAR IS OVER 5537, THE WITNESSES 2363, THE X FROM OUTER SPACE 34585, THE YOUNG GIRLS OF ROCHEFORT 19606, TWO MEN IN MANHATTAN 19779, UNDER THE BOMBS 9811, VEXILLE 11659, VIVA MARIA! 29077, WASABI 15557, WATER LILIES 33483, WEEKEND 19257, WINGED MIGRATION 31658, YOU ONLY LIVE TWICE 18197, ZATOICHI THE OUTLAW src, edge_attr, dst 26123, has_tags, 36874 26123, in_language, 36874 26123, release_year, 17315 14771, has_genre, 14724 14771, in_language, 6012 10244, in_language, 36874 10244, release_year, 36163 13496, in_language, 6012 13496, release_year, 17315 31672, in_language, 6012 31672, in_language, 36874 21128, in_language, 6012 21128, release_year, 17315 31322, in_language, 6012 31322, release_year, 36163 30795, directed_by, 14433 30795, has_genre, 14724 30795, has_tags, 14433 30795, in_language, 6012 23893, in_language, 36874 23893, release_year, 36163 16426, in_language, 6012 16426, release_year, 36163 22690, in_language, 36874 22690, release_year, 17315 26908, directed_by, 14433 26908, has_tags, 14433 26908, in_language, 6012 26908, written_by, 14433 33881, in_language, 6012 33881, release_year, 17315 12252, in_language, 36874 12252, release_year, 36163 38311, in_language, 6012 38311, in_language, 36874 13627, in_language, 6012 13627, release_year, 17315 38495, has_genre, 14724 38495, in_language, 6012 26635, in_language, 36874 26635, release_year, 17315 18405, directed_by, 14433 18405, has_tags, 14433 18405, in_language, 6012 22667, in_language, 6012 22667, release_year, 17315 24410, in_language, 6012 24410, in_language, 36874 962, in_language, 36874 962, release_year, 17315 8333, directed_by, 14433 8333, has_genre, 14724 8333, has_tags, 14433 8333, in_language, 6012 8333, written_by, 14433 37699, has_genre, 14724 37699, in_language, 6012 28864, in_language, 36874 28864, release_year, 17315 3553, in_language, 6012 3553, in_language, 36874 31911, in_language, 6012 31911, release_year, 17315 9613, in_language, 6012 9613, release_year, 17315 24208, has_tags, 6012 24208, in_language, 6012 24208, release_year, 17315 24305, has_genre, 14724 24305, in_language, 6012 2685, has_genre, 14724 2685, in_language, 6012 14772, has_tags, 36874 14772, in_language, 36874 14772, release_year, 36163 22352, has_tags, 6012 22352, in_language, 6012 22352, release_year, 17315 24812, directed_by, 14433 24812, has_tags, 14433 24812, in_language, 6012 24812, written_by, 14433 23661, has_genre, 14724 23661, has_tags, 14724 23661, in_language, 6012 23661, release_year, 36163 9792, has_genre, 14724 9792, in_language, 6012 11735, in_language, 6012 11735, release_year, 17315 22804, in_language, 36874 22804, release_year, 17315 10560, has_genre, 14724 10560, in_language, 6012 29660, directed_by, 14433 29660, has_tags, 14433 29660, in_language, 6012 29660, written_by, 14433 11170, in_language, 6012 11170, release_year, 17315 12100, in_language, 6012 12100, release_year, 36163 7745, directed_by, 14433 7745, has_tags, 14433 7745, in_language, 6012 7745, written_by, 14433 11056, has_genre, 14724 11056, in_language, 6012 16348, has_tags, 6012 16348, in_language, 6012 16348, release_year, 17315 5311, in_language, 6012 5311, release_year, 36163 21835, has_genre, 14724 21835, in_language, 6012 21835, release_year, 36163 29620, has_genre, 14724 29620, in_language, 6012 9019, in_language, 6012 9019, release_year, 17315 15938, has_genre, 14724 15938, has_tags, 14724 15938, in_language, 6012 18478, in_language, 6012 18478, release_year, 17315 25217, in_language, 36874 25217, release_year, 36163 39915, in_language, 6012 39915, release_year, 17315 15772, in_language, 36874 15772, release_year, 17315 10280, in_language, 36874 10280, release_year, 36163 17447, in_language, 6012 17447, release_year, 17315 8436, directed_by, 14433 8436, in_language, 6012 8436, written_by, 14433 36983, in_language, 36874 36983, release_year, 17315 35926, has_genre, 14724 35926, in_language, 6012 24351, in_language, 6012 24351, release_year, 17315 14557, has_genre, 14724 14557, in_language, 6012 21712, has_tags, 6012 21712, in_language, 6012 21712, release_year, 17315 26236, has_genre, 14724 26236, in_language, 6012 21280, in_language, 6012 21280, release_year, 17315 38918, has_genre, 14724 38918, in_language, 6012 28928, directed_by, 14433 28928, has_tags, 14433 28928, in_language, 6012 29103, in_language, 6012 29103, release_year, 17315 9799, has_genre, 14724 9799, in_language, 6012 17428, directed_by, 14433 17428, has_tags, 14433 17428, in_language, 6012 21696, has_genre, 14724 21696, in_language, 6012 21696, release_year, 17315 21821, in_language, 36874 21821, release_year, 17315 27909, in_language, 6012 27909, release_year, 17315 26678, has_genre, 14724 26678, in_language, 6012 11654, directed_by, 14433 11654, has_genre, 14724 11654, in_language, 6012 11654, release_year, 36163 11654, starred_actors, 1677 11654, written_by, 14433 30481, in_language, 6012 30481, release_year, 36163 31589, in_language, 6012 31589, starred_actors, 1677 5537, in_language, 6012 5537, release_year, 17315 2363, in_language, 36874 2363, release_year, 36163 34585, in_language, 6012 34585, release_year, 36163 19606, has_genre, 14724 19606, in_language, 6012 19779, in_language, 6012 19779, release_year, 17315 9811, in_language, 36874 9811, release_year, 17315 11659, directed_by, 14433 11659, has_tags, 6012 11659, has_tags, 14433 11659, in_language, 6012 11659, written_by, 14433 29077, in_language, 6012 29077, in_language, 36874 15557, in_language, 6012 15557, release_year, 17315 33483, has_tags, 6012 33483, in_language, 6012 33483, release_year, 36163 19257, has_tags, 23951 19257, in_language, 6012 31658, in_language, 36874 31658, release_year, 36163 18197, in_language, 36874 18197, release_year, 36163 Question: For what reason are BIRDS, DORORO, and THE THIEF OF PARIS associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BIRDS", "DORORO", "THE THIEF OF PARIS" ], "valid_edges": [ [ "5 CENTIMETERS PER SECOND", "has_tags", "JAPANESE" ], [ "5 CENTIMETERS PER SECOND", "in_language", "JAPANESE" ], [ "5 CENTIMETERS PER SECOND", "release_year", "2007" ], [ "A CAT IN PARIS", "has_genre", "CRIME" ], [ "A CAT IN PARIS", "in_language", "FRENCH" ], [ "A COLT IS MY PASSPORT", "in_language", "JAPANESE" ], [ "A COLT IS MY PASSPORT", "release_year", "1967" ], [ "A SECRET", "in_language", "FRENCH" ], [ "A SECRET", "release_year", "2007" ], [ "A.K.", "in_language", "FRENCH" ], [ "A.K.", "in_language", "JAPANESE" ], [ "ACTRICES", "in_language", "FRENCH" ], [ "ACTRICES", "release_year", "2007" ], [ "ASTERIX THE GAUL", "in_language", "FRENCH" ], [ "ASTERIX THE GAUL", "release_year", "1967" ], [ "ATLANTIC CITY", "directed_by", "LOUIS MALLE" ], [ "ATLANTIC CITY", "has_genre", "CRIME" ], [ "ATLANTIC CITY", "has_tags", "LOUIS MALLE" ], [ "ATLANTIC CITY", "in_language", "FRENCH" ], [ "BEACH RED", "in_language", "JAPANESE" ], [ "BEACH RED", "release_year", "1967" ], [ "BELLE DE JOUR", "in_language", "FRENCH" ], [ "BELLE DE JOUR", "release_year", "1967" ], [ "BIG MAN JAPAN", "in_language", "JAPANESE" ], [ "BIG MAN JAPAN", "release_year", "2007" ], [ "BLACK MOON", "directed_by", "LOUIS MALLE" ], [ "BLACK MOON", "has_tags", "LOUIS MALLE" ], [ "BLACK MOON", "in_language", "FRENCH" ], [ "BLACK MOON", "written_by", "LOUIS MALLE" ], [ "BOARDING GATE", "in_language", "FRENCH" ], [ "BOARDING GATE", "release_year", "2007" ], [ "BRANDED TO KILL", "in_language", "JAPANESE" ], [ "BRANDED TO KILL", "release_year", "1967" ], [ "CHAOS", "in_language", "FRENCH" ], [ "CHAOS", "in_language", "JAPANESE" ], [ "CONVERSATIONS WITH MY GARDENER", "in_language", "FRENCH" ], [ "CONVERSATIONS WITH MY GARDENER", "release_year", "2007" ], [ "CRIME SPREE", "has_genre", "CRIME" ], [ "CRIME SPREE", "in_language", "FRENCH" ], [ "CROWS ZERO", "in_language", "JAPANESE" ], [ "CROWS ZERO", "release_year", "2007" ], [ "DAMAGE", "directed_by", "LOUIS MALLE" ], [ "DAMAGE", "has_tags", "LOUIS MALLE" ], [ "DAMAGE", "in_language", "FRENCH" ], [ "DAYS OF DARKNESS", "in_language", "FRENCH" ], [ "DAYS OF DARKNESS", "release_year", "2007" ], [ "DEMONLOVER", "in_language", "FRENCH" ], [ "DEMONLOVER", "in_language", "JAPANESE" ], [ "DORORO", "in_language", "JAPANESE" ], [ "DORORO", "release_year", "2007" ], [ "ELEVATOR TO THE GALLOWS", "directed_by", "LOUIS MALLE" ], [ "ELEVATOR TO THE GALLOWS", "has_genre", "CRIME" ], [ "ELEVATOR TO THE GALLOWS", "has_tags", "LOUIS MALLE" ], [ "ELEVATOR TO THE GALLOWS", "in_language", "FRENCH" ], [ "ELEVATOR TO THE GALLOWS", "written_by", "LOUIS MALLE" ], [ "FRENCH CONNECTION II", "has_genre", "CRIME" ], [ "FRENCH CONNECTION II", "in_language", "FRENCH" ], [ "GLORY TO THE FILMMAKER!", "in_language", "JAPANESE" ], [ "GLORY TO THE FILMMAKER!", "release_year", "2007" ], [ "GODZILLA", "in_language", "FRENCH" ], [ "GODZILLA", "in_language", "JAPANESE" ], [ "HEARTBEAT DETECTOR", "in_language", "FRENCH" ], [ "HEARTBEAT DETECTOR", "release_year", "2007" ], [ "HUNTING AND GATHERING", "in_language", "FRENCH" ], [ "HUNTING AND GATHERING", "release_year", "2007" ], [ "INSIDE", "has_tags", "FRENCH" ], [ "INSIDE", "in_language", "FRENCH" ], [ "INSIDE", "release_year", "2007" ], [ "JCVD", "has_genre", "CRIME" ], [ "JCVD", "in_language", "FRENCH" ], [ "JUDEX", "has_genre", "CRIME" ], [ "JUDEX", "in_language", "FRENCH" ], [ "KING KONG ESCAPES", "has_tags", "JAPANESE" ], [ "KING KONG ESCAPES", "in_language", "JAPANESE" ], [ "KING KONG ESCAPES", "release_year", "1967" ], [ "LA VIE EN ROSE", "has_tags", "FRENCH" ], [ "LA VIE EN ROSE", "in_language", "FRENCH" ], [ "LA VIE EN ROSE", "release_year", "2007" ], [ "LACOMBE, LUCIEN", "directed_by", "LOUIS MALLE" ], [ "LACOMBE, LUCIEN", "has_tags", "LOUIS MALLE" ], [ "LACOMBE, LUCIEN", "in_language", "FRENCH" ], [ "LACOMBE, LUCIEN", "written_by", "LOUIS MALLE" ], [ "LE SAMOURAÏ", "has_genre", "CRIME" ], [ "LE SAMOURAÏ", "has_tags", "CRIME" ], [ "LE SAMOURAÏ", "in_language", "FRENCH" ], [ "LE SAMOURAÏ", "release_year", "1967" ], [ "LOVE CRIME", "has_genre", "CRIME" ], [ "LOVE CRIME", "in_language", "FRENCH" ], [ "LOVE SONGS", "in_language", "FRENCH" ], [ "LOVE SONGS", "release_year", "2007" ], [ "LUST, CAUTION", "in_language", "JAPANESE" ], [ "LUST, CAUTION", "release_year", "2007" ], [ "MAN BITES DOG", "has_genre", "CRIME" ], [ "MAN BITES DOG", "in_language", "FRENCH" ], [ "MAY FOOLS", "directed_by", "LOUIS MALLE" ], [ "MAY FOOLS", "has_tags", "LOUIS MALLE" ], [ "MAY FOOLS", "in_language", "FRENCH" ], [ "MAY FOOLS", "written_by", "LOUIS MALLE" ], [ "MOLIÈRE", "in_language", "FRENCH" ], [ "MOLIÈRE", "release_year", "2007" ], [ "MOUCHETTE", "in_language", "FRENCH" ], [ "MOUCHETTE", "release_year", "1967" ], [ "MURMUR OF THE HEART", "directed_by", "LOUIS MALLE" ], [ "MURMUR OF THE HEART", "has_tags", "LOUIS MALLE" ], [ "MURMUR OF THE HEART", "in_language", "FRENCH" ], [ "MURMUR OF THE HEART", "written_by", "LOUIS MALLE" ], [ "PAULETTE", "has_genre", "CRIME" ], [ "PAULETTE", "in_language", "FRENCH" ], [ "PERSEPOLIS", "has_tags", "FRENCH" ], [ "PERSEPOLIS", "in_language", "FRENCH" ], [ "PERSEPOLIS", "release_year", "2007" ], [ "PLAYTIME", "in_language", "FRENCH" ], [ "PLAYTIME", "release_year", "1967" ], [ "POINT BLANK", "has_genre", "CRIME" ], [ "POINT BLANK", "in_language", "FRENCH" ], [ "POINT BLANK", "release_year", "1967" ], [ "POLICE", "has_genre", "CRIME" ], [ "POLICE", "in_language", "FRENCH" ], [ "RATATOUILLE", "in_language", "FRENCH" ], [ "RATATOUILLE", "release_year", "2007" ], [ "RIFIFI", "has_genre", "CRIME" ], [ "RIFIFI", "has_tags", "CRIME" ], [ "RIFIFI", "in_language", "FRENCH" ], [ "RUSH HOUR 3", "in_language", "FRENCH" ], [ "RUSH HOUR 3", "release_year", "2007" ], [ "SAMURAI REBELLION", "in_language", "JAPANESE" ], [ "SAMURAI REBELLION", "release_year", "1967" ], [ "SHALL WE KISS?", "in_language", "FRENCH" ], [ "SHALL WE KISS?", "release_year", "2007" ], [ "SILK", "in_language", "JAPANESE" ], [ "SILK", "release_year", "2007" ], [ "SON OF GODZILLA", "in_language", "JAPANESE" ], [ "SON OF GODZILLA", "release_year", "1967" ], [ "SON OF RAMBOW", "in_language", "FRENCH" ], [ "SON OF RAMBOW", "release_year", "2007" ], [ "SPIRITS OF THE DEAD", "directed_by", "LOUIS MALLE" ], [ "SPIRITS OF THE DEAD", "in_language", "FRENCH" ], [ "SPIRITS OF THE DEAD", "written_by", "LOUIS MALLE" ], [ "SUMMER DAYS WITH COO", "in_language", "JAPANESE" ], [ "SUMMER DAYS WITH COO", "release_year", "2007" ], [ "SÉRIE NOIRE", "has_genre", "CRIME" ], [ "SÉRIE NOIRE", "in_language", "FRENCH" ], [ "TAXI 4", "in_language", "FRENCH" ], [ "TAXI 4", "release_year", "2007" ], [ "THE ADVENTURES OF ARSÈNE LUPIN", "has_genre", "CRIME" ], [ "THE ADVENTURES OF ARSÈNE LUPIN", "in_language", "FRENCH" ], [ "THE CLASS", "has_tags", "FRENCH" ], [ "THE CLASS", "in_language", "FRENCH" ], [ "THE CLASS", "release_year", "2007" ], [ "THE CRIME OF MONSIEUR LANGE", "has_genre", "CRIME" ], [ "THE CRIME OF MONSIEUR LANGE", "in_language", "FRENCH" ], [ "THE DUCHESS OF LANGEAIS", "in_language", "FRENCH" ], [ "THE DUCHESS OF LANGEAIS", "release_year", "2007" ], [ "THE FAMILY", "has_genre", "CRIME" ], [ "THE FAMILY", "in_language", "FRENCH" ], [ "THE FIRE WITHIN", "directed_by", "LOUIS MALLE" ], [ "THE FIRE WITHIN", "has_tags", "LOUIS MALLE" ], [ "THE FIRE WITHIN", "in_language", "FRENCH" ], [ "THE LAST MISTRESS", "in_language", "FRENCH" ], [ "THE LAST MISTRESS", "release_year", "2007" ], [ "THE LOST SON", "has_genre", "CRIME" ], [ "THE LOST SON", "in_language", "FRENCH" ], [ "THE LOVERS", "directed_by", "LOUIS MALLE" ], [ "THE LOVERS", "has_tags", "LOUIS MALLE" ], [ "THE LOVERS", "in_language", "FRENCH" ], [ "THE MAN FROM LONDON", "has_genre", "CRIME" ], [ "THE MAN FROM LONDON", "in_language", "FRENCH" ], [ "THE MAN FROM LONDON", "release_year", "2007" ], [ "THE MOURNING FOREST", "in_language", "JAPANESE" ], [ "THE MOURNING FOREST", "release_year", "2007" ], [ "THE SECRET OF THE GRAIN", "in_language", "FRENCH" ], [ "THE SECRET OF THE GRAIN", "release_year", "2007" ], [ "THE SWINDLE", "has_genre", "CRIME" ], [ "THE SWINDLE", "in_language", "FRENCH" ], [ "THE THIEF OF PARIS", "directed_by", "LOUIS MALLE" ], [ "THE THIEF OF PARIS", "has_genre", "CRIME" ], [ "THE THIEF OF PARIS", "in_language", "FRENCH" ], [ "THE THIEF OF PARIS", "release_year", "1967" ], [ "THE THIEF OF PARIS", "starred_actors", "GENEVIÈVE BUJOLD" ], [ "THE THIEF OF PARIS", "written_by", "LOUIS MALLE" ], [ "THE TWO OF US", "in_language", "FRENCH" ], [ "THE TWO OF US", "release_year", "1967" ], [ "THE WAR IS OVER", "in_language", "FRENCH" ], [ "THE WAR IS OVER", "starred_actors", "GENEVIÈVE BUJOLD" ], [ "THE WITNESSES", "in_language", "FRENCH" ], [ "THE WITNESSES", "release_year", "2007" ], [ "THE X FROM OUTER SPACE", "in_language", "JAPANESE" ], [ "THE X FROM OUTER SPACE", "release_year", "1967" ], [ "THE YOUNG GIRLS OF ROCHEFORT", "in_language", "FRENCH" ], [ "THE YOUNG GIRLS OF ROCHEFORT", "release_year", "1967" ], [ "TWO MEN IN MANHATTAN", "has_genre", "CRIME" ], [ "TWO MEN IN MANHATTAN", "in_language", "FRENCH" ], [ "UNDER THE BOMBS", "in_language", "FRENCH" ], [ "UNDER THE BOMBS", "release_year", "2007" ], [ "VEXILLE", "in_language", "JAPANESE" ], [ "VEXILLE", "release_year", "2007" ], [ "VIVA MARIA!", "directed_by", "LOUIS MALLE" ], [ "VIVA MARIA!", "has_tags", "FRENCH" ], [ "VIVA MARIA!", "has_tags", "LOUIS MALLE" ], [ "VIVA MARIA!", "in_language", "FRENCH" ], [ "VIVA MARIA!", "written_by", "LOUIS MALLE" ], [ "WASABI", "in_language", "FRENCH" ], [ "WASABI", "in_language", "JAPANESE" ], [ "WATER LILIES", "in_language", "FRENCH" ], [ "WATER LILIES", "release_year", "2007" ], [ "WEEKEND", "has_tags", "FRENCH" ], [ "WEEKEND", "in_language", "FRENCH" ], [ "WEEKEND", "release_year", "1967" ], [ "WINGED MIGRATION", "has_tags", "BIRDS" ], [ "WINGED MIGRATION", "in_language", "FRENCH" ], [ "YOU ONLY LIVE TWICE", "in_language", "JAPANESE" ], [ "YOU ONLY LIVE TWICE", "release_year", "1967" ], [ "ZATOICHI THE OUTLAW", "in_language", "JAPANESE" ], [ "ZATOICHI THE OUTLAW", "release_year", "1967" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 31196, 1974 31365, COCKFIGHTER 4119, DYLAN MCDERMOTT 35711, HARRY DEAN STANTON 7083, SONNY 15352, THE QUESTOR TAPES 30585, TWISTER 16336, WHERE SLEEPING DOGS LIE src, edge_attr, dst 31365, release_year, 31196 31365, starred_actors, 35711 7083, starred_actors, 35711 15352, release_year, 31196 30585, starred_actors, 4119 30585, starred_actors, 35711 16336, starred_actors, 4119 Question: How are SONNY, THE QUESTOR TAPES, and WHERE SLEEPING DOGS LIE related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "SONNY", "THE QUESTOR TAPES", "WHERE SLEEPING DOGS LIE" ], "valid_edges": [ [ "COCKFIGHTER", "release_year", "1974" ], [ "COCKFIGHTER", "starred_actors", "HARRY DEAN STANTON" ], [ "SONNY", "starred_actors", "HARRY DEAN STANTON" ], [ "THE QUESTOR TAPES", "release_year", "1974" ], [ "TWISTER", "starred_actors", "DYLAN MCDERMOTT" ], [ "TWISTER", "starred_actors", "HARRY DEAN STANTON" ], [ "WHERE SLEEPING DOGS LIE", "starred_actors", "DYLAN MCDERMOTT" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 30407, ALIEN 31076, CHARLOTTE STEWART 38780, DETENTION 9611, ERASERHEAD 35711, HARRY DEAN STANTON 5870, HORROR 37497, NATIONAL FILM REGISTRY 34822, SHANLEY CASWELL src, edge_attr, dst 30407, has_genre, 5870 30407, has_tags, 30407 30407, has_tags, 5870 30407, has_tags, 37497 30407, starred_actors, 35711 38780, has_genre, 5870 38780, starred_actors, 34822 9611, has_genre, 5870 9611, has_tags, 37497 9611, starred_actors, 31076 Question: How are CHARLOTTE STEWART, HARRY DEAN STANTON, and SHANLEY CASWELL related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "CHARLOTTE STEWART", "HARRY DEAN STANTON", "SHANLEY CASWELL" ], "valid_edges": [ [ "ALIEN", "has_genre", "HORROR" ], [ "ALIEN", "has_tags", "ALIEN" ], [ "ALIEN", "has_tags", "HORROR" ], [ "ALIEN", "has_tags", "NATIONAL FILM REGISTRY" ], [ "ALIEN", "starred_actors", "HARRY DEAN STANTON" ], [ "DETENTION", "has_genre", "HORROR" ], [ "DETENTION", "starred_actors", "SHANLEY CASWELL" ], [ "ERASERHEAD", "has_genre", "HORROR" ], [ "ERASERHEAD", "has_tags", "NATIONAL FILM REGISTRY" ], [ "ERASERHEAD", "starred_actors", "CHARLOTTE STEWART" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35798, 2010 14531, A HORRIBLE WAY TO DIE 24480, A NIGHTMARE ON ELM STREET 25532, ALICE IN MURDERLAND 23960, ATROCIOUS 19826, BLACK DEATH 31909, BLACK SWAN 30352, BURNING BRIGHT 30430, CHERRY TREE LANE 20871, DEVIL 13272, DON'T BE AFRAID OF THE DARK 26523, EXORCISMUS 29544, FORGET ME NOT 3553, GODZILLA 3120, GODZILLA VS. DESTOROYAH 25799, GODZILLA VS. KING GHIDORAH 20909, GODZILLA VS. MECHAGODZILLA II 5451, GROWTH 5870, HORROR 31812, I AM LEGEND 15439, I SPIT ON YOUR GRAVE 16313, INSIDIOUS 36874, JAPANESE 19452, JULIA'S EYES 28584, KAIJU 29987, LET ME IN 29511, MEGA PIRANHA 1943, MEGUMI ODAKA 15489, MIRRORS 2 39644, MUTANTS 2831, MY SOUL TO TAKE 38244, NINE DEAD 39250, PARANORMAL ACTIVITY 2 21830, PIRANHA 3D 36052, PROWL 33753, PSYCHOSIS 11677, RAMMBOCK 6924, ROAD KILL 1722, SHARKTOPUS 25057, STREETDANCE 3D 10173, TAKAO OKAWARA 25463, THE CRAZIES 16105, THE DEAD 14270, THE FINAL 18609, THE LAST EXORCISM 1250, THE LAST MAN ON EARTH 28305, THE MAZE 12531, THE PRESENCE 22440, THE REEF 16147, THE SILENT HOUSE 12533, THE TORTURED 497, THE TRAVELER 8494, THE VIOLENT KIND 35250, THE WARD 23343, THE WOLFMAN 20520, TOHO 15490, WE ARE THE NIGHT 18049, WE ARE WHAT WE ARE src, edge_attr, dst 14531, has_genre, 5870 14531, release_year, 35798 24480, has_genre, 5870 24480, has_tags, 5870 24480, release_year, 35798 25532, has_genre, 5870 25532, release_year, 35798 23960, has_genre, 5870 23960, release_year, 35798 19826, has_genre, 5870 19826, release_year, 35798 31909, has_tags, 5870 31909, release_year, 35798 30352, has_genre, 5870 30352, release_year, 35798 30430, has_genre, 5870 30430, release_year, 35798 20871, has_genre, 5870 20871, has_tags, 5870 20871, release_year, 35798 13272, has_genre, 5870 13272, release_year, 35798 26523, has_genre, 5870 26523, release_year, 35798 29544, has_genre, 5870 29544, release_year, 35798 3553, has_genre, 5870 3120, directed_by, 10173 3120, has_tags, 3553 3120, has_tags, 28584 3120, has_tags, 20520 3120, in_language, 36874 3120, starred_actors, 1943 25799, has_tags, 3553 25799, has_tags, 28584 25799, has_tags, 20520 25799, in_language, 36874 25799, starred_actors, 1943 20909, directed_by, 10173 20909, has_tags, 3553 20909, has_tags, 28584 20909, has_tags, 20520 20909, in_language, 36874 20909, starred_actors, 1943 5451, has_genre, 5870 5451, release_year, 35798 31812, has_tags, 5870 31812, has_tags, 31812 31812, has_tags, 39644 15439, has_genre, 5870 15439, release_year, 35798 16313, has_genre, 5870 16313, has_tags, 5870 16313, release_year, 35798 19452, has_genre, 5870 19452, release_year, 35798 29987, has_genre, 5870 29987, has_tags, 5870 29987, release_year, 35798 29511, has_genre, 5870 29511, release_year, 35798 15489, has_genre, 5870 15489, release_year, 35798 39644, has_genre, 5870 2831, has_genre, 5870 2831, release_year, 35798 38244, has_genre, 5870 38244, release_year, 35798 39250, has_genre, 5870 39250, release_year, 35798 21830, has_genre, 5870 21830, release_year, 35798 36052, has_genre, 5870 36052, release_year, 35798 33753, has_genre, 5870 33753, release_year, 35798 11677, has_genre, 5870 11677, release_year, 35798 6924, has_genre, 5870 6924, has_tags, 5870 6924, release_year, 35798 1722, has_genre, 5870 1722, release_year, 35798 25057, release_year, 35798 25463, has_genre, 5870 25463, release_year, 35798 16105, has_genre, 5870 16105, release_year, 35798 14270, has_genre, 5870 14270, release_year, 35798 18609, has_genre, 5870 18609, release_year, 35798 1250, has_genre, 5870 1250, has_tags, 31812 28305, has_genre, 5870 28305, release_year, 35798 12531, has_genre, 5870 12531, release_year, 35798 22440, has_genre, 5870 22440, release_year, 35798 16147, has_genre, 5870 16147, release_year, 35798 12533, has_genre, 5870 12533, release_year, 35798 497, has_genre, 5870 497, release_year, 35798 8494, has_genre, 5870 8494, release_year, 35798 35250, has_genre, 5870 35250, release_year, 35798 23343, has_genre, 5870 23343, release_year, 35798 15490, has_genre, 5870 15490, release_year, 35798 18049, has_genre, 5870 18049, has_tags, 5870 18049, release_year, 35798 Question: In what context are MEGUMI ODAKA, STREETDANCE 3D, and THE LAST MAN ON EARTH connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "MEGUMI ODAKA", "STREETDANCE 3D", "THE LAST MAN ON EARTH" ], "valid_edges": [ [ "A HORRIBLE WAY TO DIE", "has_genre", "HORROR" ], [ "A HORRIBLE WAY TO DIE", "release_year", "2010" ], [ "A NIGHTMARE ON ELM STREET", "has_genre", "HORROR" ], [ "A NIGHTMARE ON ELM STREET", "has_tags", "HORROR" ], [ "A NIGHTMARE ON ELM STREET", "release_year", "2010" ], [ "ALICE IN MURDERLAND", "has_genre", "HORROR" ], [ "ALICE IN MURDERLAND", "release_year", "2010" ], [ "ATROCIOUS", "has_genre", "HORROR" ], [ "ATROCIOUS", "release_year", "2010" ], [ "BLACK DEATH", "has_genre", "HORROR" ], [ "BLACK DEATH", "release_year", "2010" ], [ "BLACK SWAN", "has_tags", "HORROR" ], [ "BLACK SWAN", "release_year", "2010" ], [ "BURNING BRIGHT", "has_genre", "HORROR" ], [ "BURNING BRIGHT", "release_year", "2010" ], [ "CHERRY TREE LANE", "has_genre", "HORROR" ], [ "CHERRY TREE LANE", "release_year", "2010" ], [ "DEVIL", "has_genre", "HORROR" ], [ "DEVIL", "has_tags", "HORROR" ], [ "DEVIL", "release_year", "2010" ], [ "DON'T BE AFRAID OF THE DARK", "has_genre", "HORROR" ], [ "DON'T BE AFRAID OF THE DARK", "release_year", "2010" ], [ "EXORCISMUS", "has_genre", "HORROR" ], [ "EXORCISMUS", "release_year", "2010" ], [ "FORGET ME NOT", "has_genre", "HORROR" ], [ "FORGET ME NOT", "release_year", "2010" ], [ "GODZILLA", "has_genre", "HORROR" ], [ "GODZILLA VS. DESTOROYAH", "directed_by", "TAKAO OKAWARA" ], [ "GODZILLA VS. DESTOROYAH", "has_tags", "GODZILLA" ], [ "GODZILLA VS. DESTOROYAH", "has_tags", "KAIJU" ], [ "GODZILLA VS. DESTOROYAH", "has_tags", "TOHO" ], [ "GODZILLA VS. DESTOROYAH", "in_language", "JAPANESE" ], [ "GODZILLA VS. DESTOROYAH", "starred_actors", "MEGUMI ODAKA" ], [ "GODZILLA VS. KING GHIDORAH", "has_tags", "GODZILLA" ], [ "GODZILLA VS. KING GHIDORAH", "has_tags", "KAIJU" ], [ "GODZILLA VS. KING GHIDORAH", "has_tags", "TOHO" ], [ "GODZILLA VS. KING GHIDORAH", "in_language", "JAPANESE" ], [ "GODZILLA VS. KING GHIDORAH", "starred_actors", "MEGUMI ODAKA" ], [ "GODZILLA VS. MECHAGODZILLA II", "directed_by", "TAKAO OKAWARA" ], [ "GODZILLA VS. MECHAGODZILLA II", "has_tags", "GODZILLA" ], [ "GODZILLA VS. MECHAGODZILLA II", "has_tags", "KAIJU" ], [ "GODZILLA VS. MECHAGODZILLA II", "has_tags", "TOHO" ], [ "GODZILLA VS. MECHAGODZILLA II", "in_language", "JAPANESE" ], [ "GODZILLA VS. MECHAGODZILLA II", "starred_actors", "MEGUMI ODAKA" ], [ "GROWTH", "has_genre", "HORROR" ], [ "GROWTH", "release_year", "2010" ], [ "I AM LEGEND", "has_tags", "HORROR" ], [ "I AM LEGEND", "has_tags", "I AM LEGEND" ], [ "I AM LEGEND", "has_tags", "MUTANTS" ], [ "I SPIT ON YOUR GRAVE", "has_genre", "HORROR" ], [ "I SPIT ON YOUR GRAVE", "release_year", "2010" ], [ "INSIDIOUS", "has_genre", "HORROR" ], [ "INSIDIOUS", "has_tags", "HORROR" ], [ "INSIDIOUS", "release_year", "2010" ], [ "JULIA'S EYES", "has_genre", "HORROR" ], [ "JULIA'S EYES", "release_year", "2010" ], [ "LET ME IN", "has_genre", "HORROR" ], [ "LET ME IN", "has_tags", "HORROR" ], [ "LET ME IN", "release_year", "2010" ], [ "MEGA PIRANHA", "has_genre", "HORROR" ], [ "MEGA PIRANHA", "release_year", "2010" ], [ "MIRRORS 2", "has_genre", "HORROR" ], [ "MIRRORS 2", "release_year", "2010" ], [ "MUTANTS", "has_genre", "HORROR" ], [ "MY SOUL TO TAKE", "has_genre", "HORROR" ], [ "MY SOUL TO TAKE", "release_year", "2010" ], [ "NINE DEAD", "has_genre", "HORROR" ], [ "NINE DEAD", "release_year", "2010" ], [ "PARANORMAL ACTIVITY 2", "has_genre", "HORROR" ], [ "PARANORMAL ACTIVITY 2", "release_year", "2010" ], [ "PIRANHA 3D", "has_genre", "HORROR" ], [ "PIRANHA 3D", "release_year", "2010" ], [ "PROWL", "has_genre", "HORROR" ], [ "PROWL", "release_year", "2010" ], [ "PSYCHOSIS", "has_genre", "HORROR" ], [ "PSYCHOSIS", "release_year", "2010" ], [ "RAMMBOCK", "has_genre", "HORROR" ], [ "RAMMBOCK", "release_year", "2010" ], [ "ROAD KILL", "has_genre", "HORROR" ], [ "ROAD KILL", "has_tags", "HORROR" ], [ "ROAD KILL", "release_year", "2010" ], [ "SHARKTOPUS", "has_genre", "HORROR" ], [ "SHARKTOPUS", "release_year", "2010" ], [ "STREETDANCE 3D", "release_year", "2010" ], [ "THE CRAZIES", "has_genre", "HORROR" ], [ "THE CRAZIES", "release_year", "2010" ], [ "THE DEAD", "has_genre", "HORROR" ], [ "THE DEAD", "release_year", "2010" ], [ "THE FINAL", "has_genre", "HORROR" ], [ "THE FINAL", "release_year", "2010" ], [ "THE LAST EXORCISM", "has_genre", "HORROR" ], [ "THE LAST EXORCISM", "release_year", "2010" ], [ "THE LAST MAN ON EARTH", "has_genre", "HORROR" ], [ "THE LAST MAN ON EARTH", "has_tags", "I AM LEGEND" ], [ "THE MAZE", "has_genre", "HORROR" ], [ "THE MAZE", "release_year", "2010" ], [ "THE PRESENCE", "has_genre", "HORROR" ], [ "THE PRESENCE", "release_year", "2010" ], [ "THE REEF", "has_genre", "HORROR" ], [ "THE REEF", "release_year", "2010" ], [ "THE SILENT HOUSE", "has_genre", "HORROR" ], [ "THE SILENT HOUSE", "release_year", "2010" ], [ "THE TORTURED", "has_genre", "HORROR" ], [ "THE TORTURED", "release_year", "2010" ], [ "THE TRAVELER", "has_genre", "HORROR" ], [ "THE TRAVELER", "release_year", "2010" ], [ "THE VIOLENT KIND", "has_genre", "HORROR" ], [ "THE VIOLENT KIND", "release_year", "2010" ], [ "THE WARD", "has_genre", "HORROR" ], [ "THE WARD", "release_year", "2010" ], [ "THE WOLFMAN", "has_genre", "HORROR" ], [ "THE WOLFMAN", "release_year", "2010" ], [ "WE ARE THE NIGHT", "has_genre", "HORROR" ], [ "WE ARE THE NIGHT", "release_year", "2010" ], [ "WE ARE WHAT WE ARE", "has_genre", "HORROR" ], [ "WE ARE WHAT WE ARE", "has_tags", "HORROR" ], [ "WE ARE WHAT WE ARE", "release_year", "2010" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 1006, 1996 10067, BERNIE 10569, DANIEL CHUBA 21909, ROBIN TUNNEY 19392, SKIP HOLLANDSWORTH 31004, SUPERNOVA 20760, THE CRAFT 29745, WITCHCRAFT src, edge_attr, dst 10067, release_year, 1006 10067, written_by, 19392 31004, has_tags, 21909 31004, written_by, 10569 20760, has_tags, 21909 20760, has_tags, 29745 20760, release_year, 1006 20760, starred_actors, 21909 Question: How are DANIEL CHUBA, SKIP HOLLANDSWORTH, and WITCHCRAFT related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "DANIEL CHUBA", "SKIP HOLLANDSWORTH", "WITCHCRAFT" ], "valid_edges": [ [ "BERNIE", "release_year", "1996" ], [ "BERNIE", "written_by", "SKIP HOLLANDSWORTH" ], [ "SUPERNOVA", "has_tags", "ROBIN TUNNEY" ], [ "SUPERNOVA", "written_by", "DANIEL CHUBA" ], [ "THE CRAFT", "has_tags", "ROBIN TUNNEY" ], [ "THE CRAFT", "has_tags", "WITCHCRAFT" ], [ "THE CRAFT", "release_year", "1996" ], [ "THE CRAFT", "starred_actors", "ROBIN TUNNEY" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 26310, 1947 1567, 1954 35063, 1976 9152, A STAR IS BORN 21857, ALAN CAMPBELL 21816, APARTHEID 10045, BD-R 5189, CAPE FEAR 39676, CROSSFIRE 6692, DISTRICT 9 18693, FILM NOIR 37497, NATIONAL FILM REGISTRY 21620, NOIR 14276, OUT OF THE PAST 28729, REMAKE 18515, RIVER OF NO RETURN 17979, ROBERT DE NIRO 39935, ROBERT MITCHUM 697, ROBERT RYAN 30447, RYAN'S DAUGHTER 1899, SECRET CEREMONY 37368, THE BIG SLEEP 34379, THE HUNTERS 30201, THE LAST TYCOON 27599, THE NIGHT OF THE HUNTER 7478, THE RACKET 24811, THRILLER 27872, TRACK OF THE CAT 14843, WHERE DANGER LIVES 24706, WILLIAM A. WELLMAN src, edge_attr, dst 9152, directed_by, 24706 9152, has_tags, 10045 9152, has_tags, 37497 9152, has_tags, 24706 9152, release_year, 1567 9152, release_year, 35063 9152, written_by, 21857 9152, written_by, 24706 5189, has_genre, 24811 5189, has_tags, 28729 5189, has_tags, 17979 5189, has_tags, 39935 5189, has_tags, 24811 5189, starred_actors, 17979 5189, starred_actors, 39935 39676, has_tags, 10045 39676, release_year, 26310 39676, starred_actors, 39935 39676, starred_actors, 697 6692, has_genre, 24811 6692, has_tags, 21816 6692, has_tags, 24811 14276, has_tags, 18693 14276, has_tags, 37497 14276, has_tags, 21620 14276, release_year, 26310 14276, starred_actors, 39935 18515, release_year, 1567 18515, starred_actors, 39935 30447, has_tags, 10045 30447, has_tags, 39935 30447, starred_actors, 39935 1899, has_tags, 10045 1899, starred_actors, 39935 37368, has_tags, 10045 37368, has_tags, 18693 37368, has_tags, 37497 37368, has_tags, 21620 37368, has_tags, 28729 37368, starred_actors, 39935 34379, has_genre, 24811 34379, starred_actors, 39935 30201, release_year, 35063 30201, starred_actors, 17979 30201, starred_actors, 39935 27599, directed_by, 39935 27599, has_tags, 10045 27599, has_tags, 37497 27599, has_tags, 39935 27599, has_tags, 24811 27599, starred_actors, 39935 7478, has_tags, 10045 7478, starred_actors, 39935 7478, starred_actors, 697 27872, directed_by, 24706 27872, release_year, 1567 27872, starred_actors, 39935 14843, has_genre, 24811 14843, starred_actors, 39935 Question: In what context are ALAN CAMPBELL, APARTHEID, and ROBERT MITCHUM connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ALAN CAMPBELL", "APARTHEID", "ROBERT MITCHUM" ], "valid_edges": [ [ "A STAR IS BORN", "directed_by", "WILLIAM A. WELLMAN" ], [ "A STAR IS BORN", "has_tags", "BD-R" ], [ "A STAR IS BORN", "has_tags", "NATIONAL FILM REGISTRY" ], [ "A STAR IS BORN", "has_tags", "WILLIAM A. WELLMAN" ], [ "A STAR IS BORN", "release_year", "1954" ], [ "A STAR IS BORN", "release_year", "1976" ], [ "A STAR IS BORN", "written_by", "ALAN CAMPBELL" ], [ "A STAR IS BORN", "written_by", "WILLIAM A. WELLMAN" ], [ "CAPE FEAR", "has_genre", "THRILLER" ], [ "CAPE FEAR", "has_tags", "REMAKE" ], [ "CAPE FEAR", "has_tags", "ROBERT DE NIRO" ], [ "CAPE FEAR", "has_tags", "ROBERT MITCHUM" ], [ "CAPE FEAR", "has_tags", "THRILLER" ], [ "CAPE FEAR", "starred_actors", "ROBERT DE NIRO" ], [ "CAPE FEAR", "starred_actors", "ROBERT MITCHUM" ], [ "CROSSFIRE", "has_tags", "BD-R" ], [ "CROSSFIRE", "release_year", "1947" ], [ "CROSSFIRE", "starred_actors", "ROBERT MITCHUM" ], [ "CROSSFIRE", "starred_actors", "ROBERT RYAN" ], [ "DISTRICT 9", "has_genre", "THRILLER" ], [ "DISTRICT 9", "has_tags", "APARTHEID" ], [ "DISTRICT 9", "has_tags", "THRILLER" ], [ "OUT OF THE PAST", "has_tags", "FILM NOIR" ], [ "OUT OF THE PAST", "has_tags", "NATIONAL FILM REGISTRY" ], [ "OUT OF THE PAST", "has_tags", "NOIR" ], [ "OUT OF THE PAST", "release_year", "1947" ], [ "OUT OF THE PAST", "starred_actors", "ROBERT MITCHUM" ], [ "RIVER OF NO RETURN", "release_year", "1954" ], [ "RIVER OF NO RETURN", "starred_actors", "ROBERT MITCHUM" ], [ "RYAN'S DAUGHTER", "has_tags", "BD-R" ], [ "RYAN'S DAUGHTER", "has_tags", "ROBERT MITCHUM" ], [ "RYAN'S DAUGHTER", "starred_actors", "ROBERT MITCHUM" ], [ "SECRET CEREMONY", "has_tags", "BD-R" ], [ "SECRET CEREMONY", "starred_actors", "ROBERT MITCHUM" ], [ "THE BIG SLEEP", "has_tags", "BD-R" ], [ "THE BIG SLEEP", "has_tags", "FILM NOIR" ], [ "THE BIG SLEEP", "has_tags", "NATIONAL FILM REGISTRY" ], [ "THE BIG SLEEP", "has_tags", "NOIR" ], [ "THE BIG SLEEP", "has_tags", "REMAKE" ], [ "THE BIG SLEEP", "starred_actors", "ROBERT MITCHUM" ], [ "THE HUNTERS", "has_genre", "THRILLER" ], [ "THE HUNTERS", "starred_actors", "ROBERT MITCHUM" ], [ "THE LAST TYCOON", "release_year", "1976" ], [ "THE LAST TYCOON", "starred_actors", "ROBERT DE NIRO" ], [ "THE LAST TYCOON", "starred_actors", "ROBERT MITCHUM" ], [ "THE NIGHT OF THE HUNTER", "directed_by", "ROBERT MITCHUM" ], [ "THE NIGHT OF THE HUNTER", "has_tags", "BD-R" ], [ "THE NIGHT OF THE HUNTER", "has_tags", "NATIONAL FILM REGISTRY" ], [ "THE NIGHT OF THE HUNTER", "has_tags", "ROBERT MITCHUM" ], [ "THE NIGHT OF THE HUNTER", "has_tags", "THRILLER" ], [ "THE NIGHT OF THE HUNTER", "starred_actors", "ROBERT MITCHUM" ], [ "THE RACKET", "has_tags", "BD-R" ], [ "THE RACKET", "starred_actors", "ROBERT MITCHUM" ], [ "THE RACKET", "starred_actors", "ROBERT RYAN" ], [ "TRACK OF THE CAT", "directed_by", "WILLIAM A. WELLMAN" ], [ "TRACK OF THE CAT", "release_year", "1954" ], [ "TRACK OF THE CAT", "starred_actors", "ROBERT MITCHUM" ], [ "WHERE DANGER LIVES", "has_genre", "THRILLER" ], [ "WHERE DANGER LIVES", "starred_actors", "ROBERT MITCHUM" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 16055, 1983 1006, 1996 30269, AMITYVILLE 3-D 22951, DON'T BE A MENACE TO SOUTH CENTRAL WHILE DRINKING YOUR JUICE IN THE HOOD 37940, OCTOPUSSY 36554, ROGER MOORE 12863, THE QUEST 6911, THE WILD GEESE src, edge_attr, dst 30269, release_year, 16055 22951, release_year, 1006 37940, has_tags, 36554 37940, release_year, 16055 37940, starred_actors, 36554 12863, has_tags, 36554 12863, release_year, 1006 12863, starred_actors, 36554 6911, has_tags, 36554 6911, starred_actors, 36554 Question: For what reason are AMITYVILLE 3-D, DON'T BE A MENACE TO SOUTH CENTRAL WHILE DRINKING YOUR JUICE IN THE HOOD, and THE WILD GEESE associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "AMITYVILLE 3-D", "DON'T BE A MENACE TO SOUTH CENTRAL WHILE DRINKING YOUR JUICE IN THE HOOD", "THE WILD GEESE" ], "valid_edges": [ [ "AMITYVILLE 3-D", "release_year", "1983" ], [ "DON'T BE A MENACE TO SOUTH CENTRAL WHILE DRINKING YOUR JUICE IN THE HOOD", "release_year", "1996" ], [ "OCTOPUSSY", "has_tags", "ROGER MOORE" ], [ "OCTOPUSSY", "release_year", "1983" ], [ "OCTOPUSSY", "starred_actors", "ROGER MOORE" ], [ "THE QUEST", "has_tags", "ROGER MOORE" ], [ "THE QUEST", "release_year", "1996" ], [ "THE QUEST", "starred_actors", "ROGER MOORE" ], [ "THE WILD GEESE", "has_tags", "ROGER MOORE" ], [ "THE WILD GEESE", "starred_actors", "ROGER MOORE" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 4151, A THIN LINE BETWEEN LOVE AND HATE 30463, COMEDY 1221, GONE FISHIN' 6886, HOUSEFULL 2 36949, JALLA! JALLA! 37303, LALEH POURKARIM 28563, LYNN WHITFIELD 13879, SAJID NADIADWALA src, edge_attr, dst 4151, has_genre, 30463 4151, starred_actors, 28563 1221, has_genre, 30463 1221, starred_actors, 28563 6886, has_genre, 30463 6886, written_by, 13879 36949, has_genre, 30463 36949, starred_actors, 37303 Question: For what reason are LALEH POURKARIM, LYNN WHITFIELD, and SAJID NADIADWALA associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "LALEH POURKARIM", "LYNN WHITFIELD", "SAJID NADIADWALA" ], "valid_edges": [ [ "A THIN LINE BETWEEN LOVE AND HATE", "has_genre", "COMEDY" ], [ "A THIN LINE BETWEEN LOVE AND HATE", "starred_actors", "LYNN WHITFIELD" ], [ "GONE FISHIN'", "has_genre", "COMEDY" ], [ "GONE FISHIN'", "starred_actors", "LYNN WHITFIELD" ], [ "HOUSEFULL 2", "has_genre", "COMEDY" ], [ "HOUSEFULL 2", "written_by", "SAJID NADIADWALA" ], [ "JALLA! JALLA!", "has_genre", "COMEDY" ], [ "JALLA! JALLA!", "starred_actors", "LALEH POURKARIM" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6347, $ 7977, 1969 37484, 2004 1698, ALFIE 6360, AROUND THE BEND 10045, BD-R 16823, BITE THE BULLET 11108, BRUTE FORCE 32075, BURN! 35953, BUTCH CASSIDY AND THE SUNDANCE KID 3034, CAT ON A HOT TIN ROOF 39676, CROSSFIRE 13424, DON'T DRINK THE WATER 4728, EASY RIDER 26308, FLIPPER 32144, FRANKENSTEIN MUST BE DESTROYED 2048, GOODBYE, MR. CHIPS 4709, GREAT EXPECTATIONS 12244, GUYS AND DOLLS 20941, HAMLET 12087, HOWL'S MOVING CASTLE 30184, IN COLD BLOOD 36637, JAMES B. CLARK 25901, JEAN SIMMONS 18020, JOHN FORSYTHE 13763, KES 4929, KEY LARGO 14156, KING SOLOMON'S MINES 28933, LAST SUMMER 26370, MADHOUSE 22678, MARLOWE 6543, MODEL SHOP 16139, MY SIDE OF THE MOUNTAIN 18184, OKLAHOMA! 33617, RED DUST 8614, RICHARD BROOKS 38066, SHIRLEY JONES 420, SUPPORT YOUR LOCAL SHERIFF! 24926, SWEET CHARITY 31619, THE ARRANGEMENT 2915, THE BROTHERS KARAMAZOV 31280, THE CATERED AFFAIR 7763, THE CHEYENNE SOCIAL CLUB 557, THE HAPPY ENDING 7447, THE INCREDIBLES 1443, THE ITALIAN JOB 30690, THE LADYKILLERS 1368, THE MILKY WAY 28118, THE MUSIC MAN 31953, THE PRIME OF MISS JEAN BRODIE 19768, THE PROFESSIONALS 23847, THE STEPFORD WIVES 18433, THE WILD BUNCH 37252, WOMEN IN LOVE 31083, WRONG IS RIGHT 38760, Z src, edge_attr, dst 6347, directed_by, 8614 6347, has_tags, 10045 6347, written_by, 8614 1698, has_tags, 10045 1698, release_year, 37484 6360, release_year, 37484 16823, directed_by, 8614 16823, has_tags, 10045 16823, written_by, 8614 11108, has_tags, 10045 11108, written_by, 8614 32075, has_tags, 10045 32075, release_year, 7977 35953, has_tags, 10045 35953, release_year, 7977 3034, directed_by, 8614 3034, has_tags, 10045 3034, has_tags, 8614 3034, written_by, 8614 39676, has_tags, 10045 39676, written_by, 8614 13424, has_tags, 10045 13424, release_year, 7977 4728, has_tags, 10045 4728, release_year, 7977 26308, directed_by, 36637 26308, has_tags, 10045 32144, has_tags, 10045 32144, release_year, 7977 2048, has_tags, 10045 2048, release_year, 7977 4709, has_tags, 10045 4709, starred_actors, 25901 12244, has_tags, 10045 12244, starred_actors, 25901 20941, has_tags, 10045 20941, release_year, 7977 12087, has_tags, 10045 12087, release_year, 37484 30184, directed_by, 8614 30184, has_tags, 10045 30184, has_tags, 8614 30184, starred_actors, 18020 30184, written_by, 8614 13763, has_tags, 10045 13763, release_year, 7977 4929, has_tags, 10045 4929, written_by, 8614 14156, has_tags, 10045 14156, release_year, 37484 28933, has_tags, 10045 28933, release_year, 7977 26370, has_tags, 10045 26370, release_year, 37484 22678, has_tags, 10045 22678, release_year, 7977 6543, has_tags, 10045 6543, release_year, 7977 16139, directed_by, 36637 16139, release_year, 7977 18184, has_tags, 10045 18184, has_tags, 38066 33617, has_tags, 10045 33617, release_year, 37484 420, has_tags, 10045 420, release_year, 7977 24926, has_tags, 10045 24926, release_year, 7977 31619, has_tags, 10045 31619, release_year, 7977 2915, directed_by, 8614 2915, has_tags, 10045 2915, written_by, 8614 31280, directed_by, 8614 31280, has_tags, 10045 31280, has_tags, 8614 7763, has_tags, 10045 7763, starred_actors, 38066 557, directed_by, 8614 557, has_tags, 10045 557, release_year, 7977 557, starred_actors, 25901 557, starred_actors, 18020 557, starred_actors, 38066 557, written_by, 8614 7447, has_tags, 10045 7447, release_year, 37484 1443, has_tags, 10045 1443, release_year, 7977 30690, has_tags, 10045 30690, release_year, 37484 1368, has_tags, 10045 1368, release_year, 7977 28118, has_tags, 10045 28118, has_tags, 38066 28118, starred_actors, 38066 31953, has_tags, 10045 31953, release_year, 7977 19768, directed_by, 8614 19768, has_tags, 10045 19768, has_tags, 8614 19768, written_by, 8614 23847, has_tags, 10045 23847, release_year, 37484 18433, has_tags, 10045 18433, release_year, 7977 37252, has_tags, 10045 37252, release_year, 7977 31083, directed_by, 8614 31083, has_tags, 10045 31083, written_by, 8614 38760, has_tags, 10045 38760, release_year, 7977 Question: How are AROUND THE BEND, JAMES B. CLARK, and THE HAPPY ENDING related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "AROUND THE BEND", "JAMES B. CLARK", "THE HAPPY ENDING" ], "valid_edges": [ [ "$", "directed_by", "RICHARD BROOKS" ], [ "$", "has_tags", "BD-R" ], [ "$", "written_by", "RICHARD BROOKS" ], [ "ALFIE", "has_tags", "BD-R" ], [ "ALFIE", "release_year", "2004" ], [ "AROUND THE BEND", "release_year", "2004" ], [ "BITE THE BULLET", "directed_by", "RICHARD BROOKS" ], [ "BITE THE BULLET", "has_tags", "BD-R" ], [ "BITE THE BULLET", "written_by", "RICHARD BROOKS" ], [ "BRUTE FORCE", "has_tags", "BD-R" ], [ "BRUTE FORCE", "written_by", "RICHARD BROOKS" ], [ "BURN!", "has_tags", "BD-R" ], [ "BURN!", "release_year", "1969" ], [ "BUTCH CASSIDY AND THE SUNDANCE KID", "has_tags", "BD-R" ], [ "BUTCH CASSIDY AND THE SUNDANCE KID", "release_year", "1969" ], [ "CAT ON A HOT TIN ROOF", "directed_by", "RICHARD BROOKS" ], [ "CAT ON A HOT TIN ROOF", "has_tags", "BD-R" ], [ "CAT ON A HOT TIN ROOF", "has_tags", "RICHARD BROOKS" ], [ "CAT ON A HOT TIN ROOF", "written_by", "RICHARD BROOKS" ], [ "CROSSFIRE", "has_tags", "BD-R" ], [ "CROSSFIRE", "written_by", "RICHARD BROOKS" ], [ "DON'T DRINK THE WATER", "has_tags", "BD-R" ], [ "DON'T DRINK THE WATER", "release_year", "1969" ], [ "EASY RIDER", "has_tags", "BD-R" ], [ "EASY RIDER", "release_year", "1969" ], [ "FLIPPER", "directed_by", "JAMES B. CLARK" ], [ "FLIPPER", "has_tags", "BD-R" ], [ "FRANKENSTEIN MUST BE DESTROYED", "has_tags", "BD-R" ], [ "FRANKENSTEIN MUST BE DESTROYED", "release_year", "1969" ], [ "GOODBYE, MR. CHIPS", "has_tags", "BD-R" ], [ "GOODBYE, MR. CHIPS", "release_year", "1969" ], [ "GREAT EXPECTATIONS", "has_tags", "BD-R" ], [ "GREAT EXPECTATIONS", "starred_actors", "JEAN SIMMONS" ], [ "GUYS AND DOLLS", "has_tags", "BD-R" ], [ "GUYS AND DOLLS", "starred_actors", "JEAN SIMMONS" ], [ "HAMLET", "has_tags", "BD-R" ], [ "HAMLET", "release_year", "1969" ], [ "HOWL'S MOVING CASTLE", "has_tags", "BD-R" ], [ "HOWL'S MOVING CASTLE", "release_year", "2004" ], [ "IN COLD BLOOD", "directed_by", "RICHARD BROOKS" ], [ "IN COLD BLOOD", "has_tags", "BD-R" ], [ "IN COLD BLOOD", "has_tags", "RICHARD BROOKS" ], [ "IN COLD BLOOD", "starred_actors", "JOHN FORSYTHE" ], [ "IN COLD BLOOD", "written_by", "RICHARD BROOKS" ], [ "KES", "has_tags", "BD-R" ], [ "KES", "release_year", "1969" ], [ "KEY LARGO", "has_tags", "BD-R" ], [ "KEY LARGO", "written_by", "RICHARD BROOKS" ], [ "KING SOLOMON'S MINES", "has_tags", "BD-R" ], [ "KING SOLOMON'S MINES", "release_year", "2004" ], [ "LAST SUMMER", "has_tags", "BD-R" ], [ "LAST SUMMER", "release_year", "1969" ], [ "MADHOUSE", "has_tags", "BD-R" ], [ "MADHOUSE", "release_year", "2004" ], [ "MARLOWE", "has_tags", "BD-R" ], [ "MARLOWE", "release_year", "1969" ], [ "MODEL SHOP", "has_tags", "BD-R" ], [ "MODEL SHOP", "release_year", "1969" ], [ "MY SIDE OF THE MOUNTAIN", "directed_by", "JAMES B. CLARK" ], [ "MY SIDE OF THE MOUNTAIN", "release_year", "1969" ], [ "OKLAHOMA!", "has_tags", "BD-R" ], [ "OKLAHOMA!", "has_tags", "SHIRLEY JONES" ], [ "RED DUST", "has_tags", "BD-R" ], [ "RED DUST", "release_year", "2004" ], [ "SUPPORT YOUR LOCAL SHERIFF!", "has_tags", "BD-R" ], [ "SUPPORT YOUR LOCAL SHERIFF!", "release_year", "1969" ], [ "SWEET CHARITY", "has_tags", "BD-R" ], [ "SWEET CHARITY", "release_year", "1969" ], [ "THE ARRANGEMENT", "has_tags", "BD-R" ], [ "THE ARRANGEMENT", "release_year", "1969" ], [ "THE BROTHERS KARAMAZOV", "directed_by", "RICHARD BROOKS" ], [ "THE BROTHERS KARAMAZOV", "has_tags", "BD-R" ], [ "THE BROTHERS KARAMAZOV", "written_by", "RICHARD BROOKS" ], [ "THE CATERED AFFAIR", "directed_by", "RICHARD BROOKS" ], [ "THE CATERED AFFAIR", "has_tags", "BD-R" ], [ "THE CATERED AFFAIR", "has_tags", "RICHARD BROOKS" ], [ "THE CHEYENNE SOCIAL CLUB", "has_tags", "BD-R" ], [ "THE CHEYENNE SOCIAL CLUB", "starred_actors", "SHIRLEY JONES" ], [ "THE HAPPY ENDING", "directed_by", "RICHARD BROOKS" ], [ "THE HAPPY ENDING", "has_tags", "BD-R" ], [ "THE HAPPY ENDING", "release_year", "1969" ], [ "THE HAPPY ENDING", "starred_actors", "JEAN SIMMONS" ], [ "THE HAPPY ENDING", "starred_actors", "JOHN FORSYTHE" ], [ "THE HAPPY ENDING", "starred_actors", "SHIRLEY JONES" ], [ "THE HAPPY ENDING", "written_by", "RICHARD BROOKS" ], [ "THE INCREDIBLES", "has_tags", "BD-R" ], [ "THE INCREDIBLES", "release_year", "2004" ], [ "THE ITALIAN JOB", "has_tags", "BD-R" ], [ "THE ITALIAN JOB", "release_year", "1969" ], [ "THE LADYKILLERS", "has_tags", "BD-R" ], [ "THE LADYKILLERS", "release_year", "2004" ], [ "THE MILKY WAY", "has_tags", "BD-R" ], [ "THE MILKY WAY", "release_year", "1969" ], [ "THE MUSIC MAN", "has_tags", "BD-R" ], [ "THE MUSIC MAN", "has_tags", "SHIRLEY JONES" ], [ "THE MUSIC MAN", "starred_actors", "SHIRLEY JONES" ], [ "THE PRIME OF MISS JEAN BRODIE", "has_tags", "BD-R" ], [ "THE PRIME OF MISS JEAN BRODIE", "release_year", "1969" ], [ "THE PROFESSIONALS", "directed_by", "RICHARD BROOKS" ], [ "THE PROFESSIONALS", "has_tags", "BD-R" ], [ "THE PROFESSIONALS", "has_tags", "RICHARD BROOKS" ], [ "THE PROFESSIONALS", "written_by", "RICHARD BROOKS" ], [ "THE STEPFORD WIVES", "has_tags", "BD-R" ], [ "THE STEPFORD WIVES", "release_year", "2004" ], [ "THE WILD BUNCH", "has_tags", "BD-R" ], [ "THE WILD BUNCH", "release_year", "1969" ], [ "WOMEN IN LOVE", "has_tags", "BD-R" ], [ "WOMEN IN LOVE", "release_year", "1969" ], [ "WRONG IS RIGHT", "directed_by", "RICHARD BROOKS" ], [ "WRONG IS RIGHT", "has_tags", "BD-R" ], [ "WRONG IS RIGHT", "written_by", "RICHARD BROOKS" ], [ "Z", "has_tags", "BD-R" ], [ "Z", "release_year", "1969" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 13464, 10 THINGS I HATE ABOUT YOU 8486, 1999 5620, 200 CIGARETTES 30146, A CHRISTMAS CAROL 38367, A MAP OF THE WORLD 27672, A ROOM FOR ROMEO BRASS 5152, A SLIPPING-DOWN LIFE 30464, A WALK ON THE MOON 35603, AGNES BROWNE 30284, ALL ABOUT MY MOTHER 39271, ALL FALL DOWN 17254, AMERICAN BEAUTY 17481, ANGELA'S ASHES 29422, ANNA AND THE KING 17320, ANY GIVEN SUNDAY 25586, ARLINGTON ROAD 7908, AUDITION 10045, BD-R 26205, BEING JOHN MALKOVICH 2067, BETWEEN YOUR LEGS 8900, BICENTENNIAL MAN 37569, BOYS DON'T CRY 21035, BRINGING OUT THE DEAD 7918, BROKEDOWN PALACE 26642, BUS STOP 3291, CATFISH IN BLACK BEAN SAUCE 32880, COME BACK, LITTLE SHEBA 23710, CRADLE WILL ROCK 32140, CRAZY IN ALABAMA 27377, CRUEL INTENTIONS 13007, DETERRENCE 36212, DRAMA 12319, DREAMING OF JOSEPH LEES 7568, EAST IS EAST 34555, FLAWLESS 17478, FOOLISH 9161, FOR LOVE OF THE GAME 5820, FOREVER MINE 22532, GIRL, INTERRUPTED 35657, GLORIA 10457, GOYA IN BORDEAUX 19204, GUINEVERE 24044, IMMEDIATE FAMILY 27446, IN TOO DEEP 35335, IT ALL STARTS TODAY 4912, JAKOB THE LIAR 14391, JOE THE KING 4864, JOSHUA LOGAN 27249, JUDY BERLIN 21224, KIKUJIRO 22333, KING OF COMEDY 18023, LIBERTY HEIGHTS 7104, LIFE 21197, LIGHT IT UP 4052, LIMBO 28092, MAGNOLIA 37867, MAN ON THE MOON 6649, MANSFIELD PARK 6559, MESSAGE IN A BOTTLE 30008, MISS JULIE 19598, MOLLY 14977, MOLOCH 16428, MUMFORD 3018, MUSIC OF THE HEART 33718, MYSTERY, ALASKA 493, ONE MAN'S HERO 18987, ONEGIN 35341, ORFEU 24816, OXYGEN 439, PICNIC 35054, PLAY IT TO THE BONE 21386, POLA X 10039, PUPS 16964, PUSHING TIN 9963, RANDOM HEARTS 27631, RIDE WITH THE DEVIL 12234, RKO 281 27556, ROGUE TRADER 8379, ROMANCE 15252, SCREWED IN TALLINN 36167, SOFT FRUIT 1469, SOPHIE'S WORLD 8753, SPLENDOR IN THE GRASS 32984, SWEET AND LOWDOWN 4157, THE BEST MAN 27111, THE BIG KAHUNA 22372, THE CIDER HOUSE RULES 6150, THE CONFESSION 25509, THE DEBT 791, THE DEEP END OF THE OCEAN 8141, THE END OF THE AFFAIR 14220, THE FIVE SENSES 12748, THE GLASS AGENCY 35367, THE GREEN MILE 16209, THE HI-LINE 12439, THE INSIDER 34986, THE JOYRIDERS 37562, THE KING AND I 9799, THE LOST SON 27045, THE MISSION 5098, THE MOMENT AFTER 37200, THE STORY OF US 9301, THE STRAIGHT STORY 11235, THE SUBURBANS 13405, THE THIRD MIRACLE 12026, THE TIC CODE 12801, THE VIRGIN SUICIDES 39253, THE WAR ZONE 14983, THE WINSLOW BOY 332, TOPSY-TURVY 308, TRUE CRIME 13101, TUMBLEWEEDS 9202, VARSITY BLUES 16102, WILDFLOWERS 16030, WILLIAM INGE 30288, WISCONSIN DEATH TRIP 6567, WITH FIRE AND SWORD 31862, WONDERLAND src, edge_attr, dst 13464, has_genre, 36212 13464, release_year, 8486 5620, has_genre, 36212 5620, release_year, 8486 30146, has_genre, 36212 30146, release_year, 8486 38367, has_genre, 36212 38367, release_year, 8486 27672, has_genre, 36212 27672, release_year, 8486 5152, has_genre, 36212 5152, release_year, 8486 30464, has_genre, 36212 30464, release_year, 8486 35603, has_genre, 36212 35603, release_year, 8486 30284, has_genre, 36212 30284, release_year, 8486 39271, has_genre, 36212 39271, written_by, 16030 17254, has_genre, 36212 17254, has_tags, 36212 17254, release_year, 8486 17481, has_genre, 36212 17481, release_year, 8486 29422, has_genre, 36212 29422, release_year, 8486 17320, has_genre, 36212 17320, release_year, 8486 25586, has_genre, 36212 25586, release_year, 8486 7908, has_genre, 36212 7908, release_year, 8486 26205, has_genre, 36212 26205, has_tags, 36212 26205, release_year, 8486 2067, has_genre, 36212 2067, release_year, 8486 8900, has_genre, 36212 8900, release_year, 8486 37569, has_genre, 36212 37569, has_tags, 36212 37569, release_year, 8486 21035, has_genre, 36212 21035, has_tags, 36212 21035, release_year, 8486 7918, has_genre, 36212 7918, release_year, 8486 26642, directed_by, 4864 26642, has_genre, 36212 26642, has_tags, 10045 26642, written_by, 16030 3291, has_genre, 36212 3291, release_year, 8486 32880, has_genre, 36212 32880, written_by, 16030 23710, has_genre, 36212 23710, release_year, 8486 32140, has_genre, 36212 32140, release_year, 8486 27377, has_genre, 36212 27377, release_year, 8486 13007, has_genre, 36212 13007, release_year, 8486 12319, has_genre, 36212 12319, release_year, 8486 7568, has_genre, 36212 7568, release_year, 8486 34555, has_genre, 36212 34555, release_year, 8486 17478, has_genre, 36212 17478, release_year, 8486 9161, has_genre, 36212 9161, release_year, 8486 5820, has_genre, 36212 5820, release_year, 8486 22532, has_genre, 36212 22532, has_tags, 36212 22532, release_year, 8486 35657, has_genre, 36212 35657, release_year, 8486 10457, has_genre, 36212 10457, release_year, 8486 19204, has_genre, 36212 19204, release_year, 8486 24044, has_genre, 36212 27446, has_genre, 36212 27446, release_year, 8486 35335, has_genre, 36212 35335, release_year, 8486 4912, has_genre, 36212 4912, release_year, 8486 14391, has_genre, 36212 14391, release_year, 8486 27249, has_genre, 36212 27249, release_year, 8486 21224, has_genre, 36212 21224, release_year, 8486 22333, has_genre, 36212 22333, release_year, 8486 18023, has_genre, 36212 18023, release_year, 8486 7104, has_genre, 36212 7104, release_year, 8486 21197, has_genre, 36212 21197, release_year, 8486 4052, has_genre, 36212 4052, release_year, 8486 28092, has_genre, 36212 28092, release_year, 8486 37867, has_genre, 36212 37867, release_year, 8486 6649, has_genre, 36212 6649, release_year, 8486 6559, has_genre, 36212 6559, release_year, 8486 30008, has_genre, 36212 30008, release_year, 8486 19598, has_genre, 36212 19598, release_year, 8486 14977, has_genre, 36212 14977, release_year, 8486 16428, has_genre, 36212 16428, release_year, 8486 3018, has_genre, 36212 3018, release_year, 8486 33718, has_genre, 36212 33718, release_year, 8486 493, has_genre, 36212 493, release_year, 8486 18987, has_genre, 36212 18987, release_year, 8486 35341, has_genre, 36212 35341, release_year, 8486 24816, release_year, 8486 439, directed_by, 4864 439, has_genre, 36212 439, has_tags, 10045 439, has_tags, 4864 439, written_by, 16030 35054, has_genre, 36212 35054, release_year, 8486 21386, has_genre, 36212 21386, release_year, 8486 10039, has_genre, 36212 10039, release_year, 8486 16964, has_genre, 36212 16964, release_year, 8486 9963, has_genre, 36212 9963, release_year, 8486 27631, has_genre, 36212 27631, release_year, 8486 12234, has_genre, 36212 12234, release_year, 8486 27556, has_genre, 36212 27556, release_year, 8486 8379, has_genre, 36212 8379, release_year, 8486 15252, has_genre, 36212 15252, release_year, 8486 36167, has_genre, 36212 36167, release_year, 8486 1469, has_genre, 36212 1469, release_year, 8486 8753, has_genre, 36212 8753, has_tags, 10045 8753, written_by, 16030 32984, has_genre, 36212 32984, release_year, 8486 4157, has_genre, 36212 4157, release_year, 8486 27111, has_genre, 36212 27111, release_year, 8486 22372, has_genre, 36212 22372, has_tags, 36212 22372, release_year, 8486 6150, has_genre, 36212 6150, release_year, 8486 25509, has_genre, 36212 25509, release_year, 8486 791, has_genre, 36212 791, release_year, 8486 8141, has_genre, 36212 8141, release_year, 8486 14220, has_genre, 36212 14220, release_year, 8486 12748, has_genre, 36212 12748, release_year, 8486 35367, has_genre, 36212 35367, has_tags, 36212 35367, release_year, 8486 16209, has_genre, 36212 16209, release_year, 8486 12439, has_genre, 36212 12439, has_tags, 36212 12439, release_year, 8486 34986, has_genre, 36212 34986, release_year, 8486 37562, has_genre, 36212 37562, release_year, 8486 9799, has_genre, 36212 9799, release_year, 8486 27045, has_genre, 36212 27045, release_year, 8486 5098, has_genre, 36212 5098, release_year, 8486 37200, has_genre, 36212 37200, has_tags, 36212 37200, release_year, 8486 9301, has_genre, 36212 9301, release_year, 8486 11235, has_genre, 36212 11235, release_year, 8486 13405, has_genre, 36212 13405, release_year, 8486 12026, has_genre, 36212 12026, release_year, 8486 12801, has_genre, 36212 12801, release_year, 8486 39253, has_genre, 36212 39253, release_year, 8486 14983, has_genre, 36212 14983, release_year, 8486 332, has_genre, 36212 332, release_year, 8486 308, has_genre, 36212 308, release_year, 8486 13101, has_genre, 36212 13101, release_year, 8486 9202, has_genre, 36212 9202, release_year, 8486 16102, has_genre, 36212 16102, release_year, 8486 30288, has_genre, 36212 30288, release_year, 8486 6567, has_genre, 36212 6567, release_year, 8486 31862, has_genre, 36212 31862, release_year, 8486 Question: In what context are IMMEDIATE FAMILY, OXYGEN, and WILLIAM INGE connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "IMMEDIATE FAMILY", "OXYGEN", "WILLIAM INGE" ], "valid_edges": [ [ "10 THINGS I HATE ABOUT YOU", "has_genre", "DRAMA" ], [ "10 THINGS I HATE ABOUT YOU", "release_year", "1999" ], [ "200 CIGARETTES", "has_genre", "DRAMA" ], [ "200 CIGARETTES", "release_year", "1999" ], [ "A CHRISTMAS CAROL", "has_genre", "DRAMA" ], [ "A CHRISTMAS CAROL", "release_year", "1999" ], [ "A MAP OF THE WORLD", "has_genre", "DRAMA" ], [ "A MAP OF THE WORLD", "release_year", "1999" ], [ "A ROOM FOR ROMEO BRASS", "has_genre", "DRAMA" ], [ "A ROOM FOR ROMEO BRASS", "release_year", "1999" ], [ "A SLIPPING-DOWN LIFE", "has_genre", "DRAMA" ], [ "A SLIPPING-DOWN LIFE", "release_year", "1999" ], [ "A WALK ON THE MOON", "has_genre", "DRAMA" ], [ "A WALK ON THE MOON", "release_year", "1999" ], [ "AGNES BROWNE", "has_genre", "DRAMA" ], [ "AGNES BROWNE", "release_year", "1999" ], [ "ALL ABOUT MY MOTHER", "has_genre", "DRAMA" ], [ "ALL ABOUT MY MOTHER", "release_year", "1999" ], [ "ALL FALL DOWN", "has_genre", "DRAMA" ], [ "ALL FALL DOWN", "written_by", "WILLIAM INGE" ], [ "AMERICAN BEAUTY", "has_genre", "DRAMA" ], [ "AMERICAN BEAUTY", "has_tags", "DRAMA" ], [ "AMERICAN BEAUTY", "release_year", "1999" ], [ "ANGELA'S ASHES", "has_genre", "DRAMA" ], [ "ANGELA'S ASHES", "release_year", "1999" ], [ "ANNA AND THE KING", "has_genre", "DRAMA" ], [ "ANNA AND THE KING", "release_year", "1999" ], [ "ANY GIVEN SUNDAY", "has_genre", "DRAMA" ], [ "ANY GIVEN SUNDAY", "release_year", "1999" ], [ "ARLINGTON ROAD", "has_genre", "DRAMA" ], [ "ARLINGTON ROAD", "release_year", "1999" ], [ "AUDITION", "has_genre", "DRAMA" ], [ "AUDITION", "release_year", "1999" ], [ "BEING JOHN MALKOVICH", "has_genre", "DRAMA" ], [ "BEING JOHN MALKOVICH", "has_tags", "DRAMA" ], [ "BEING JOHN MALKOVICH", "release_year", "1999" ], [ "BETWEEN YOUR LEGS", "has_genre", "DRAMA" ], [ "BETWEEN YOUR LEGS", "release_year", "1999" ], [ "BICENTENNIAL MAN", "has_genre", "DRAMA" ], [ "BICENTENNIAL MAN", "release_year", "1999" ], [ "BOYS DON'T CRY", "has_genre", "DRAMA" ], [ "BOYS DON'T CRY", "has_tags", "DRAMA" ], [ "BOYS DON'T CRY", "release_year", "1999" ], [ "BRINGING OUT THE DEAD", "has_genre", "DRAMA" ], [ "BRINGING OUT THE DEAD", "has_tags", "DRAMA" ], [ "BRINGING OUT THE DEAD", "release_year", "1999" ], [ "BROKEDOWN PALACE", "has_genre", "DRAMA" ], [ "BROKEDOWN PALACE", "release_year", "1999" ], [ "BUS STOP", "directed_by", "JOSHUA LOGAN" ], [ "BUS STOP", "has_genre", "DRAMA" ], [ "BUS STOP", "has_tags", "BD-R" ], [ "BUS STOP", "written_by", "WILLIAM INGE" ], [ "CATFISH IN BLACK BEAN SAUCE", "has_genre", "DRAMA" ], [ "CATFISH IN BLACK BEAN SAUCE", "release_year", "1999" ], [ "COME BACK, LITTLE SHEBA", "has_genre", "DRAMA" ], [ "COME BACK, LITTLE SHEBA", "written_by", "WILLIAM INGE" ], [ "CRADLE WILL ROCK", "has_genre", "DRAMA" ], [ "CRADLE WILL ROCK", "release_year", "1999" ], [ "CRAZY IN ALABAMA", "has_genre", "DRAMA" ], [ "CRAZY IN ALABAMA", "release_year", "1999" ], [ "CRUEL INTENTIONS", "has_genre", "DRAMA" ], [ "CRUEL INTENTIONS", "release_year", "1999" ], [ "DETERRENCE", "has_genre", "DRAMA" ], [ "DETERRENCE", "release_year", "1999" ], [ "DREAMING OF JOSEPH LEES", "has_genre", "DRAMA" ], [ "DREAMING OF JOSEPH LEES", "release_year", "1999" ], [ "EAST IS EAST", "has_genre", "DRAMA" ], [ "EAST IS EAST", "release_year", "1999" ], [ "FLAWLESS", "has_genre", "DRAMA" ], [ "FLAWLESS", "release_year", "1999" ], [ "FOOLISH", "has_genre", "DRAMA" ], [ "FOOLISH", "release_year", "1999" ], [ "FOR LOVE OF THE GAME", "has_genre", "DRAMA" ], [ "FOR LOVE OF THE GAME", "release_year", "1999" ], [ "FOREVER MINE", "has_genre", "DRAMA" ], [ "FOREVER MINE", "release_year", "1999" ], [ "GIRL, INTERRUPTED", "has_genre", "DRAMA" ], [ "GIRL, INTERRUPTED", "has_tags", "DRAMA" ], [ "GIRL, INTERRUPTED", "release_year", "1999" ], [ "GLORIA", "has_genre", "DRAMA" ], [ "GLORIA", "release_year", "1999" ], [ "GOYA IN BORDEAUX", "has_genre", "DRAMA" ], [ "GOYA IN BORDEAUX", "release_year", "1999" ], [ "GUINEVERE", "has_genre", "DRAMA" ], [ "GUINEVERE", "release_year", "1999" ], [ "IMMEDIATE FAMILY", "has_genre", "DRAMA" ], [ "IN TOO DEEP", "has_genre", "DRAMA" ], [ "IN TOO DEEP", "release_year", "1999" ], [ "IT ALL STARTS TODAY", "has_genre", "DRAMA" ], [ "IT ALL STARTS TODAY", "release_year", "1999" ], [ "JAKOB THE LIAR", "has_genre", "DRAMA" ], [ "JAKOB THE LIAR", "release_year", "1999" ], [ "JOE THE KING", "has_genre", "DRAMA" ], [ "JOE THE KING", "release_year", "1999" ], [ "JUDY BERLIN", "has_genre", "DRAMA" ], [ "JUDY BERLIN", "release_year", "1999" ], [ "KIKUJIRO", "has_genre", "DRAMA" ], [ "KIKUJIRO", "release_year", "1999" ], [ "KING OF COMEDY", "has_genre", "DRAMA" ], [ "KING OF COMEDY", "release_year", "1999" ], [ "LIBERTY HEIGHTS", "has_genre", "DRAMA" ], [ "LIBERTY HEIGHTS", "release_year", "1999" ], [ "LIFE", "has_genre", "DRAMA" ], [ "LIFE", "release_year", "1999" ], [ "LIGHT IT UP", "has_genre", "DRAMA" ], [ "LIGHT IT UP", "release_year", "1999" ], [ "LIMBO", "has_genre", "DRAMA" ], [ "LIMBO", "release_year", "1999" ], [ "MAGNOLIA", "has_genre", "DRAMA" ], [ "MAGNOLIA", "release_year", "1999" ], [ "MAN ON THE MOON", "has_genre", "DRAMA" ], [ "MAN ON THE MOON", "release_year", "1999" ], [ "MANSFIELD PARK", "has_genre", "DRAMA" ], [ "MANSFIELD PARK", "release_year", "1999" ], [ "MESSAGE IN A BOTTLE", "has_genre", "DRAMA" ], [ "MESSAGE IN A BOTTLE", "release_year", "1999" ], [ "MISS JULIE", "has_genre", "DRAMA" ], [ "MISS JULIE", "release_year", "1999" ], [ "MOLLY", "has_genre", "DRAMA" ], [ "MOLLY", "release_year", "1999" ], [ "MOLOCH", "has_genre", "DRAMA" ], [ "MOLOCH", "release_year", "1999" ], [ "MUMFORD", "has_genre", "DRAMA" ], [ "MUMFORD", "release_year", "1999" ], [ "MUSIC OF THE HEART", "has_genre", "DRAMA" ], [ "MUSIC OF THE HEART", "release_year", "1999" ], [ "MYSTERY, ALASKA", "has_genre", "DRAMA" ], [ "MYSTERY, ALASKA", "release_year", "1999" ], [ "ONE MAN'S HERO", "has_genre", "DRAMA" ], [ "ONE MAN'S HERO", "release_year", "1999" ], [ "ONEGIN", "has_genre", "DRAMA" ], [ "ONEGIN", "release_year", "1999" ], [ "ORFEU", "has_genre", "DRAMA" ], [ "ORFEU", "release_year", "1999" ], [ "OXYGEN", "release_year", "1999" ], [ "PICNIC", "directed_by", "JOSHUA LOGAN" ], [ "PICNIC", "has_genre", "DRAMA" ], [ "PICNIC", "has_tags", "BD-R" ], [ "PICNIC", "has_tags", "JOSHUA LOGAN" ], [ "PICNIC", "written_by", "WILLIAM INGE" ], [ "PLAY IT TO THE BONE", "has_genre", "DRAMA" ], [ "PLAY IT TO THE BONE", "release_year", "1999" ], [ "POLA X", "has_genre", "DRAMA" ], [ "POLA X", "release_year", "1999" ], [ "PUPS", "has_genre", "DRAMA" ], [ "PUPS", "release_year", "1999" ], [ "PUSHING TIN", "has_genre", "DRAMA" ], [ "PUSHING TIN", "release_year", "1999" ], [ "RANDOM HEARTS", "has_genre", "DRAMA" ], [ "RANDOM HEARTS", "release_year", "1999" ], [ "RIDE WITH THE DEVIL", "has_genre", "DRAMA" ], [ "RIDE WITH THE DEVIL", "release_year", "1999" ], [ "RKO 281", "has_genre", "DRAMA" ], [ "RKO 281", "release_year", "1999" ], [ "ROGUE TRADER", "has_genre", "DRAMA" ], [ "ROGUE TRADER", "release_year", "1999" ], [ "ROMANCE", "has_genre", "DRAMA" ], [ "ROMANCE", "release_year", "1999" ], [ "SCREWED IN TALLINN", "has_genre", "DRAMA" ], [ "SCREWED IN TALLINN", "release_year", "1999" ], [ "SOFT FRUIT", "has_genre", "DRAMA" ], [ "SOFT FRUIT", "release_year", "1999" ], [ "SOPHIE'S WORLD", "has_genre", "DRAMA" ], [ "SOPHIE'S WORLD", "release_year", "1999" ], [ "SPLENDOR IN THE GRASS", "has_genre", "DRAMA" ], [ "SPLENDOR IN THE GRASS", "has_tags", "BD-R" ], [ "SPLENDOR IN THE GRASS", "written_by", "WILLIAM INGE" ], [ "SWEET AND LOWDOWN", "has_genre", "DRAMA" ], [ "SWEET AND LOWDOWN", "release_year", "1999" ], [ "THE BEST MAN", "has_genre", "DRAMA" ], [ "THE BEST MAN", "release_year", "1999" ], [ "THE BIG KAHUNA", "has_genre", "DRAMA" ], [ "THE BIG KAHUNA", "release_year", "1999" ], [ "THE CIDER HOUSE RULES", "has_genre", "DRAMA" ], [ "THE CIDER HOUSE RULES", "has_tags", "DRAMA" ], [ "THE CIDER HOUSE RULES", "release_year", "1999" ], [ "THE CONFESSION", "has_genre", "DRAMA" ], [ "THE CONFESSION", "release_year", "1999" ], [ "THE DEBT", "has_genre", "DRAMA" ], [ "THE DEBT", "release_year", "1999" ], [ "THE DEEP END OF THE OCEAN", "has_genre", "DRAMA" ], [ "THE DEEP END OF THE OCEAN", "release_year", "1999" ], [ "THE END OF THE AFFAIR", "has_genre", "DRAMA" ], [ "THE END OF THE AFFAIR", "release_year", "1999" ], [ "THE FIVE SENSES", "has_genre", "DRAMA" ], [ "THE FIVE SENSES", "release_year", "1999" ], [ "THE GLASS AGENCY", "has_genre", "DRAMA" ], [ "THE GLASS AGENCY", "release_year", "1999" ], [ "THE GREEN MILE", "has_genre", "DRAMA" ], [ "THE GREEN MILE", "has_tags", "DRAMA" ], [ "THE GREEN MILE", "release_year", "1999" ], [ "THE HI-LINE", "has_genre", "DRAMA" ], [ "THE HI-LINE", "release_year", "1999" ], [ "THE INSIDER", "has_genre", "DRAMA" ], [ "THE INSIDER", "has_tags", "DRAMA" ], [ "THE INSIDER", "release_year", "1999" ], [ "THE JOYRIDERS", "has_genre", "DRAMA" ], [ "THE JOYRIDERS", "release_year", "1999" ], [ "THE KING AND I", "has_genre", "DRAMA" ], [ "THE KING AND I", "release_year", "1999" ], [ "THE LOST SON", "has_genre", "DRAMA" ], [ "THE LOST SON", "release_year", "1999" ], [ "THE MISSION", "has_genre", "DRAMA" ], [ "THE MISSION", "release_year", "1999" ], [ "THE MOMENT AFTER", "has_genre", "DRAMA" ], [ "THE MOMENT AFTER", "release_year", "1999" ], [ "THE STORY OF US", "has_genre", "DRAMA" ], [ "THE STORY OF US", "has_tags", "DRAMA" ], [ "THE STORY OF US", "release_year", "1999" ], [ "THE STRAIGHT STORY", "has_genre", "DRAMA" ], [ "THE STRAIGHT STORY", "release_year", "1999" ], [ "THE SUBURBANS", "has_genre", "DRAMA" ], [ "THE SUBURBANS", "release_year", "1999" ], [ "THE THIRD MIRACLE", "has_genre", "DRAMA" ], [ "THE THIRD MIRACLE", "release_year", "1999" ], [ "THE TIC CODE", "has_genre", "DRAMA" ], [ "THE TIC CODE", "release_year", "1999" ], [ "THE VIRGIN SUICIDES", "has_genre", "DRAMA" ], [ "THE VIRGIN SUICIDES", "release_year", "1999" ], [ "THE WAR ZONE", "has_genre", "DRAMA" ], [ "THE WAR ZONE", "release_year", "1999" ], [ "THE WINSLOW BOY", "has_genre", "DRAMA" ], [ "THE WINSLOW BOY", "release_year", "1999" ], [ "TOPSY-TURVY", "has_genre", "DRAMA" ], [ "TOPSY-TURVY", "release_year", "1999" ], [ "TRUE CRIME", "has_genre", "DRAMA" ], [ "TRUE CRIME", "release_year", "1999" ], [ "TUMBLEWEEDS", "has_genre", "DRAMA" ], [ "TUMBLEWEEDS", "release_year", "1999" ], [ "VARSITY BLUES", "has_genre", "DRAMA" ], [ "VARSITY BLUES", "release_year", "1999" ], [ "WILDFLOWERS", "has_genre", "DRAMA" ], [ "WILDFLOWERS", "release_year", "1999" ], [ "WISCONSIN DEATH TRIP", "has_genre", "DRAMA" ], [ "WISCONSIN DEATH TRIP", "release_year", "1999" ], [ "WITH FIRE AND SWORD", "has_genre", "DRAMA" ], [ "WITH FIRE AND SWORD", "release_year", "1999" ], [ "WONDERLAND", "has_genre", "DRAMA" ], [ "WONDERLAND", "release_year", "1999" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35935, 2002 26646, 3000 MILES TO GRACELAND 6094, A CLOCKWORK ORANGE 6938, ALPHA DOG 20584, AMERICAN GANGSTER 26048, AMERICAN HUSTLE 446, ANTHONY HOPKINS 23328, APPALOOSA 23513, ASH WEDNESDAY 27270, ASSASSINATION TANGO 5247, BAD LIEUTENANT 8402, BANGKOK DANGEROUS 23466, BEFORE THE DEVIL KNOWS YOU'RE DEAD 12228, BETTER LUCK TOMORROW 16956, BLUE COLLAR 20906, BRETT RATNER 12698, BUDD BOETTICHER 13639, BUGSY 33362, CARLITO'S WAY 27059, CATCH ME IF YOU CAN 33387, CITY OF GOD 29115, CITY OF INDUSTRY 28269, CLOCKERS 6871, COP LAND 32049, CRASH 14724, CRIME 7393, CRIME AND PUNISHMENT 38495, CRIME SPREE 17819, DARK BLUE 14292, DEAD IN THE WATER 11826, DEEP COVER 36982, DESPERATE HOURS 23027, DEUCES WILD 5795, DINNER RUSH 15961, DON 15892, DONNIE BRASCO 21021, DRIVE 12628, EASTERN PROMISES 26366, EDWARD NORTON 34642, EMILY WATSON 21123, FARGO 19755, FBI 18217, FIND ME GUILTY 34555, FLAWLESS 1033, FRACTURE 8690, FROZEN RIVER 17400, GANGS OF NEW YORK 23299, GET CARTER 35838, GUNCRAZY 17938, HANNIBAL 25245, HANNIBAL LECTER 18431, HARD EIGHT 13846, HARSH TIMES 31948, HARVEY KEITEL 33673, HEAT 26297, HIGH CRIMES 34072, I'M NOT SCARED 978, INFERNAL AFFAIRS 7264, INSIDE MAN 24305, JCVD 15615, JENNIFER LEITZES 11346, KISS KISS BANG BANG 3854, LAYER CAKE 38120, LORD OF WAR 33950, LUCKY NUMBER SLEVIN 10560, MAN BITES DOG 37052, MANHUNTER 33561, MEAN STREETS 38677, MIAMI VICE 3487, MINORITY REPORT 27827, MONSTER 10883, MONTANA 13217, NARC 1922, NINE QUEENS 29893, NO GOOD DEED 32999, PAID IN FULL 8869, PAYBACK 14744, PEOPLE I KNOW 27139, PHILIP SEYMOUR HOFFMAN 26716, PRIDE AND GLORY 31741, PRIMAL FEAR 26174, PULP FICTION 13081, R 2169, RANSOM 4523, RED DRAGON 28729, REMAKE 32357, RESERVOIR DOGS 25098, RICOCHET 5844, RIGHTEOUS KILL 19362, RISING SUN 1507, ROAD TO PERDITION 8516, ROBOCOP 9206, ROCKNROLLA 31913, ROMEO IS BLEEDING 5362, ROUNDERS 32607, RUSH HOUR 8020, SAFE MEN 6056, SCARFACE 10981, SERIAL KILLER 27807, SHAFT 1472, SHOTTAS 12333, SMOKIN' ACES 7083, SONNY 35843, SPUN 4722, STATE PROPERTY 5170, STEALING HARVARD 33414, SWEET SIXTEEN 30932, SWORDFISH 12, TAXI DRIVER 2512, THE BANK JOB 26674, THE BIG EASY 14696, THE BIG LEBOWSKI 11948, THE BLACK DAHLIA 16900, THE CALL 32597, THE DANCER UPSTAIRS 18997, THE DEPARTED 6524, THE GETAWAY 35551, THE GODFATHER 17518, THE GOOD THIEF 13052, THE HARD WORD 29952, THE INTERNATIONAL 33807, THE KILLER IS LOOSE 16288, THE LARAMIE PROJECT 11559, THE LOOKOUT 13761, THE ROOKIE 14250, THE SALTON SEA 14886, THE SCORE 37148, THE SECRET IN THEIR EYES 27836, THE SILENCE OF THE LAMBS 19084, THE TOWN 22820, THE UNITED STATES OF LELAND 9390, THE UNTOUCHABLES 20880, THE USUAL SUSPECTS 26758, THICK AS THIEVES 9726, THOMAS HARRIS 37331, TO DIE FOR 33836, TRAFFIC 17169, TRAINING DAY 27559, TRAINSPOTTING 24487, TRAPPED 10720, TRIGGERMEN 6485, TRIXIE 22588, WANTED 6770, WE OWN THE NIGHT 37210, ZODIAC src, edge_attr, dst 26646, has_genre, 14724 26646, has_tags, 13081 6094, has_genre, 14724 6094, has_tags, 13081 6938, has_genre, 14724 6938, has_tags, 13081 20584, has_genre, 14724 20584, has_tags, 14724 20584, has_tags, 13081 26048, has_genre, 14724 26048, has_tags, 13081 23328, has_genre, 14724 23328, has_tags, 13081 23513, has_genre, 14724 23513, release_year, 35935 27270, has_genre, 14724 27270, release_year, 35935 5247, has_genre, 14724 5247, has_tags, 31948 5247, starred_actors, 31948 8402, has_genre, 14724 8402, has_tags, 14724 8402, has_tags, 13081 23466, has_genre, 14724 23466, has_tags, 27139 23466, has_tags, 13081 23466, starred_actors, 27139 12228, has_genre, 14724 12228, release_year, 35935 16956, has_genre, 14724 16956, starred_actors, 31948 13639, has_genre, 14724 13639, starred_actors, 31948 33362, has_genre, 14724 33362, has_tags, 14724 33362, has_tags, 13081 27059, has_genre, 14724 27059, has_tags, 14724 27059, release_year, 35935 33387, has_genre, 14724 33387, has_tags, 14724 33387, has_tags, 13081 33387, release_year, 35935 29115, has_genre, 14724 29115, starred_actors, 31948 28269, has_genre, 14724 28269, starred_actors, 31948 6871, has_genre, 14724 6871, has_tags, 14724 6871, starred_actors, 31948 32049, has_tags, 14724 32049, has_tags, 13081 7393, has_genre, 14724 7393, release_year, 35935 38495, has_genre, 14724 38495, starred_actors, 31948 17819, has_genre, 14724 17819, release_year, 35935 14292, has_genre, 14724 14292, release_year, 35935 11826, has_genre, 14724 11826, has_tags, 13081 36982, has_genre, 14724 36982, starred_actors, 446 23027, has_genre, 14724 23027, release_year, 35935 5795, has_genre, 14724 5795, has_tags, 13081 15961, has_genre, 14724 15961, has_tags, 28729 15892, has_genre, 14724 15892, has_tags, 19755 21021, has_genre, 14724 21021, has_tags, 14724 21021, has_tags, 13081 12628, has_genre, 14724 12628, has_tags, 13081 21123, has_genre, 14724 21123, has_tags, 14724 21123, has_tags, 13081 18217, has_genre, 14724 18217, has_tags, 13081 34555, has_genre, 14724 34555, has_tags, 27139 34555, starred_actors, 27139 1033, has_genre, 14724 1033, has_tags, 446 1033, has_tags, 13081 1033, starred_actors, 446 8690, has_genre, 14724 8690, has_tags, 13081 17400, has_genre, 14724 17400, has_tags, 13081 17400, release_year, 35935 23299, has_genre, 14724 23299, has_tags, 28729 35838, has_genre, 14724 35838, has_tags, 28729 17938, has_genre, 14724 17938, has_tags, 446 17938, has_tags, 25245 17938, has_tags, 13081 17938, has_tags, 10981 17938, has_tags, 9726 17938, starred_actors, 446 17938, written_by, 9726 18431, has_genre, 14724 18431, has_tags, 27139 13846, has_genre, 14724 13846, has_tags, 13081 33673, has_genre, 14724 33673, has_tags, 14724 33673, has_tags, 13081 26297, has_genre, 14724 26297, release_year, 35935 34072, has_genre, 14724 34072, has_tags, 14724 34072, has_tags, 13081 978, has_genre, 14724 978, has_tags, 13081 978, release_year, 35935 7264, has_genre, 14724 7264, has_tags, 13081 24305, has_genre, 14724 24305, has_tags, 13081 11346, has_genre, 14724 11346, has_tags, 13081 3854, has_genre, 14724 3854, has_tags, 13081 38120, has_genre, 14724 38120, has_tags, 14724 38120, has_tags, 13081 33950, has_genre, 14724 33950, has_tags, 14724 33950, has_tags, 13081 10560, has_genre, 14724 10560, has_tags, 10981 37052, has_genre, 14724 37052, has_tags, 19755 37052, has_tags, 25245 37052, has_tags, 10981 37052, written_by, 9726 33561, has_genre, 14724 33561, has_tags, 31948 33561, has_tags, 13081 33561, starred_actors, 31948 38677, has_genre, 14724 38677, has_tags, 13081 3487, has_tags, 14724 3487, release_year, 35935 27827, has_genre, 14724 27827, has_tags, 14724 27827, has_tags, 10981 10883, directed_by, 15615 10883, has_genre, 14724 13217, has_genre, 14724 13217, release_year, 35935 1922, has_genre, 14724 1922, has_tags, 14724 1922, has_tags, 13081 29893, has_genre, 14724 29893, release_year, 35935 32999, has_genre, 14724 32999, release_year, 35935 8869, has_genre, 14724 8869, has_tags, 13081 14744, has_genre, 14724 14744, has_tags, 13081 14744, release_year, 35935 26716, has_genre, 14724 26716, has_tags, 26366 26716, has_tags, 13081 26716, starred_actors, 26366 31741, has_genre, 14724 31741, has_tags, 14724 31741, has_tags, 26366 26174, has_genre, 14724 26174, has_tags, 14724 26174, has_tags, 13081 2169, has_genre, 14724 2169, has_tags, 13081 4523, directed_by, 20906 4523, has_genre, 14724 4523, has_tags, 446 4523, has_tags, 20906 4523, has_tags, 26366 4523, has_tags, 34642 4523, has_tags, 19755 4523, has_tags, 25245 4523, has_tags, 31948 4523, has_tags, 27139 4523, has_tags, 13081 4523, has_tags, 28729 4523, has_tags, 10981 4523, release_year, 35935 4523, starred_actors, 446 4523, starred_actors, 26366 4523, starred_actors, 31948 4523, written_by, 9726 32357, has_genre, 14724 32357, has_tags, 14724 32357, has_tags, 31948 32357, starred_actors, 31948 25098, has_genre, 14724 25098, has_tags, 13081 5844, has_genre, 14724 5844, has_tags, 13081 19362, has_genre, 14724 19362, has_tags, 14724 19362, starred_actors, 31948 1507, has_genre, 14724 1507, release_year, 35935 8516, has_tags, 14724 8516, has_tags, 28729 9206, has_genre, 14724 9206, has_tags, 14724 9206, has_tags, 13081 31913, has_genre, 14724 31913, has_tags, 13081 5362, has_genre, 14724 5362, has_tags, 26366 32607, directed_by, 20906 32607, has_tags, 20906 32607, has_tags, 14724 8020, has_genre, 14724 8020, has_tags, 13081 6056, has_genre, 14724 6056, has_tags, 14724 6056, has_tags, 28729 27807, has_genre, 14724 27807, has_tags, 28729 1472, has_genre, 14724 1472, release_year, 35935 12333, has_genre, 14724 12333, has_tags, 13081 7083, has_genre, 14724 7083, release_year, 35935 35843, has_genre, 14724 35843, release_year, 35935 4722, has_genre, 14724 4722, release_year, 35935 5170, has_genre, 14724 5170, release_year, 35935 33414, has_genre, 14724 33414, has_tags, 13081 33414, release_year, 35935 30932, has_genre, 14724 30932, has_tags, 13081 12, has_genre, 14724 12, has_tags, 31948 2512, has_genre, 14724 2512, has_tags, 13081 26674, has_genre, 14724 26674, has_tags, 13081 14696, has_genre, 14724 14696, has_tags, 14724 14696, has_tags, 27139 11948, has_genre, 14724 11948, has_tags, 13081 16900, has_genre, 14724 16900, has_tags, 10981 32597, has_genre, 14724 32597, release_year, 35935 18997, has_genre, 14724 18997, has_tags, 14724 18997, has_tags, 13081 18997, has_tags, 28729 6524, has_genre, 14724 6524, has_tags, 28729 35551, has_genre, 14724 35551, has_tags, 14724 35551, has_tags, 13081 17518, has_genre, 14724 17518, release_year, 35935 13052, has_genre, 14724 13052, release_year, 35935 29952, has_genre, 14724 29952, has_tags, 13081 33807, directed_by, 12698 33807, has_genre, 14724 16288, has_genre, 14724 16288, release_year, 35935 11559, has_genre, 14724 11559, has_tags, 13081 13761, has_genre, 14724 13761, release_year, 35935 14250, has_genre, 14724 14250, release_year, 35935 14886, has_genre, 14724 14886, has_tags, 26366 14886, starred_actors, 26366 37148, has_tags, 14724 37148, has_tags, 13081 27836, has_tags, 446 27836, has_tags, 14724 27836, has_tags, 19755 27836, has_tags, 25245 27836, has_tags, 10981 27836, has_tags, 9726 27836, written_by, 9726 19084, has_genre, 14724 19084, has_tags, 14724 19084, has_tags, 13081 22820, has_genre, 14724 22820, has_tags, 13081 9390, has_genre, 14724 9390, has_tags, 14724 9390, has_tags, 13081 20880, has_genre, 14724 20880, has_tags, 14724 20880, has_tags, 13081 26758, has_genre, 14724 26758, has_tags, 13081 37331, has_genre, 14724 37331, has_tags, 13081 33836, has_genre, 14724 33836, has_tags, 13081 17169, has_genre, 14724 17169, has_tags, 13081 27559, has_tags, 14724 27559, has_tags, 13081 24487, has_genre, 14724 24487, release_year, 35935 10720, has_genre, 14724 10720, has_tags, 14724 10720, release_year, 35935 6485, has_genre, 14724 6485, starred_actors, 34642 22588, has_genre, 14724 22588, has_tags, 13081 6770, has_genre, 14724 6770, has_tags, 13081 37210, has_genre, 14724 37210, has_tags, 14724 37210, has_tags, 13081 37210, has_tags, 10981 Question: For what reason are BUDD BOETTICHER, JENNIFER LEITZES, and RED DRAGON associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BUDD BOETTICHER", "JENNIFER LEITZES", "RED DRAGON" ], "valid_edges": [ [ "3000 MILES TO GRACELAND", "has_genre", "CRIME" ], [ "3000 MILES TO GRACELAND", "has_tags", "R" ], [ "A CLOCKWORK ORANGE", "has_genre", "CRIME" ], [ "A CLOCKWORK ORANGE", "has_tags", "R" ], [ "ALPHA DOG", "has_genre", "CRIME" ], [ "ALPHA DOG", "has_tags", "R" ], [ "AMERICAN GANGSTER", "has_genre", "CRIME" ], [ "AMERICAN GANGSTER", "has_tags", "CRIME" ], [ "AMERICAN GANGSTER", "has_tags", "R" ], [ "AMERICAN HUSTLE", "has_genre", "CRIME" ], [ "AMERICAN HUSTLE", "has_tags", "R" ], [ "APPALOOSA", "has_genre", "CRIME" ], [ "APPALOOSA", "has_tags", "R" ], [ "ASH WEDNESDAY", "has_genre", "CRIME" ], [ "ASH WEDNESDAY", "release_year", "2002" ], [ "ASSASSINATION TANGO", "has_genre", "CRIME" ], [ "ASSASSINATION TANGO", "release_year", "2002" ], [ "BAD LIEUTENANT", "has_genre", "CRIME" ], [ "BAD LIEUTENANT", "has_tags", "HARVEY KEITEL" ], [ "BAD LIEUTENANT", "starred_actors", "HARVEY KEITEL" ], [ "BANGKOK DANGEROUS", "has_genre", "CRIME" ], [ "BANGKOK DANGEROUS", "has_tags", "CRIME" ], [ "BANGKOK DANGEROUS", "has_tags", "R" ], [ "BEFORE THE DEVIL KNOWS YOU'RE DEAD", "has_genre", "CRIME" ], [ "BEFORE THE DEVIL KNOWS YOU'RE DEAD", "has_tags", "PHILIP SEYMOUR HOFFMAN" ], [ "BEFORE THE DEVIL KNOWS YOU'RE DEAD", "has_tags", "R" ], [ "BEFORE THE DEVIL KNOWS YOU'RE DEAD", "starred_actors", "PHILIP SEYMOUR HOFFMAN" ], [ "BETTER LUCK TOMORROW", "has_genre", "CRIME" ], [ "BETTER LUCK TOMORROW", "release_year", "2002" ], [ "BLUE COLLAR", "has_genre", "CRIME" ], [ "BLUE COLLAR", "starred_actors", "HARVEY KEITEL" ], [ "BUGSY", "has_genre", "CRIME" ], [ "BUGSY", "starred_actors", "HARVEY KEITEL" ], [ "CARLITO'S WAY", "has_genre", "CRIME" ], [ "CARLITO'S WAY", "has_tags", "CRIME" ], [ "CARLITO'S WAY", "has_tags", "R" ], [ "CATCH ME IF YOU CAN", "has_genre", "CRIME" ], [ "CATCH ME IF YOU CAN", "has_tags", "CRIME" ], [ "CATCH ME IF YOU CAN", "release_year", "2002" ], [ "CITY OF GOD", "has_genre", "CRIME" ], [ "CITY OF GOD", "has_tags", "CRIME" ], [ "CITY OF GOD", "has_tags", "R" ], [ "CITY OF GOD", "release_year", "2002" ], [ "CITY OF INDUSTRY", "has_genre", "CRIME" ], [ "CITY OF INDUSTRY", "starred_actors", "HARVEY KEITEL" ], [ "CLOCKERS", "has_genre", "CRIME" ], [ "CLOCKERS", "starred_actors", "HARVEY KEITEL" ], [ "COP LAND", "has_genre", "CRIME" ], [ "COP LAND", "has_tags", "CRIME" ], [ "COP LAND", "starred_actors", "HARVEY KEITEL" ], [ "CRASH", "has_tags", "CRIME" ], [ "CRASH", "has_tags", "R" ], [ "CRIME AND PUNISHMENT", "has_genre", "CRIME" ], [ "CRIME AND PUNISHMENT", "release_year", "2002" ], [ "CRIME SPREE", "has_genre", "CRIME" ], [ "CRIME SPREE", "starred_actors", "HARVEY KEITEL" ], [ "DARK BLUE", "has_genre", "CRIME" ], [ "DARK BLUE", "release_year", "2002" ], [ "DEAD IN THE WATER", "has_genre", "CRIME" ], [ "DEAD IN THE WATER", "release_year", "2002" ], [ "DEEP COVER", "has_genre", "CRIME" ], [ "DEEP COVER", "has_tags", "R" ], [ "DESPERATE HOURS", "has_genre", "CRIME" ], [ "DESPERATE HOURS", "starred_actors", "ANTHONY HOPKINS" ], [ "DEUCES WILD", "has_genre", "CRIME" ], [ "DEUCES WILD", "release_year", "2002" ], [ "DINNER RUSH", "has_genre", "CRIME" ], [ "DINNER RUSH", "has_tags", "R" ], [ "DON", "has_genre", "CRIME" ], [ "DON", "has_tags", "REMAKE" ], [ "DONNIE BRASCO", "has_genre", "CRIME" ], [ "DONNIE BRASCO", "has_tags", "FBI" ], [ "DRIVE", "has_genre", "CRIME" ], [ "DRIVE", "has_tags", "CRIME" ], [ "DRIVE", "has_tags", "R" ], [ "EASTERN PROMISES", "has_genre", "CRIME" ], [ "EASTERN PROMISES", "has_tags", "R" ], [ "FARGO", "has_genre", "CRIME" ], [ "FARGO", "has_tags", "CRIME" ], [ "FARGO", "has_tags", "R" ], [ "FIND ME GUILTY", "has_genre", "CRIME" ], [ "FIND ME GUILTY", "has_tags", "R" ], [ "FLAWLESS", "has_genre", "CRIME" ], [ "FLAWLESS", "has_tags", "PHILIP SEYMOUR HOFFMAN" ], [ "FLAWLESS", "starred_actors", "PHILIP SEYMOUR HOFFMAN" ], [ "FRACTURE", "has_genre", "CRIME" ], [ "FRACTURE", "has_tags", "ANTHONY HOPKINS" ], [ "FRACTURE", "has_tags", "R" ], [ "FRACTURE", "starred_actors", "ANTHONY HOPKINS" ], [ "FROZEN RIVER", "has_genre", "CRIME" ], [ "FROZEN RIVER", "has_tags", "R" ], [ "GANGS OF NEW YORK", "has_genre", "CRIME" ], [ "GANGS OF NEW YORK", "has_tags", "R" ], [ "GANGS OF NEW YORK", "release_year", "2002" ], [ "GET CARTER", "has_genre", "CRIME" ], [ "GET CARTER", "has_tags", "REMAKE" ], [ "GUNCRAZY", "has_genre", "CRIME" ], [ "GUNCRAZY", "has_tags", "REMAKE" ], [ "HANNIBAL", "has_genre", "CRIME" ], [ "HANNIBAL", "has_tags", "ANTHONY HOPKINS" ], [ "HANNIBAL", "has_tags", "HANNIBAL LECTER" ], [ "HANNIBAL", "has_tags", "R" ], [ "HANNIBAL", "has_tags", "SERIAL KILLER" ], [ "HANNIBAL", "has_tags", "THOMAS HARRIS" ], [ "HANNIBAL", "starred_actors", "ANTHONY HOPKINS" ], [ "HANNIBAL", "written_by", "THOMAS HARRIS" ], [ "HARD EIGHT", "has_genre", "CRIME" ], [ "HARD EIGHT", "has_tags", "PHILIP SEYMOUR HOFFMAN" ], [ "HARSH TIMES", "has_genre", "CRIME" ], [ "HARSH TIMES", "has_tags", "R" ], [ "HEAT", "has_genre", "CRIME" ], [ "HEAT", "has_tags", "CRIME" ], [ "HEAT", "has_tags", "R" ], [ "HIGH CRIMES", "has_genre", "CRIME" ], [ "HIGH CRIMES", "release_year", "2002" ], [ "I'M NOT SCARED", "has_genre", "CRIME" ], [ "I'M NOT SCARED", "has_tags", "CRIME" ], [ "I'M NOT SCARED", "has_tags", "R" ], [ "INFERNAL AFFAIRS", "has_genre", "CRIME" ], [ "INFERNAL AFFAIRS", "has_tags", "R" ], [ "INFERNAL AFFAIRS", "release_year", "2002" ], [ "INSIDE MAN", "has_genre", "CRIME" ], [ "INSIDE MAN", "has_tags", "R" ], [ "JCVD", "has_genre", "CRIME" ], [ "JCVD", "has_tags", "R" ], [ "KISS KISS BANG BANG", "has_genre", "CRIME" ], [ "KISS KISS BANG BANG", "has_tags", "R" ], [ "LAYER CAKE", "has_genre", "CRIME" ], [ "LAYER CAKE", "has_tags", "R" ], [ "LORD OF WAR", "has_genre", "CRIME" ], [ "LORD OF WAR", "has_tags", "CRIME" ], [ "LORD OF WAR", "has_tags", "R" ], [ "LUCKY NUMBER SLEVIN", "has_genre", "CRIME" ], [ "LUCKY NUMBER SLEVIN", "has_tags", "CRIME" ], [ "LUCKY NUMBER SLEVIN", "has_tags", "R" ], [ "MAN BITES DOG", "has_genre", "CRIME" ], [ "MAN BITES DOG", "has_tags", "SERIAL KILLER" ], [ "MANHUNTER", "has_genre", "CRIME" ], [ "MANHUNTER", "has_tags", "FBI" ], [ "MANHUNTER", "has_tags", "HANNIBAL LECTER" ], [ "MANHUNTER", "has_tags", "SERIAL KILLER" ], [ "MANHUNTER", "written_by", "THOMAS HARRIS" ], [ "MEAN STREETS", "has_genre", "CRIME" ], [ "MEAN STREETS", "has_tags", "HARVEY KEITEL" ], [ "MEAN STREETS", "has_tags", "R" ], [ "MEAN STREETS", "starred_actors", "HARVEY KEITEL" ], [ "MIAMI VICE", "has_genre", "CRIME" ], [ "MIAMI VICE", "has_tags", "R" ], [ "MINORITY REPORT", "has_tags", "CRIME" ], [ "MINORITY REPORT", "release_year", "2002" ], [ "MONSTER", "has_genre", "CRIME" ], [ "MONSTER", "has_tags", "CRIME" ], [ "MONSTER", "has_tags", "SERIAL KILLER" ], [ "MONTANA", "directed_by", "JENNIFER LEITZES" ], [ "MONTANA", "has_genre", "CRIME" ], [ "NARC", "has_genre", "CRIME" ], [ "NARC", "release_year", "2002" ], [ "NINE QUEENS", "has_genre", "CRIME" ], [ "NINE QUEENS", "has_tags", "CRIME" ], [ "NINE QUEENS", "has_tags", "R" ], [ "NO GOOD DEED", "has_genre", "CRIME" ], [ "NO GOOD DEED", "release_year", "2002" ], [ "PAID IN FULL", "has_genre", "CRIME" ], [ "PAID IN FULL", "release_year", "2002" ], [ "PAYBACK", "has_genre", "CRIME" ], [ "PAYBACK", "has_tags", "R" ], [ "PEOPLE I KNOW", "has_genre", "CRIME" ], [ "PEOPLE I KNOW", "has_tags", "R" ], [ "PEOPLE I KNOW", "release_year", "2002" ], [ "PRIDE AND GLORY", "has_genre", "CRIME" ], [ "PRIDE AND GLORY", "has_tags", "EDWARD NORTON" ], [ "PRIDE AND GLORY", "has_tags", "R" ], [ "PRIDE AND GLORY", "starred_actors", "EDWARD NORTON" ], [ "PRIMAL FEAR", "has_genre", "CRIME" ], [ "PRIMAL FEAR", "has_tags", "CRIME" ], [ "PRIMAL FEAR", "has_tags", "EDWARD NORTON" ], [ "PULP FICTION", "has_genre", "CRIME" ], [ "PULP FICTION", "has_tags", "CRIME" ], [ "PULP FICTION", "has_tags", "R" ], [ "RANSOM", "has_genre", "CRIME" ], [ "RANSOM", "has_tags", "R" ], [ "RED DRAGON", "directed_by", "BRETT RATNER" ], [ "RED DRAGON", "has_genre", "CRIME" ], [ "RED DRAGON", "has_tags", "ANTHONY HOPKINS" ], [ "RED DRAGON", "has_tags", "BRETT RATNER" ], [ "RED DRAGON", "has_tags", "EDWARD NORTON" ], [ "RED DRAGON", "has_tags", "EMILY WATSON" ], [ "RED DRAGON", "has_tags", "FBI" ], [ "RED DRAGON", "has_tags", "HANNIBAL LECTER" ], [ "RED DRAGON", "has_tags", "HARVEY KEITEL" ], [ "RED DRAGON", "has_tags", "PHILIP SEYMOUR HOFFMAN" ], [ "RED DRAGON", "has_tags", "R" ], [ "RED DRAGON", "has_tags", "REMAKE" ], [ "RED DRAGON", "has_tags", "SERIAL KILLER" ], [ "RED DRAGON", "release_year", "2002" ], [ "RED DRAGON", "starred_actors", "ANTHONY HOPKINS" ], [ "RED DRAGON", "starred_actors", "EDWARD NORTON" ], [ "RED DRAGON", "starred_actors", "HARVEY KEITEL" ], [ "RED DRAGON", "written_by", "THOMAS HARRIS" ], [ "RESERVOIR DOGS", "has_genre", "CRIME" ], [ "RESERVOIR DOGS", "has_tags", "CRIME" ], [ "RESERVOIR DOGS", "has_tags", "HARVEY KEITEL" ], [ "RESERVOIR DOGS", "starred_actors", "HARVEY KEITEL" ], [ "RICOCHET", "has_genre", "CRIME" ], [ "RICOCHET", "has_tags", "R" ], [ "RIGHTEOUS KILL", "has_genre", "CRIME" ], [ "RIGHTEOUS KILL", "has_tags", "R" ], [ "RISING SUN", "has_genre", "CRIME" ], [ "RISING SUN", "has_tags", "CRIME" ], [ "RISING SUN", "starred_actors", "HARVEY KEITEL" ], [ "ROAD TO PERDITION", "has_genre", "CRIME" ], [ "ROAD TO PERDITION", "release_year", "2002" ], [ "ROBOCOP", "has_tags", "CRIME" ], [ "ROBOCOP", "has_tags", "REMAKE" ], [ "ROCKNROLLA", "has_genre", "CRIME" ], [ "ROCKNROLLA", "has_tags", "CRIME" ], [ "ROCKNROLLA", "has_tags", "R" ], [ "ROMEO IS BLEEDING", "has_genre", "CRIME" ], [ "ROMEO IS BLEEDING", "has_tags", "R" ], [ "ROUNDERS", "has_genre", "CRIME" ], [ "ROUNDERS", "has_tags", "EDWARD NORTON" ], [ "RUSH HOUR", "directed_by", "BRETT RATNER" ], [ "RUSH HOUR", "has_tags", "BRETT RATNER" ], [ "RUSH HOUR", "has_tags", "CRIME" ], [ "SAFE MEN", "has_genre", "CRIME" ], [ "SAFE MEN", "has_tags", "R" ], [ "SCARFACE", "has_genre", "CRIME" ], [ "SCARFACE", "has_tags", "CRIME" ], [ "SCARFACE", "has_tags", "REMAKE" ], [ "SHAFT", "has_genre", "CRIME" ], [ "SHAFT", "has_tags", "REMAKE" ], [ "SHOTTAS", "has_genre", "CRIME" ], [ "SHOTTAS", "release_year", "2002" ], [ "SMOKIN' ACES", "has_genre", "CRIME" ], [ "SMOKIN' ACES", "has_tags", "R" ], [ "SONNY", "has_genre", "CRIME" ], [ "SONNY", "release_year", "2002" ], [ "SPUN", "has_genre", "CRIME" ], [ "SPUN", "release_year", "2002" ], [ "STATE PROPERTY", "has_genre", "CRIME" ], [ "STATE PROPERTY", "release_year", "2002" ], [ "STEALING HARVARD", "has_genre", "CRIME" ], [ "STEALING HARVARD", "release_year", "2002" ], [ "SWEET SIXTEEN", "has_genre", "CRIME" ], [ "SWEET SIXTEEN", "has_tags", "R" ], [ "SWEET SIXTEEN", "release_year", "2002" ], [ "SWORDFISH", "has_genre", "CRIME" ], [ "SWORDFISH", "has_tags", "R" ], [ "TAXI DRIVER", "has_genre", "CRIME" ], [ "TAXI DRIVER", "has_tags", "HARVEY KEITEL" ], [ "THE BANK JOB", "has_genre", "CRIME" ], [ "THE BANK JOB", "has_tags", "R" ], [ "THE BIG EASY", "has_genre", "CRIME" ], [ "THE BIG EASY", "has_tags", "R" ], [ "THE BIG LEBOWSKI", "has_genre", "CRIME" ], [ "THE BIG LEBOWSKI", "has_tags", "CRIME" ], [ "THE BIG LEBOWSKI", "has_tags", "PHILIP SEYMOUR HOFFMAN" ], [ "THE BLACK DAHLIA", "has_genre", "CRIME" ], [ "THE BLACK DAHLIA", "has_tags", "R" ], [ "THE CALL", "has_genre", "CRIME" ], [ "THE CALL", "has_tags", "SERIAL KILLER" ], [ "THE DANCER UPSTAIRS", "has_genre", "CRIME" ], [ "THE DANCER UPSTAIRS", "release_year", "2002" ], [ "THE DEPARTED", "has_genre", "CRIME" ], [ "THE DEPARTED", "has_tags", "CRIME" ], [ "THE DEPARTED", "has_tags", "R" ], [ "THE DEPARTED", "has_tags", "REMAKE" ], [ "THE GETAWAY", "has_genre", "CRIME" ], [ "THE GETAWAY", "has_tags", "REMAKE" ], [ "THE GODFATHER", "has_genre", "CRIME" ], [ "THE GODFATHER", "has_tags", "CRIME" ], [ "THE GODFATHER", "has_tags", "R" ], [ "THE GOOD THIEF", "has_genre", "CRIME" ], [ "THE GOOD THIEF", "release_year", "2002" ], [ "THE HARD WORD", "has_genre", "CRIME" ], [ "THE HARD WORD", "release_year", "2002" ], [ "THE INTERNATIONAL", "has_genre", "CRIME" ], [ "THE INTERNATIONAL", "has_tags", "R" ], [ "THE KILLER IS LOOSE", "directed_by", "BUDD BOETTICHER" ], [ "THE KILLER IS LOOSE", "has_genre", "CRIME" ], [ "THE LARAMIE PROJECT", "has_genre", "CRIME" ], [ "THE LARAMIE PROJECT", "release_year", "2002" ], [ "THE LOOKOUT", "has_genre", "CRIME" ], [ "THE LOOKOUT", "has_tags", "R" ], [ "THE ROOKIE", "has_genre", "CRIME" ], [ "THE ROOKIE", "release_year", "2002" ], [ "THE SALTON SEA", "has_genre", "CRIME" ], [ "THE SALTON SEA", "release_year", "2002" ], [ "THE SCORE", "has_genre", "CRIME" ], [ "THE SCORE", "has_tags", "EDWARD NORTON" ], [ "THE SCORE", "starred_actors", "EDWARD NORTON" ], [ "THE SECRET IN THEIR EYES", "has_tags", "CRIME" ], [ "THE SECRET IN THEIR EYES", "has_tags", "R" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "ANTHONY HOPKINS" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "CRIME" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "FBI" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "HANNIBAL LECTER" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "SERIAL KILLER" ], [ "THE SILENCE OF THE LAMBS", "has_tags", "THOMAS HARRIS" ], [ "THE SILENCE OF THE LAMBS", "written_by", "THOMAS HARRIS" ], [ "THE TOWN", "has_genre", "CRIME" ], [ "THE TOWN", "has_tags", "CRIME" ], [ "THE TOWN", "has_tags", "R" ], [ "THE UNITED STATES OF LELAND", "has_genre", "CRIME" ], [ "THE UNITED STATES OF LELAND", "has_tags", "R" ], [ "THE UNTOUCHABLES", "has_genre", "CRIME" ], [ "THE UNTOUCHABLES", "has_tags", "CRIME" ], [ "THE UNTOUCHABLES", "has_tags", "R" ], [ "THE USUAL SUSPECTS", "has_genre", "CRIME" ], [ "THE USUAL SUSPECTS", "has_tags", "CRIME" ], [ "THE USUAL SUSPECTS", "has_tags", "R" ], [ "THICK AS THIEVES", "has_genre", "CRIME" ], [ "THICK AS THIEVES", "has_tags", "R" ], [ "TO DIE FOR", "has_genre", "CRIME" ], [ "TO DIE FOR", "has_tags", "R" ], [ "TRAFFIC", "has_genre", "CRIME" ], [ "TRAFFIC", "has_tags", "R" ], [ "TRAINING DAY", "has_genre", "CRIME" ], [ "TRAINING DAY", "has_tags", "R" ], [ "TRAINSPOTTING", "has_tags", "CRIME" ], [ "TRAINSPOTTING", "has_tags", "R" ], [ "TRAPPED", "has_genre", "CRIME" ], [ "TRAPPED", "release_year", "2002" ], [ "TRIGGERMEN", "has_genre", "CRIME" ], [ "TRIGGERMEN", "has_tags", "CRIME" ], [ "TRIGGERMEN", "release_year", "2002" ], [ "TRIXIE", "has_genre", "CRIME" ], [ "TRIXIE", "starred_actors", "EMILY WATSON" ], [ "WANTED", "has_genre", "CRIME" ], [ "WANTED", "has_tags", "R" ], [ "WE OWN THE NIGHT", "has_genre", "CRIME" ], [ "WE OWN THE NIGHT", "has_tags", "R" ], [ "ZODIAC", "has_genre", "CRIME" ], [ "ZODIAC", "has_tags", "CRIME" ], [ "ZODIAC", "has_tags", "R" ], [ "ZODIAC", "has_tags", "SERIAL KILLER" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 5658, BAD BOY BUBBY 35468, CLEAN SLATE 30463, COMEDY 36280, MR. BEAN'S HOLIDAY 20191, NICHOLAS HOPE src, edge_attr, dst 5658, has_genre, 30463 5658, starred_actors, 20191 35468, has_genre, 30463 36280, has_genre, 30463 Question: For what reason are CLEAN SLATE, MR. BEAN'S HOLIDAY, and NICHOLAS HOPE associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "CLEAN SLATE", "MR. BEAN'S HOLIDAY", "NICHOLAS HOPE" ], "valid_edges": [ [ "BAD BOY BUBBY", "has_genre", "COMEDY" ], [ "BAD BOY BUBBY", "starred_actors", "NICHOLAS HOPE" ], [ "CLEAN SLATE", "has_genre", "COMEDY" ], [ "MR. BEAN'S HOLIDAY", "has_genre", "COMEDY" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 17315, 2007 7795, BEN CHAPLIN 6012, FRENCH 6480, GERMAN 5870, HORROR 35224, LOST SOULS 31784, PAUL A. PARTAIN 16348, PERSEPOLIS 36258, TALI-IHANTALA 1944 19649, THE COUNTERFEITERS 429, THE SEARCH 38051, THE TEXAS CHAIN SAW MASSACRE 31126, THE WATER HORSE 19779, UNDER THE BOMBS 22214, WAR src, edge_attr, dst 35224, has_genre, 5870 35224, starred_actors, 7795 16348, has_tags, 6012 16348, has_tags, 22214 16348, in_language, 6012 36258, has_genre, 22214 36258, in_language, 6480 19649, has_genre, 22214 19649, in_language, 6480 429, has_genre, 22214 429, in_language, 6012 429, in_language, 6480 38051, has_genre, 5870 38051, has_tags, 5870 38051, starred_actors, 31784 31126, release_year, 17315 31126, starred_actors, 7795 19779, has_genre, 22214 19779, in_language, 6012 22214, release_year, 17315 Question: In what context are BEN CHAPLIN, PAUL A. PARTAIN, and THE SEARCH connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BEN CHAPLIN", "PAUL A. PARTAIN", "THE SEARCH" ], "valid_edges": [ [ "LOST SOULS", "has_genre", "HORROR" ], [ "LOST SOULS", "starred_actors", "BEN CHAPLIN" ], [ "PERSEPOLIS", "has_tags", "FRENCH" ], [ "PERSEPOLIS", "has_tags", "WAR" ], [ "PERSEPOLIS", "in_language", "FRENCH" ], [ "TALI-IHANTALA 1944", "has_genre", "WAR" ], [ "TALI-IHANTALA 1944", "in_language", "GERMAN" ], [ "THE COUNTERFEITERS", "has_genre", "WAR" ], [ "THE COUNTERFEITERS", "in_language", "GERMAN" ], [ "THE SEARCH", "has_genre", "WAR" ], [ "THE SEARCH", "in_language", "FRENCH" ], [ "THE SEARCH", "in_language", "GERMAN" ], [ "THE TEXAS CHAIN SAW MASSACRE", "has_genre", "HORROR" ], [ "THE TEXAS CHAIN SAW MASSACRE", "has_tags", "HORROR" ], [ "THE TEXAS CHAIN SAW MASSACRE", "starred_actors", "PAUL A. PARTAIN" ], [ "THE WATER HORSE", "release_year", "2007" ], [ "THE WATER HORSE", "starred_actors", "BEN CHAPLIN" ], [ "UNDER THE BOMBS", "has_genre", "WAR" ], [ "UNDER THE BOMBS", "in_language", "FRENCH" ], [ "WAR", "release_year", "2007" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 17480, 1988 27261, 2009 18599, BILOXI BLUES 9551, KYLE MCCULLOCH 25508, MATTHEW BRODERICK 3977, TALES FROM THE GIMLI HOSPITAL 18158, THE PERFECT GAME 11574, TORCH SONG TRILOGY 37971, W. WILLIAM WINOKUR 31035, WONDERFUL WORLD src, edge_attr, dst 18599, release_year, 17480 18599, starred_actors, 25508 3977, release_year, 17480 3977, starred_actors, 9551 18158, release_year, 27261 18158, written_by, 37971 11574, has_tags, 25508 11574, release_year, 17480 11574, starred_actors, 25508 31035, release_year, 27261 31035, starred_actors, 25508 Question: How are KYLE MCCULLOCH, MATTHEW BRODERICK, and W. WILLIAM WINOKUR related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "KYLE MCCULLOCH", "MATTHEW BRODERICK", "W. WILLIAM WINOKUR" ], "valid_edges": [ [ "BILOXI BLUES", "release_year", "1988" ], [ "BILOXI BLUES", "starred_actors", "MATTHEW BRODERICK" ], [ "TALES FROM THE GIMLI HOSPITAL", "release_year", "1988" ], [ "TALES FROM THE GIMLI HOSPITAL", "starred_actors", "KYLE MCCULLOCH" ], [ "THE PERFECT GAME", "release_year", "2009" ], [ "THE PERFECT GAME", "written_by", "W. WILLIAM WINOKUR" ], [ "TORCH SONG TRILOGY", "has_tags", "MATTHEW BRODERICK" ], [ "TORCH SONG TRILOGY", "release_year", "1988" ], [ "TORCH SONG TRILOGY", "starred_actors", "MATTHEW BRODERICK" ], [ "WONDERFUL WORLD", "release_year", "2009" ], [ "WONDERFUL WORLD", "starred_actors", "MATTHEW BRODERICK" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 27810, 1968 4816, BANDOLERO! 29521, EASY VIRTUE 8406, FIRECREEK 39775, IN YOUR EYES 7736, JAMES STEWART 23224, KRISTIN SCOTT THOMAS 8379, ROMANCE 2738, ROMEO AND JULIET 22580, SUITE FRANÇAISE 27995, THE FAR COUNTRY src, edge_attr, dst 4816, release_year, 27810 4816, starred_actors, 7736 29521, has_genre, 8379 29521, starred_actors, 23224 8406, release_year, 27810 8406, starred_actors, 7736 39775, has_genre, 8379 2738, has_genre, 8379 2738, has_tags, 8379 2738, release_year, 27810 22580, has_genre, 8379 22580, starred_actors, 23224 27995, has_genre, 8379 27995, starred_actors, 7736 Question: For what reason are FIRECREEK, IN YOUR EYES, and KRISTIN SCOTT THOMAS associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "FIRECREEK", "IN YOUR EYES", "KRISTIN SCOTT THOMAS" ], "valid_edges": [ [ "BANDOLERO!", "release_year", "1968" ], [ "BANDOLERO!", "starred_actors", "JAMES STEWART" ], [ "EASY VIRTUE", "has_genre", "ROMANCE" ], [ "EASY VIRTUE", "starred_actors", "KRISTIN SCOTT THOMAS" ], [ "FIRECREEK", "release_year", "1968" ], [ "FIRECREEK", "starred_actors", "JAMES STEWART" ], [ "IN YOUR EYES", "has_genre", "ROMANCE" ], [ "ROMEO AND JULIET", "has_genre", "ROMANCE" ], [ "ROMEO AND JULIET", "has_tags", "ROMANCE" ], [ "ROMEO AND JULIET", "release_year", "1968" ], [ "SUITE FRANÇAISE", "has_genre", "ROMANCE" ], [ "SUITE FRANÇAISE", "starred_actors", "KRISTIN SCOTT THOMAS" ], [ "THE FAR COUNTRY", "has_genre", "ROMANCE" ], [ "THE FAR COUNTRY", "starred_actors", "JAMES STEWART" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 25221, 1981 27261, 2009 35798, 2010 673, 9 2992, A VERY POTTER MUSICAL 39013, A VERY POTTER SEQUEL 20762, BONNIE GRUESEN 3693, BRIAN HOLDEN 8709, CHRISTOPHER PLUMMER 14850, DARREN CRISS 11528, DEE HEPBURN 2807, GREGORY'S GIRL 5887, HARRY POTTER 7709, HELEN MIRREN 28207, JOEY RICHTER 8481, LAUREN LOPEZ 4018, MATT LANG 24593, MUSICAL 3347, MY DOG TULIP 18396, NICK LANG 27541, PARODY 38000, STATE OF PLAY 5081, THE IMAGINARIUM OF DOCTOR PARNASSUS 29682, THE LAST STATION src, edge_attr, dst 25221, release_year, 27261 35798, starred_actors, 7709 673, has_tags, 8709 673, release_year, 27261 673, starred_actors, 8709 2992, directed_by, 4018 2992, has_genre, 24593 2992, has_tags, 5887 2992, has_tags, 27541 2992, release_year, 27261 2992, starred_actors, 20762 2992, starred_actors, 14850 2992, starred_actors, 28207 2992, starred_actors, 8481 2992, written_by, 3693 2992, written_by, 4018 2992, written_by, 18396 39013, directed_by, 4018 39013, has_genre, 24593 39013, has_tags, 5887 39013, has_tags, 27541 39013, release_year, 35798 39013, starred_actors, 20762 39013, starred_actors, 14850 39013, starred_actors, 28207 39013, starred_actors, 8481 39013, written_by, 3693 39013, written_by, 4018 39013, written_by, 18396 2807, release_year, 25221 2807, starred_actors, 11528 3347, release_year, 27261 3347, starred_actors, 8709 38000, has_tags, 7709 38000, release_year, 27261 38000, starred_actors, 7709 5081, has_tags, 8709 5081, release_year, 27261 5081, starred_actors, 8709 29682, has_tags, 7709 29682, release_year, 27261 29682, starred_actors, 8709 29682, starred_actors, 7709 Question: For what reason are BRIAN HOLDEN, DEE HEPBURN, and THE LAST STATION associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BRIAN HOLDEN", "DEE HEPBURN", "THE LAST STATION" ], "valid_edges": [ [ "1981", "release_year", "2009" ], [ "2010", "starred_actors", "HELEN MIRREN" ], [ "9", "has_tags", "CHRISTOPHER PLUMMER" ], [ "9", "release_year", "2009" ], [ "9", "starred_actors", "CHRISTOPHER PLUMMER" ], [ "A VERY POTTER MUSICAL", "directed_by", "MATT LANG" ], [ "A VERY POTTER MUSICAL", "has_genre", "MUSICAL" ], [ "A VERY POTTER MUSICAL", "has_tags", "HARRY POTTER" ], [ "A VERY POTTER MUSICAL", "has_tags", "PARODY" ], [ "A VERY POTTER MUSICAL", "release_year", "2009" ], [ "A VERY POTTER MUSICAL", "starred_actors", "BONNIE GRUESEN" ], [ "A VERY POTTER MUSICAL", "starred_actors", "DARREN CRISS" ], [ "A VERY POTTER MUSICAL", "starred_actors", "JOEY RICHTER" ], [ "A VERY POTTER MUSICAL", "starred_actors", "LAUREN LOPEZ" ], [ "A VERY POTTER MUSICAL", "written_by", "BRIAN HOLDEN" ], [ "A VERY POTTER MUSICAL", "written_by", "MATT LANG" ], [ "A VERY POTTER MUSICAL", "written_by", "NICK LANG" ], [ "A VERY POTTER SEQUEL", "directed_by", "MATT LANG" ], [ "A VERY POTTER SEQUEL", "has_genre", "MUSICAL" ], [ "A VERY POTTER SEQUEL", "has_tags", "HARRY POTTER" ], [ "A VERY POTTER SEQUEL", "has_tags", "PARODY" ], [ "A VERY POTTER SEQUEL", "release_year", "2010" ], [ "A VERY POTTER SEQUEL", "starred_actors", "BONNIE GRUESEN" ], [ "A VERY POTTER SEQUEL", "starred_actors", "DARREN CRISS" ], [ "A VERY POTTER SEQUEL", "starred_actors", "JOEY RICHTER" ], [ "A VERY POTTER SEQUEL", "starred_actors", "LAUREN LOPEZ" ], [ "A VERY POTTER SEQUEL", "written_by", "BRIAN HOLDEN" ], [ "A VERY POTTER SEQUEL", "written_by", "MATT LANG" ], [ "A VERY POTTER SEQUEL", "written_by", "NICK LANG" ], [ "GREGORY'S GIRL", "release_year", "1981" ], [ "GREGORY'S GIRL", "starred_actors", "DEE HEPBURN" ], [ "MY DOG TULIP", "release_year", "2009" ], [ "MY DOG TULIP", "starred_actors", "CHRISTOPHER PLUMMER" ], [ "STATE OF PLAY", "has_tags", "HELEN MIRREN" ], [ "STATE OF PLAY", "release_year", "2009" ], [ "STATE OF PLAY", "starred_actors", "HELEN MIRREN" ], [ "THE IMAGINARIUM OF DOCTOR PARNASSUS", "has_tags", "CHRISTOPHER PLUMMER" ], [ "THE IMAGINARIUM OF DOCTOR PARNASSUS", "release_year", "2009" ], [ "THE IMAGINARIUM OF DOCTOR PARNASSUS", "starred_actors", "CHRISTOPHER PLUMMER" ], [ "THE LAST STATION", "has_tags", "HELEN MIRREN" ], [ "THE LAST STATION", "release_year", "2009" ], [ "THE LAST STATION", "starred_actors", "CHRISTOPHER PLUMMER" ], [ "THE LAST STATION", "starred_actors", "HELEN MIRREN" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 10045, BD-R 2890, DESERT HEARTS 6565, DESPERATE JOURNEY 36212, DRAMA 37934, HELEN SHAVER 27893, POSSESSED 11501, RAYMOND MASSEY 24053, RUGGERO MACCARI 40001, SCENT OF A WOMAN 1277, THE COLOR OF MONEY 30987, THE FOUNTAINHEAD 31851, THE PRISONER OF ZENDA src, edge_attr, dst 2890, has_genre, 36212 2890, starred_actors, 37934 6565, has_genre, 36212 6565, starred_actors, 11501 27893, has_genre, 36212 27893, has_tags, 10045 27893, starred_actors, 11501 40001, has_genre, 36212 40001, has_tags, 36212 40001, written_by, 24053 1277, has_genre, 36212 1277, starred_actors, 37934 30987, has_genre, 36212 30987, starred_actors, 11501 31851, has_genre, 36212 31851, has_tags, 10045 31851, starred_actors, 11501 Question: In what context are HELEN SHAVER, RAYMOND MASSEY, and RUGGERO MACCARI connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "HELEN SHAVER", "RAYMOND MASSEY", "RUGGERO MACCARI" ], "valid_edges": [ [ "DESERT HEARTS", "has_genre", "DRAMA" ], [ "DESERT HEARTS", "starred_actors", "HELEN SHAVER" ], [ "DESPERATE JOURNEY", "has_genre", "DRAMA" ], [ "DESPERATE JOURNEY", "starred_actors", "RAYMOND MASSEY" ], [ "POSSESSED", "has_genre", "DRAMA" ], [ "POSSESSED", "has_tags", "BD-R" ], [ "POSSESSED", "starred_actors", "RAYMOND MASSEY" ], [ "SCENT OF A WOMAN", "has_genre", "DRAMA" ], [ "SCENT OF A WOMAN", "has_tags", "DRAMA" ], [ "SCENT OF A WOMAN", "written_by", "RUGGERO MACCARI" ], [ "THE COLOR OF MONEY", "has_genre", "DRAMA" ], [ "THE COLOR OF MONEY", "starred_actors", "HELEN SHAVER" ], [ "THE FOUNTAINHEAD", "has_genre", "DRAMA" ], [ "THE FOUNTAINHEAD", "starred_actors", "RAYMOND MASSEY" ], [ "THE PRISONER OF ZENDA", "has_genre", "DRAMA" ], [ "THE PRISONER OF ZENDA", "has_tags", "BD-R" ], [ "THE PRISONER OF ZENDA", "starred_actors", "RAYMOND MASSEY" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 658, 2012 22920, CHUNGKING EXPRESS 26193, ELECTION 31783, ENGLISH 24535, GAME CHANGE 12060, GARY LOCKWOOD 26001, HONG KONG 978, INFERNAL AFFAIRS 39252, JOHNNIE TO 6543, MODEL SHOP 31252, V/H/S 10735, VENGEANCE src, edge_attr, dst 22920, has_tags, 26001 22920, in_language, 31783 26193, directed_by, 39252 26193, has_tags, 26001 26193, has_tags, 39252 24535, has_tags, 26193 24535, release_year, 658 978, has_tags, 26001 978, in_language, 31783 6543, in_language, 31783 6543, starred_actors, 12060 31252, release_year, 658 10735, directed_by, 39252 10735, has_tags, 26001 10735, has_tags, 39252 10735, has_tags, 10735 10735, in_language, 31783 Question: How are GARY LOCKWOOD, HONG KONG, and V/H/S related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "GARY LOCKWOOD", "HONG KONG", "V/H/S" ], "valid_edges": [ [ "CHUNGKING EXPRESS", "has_tags", "HONG KONG" ], [ "CHUNGKING EXPRESS", "in_language", "ENGLISH" ], [ "ELECTION", "directed_by", "JOHNNIE TO" ], [ "ELECTION", "has_tags", "HONG KONG" ], [ "ELECTION", "has_tags", "JOHNNIE TO" ], [ "GAME CHANGE", "has_tags", "ELECTION" ], [ "GAME CHANGE", "release_year", "2012" ], [ "INFERNAL AFFAIRS", "has_tags", "HONG KONG" ], [ "INFERNAL AFFAIRS", "in_language", "ENGLISH" ], [ "MODEL SHOP", "in_language", "ENGLISH" ], [ "MODEL SHOP", "starred_actors", "GARY LOCKWOOD" ], [ "V/H/S", "release_year", "2012" ], [ "VENGEANCE", "directed_by", "JOHNNIE TO" ], [ "VENGEANCE", "has_tags", "HONG KONG" ], [ "VENGEANCE", "has_tags", "JOHNNIE TO" ], [ "VENGEANCE", "has_tags", "VENGEANCE" ], [ "VENGEANCE", "in_language", "ENGLISH" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 26257, 1994 1097, 2003 12144, BAD BOYS 34486, EILA 6666, FINNISH 39394, SHOPPING 1124, TAKE CARE OF YOUR SCARF, TATIANA 38099, THE HOME OF DARK BUTTERFLIES 29084, TWENTYNINE PALMS 6867, YOUNG GODS src, edge_attr, dst 12144, in_language, 6666 12144, release_year, 1097 34486, in_language, 6666 34486, release_year, 1097 39394, release_year, 26257 1124, in_language, 6666 1124, release_year, 26257 38099, in_language, 6666 29084, release_year, 1097 6867, in_language, 6666 6867, release_year, 1097 Question: How are SHOPPING, THE HOME OF DARK BUTTERFLIES, and TWENTYNINE PALMS related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "SHOPPING", "THE HOME OF DARK BUTTERFLIES", "TWENTYNINE PALMS" ], "valid_edges": [ [ "BAD BOYS", "in_language", "FINNISH" ], [ "BAD BOYS", "release_year", "2003" ], [ "EILA", "in_language", "FINNISH" ], [ "EILA", "release_year", "2003" ], [ "SHOPPING", "release_year", "1994" ], [ "TAKE CARE OF YOUR SCARF, TATIANA", "in_language", "FINNISH" ], [ "TAKE CARE OF YOUR SCARF, TATIANA", "release_year", "1994" ], [ "THE HOME OF DARK BUTTERFLIES", "in_language", "FINNISH" ], [ "TWENTYNINE PALMS", "release_year", "2003" ], [ "YOUNG GODS", "in_language", "FINNISH" ], [ "YOUNG GODS", "release_year", "2003" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 14259, 1997 35288, ALL OR NOTHING 37672, ANOTHER YEAR 16654, BRITISH 33121, CAREER GIRLS 31728, CHARLY 30463, COMEDY 30946, DANIEL BENZALI 36212, DRAMA 36942, HAPPY-GO-LUCKY 10555, HIGH HOPES 24353, MIKE LEIGH 9635, MR. TURNER 33425, MURDER AT 1600 3159, NAKED 4629, RUTH SHEEN 332, TOPSY-TURVY 22949, VERA DRAKE src, edge_attr, dst 35288, directed_by, 24353 35288, has_genre, 36212 35288, has_tags, 24353 35288, written_by, 24353 37672, directed_by, 24353 37672, has_genre, 36212 37672, has_tags, 16654 37672, has_tags, 24353 37672, starred_actors, 4629 37672, written_by, 24353 33121, directed_by, 24353 33121, has_genre, 36212 33121, has_tags, 24353 33121, release_year, 14259 33121, written_by, 24353 31728, has_genre, 36212 36942, directed_by, 24353 36942, has_genre, 30463 36942, has_genre, 36212 36942, has_tags, 16654 36942, has_tags, 36212 36942, has_tags, 24353 36942, written_by, 24353 10555, directed_by, 24353 10555, has_genre, 30463 10555, has_genre, 36212 10555, has_tags, 24353 10555, starred_actors, 4629 10555, written_by, 24353 9635, directed_by, 24353 9635, has_genre, 36212 9635, has_tags, 24353 9635, written_by, 24353 33425, release_year, 14259 33425, starred_actors, 30946 3159, directed_by, 24353 3159, has_genre, 36212 3159, has_tags, 16654 3159, has_tags, 24353 3159, written_by, 24353 332, directed_by, 24353 332, has_genre, 36212 332, has_tags, 24353 332, written_by, 24353 22949, directed_by, 24353 22949, has_genre, 36212 22949, has_tags, 24353 22949, written_by, 24353 Question: In what context are CHARLY, DANIEL BENZALI, and MIKE LEIGH connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "CHARLY", "DANIEL BENZALI", "MIKE LEIGH" ], "valid_edges": [ [ "ALL OR NOTHING", "directed_by", "MIKE LEIGH" ], [ "ALL OR NOTHING", "has_genre", "DRAMA" ], [ "ALL OR NOTHING", "has_tags", "MIKE LEIGH" ], [ "ALL OR NOTHING", "written_by", "MIKE LEIGH" ], [ "ANOTHER YEAR", "directed_by", "MIKE LEIGH" ], [ "ANOTHER YEAR", "has_genre", "DRAMA" ], [ "ANOTHER YEAR", "has_tags", "BRITISH" ], [ "ANOTHER YEAR", "has_tags", "MIKE LEIGH" ], [ "ANOTHER YEAR", "starred_actors", "RUTH SHEEN" ], [ "ANOTHER YEAR", "written_by", "MIKE LEIGH" ], [ "CAREER GIRLS", "directed_by", "MIKE LEIGH" ], [ "CAREER GIRLS", "has_genre", "DRAMA" ], [ "CAREER GIRLS", "has_tags", "MIKE LEIGH" ], [ "CAREER GIRLS", "release_year", "1997" ], [ "CAREER GIRLS", "written_by", "MIKE LEIGH" ], [ "CHARLY", "has_genre", "DRAMA" ], [ "HAPPY-GO-LUCKY", "directed_by", "MIKE LEIGH" ], [ "HAPPY-GO-LUCKY", "has_genre", "COMEDY" ], [ "HAPPY-GO-LUCKY", "has_genre", "DRAMA" ], [ "HAPPY-GO-LUCKY", "has_tags", "BRITISH" ], [ "HAPPY-GO-LUCKY", "has_tags", "DRAMA" ], [ "HAPPY-GO-LUCKY", "has_tags", "MIKE LEIGH" ], [ "HAPPY-GO-LUCKY", "written_by", "MIKE LEIGH" ], [ "HIGH HOPES", "directed_by", "MIKE LEIGH" ], [ "HIGH HOPES", "has_genre", "COMEDY" ], [ "HIGH HOPES", "has_genre", "DRAMA" ], [ "HIGH HOPES", "has_tags", "MIKE LEIGH" ], [ "HIGH HOPES", "starred_actors", "RUTH SHEEN" ], [ "HIGH HOPES", "written_by", "MIKE LEIGH" ], [ "MR. TURNER", "directed_by", "MIKE LEIGH" ], [ "MR. TURNER", "has_genre", "DRAMA" ], [ "MR. TURNER", "has_tags", "MIKE LEIGH" ], [ "MR. TURNER", "written_by", "MIKE LEIGH" ], [ "MURDER AT 1600", "release_year", "1997" ], [ "MURDER AT 1600", "starred_actors", "DANIEL BENZALI" ], [ "NAKED", "directed_by", "MIKE LEIGH" ], [ "NAKED", "has_genre", "DRAMA" ], [ "NAKED", "has_tags", "BRITISH" ], [ "NAKED", "has_tags", "MIKE LEIGH" ], [ "NAKED", "written_by", "MIKE LEIGH" ], [ "TOPSY-TURVY", "directed_by", "MIKE LEIGH" ], [ "TOPSY-TURVY", "has_genre", "DRAMA" ], [ "TOPSY-TURVY", "has_tags", "MIKE LEIGH" ], [ "TOPSY-TURVY", "written_by", "MIKE LEIGH" ], [ "VERA DRAKE", "directed_by", "MIKE LEIGH" ], [ "VERA DRAKE", "has_genre", "DRAMA" ], [ "VERA DRAKE", "has_tags", "MIKE LEIGH" ], [ "VERA DRAKE", "written_by", "MIKE LEIGH" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 26762, 2008 24116, COLLEGE 14144, CRAZY LOVE 18087, DAN KLORES 12841, DOCUMENTARY 17682, EXAMINED LIFE 18916, JAMES W. HORNE 4238, JUDITH BUTLER src, edge_attr, dst 24116, directed_by, 18916 24116, has_tags, 18916 24116, release_year, 26762 14144, directed_by, 18087 14144, has_genre, 12841 14144, written_by, 18087 17682, has_genre, 12841 17682, release_year, 26762 17682, starred_actors, 4238 Question: For what reason are DAN KLORES, JAMES W. HORNE, and JUDITH BUTLER associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "DAN KLORES", "JAMES W. HORNE", "JUDITH BUTLER" ], "valid_edges": [ [ "COLLEGE", "directed_by", "JAMES W. HORNE" ], [ "COLLEGE", "has_tags", "JAMES W. HORNE" ], [ "COLLEGE", "release_year", "2008" ], [ "CRAZY LOVE", "directed_by", "DAN KLORES" ], [ "CRAZY LOVE", "has_genre", "DOCUMENTARY" ], [ "CRAZY LOVE", "written_by", "DAN KLORES" ], [ "EXAMINED LIFE", "has_genre", "DOCUMENTARY" ], [ "EXAMINED LIFE", "release_year", "2008" ], [ "EXAMINED LIFE", "starred_actors", "JUDITH BUTLER" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 3702, 1995 8486, 1999 22189, BEFORE SUNRISE 6230, BYE-BYE 30463, COMEDY 36212, DRAMA 20071, FLIRT 21828, HUMAN TRAFFIC 11499, JOHN SIMM 33519, LA HAINE 14601, LES MISÉRABLES 36083, MIRANDA 35637, MITCH MULLANY 22915, MONDO 8379, ROMANCE 4689, SABRINA 8605, THE BREAKS src, edge_attr, dst 22189, has_genre, 36212 22189, release_year, 3702 6230, has_genre, 36212 6230, release_year, 3702 20071, has_genre, 36212 20071, release_year, 3702 21828, release_year, 8486 21828, starred_actors, 11499 33519, has_genre, 36212 33519, release_year, 3702 14601, has_genre, 36212 14601, release_year, 3702 36083, has_genre, 30463 36083, has_genre, 8379 36083, starred_actors, 11499 22915, has_genre, 36212 22915, release_year, 3702 8379, has_genre, 36212 4689, has_genre, 36212 4689, has_tags, 36212 4689, release_year, 3702 8605, has_genre, 30463 8605, release_year, 8486 8605, written_by, 35637 Question: How are FLIRT, JOHN SIMM, and MITCH MULLANY related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "FLIRT", "JOHN SIMM", "MITCH MULLANY" ], "valid_edges": [ [ "BEFORE SUNRISE", "has_genre", "DRAMA" ], [ "BEFORE SUNRISE", "release_year", "1995" ], [ "BYE-BYE", "has_genre", "DRAMA" ], [ "BYE-BYE", "release_year", "1995" ], [ "FLIRT", "has_genre", "DRAMA" ], [ "FLIRT", "release_year", "1995" ], [ "HUMAN TRAFFIC", "release_year", "1999" ], [ "HUMAN TRAFFIC", "starred_actors", "JOHN SIMM" ], [ "LA HAINE", "has_genre", "DRAMA" ], [ "LA HAINE", "release_year", "1995" ], [ "LES MISÉRABLES", "has_genre", "DRAMA" ], [ "LES MISÉRABLES", "release_year", "1995" ], [ "MIRANDA", "has_genre", "COMEDY" ], [ "MIRANDA", "has_genre", "ROMANCE" ], [ "MIRANDA", "starred_actors", "JOHN SIMM" ], [ "MONDO", "has_genre", "DRAMA" ], [ "MONDO", "release_year", "1995" ], [ "ROMANCE", "has_genre", "DRAMA" ], [ "SABRINA", "has_genre", "DRAMA" ], [ "SABRINA", "has_tags", "DRAMA" ], [ "SABRINA", "release_year", "1995" ], [ "THE BREAKS", "has_genre", "COMEDY" ], [ "THE BREAKS", "release_year", "1999" ], [ "THE BREAKS", "written_by", "MITCH MULLANY" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 26762, 2008 30721, A BEAUTIFUL MIND 9197, AKIVA GOLDSMAN 37752, ASIMOV 5496, BOOK 22958, FAMOUS 11339, FANTASTIC VOYAGE 11565, GOOD 20625, I, ROBOT 29267, ISAAC ASIMOV 14017, JURASSIC PARK 995, MIDNIGHT IN THE GARDEN OF GOOD AND EVIL 31134, PARIS 19982, PAUL BETTANY 26434, RON HOWARD 32533, SCIENCE FICTION 1059, THE DA VINCI CODE 33995, THE GOOD EARTH 16999, ZACK AND MIRI MAKE A PORNO src, edge_attr, dst 30721, directed_by, 26434 30721, has_tags, 5496 30721, has_tags, 19982 30721, has_tags, 26434 30721, written_by, 9197 11339, has_tags, 37752 11339, has_tags, 29267 11339, has_tags, 32533 11565, release_year, 26762 20625, has_tags, 37752 20625, has_tags, 29267 20625, written_by, 9197 20625, written_by, 29267 14017, has_tags, 5496 14017, has_tags, 32533 995, has_imdb_rating, 11565 995, has_imdb_votes, 22958 995, has_tags, 5496 31134, release_year, 26762 1059, directed_by, 26434 1059, has_imdb_votes, 22958 1059, has_tags, 5496 1059, has_tags, 31134 1059, has_tags, 19982 1059, has_tags, 26434 1059, written_by, 9197 33995, has_imdb_rating, 11565 33995, has_tags, 5496 16999, release_year, 26762 Question: In what context are ASIMOV, BOOK, and ZACK AND MIRI MAKE A PORNO connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ASIMOV", "BOOK", "ZACK AND MIRI MAKE A PORNO" ], "valid_edges": [ [ "A BEAUTIFUL MIND", "directed_by", "RON HOWARD" ], [ "A BEAUTIFUL MIND", "has_tags", "BOOK" ], [ "A BEAUTIFUL MIND", "has_tags", "PAUL BETTANY" ], [ "A BEAUTIFUL MIND", "has_tags", "RON HOWARD" ], [ "A BEAUTIFUL MIND", "written_by", "AKIVA GOLDSMAN" ], [ "FANTASTIC VOYAGE", "has_tags", "ASIMOV" ], [ "FANTASTIC VOYAGE", "has_tags", "ISAAC ASIMOV" ], [ "FANTASTIC VOYAGE", "has_tags", "SCIENCE FICTION" ], [ "GOOD", "release_year", "2008" ], [ "I, ROBOT", "has_tags", "ASIMOV" ], [ "I, ROBOT", "has_tags", "ISAAC ASIMOV" ], [ "I, ROBOT", "written_by", "AKIVA GOLDSMAN" ], [ "I, ROBOT", "written_by", "ISAAC ASIMOV" ], [ "JURASSIC PARK", "has_tags", "BOOK" ], [ "JURASSIC PARK", "has_tags", "SCIENCE FICTION" ], [ "MIDNIGHT IN THE GARDEN OF GOOD AND EVIL", "has_imdb_rating", "GOOD" ], [ "MIDNIGHT IN THE GARDEN OF GOOD AND EVIL", "has_imdb_votes", "FAMOUS" ], [ "MIDNIGHT IN THE GARDEN OF GOOD AND EVIL", "has_tags", "BOOK" ], [ "PARIS", "release_year", "2008" ], [ "THE DA VINCI CODE", "directed_by", "RON HOWARD" ], [ "THE DA VINCI CODE", "has_imdb_votes", "FAMOUS" ], [ "THE DA VINCI CODE", "has_tags", "BOOK" ], [ "THE DA VINCI CODE", "has_tags", "PARIS" ], [ "THE DA VINCI CODE", "has_tags", "PAUL BETTANY" ], [ "THE DA VINCI CODE", "has_tags", "RON HOWARD" ], [ "THE DA VINCI CODE", "written_by", "AKIVA GOLDSMAN" ], [ "THE GOOD EARTH", "has_imdb_rating", "GOOD" ], [ "THE GOOD EARTH", "has_tags", "BOOK" ], [ "ZACK AND MIRI MAKE A PORNO", "release_year", "2008" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 37967, 13 SINS 26762, 2008 9377, 2014 26212, 3 DAYS TO KILL 29141, A MOST WANTED MAN 14987, A SECOND CHANCE 38646, ACHILLES AND THE TORTOISE 23806, ADDICTED 36359, AFTERWARDS 36494, ANYTHING FOR HER 8402, BANGKOK DANGEROUS 22475, BOOT CAMP 5376, CAPE NO. 7 38311, CHAOS 24400, DARK WATER 5148, DEATH RACE 24410, DEMONLOVER 2710, DEPARTURES 2827, DETROIT METAL CITY 9456, DOOMSDAY 21692, EAGLE EYE 25595, EDEN LAKE 28064, FEAR ME NOT 16516, FIFTY DEAD MEN WALKING 17653, FINE, TOTALLY FINE 26691, FREEZER 3553, GODZILLA 9642, GONE GIRL 13851, GOOD PEOPLE 10147, HOUSE 35436, HUSH 10121, IP MAN 36874, JAPANESE 33200, JESSABELLE 38731, KIKI'S DELIVERY SERVICE 26839, KILLERS 35352, KILLSHOT 7415, KISS OF DEATH 35280, LAKEVIEW TERRACE 19523, LEFT BEHIND 9396, LINEWATCH 20189, LOFT 22804, LUST, CAUTION 30350, NEXT 1128, NICOLAS CAGE 22868, NIGHTCRAWLER 29893, NO GOOD DEED 31027, NOT SAFE FOR WORK 36326, ONE MISSED CALL 12482, OPEN WINDOWS 36532, OVER YOUR DEAD BODY 21178, PATHOLOGY 20144, PONYO 12428, RAGE 29252, REASONABLE DOUBT 16595, RED 19630, RESTRAINT 37097, SABOTAGE 10303, SEEKING JUSTICE 12787, STAND BY ME DORAEMON 26269, STEREO 14619, STONEHEARST ASYLUM 32584, SUSPECT X 30003, TAKEN 23782, TAKEN 3 19565, THE 39 STEPS 20129, THE ALPHABET KILLER 23821, THE BAG MAN 30049, THE CAPTIVE 6633, THE DEAD OUTSIDE 33457, THE DEAL 10912, THE EQUALIZER 9522, THE ESCAPIST 34975, THE GUEST 9187, THE HAPPENING 8194, THE HEADLESS WOMAN 10421, THE HORSEMAN 6126, THE INCITE MILL 4223, THE INTERVIEW 13613, THE LAZARUS PROJECT 30751, THE LOFT 27657, THE MACHINE GIRL 30336, THE MIDNIGHT MEAT TRAIN 39061, THE OXFORD MURDERS 27529, THE POKER CLUB 30014, THE RAID 2 8660, THE RECKONING 20527, THE SCRIBBLER 28095, THE SIGNAL 10017, THE SKY CRAWLERS 8659, THE SNOW WHITE MURDER CASE 1330, THE SQUARE 23525, THE TWO FACES OF JANUARY 17568, THE VANISHING 21593, THE VOICES 22751, THE WICKER MAN 36616, THE WORLD OF KANAKO 24811, THRILLER 37444, TIM KRABBÉ 37920, TOKYO! 34541, TORTURED 8334, TRANSSIBERIAN 22110, TRESPASS 18451, UNTRACEABLE 33011, VALKYRIE 4375, WHEN MARNIE WAS THERE 20222, WHILE SHE WAS OUT 6262, WHITE BIRD IN A BLIZZARD 28356, WICKED BLOOD 15405, ZANDALEE src, edge_attr, dst 37967, has_genre, 24811 37967, release_year, 9377 26212, has_genre, 24811 26212, release_year, 9377 29141, has_genre, 24811 29141, has_tags, 24811 29141, release_year, 9377 14987, has_genre, 24811 14987, release_year, 9377 38646, in_language, 36874 38646, release_year, 26762 23806, has_genre, 24811 23806, release_year, 9377 36359, has_genre, 24811 36359, release_year, 26762 36494, has_genre, 24811 36494, release_year, 26762 8402, has_genre, 24811 8402, has_tags, 1128 8402, release_year, 26762 8402, starred_actors, 1128 22475, has_genre, 24811 22475, release_year, 26762 5376, in_language, 36874 5376, release_year, 26762 38311, has_genre, 24811 38311, in_language, 36874 24400, has_genre, 24811 24400, in_language, 36874 5148, has_genre, 24811 5148, release_year, 26762 24410, has_genre, 24811 24410, in_language, 36874 2710, in_language, 36874 2710, release_year, 26762 2827, in_language, 36874 2827, release_year, 26762 9456, has_genre, 24811 9456, release_year, 26762 21692, has_genre, 24811 21692, release_year, 26762 25595, has_genre, 24811 25595, release_year, 26762 28064, has_genre, 24811 28064, release_year, 26762 16516, has_genre, 24811 16516, release_year, 26762 17653, in_language, 36874 17653, release_year, 26762 26691, has_genre, 24811 26691, release_year, 9377 3553, in_language, 36874 3553, release_year, 9377 9642, has_genre, 24811 9642, has_tags, 9377 9642, has_tags, 24811 9642, release_year, 9377 13851, has_genre, 24811 13851, release_year, 9377 10147, has_tags, 36874 10147, in_language, 36874 10147, release_year, 26762 35436, has_genre, 24811 35436, release_year, 26762 10121, in_language, 36874 10121, release_year, 26762 33200, has_genre, 24811 33200, release_year, 9377 38731, in_language, 36874 38731, release_year, 9377 26839, in_language, 36874 26839, release_year, 9377 35352, has_genre, 24811 35352, release_year, 26762 7415, has_genre, 24811 7415, starred_actors, 1128 35280, has_genre, 24811 35280, release_year, 26762 19523, has_genre, 24811 19523, has_tags, 1128 19523, release_year, 9377 19523, starred_actors, 1128 9396, has_genre, 24811 9396, release_year, 26762 20189, in_language, 36874 20189, release_year, 26762 22804, has_genre, 24811 22804, in_language, 36874 30350, has_genre, 24811 30350, has_tags, 1128 30350, starred_actors, 1128 22868, has_genre, 24811 22868, has_tags, 24811 22868, release_year, 9377 29893, has_genre, 24811 29893, release_year, 9377 31027, has_genre, 24811 31027, release_year, 9377 36326, in_language, 36874 36326, release_year, 26762 12482, has_genre, 24811 12482, release_year, 9377 36532, in_language, 36874 36532, release_year, 9377 21178, has_genre, 24811 21178, release_year, 26762 20144, in_language, 36874 20144, release_year, 26762 12428, has_genre, 24811 12428, release_year, 9377 12428, starred_actors, 1128 29252, has_genre, 24811 29252, release_year, 9377 16595, has_genre, 24811 16595, release_year, 26762 19630, has_genre, 24811 19630, release_year, 26762 37097, has_genre, 24811 37097, release_year, 9377 10303, has_genre, 24811 10303, has_tags, 1128 10303, starred_actors, 1128 12787, in_language, 36874 12787, release_year, 9377 26269, has_genre, 24811 26269, release_year, 9377 14619, has_genre, 24811 14619, release_year, 9377 32584, in_language, 36874 32584, release_year, 26762 30003, has_genre, 24811 30003, has_tags, 24811 30003, release_year, 26762 23782, has_genre, 24811 23782, release_year, 9377 19565, has_genre, 24811 19565, has_tags, 24811 19565, release_year, 26762 20129, has_genre, 24811 20129, release_year, 26762 23821, has_genre, 24811 23821, release_year, 9377 30049, has_genre, 24811 30049, release_year, 9377 6633, has_genre, 24811 6633, release_year, 26762 33457, has_genre, 24811 33457, release_year, 26762 10912, has_genre, 24811 10912, release_year, 9377 9522, has_genre, 24811 9522, release_year, 26762 34975, has_genre, 24811 34975, release_year, 9377 9187, has_genre, 24811 9187, release_year, 26762 8194, has_genre, 24811 8194, release_year, 26762 10421, has_genre, 24811 10421, release_year, 26762 6126, has_genre, 24811 6126, in_language, 36874 4223, has_genre, 24811 4223, release_year, 9377 13613, has_genre, 24811 13613, release_year, 26762 30751, has_genre, 24811 30751, release_year, 9377 27657, in_language, 36874 27657, release_year, 26762 30336, has_genre, 24811 30336, release_year, 26762 39061, has_genre, 24811 39061, release_year, 26762 27529, has_genre, 24811 27529, release_year, 26762 30014, in_language, 36874 30014, release_year, 9377 8660, has_genre, 24811 8660, release_year, 9377 20527, has_genre, 24811 20527, release_year, 9377 28095, has_genre, 24811 28095, release_year, 9377 10017, has_tags, 36874 10017, in_language, 36874 10017, release_year, 26762 8659, in_language, 36874 8659, release_year, 9377 1330, has_genre, 24811 1330, release_year, 26762 23525, has_genre, 24811 23525, release_year, 9377 17568, has_genre, 24811 17568, written_by, 37444 21593, has_genre, 24811 21593, release_year, 9377 22751, has_genre, 24811 22751, has_tags, 1128 22751, starred_actors, 1128 36616, in_language, 36874 36616, release_year, 9377 37920, in_language, 36874 37920, release_year, 26762 34541, has_genre, 24811 34541, release_year, 26762 8334, has_tags, 24811 8334, release_year, 26762 22110, has_genre, 24811 22110, has_tags, 1128 22110, starred_actors, 1128 18451, has_genre, 24811 18451, release_year, 26762 33011, has_genre, 24811 33011, release_year, 26762 4375, in_language, 36874 4375, release_year, 9377 20222, has_genre, 24811 20222, release_year, 26762 6262, has_genre, 24811 6262, release_year, 9377 28356, has_genre, 24811 28356, release_year, 9377 15405, has_genre, 24811 15405, starred_actors, 1128 Question: For what reason are LEFT BEHIND, THE MACHINE GIRL, and TIM KRABBÉ associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "LEFT BEHIND", "THE MACHINE GIRL", "TIM KRABBÉ" ], "valid_edges": [ [ "13 SINS", "has_genre", "THRILLER" ], [ "13 SINS", "release_year", "2014" ], [ "3 DAYS TO KILL", "has_genre", "THRILLER" ], [ "3 DAYS TO KILL", "release_year", "2014" ], [ "A MOST WANTED MAN", "has_genre", "THRILLER" ], [ "A MOST WANTED MAN", "has_tags", "THRILLER" ], [ "A MOST WANTED MAN", "release_year", "2014" ], [ "A SECOND CHANCE", "has_genre", "THRILLER" ], [ "A SECOND CHANCE", "release_year", "2014" ], [ "ACHILLES AND THE TORTOISE", "in_language", "JAPANESE" ], [ "ACHILLES AND THE TORTOISE", "release_year", "2008" ], [ "ADDICTED", "has_genre", "THRILLER" ], [ "ADDICTED", "release_year", "2014" ], [ "AFTERWARDS", "has_genre", "THRILLER" ], [ "AFTERWARDS", "release_year", "2008" ], [ "ANYTHING FOR HER", "has_genre", "THRILLER" ], [ "ANYTHING FOR HER", "release_year", "2008" ], [ "BANGKOK DANGEROUS", "has_genre", "THRILLER" ], [ "BANGKOK DANGEROUS", "has_tags", "NICOLAS CAGE" ], [ "BANGKOK DANGEROUS", "release_year", "2008" ], [ "BANGKOK DANGEROUS", "starred_actors", "NICOLAS CAGE" ], [ "BOOT CAMP", "has_genre", "THRILLER" ], [ "BOOT CAMP", "release_year", "2008" ], [ "CAPE NO. 7", "in_language", "JAPANESE" ], [ "CAPE NO. 7", "release_year", "2008" ], [ "CHAOS", "has_genre", "THRILLER" ], [ "CHAOS", "in_language", "JAPANESE" ], [ "DARK WATER", "has_genre", "THRILLER" ], [ "DARK WATER", "in_language", "JAPANESE" ], [ "DEATH RACE", "has_genre", "THRILLER" ], [ "DEATH RACE", "release_year", "2008" ], [ "DEMONLOVER", "has_genre", "THRILLER" ], [ "DEMONLOVER", "in_language", "JAPANESE" ], [ "DEPARTURES", "in_language", "JAPANESE" ], [ "DEPARTURES", "release_year", "2008" ], [ "DETROIT METAL CITY", "in_language", "JAPANESE" ], [ "DETROIT METAL CITY", "release_year", "2008" ], [ "DOOMSDAY", "has_genre", "THRILLER" ], [ "DOOMSDAY", "release_year", "2008" ], [ "EAGLE EYE", "has_genre", "THRILLER" ], [ "EAGLE EYE", "release_year", "2008" ], [ "EDEN LAKE", "has_genre", "THRILLER" ], [ "EDEN LAKE", "release_year", "2008" ], [ "FEAR ME NOT", "has_genre", "THRILLER" ], [ "FEAR ME NOT", "release_year", "2008" ], [ "FIFTY DEAD MEN WALKING", "has_genre", "THRILLER" ], [ "FIFTY DEAD MEN WALKING", "release_year", "2008" ], [ "FINE, TOTALLY FINE", "in_language", "JAPANESE" ], [ "FINE, TOTALLY FINE", "release_year", "2008" ], [ "FREEZER", "has_genre", "THRILLER" ], [ "FREEZER", "release_year", "2014" ], [ "GODZILLA", "in_language", "JAPANESE" ], [ "GODZILLA", "release_year", "2014" ], [ "GONE GIRL", "has_genre", "THRILLER" ], [ "GONE GIRL", "has_tags", "2014" ], [ "GONE GIRL", "has_tags", "THRILLER" ], [ "GONE GIRL", "release_year", "2014" ], [ "GOOD PEOPLE", "has_genre", "THRILLER" ], [ "GOOD PEOPLE", "release_year", "2014" ], [ "HOUSE", "has_tags", "JAPANESE" ], [ "HOUSE", "in_language", "JAPANESE" ], [ "HOUSE", "release_year", "2008" ], [ "HUSH", "has_genre", "THRILLER" ], [ "HUSH", "release_year", "2008" ], [ "IP MAN", "in_language", "JAPANESE" ], [ "IP MAN", "release_year", "2008" ], [ "JESSABELLE", "has_genre", "THRILLER" ], [ "JESSABELLE", "release_year", "2014" ], [ "KIKI'S DELIVERY SERVICE", "in_language", "JAPANESE" ], [ "KIKI'S DELIVERY SERVICE", "release_year", "2014" ], [ "KILLERS", "in_language", "JAPANESE" ], [ "KILLERS", "release_year", "2014" ], [ "KILLSHOT", "has_genre", "THRILLER" ], [ "KILLSHOT", "release_year", "2008" ], [ "KISS OF DEATH", "has_genre", "THRILLER" ], [ "KISS OF DEATH", "starred_actors", "NICOLAS CAGE" ], [ "LAKEVIEW TERRACE", "has_genre", "THRILLER" ], [ "LAKEVIEW TERRACE", "release_year", "2008" ], [ "LEFT BEHIND", "has_genre", "THRILLER" ], [ "LEFT BEHIND", "has_tags", "NICOLAS CAGE" ], [ "LEFT BEHIND", "release_year", "2014" ], [ "LEFT BEHIND", "starred_actors", "NICOLAS CAGE" ], [ "LINEWATCH", "has_genre", "THRILLER" ], [ "LINEWATCH", "release_year", "2008" ], [ "LOFT", "in_language", "JAPANESE" ], [ "LOFT", "release_year", "2008" ], [ "LUST, CAUTION", "has_genre", "THRILLER" ], [ "LUST, CAUTION", "in_language", "JAPANESE" ], [ "NEXT", "has_genre", "THRILLER" ], [ "NEXT", "has_tags", "NICOLAS CAGE" ], [ "NEXT", "starred_actors", "NICOLAS CAGE" ], [ "NIGHTCRAWLER", "has_genre", "THRILLER" ], [ "NIGHTCRAWLER", "has_tags", "THRILLER" ], [ "NIGHTCRAWLER", "release_year", "2014" ], [ "NO GOOD DEED", "has_genre", "THRILLER" ], [ "NO GOOD DEED", "release_year", "2014" ], [ "NOT SAFE FOR WORK", "has_genre", "THRILLER" ], [ "NOT SAFE FOR WORK", "release_year", "2014" ], [ "ONE MISSED CALL", "in_language", "JAPANESE" ], [ "ONE MISSED CALL", "release_year", "2008" ], [ "OPEN WINDOWS", "has_genre", "THRILLER" ], [ "OPEN WINDOWS", "release_year", "2014" ], [ "OVER YOUR DEAD BODY", "in_language", "JAPANESE" ], [ "OVER YOUR DEAD BODY", "release_year", "2014" ], [ "PATHOLOGY", "has_genre", "THRILLER" ], [ "PATHOLOGY", "release_year", "2008" ], [ "PONYO", "in_language", "JAPANESE" ], [ "PONYO", "release_year", "2008" ], [ "RAGE", "has_genre", "THRILLER" ], [ "RAGE", "release_year", "2014" ], [ "RAGE", "starred_actors", "NICOLAS CAGE" ], [ "REASONABLE DOUBT", "has_genre", "THRILLER" ], [ "REASONABLE DOUBT", "release_year", "2014" ], [ "RED", "has_genre", "THRILLER" ], [ "RED", "release_year", "2008" ], [ "RESTRAINT", "has_genre", "THRILLER" ], [ "RESTRAINT", "release_year", "2008" ], [ "SABOTAGE", "has_genre", "THRILLER" ], [ "SABOTAGE", "release_year", "2014" ], [ "SEEKING JUSTICE", "has_genre", "THRILLER" ], [ "SEEKING JUSTICE", "has_tags", "NICOLAS CAGE" ], [ "SEEKING JUSTICE", "starred_actors", "NICOLAS CAGE" ], [ "STAND BY ME DORAEMON", "in_language", "JAPANESE" ], [ "STAND BY ME DORAEMON", "release_year", "2014" ], [ "STEREO", "has_genre", "THRILLER" ], [ "STEREO", "release_year", "2014" ], [ "STONEHEARST ASYLUM", "has_genre", "THRILLER" ], [ "STONEHEARST ASYLUM", "release_year", "2014" ], [ "SUSPECT X", "in_language", "JAPANESE" ], [ "SUSPECT X", "release_year", "2008" ], [ "TAKEN", "has_genre", "THRILLER" ], [ "TAKEN", "has_tags", "THRILLER" ], [ "TAKEN", "release_year", "2008" ], [ "TAKEN 3", "has_genre", "THRILLER" ], [ "TAKEN 3", "release_year", "2014" ], [ "THE 39 STEPS", "has_genre", "THRILLER" ], [ "THE 39 STEPS", "has_tags", "THRILLER" ], [ "THE 39 STEPS", "release_year", "2008" ], [ "THE ALPHABET KILLER", "has_genre", "THRILLER" ], [ "THE ALPHABET KILLER", "release_year", "2008" ], [ "THE BAG MAN", "has_genre", "THRILLER" ], [ "THE BAG MAN", "release_year", "2014" ], [ "THE CAPTIVE", "has_genre", "THRILLER" ], [ "THE CAPTIVE", "release_year", "2014" ], [ "THE DEAD OUTSIDE", "has_genre", "THRILLER" ], [ "THE DEAD OUTSIDE", "release_year", "2008" ], [ "THE DEAL", "has_genre", "THRILLER" ], [ "THE DEAL", "release_year", "2008" ], [ "THE EQUALIZER", "has_genre", "THRILLER" ], [ "THE EQUALIZER", "release_year", "2014" ], [ "THE ESCAPIST", "has_genre", "THRILLER" ], [ "THE ESCAPIST", "release_year", "2008" ], [ "THE GUEST", "has_genre", "THRILLER" ], [ "THE GUEST", "release_year", "2014" ], [ "THE HAPPENING", "has_genre", "THRILLER" ], [ "THE HAPPENING", "release_year", "2008" ], [ "THE HEADLESS WOMAN", "has_genre", "THRILLER" ], [ "THE HEADLESS WOMAN", "release_year", "2008" ], [ "THE HORSEMAN", "has_genre", "THRILLER" ], [ "THE HORSEMAN", "release_year", "2008" ], [ "THE INCITE MILL", "has_genre", "THRILLER" ], [ "THE INCITE MILL", "in_language", "JAPANESE" ], [ "THE INTERVIEW", "has_genre", "THRILLER" ], [ "THE INTERVIEW", "release_year", "2014" ], [ "THE LAZARUS PROJECT", "has_genre", "THRILLER" ], [ "THE LAZARUS PROJECT", "release_year", "2008" ], [ "THE LOFT", "has_genre", "THRILLER" ], [ "THE LOFT", "release_year", "2014" ], [ "THE MACHINE GIRL", "in_language", "JAPANESE" ], [ "THE MACHINE GIRL", "release_year", "2008" ], [ "THE MIDNIGHT MEAT TRAIN", "has_genre", "THRILLER" ], [ "THE MIDNIGHT MEAT TRAIN", "release_year", "2008" ], [ "THE OXFORD MURDERS", "has_genre", "THRILLER" ], [ "THE OXFORD MURDERS", "release_year", "2008" ], [ "THE POKER CLUB", "has_genre", "THRILLER" ], [ "THE POKER CLUB", "release_year", "2008" ], [ "THE RAID 2", "in_language", "JAPANESE" ], [ "THE RAID 2", "release_year", "2014" ], [ "THE RECKONING", "has_genre", "THRILLER" ], [ "THE RECKONING", "release_year", "2014" ], [ "THE SCRIBBLER", "has_genre", "THRILLER" ], [ "THE SCRIBBLER", "release_year", "2014" ], [ "THE SIGNAL", "has_genre", "THRILLER" ], [ "THE SIGNAL", "release_year", "2014" ], [ "THE SKY CRAWLERS", "has_tags", "JAPANESE" ], [ "THE SKY CRAWLERS", "in_language", "JAPANESE" ], [ "THE SKY CRAWLERS", "release_year", "2008" ], [ "THE SNOW WHITE MURDER CASE", "in_language", "JAPANESE" ], [ "THE SNOW WHITE MURDER CASE", "release_year", "2014" ], [ "THE SQUARE", "has_genre", "THRILLER" ], [ "THE SQUARE", "release_year", "2008" ], [ "THE TWO FACES OF JANUARY", "has_genre", "THRILLER" ], [ "THE TWO FACES OF JANUARY", "release_year", "2014" ], [ "THE VANISHING", "has_genre", "THRILLER" ], [ "THE VANISHING", "written_by", "TIM KRABBÉ" ], [ "THE VOICES", "has_genre", "THRILLER" ], [ "THE VOICES", "release_year", "2014" ], [ "THE WICKER MAN", "has_genre", "THRILLER" ], [ "THE WICKER MAN", "has_tags", "NICOLAS CAGE" ], [ "THE WICKER MAN", "starred_actors", "NICOLAS CAGE" ], [ "THE WORLD OF KANAKO", "in_language", "JAPANESE" ], [ "THE WORLD OF KANAKO", "release_year", "2014" ], [ "TOKYO!", "in_language", "JAPANESE" ], [ "TOKYO!", "release_year", "2008" ], [ "TORTURED", "has_genre", "THRILLER" ], [ "TORTURED", "release_year", "2008" ], [ "TRANSSIBERIAN", "has_tags", "THRILLER" ], [ "TRANSSIBERIAN", "release_year", "2008" ], [ "TRESPASS", "has_genre", "THRILLER" ], [ "TRESPASS", "has_tags", "NICOLAS CAGE" ], [ "TRESPASS", "starred_actors", "NICOLAS CAGE" ], [ "UNTRACEABLE", "has_genre", "THRILLER" ], [ "UNTRACEABLE", "release_year", "2008" ], [ "VALKYRIE", "has_genre", "THRILLER" ], [ "VALKYRIE", "release_year", "2008" ], [ "WHEN MARNIE WAS THERE", "in_language", "JAPANESE" ], [ "WHEN MARNIE WAS THERE", "release_year", "2014" ], [ "WHILE SHE WAS OUT", "has_genre", "THRILLER" ], [ "WHILE SHE WAS OUT", "release_year", "2008" ], [ "WHITE BIRD IN A BLIZZARD", "has_genre", "THRILLER" ], [ "WHITE BIRD IN A BLIZZARD", "release_year", "2014" ], [ "WICKED BLOOD", "has_genre", "THRILLER" ], [ "WICKED BLOOD", "release_year", "2014" ], [ "ZANDALEE", "has_genre", "THRILLER" ], [ "ZANDALEE", "starred_actors", "NICOLAS CAGE" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 36212, DRAMA 25127, GLENN WITHROW 35364, NORTH DALLAS FORTY 39846, OLEG DRACH 39963, PETER GENT 25509, THE DEBT 38801, THE MOORING 24811, THRILLER src, edge_attr, dst 35364, has_genre, 36212 35364, written_by, 39963 25509, has_genre, 36212 25509, has_genre, 24811 25509, starred_actors, 39846 38801, directed_by, 25127 38801, has_genre, 24811 38801, written_by, 25127 Question: How are GLENN WITHROW, OLEG DRACH, and PETER GENT related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "GLENN WITHROW", "OLEG DRACH", "PETER GENT" ], "valid_edges": [ [ "NORTH DALLAS FORTY", "has_genre", "DRAMA" ], [ "NORTH DALLAS FORTY", "written_by", "PETER GENT" ], [ "THE DEBT", "has_genre", "DRAMA" ], [ "THE DEBT", "has_genre", "THRILLER" ], [ "THE DEBT", "starred_actors", "OLEG DRACH" ], [ "THE MOORING", "directed_by", "GLENN WITHROW" ], [ "THE MOORING", "has_genre", "THRILLER" ], [ "THE MOORING", "written_by", "GLENN WITHROW" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8486, 1999 29422, ANNA AND THE KING 32474, EMMANUELLE 6012, FRENCH 38857, JEAN POIRET 31026, LA CAGE AUX FOLLES 24110, ONLY GOD FORGIVES 8379, ROMANCE 13090, THAI 37261, THAILAND 22505, THE HANGOVER PART II 6207, THE LOVE OF SIAM 38179, THE UNDERGROUND COMEDY MOVIE src, edge_attr, dst 29422, has_tags, 37261 29422, in_language, 13090 29422, release_year, 8486 32474, has_tags, 37261 32474, in_language, 6012 31026, has_tags, 6012 31026, in_language, 6012 31026, written_by, 38857 24110, has_tags, 37261 24110, in_language, 13090 8379, release_year, 8486 22505, has_tags, 37261 22505, in_language, 13090 6207, has_genre, 8379 6207, has_tags, 8379 6207, has_tags, 37261 6207, in_language, 13090 38179, release_year, 8486 Question: In what context are JEAN POIRET, THAILAND, and THE UNDERGROUND COMEDY MOVIE connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "JEAN POIRET", "THAILAND", "THE UNDERGROUND COMEDY MOVIE" ], "valid_edges": [ [ "ANNA AND THE KING", "has_tags", "THAILAND" ], [ "ANNA AND THE KING", "in_language", "THAI" ], [ "ANNA AND THE KING", "release_year", "1999" ], [ "EMMANUELLE", "has_tags", "THAILAND" ], [ "EMMANUELLE", "in_language", "FRENCH" ], [ "LA CAGE AUX FOLLES", "has_tags", "FRENCH" ], [ "LA CAGE AUX FOLLES", "in_language", "FRENCH" ], [ "LA CAGE AUX FOLLES", "written_by", "JEAN POIRET" ], [ "ONLY GOD FORGIVES", "has_tags", "THAILAND" ], [ "ONLY GOD FORGIVES", "in_language", "THAI" ], [ "ROMANCE", "release_year", "1999" ], [ "THE HANGOVER PART II", "has_tags", "THAILAND" ], [ "THE HANGOVER PART II", "in_language", "THAI" ], [ "THE LOVE OF SIAM", "has_genre", "ROMANCE" ], [ "THE LOVE OF SIAM", "has_tags", "ROMANCE" ], [ "THE LOVE OF SIAM", "has_tags", "THAILAND" ], [ "THE LOVE OF SIAM", "in_language", "THAI" ], [ "THE UNDERGROUND COMEDY MOVIE", "release_year", "1999" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 13408, 2001 36212, DRAMA 6012, FRENCH 28028, JESSICA CAPSHAW 25430, KARIN VIARD 5493, POLISSE 31405, THE CARDINAL 28275, THE UNHOLY WIFE 25783, TIME OUT 2194, TOM TRYON 6988, VALENTINE src, edge_attr, dst 5493, has_genre, 36212 5493, in_language, 6012 5493, starred_actors, 25430 31405, has_genre, 36212 31405, starred_actors, 2194 28275, has_genre, 36212 28275, starred_actors, 2194 25783, has_genre, 36212 25783, in_language, 6012 25783, release_year, 13408 25783, starred_actors, 25430 6988, release_year, 13408 6988, starred_actors, 28028 Question: For what reason are JESSICA CAPSHAW, KARIN VIARD, and TOM TRYON associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "JESSICA CAPSHAW", "KARIN VIARD", "TOM TRYON" ], "valid_edges": [ [ "POLISSE", "has_genre", "DRAMA" ], [ "POLISSE", "in_language", "FRENCH" ], [ "POLISSE", "starred_actors", "KARIN VIARD" ], [ "THE CARDINAL", "has_genre", "DRAMA" ], [ "THE CARDINAL", "starred_actors", "TOM TRYON" ], [ "THE UNHOLY WIFE", "has_genre", "DRAMA" ], [ "THE UNHOLY WIFE", "starred_actors", "TOM TRYON" ], [ "TIME OUT", "has_genre", "DRAMA" ], [ "TIME OUT", "in_language", "FRENCH" ], [ "TIME OUT", "release_year", "2001" ], [ "TIME OUT", "starred_actors", "KARIN VIARD" ], [ "VALENTINE", "release_year", "2001" ], [ "VALENTINE", "starred_actors", "JESSICA CAPSHAW" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15421, 100 BLOODY ACRES 24580, 2-HEADED SHARK ATTACK 658, 2012 30586, A FANTASTIC FEAR OF EVERYTHING 24466, A FUNNY MAN 25327, AFTERSHOCK 34229, ANTIVIRAL 3449, APARTMENT 1303 3D 13736, BLACK ROCK 5946, BLOODY BLOODY BIBLE CAMP 30191, CHERNOBYL DIARIES 30339, CITADEL 38598, COME OUT AND PLAY 22349, CRAWLSPACE 7316, DANISH 30529, DARK SHADOWS 33477, DARKEST NIGHT 17466, DETENTION OF THE DEAD 28770, DRACULA 3D 36212, DRAMA 6394, EXCISION 938, FRANZ SCHULZ 1041, GALLOWWALKERS 24098, GRAVE ENCOUNTERS 2 5870, HORROR 4823, HOUSE AT THE END OF THE STREET 36133, JOHN DIES AT THE END 24437, MANIAC 10848, MARTIN ZANDVLIET 38276, MIDNIGHT 10573, NO ONE LIVES 36239, PARANORMAL ACTIVITY 4 35289, PARANORMAN 17916, PIRANHA 3DD 38226, RESOLUTION 31339, RISE OF THE ZOMBIES 38815, SADAKO 3D 24992, SCARY OR DIE 15926, SILENT NIGHT 8359, SINISTER 9247, STITCHES 39265, STORAGE 24 30456, TEDDY BEAR 31162, THE ABCS OF DEATH 39296, THE APPARITION 25250, THE BARRENS 19623, THE BATTERY 16753, THE BAY 19938, THE CABIN IN THE WOODS 2974, THE COLLECTION 18592, THE DEVIL INSIDE 7153, THE DEVIL'S CARNIVAL 19064, THE HAUNTING OF HELENA 11861, THE LORDS OF SALEM 24652, THE MAN WHO LAUGHS 29525, THE PACT 2023, THE POSSESSION 18162, THE RAVEN 27003, THE THOMPSONS 3437, THE WOMAN IN BLACK 31252, V/H/S 24570, VAMPS 39464, WOULD YOU RATHER src, edge_attr, dst 15421, has_genre, 5870 15421, release_year, 658 24580, has_genre, 5870 24580, release_year, 658 30586, has_genre, 5870 30586, has_tags, 5870 30586, release_year, 658 24466, directed_by, 10848 24466, has_genre, 36212 24466, in_language, 7316 24466, written_by, 10848 25327, has_genre, 5870 25327, release_year, 658 34229, has_genre, 5870 34229, release_year, 658 3449, has_genre, 5870 3449, release_year, 658 13736, has_genre, 5870 13736, release_year, 658 5946, has_genre, 5870 5946, release_year, 658 30191, has_genre, 5870 30191, release_year, 658 30339, has_genre, 5870 30339, release_year, 658 38598, has_genre, 5870 38598, release_year, 658 22349, has_genre, 5870 22349, release_year, 658 30529, has_genre, 5870 30529, release_year, 658 33477, has_genre, 5870 33477, has_tags, 5870 33477, release_year, 658 17466, has_genre, 5870 17466, release_year, 658 28770, has_genre, 5870 28770, release_year, 658 6394, has_genre, 5870 6394, release_year, 658 1041, has_genre, 5870 1041, release_year, 658 24098, has_genre, 5870 24098, release_year, 658 4823, has_genre, 5870 4823, release_year, 658 36133, has_genre, 5870 36133, release_year, 658 24437, has_genre, 5870 24437, release_year, 658 38276, has_genre, 36212 38276, written_by, 938 10573, has_genre, 5870 10573, release_year, 658 36239, has_genre, 5870 36239, release_year, 658 35289, has_tags, 5870 35289, release_year, 658 17916, has_genre, 5870 17916, release_year, 658 38226, has_genre, 5870 38226, release_year, 658 31339, has_genre, 5870 31339, release_year, 658 38815, has_genre, 5870 38815, release_year, 658 24992, has_genre, 5870 24992, release_year, 658 15926, has_genre, 5870 15926, release_year, 658 8359, has_genre, 5870 8359, has_tags, 5870 8359, release_year, 658 9247, has_genre, 5870 9247, release_year, 658 39265, has_genre, 5870 39265, release_year, 658 30456, in_language, 7316 30456, release_year, 658 30456, written_by, 10848 31162, has_genre, 5870 31162, release_year, 658 39296, has_genre, 5870 39296, release_year, 658 25250, has_genre, 5870 25250, release_year, 658 19623, has_genre, 5870 19623, release_year, 658 16753, has_genre, 5870 16753, release_year, 658 19938, has_genre, 5870 19938, has_tags, 5870 19938, release_year, 658 2974, has_genre, 5870 2974, release_year, 658 18592, has_genre, 5870 18592, release_year, 658 7153, has_genre, 5870 7153, release_year, 658 19064, has_genre, 5870 19064, release_year, 658 11861, has_genre, 5870 11861, release_year, 658 24652, has_genre, 5870 24652, release_year, 658 29525, has_genre, 5870 29525, release_year, 658 2023, has_genre, 5870 2023, has_tags, 5870 2023, release_year, 658 18162, has_genre, 5870 18162, has_tags, 5870 18162, release_year, 658 27003, has_genre, 5870 27003, release_year, 658 3437, has_genre, 5870 3437, has_tags, 5870 3437, release_year, 658 31252, has_genre, 5870 31252, has_tags, 5870 31252, release_year, 658 24570, has_genre, 5870 24570, release_year, 658 39464, has_genre, 5870 39464, has_tags, 5870 39464, release_year, 658 Question: For what reason are DARKEST NIGHT, FRANZ SCHULZ, and MARTIN ZANDVLIET associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "DARKEST NIGHT", "FRANZ SCHULZ", "MARTIN ZANDVLIET" ], "valid_edges": [ [ "100 BLOODY ACRES", "has_genre", "HORROR" ], [ "100 BLOODY ACRES", "release_year", "2012" ], [ "2-HEADED SHARK ATTACK", "has_genre", "HORROR" ], [ "2-HEADED SHARK ATTACK", "release_year", "2012" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_genre", "HORROR" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_tags", "HORROR" ], [ "A FANTASTIC FEAR OF EVERYTHING", "release_year", "2012" ], [ "A FUNNY MAN", "directed_by", "MARTIN ZANDVLIET" ], [ "A FUNNY MAN", "has_genre", "DRAMA" ], [ "A FUNNY MAN", "in_language", "DANISH" ], [ "A FUNNY MAN", "written_by", "MARTIN ZANDVLIET" ], [ "AFTERSHOCK", "has_genre", "HORROR" ], [ "AFTERSHOCK", "release_year", "2012" ], [ "ANTIVIRAL", "has_genre", "HORROR" ], [ "ANTIVIRAL", "release_year", "2012" ], [ "APARTMENT 1303 3D", "has_genre", "HORROR" ], [ "APARTMENT 1303 3D", "release_year", "2012" ], [ "BLACK ROCK", "has_genre", "HORROR" ], [ "BLACK ROCK", "release_year", "2012" ], [ "BLOODY BLOODY BIBLE CAMP", "has_genre", "HORROR" ], [ "BLOODY BLOODY BIBLE CAMP", "release_year", "2012" ], [ "CHERNOBYL DIARIES", "has_genre", "HORROR" ], [ "CHERNOBYL DIARIES", "release_year", "2012" ], [ "CITADEL", "has_genre", "HORROR" ], [ "CITADEL", "release_year", "2012" ], [ "COME OUT AND PLAY", "has_genre", "HORROR" ], [ "COME OUT AND PLAY", "release_year", "2012" ], [ "CRAWLSPACE", "has_genre", "HORROR" ], [ "CRAWLSPACE", "release_year", "2012" ], [ "DARK SHADOWS", "has_genre", "HORROR" ], [ "DARK SHADOWS", "release_year", "2012" ], [ "DARKEST NIGHT", "has_genre", "HORROR" ], [ "DARKEST NIGHT", "has_tags", "HORROR" ], [ "DARKEST NIGHT", "release_year", "2012" ], [ "DETENTION OF THE DEAD", "has_genre", "HORROR" ], [ "DETENTION OF THE DEAD", "release_year", "2012" ], [ "DRACULA 3D", "has_genre", "HORROR" ], [ "DRACULA 3D", "release_year", "2012" ], [ "EXCISION", "has_genre", "HORROR" ], [ "EXCISION", "release_year", "2012" ], [ "GALLOWWALKERS", "has_genre", "HORROR" ], [ "GALLOWWALKERS", "release_year", "2012" ], [ "GRAVE ENCOUNTERS 2", "has_genre", "HORROR" ], [ "GRAVE ENCOUNTERS 2", "release_year", "2012" ], [ "HOUSE AT THE END OF THE STREET", "has_genre", "HORROR" ], [ "HOUSE AT THE END OF THE STREET", "release_year", "2012" ], [ "JOHN DIES AT THE END", "has_genre", "HORROR" ], [ "JOHN DIES AT THE END", "release_year", "2012" ], [ "MANIAC", "has_genre", "HORROR" ], [ "MANIAC", "release_year", "2012" ], [ "MIDNIGHT", "has_genre", "DRAMA" ], [ "MIDNIGHT", "written_by", "FRANZ SCHULZ" ], [ "NO ONE LIVES", "has_genre", "HORROR" ], [ "NO ONE LIVES", "release_year", "2012" ], [ "PARANORMAL ACTIVITY 4", "has_genre", "HORROR" ], [ "PARANORMAL ACTIVITY 4", "release_year", "2012" ], [ "PARANORMAN", "has_tags", "HORROR" ], [ "PARANORMAN", "release_year", "2012" ], [ "PIRANHA 3DD", "has_genre", "HORROR" ], [ "PIRANHA 3DD", "release_year", "2012" ], [ "RESOLUTION", "has_genre", "HORROR" ], [ "RESOLUTION", "release_year", "2012" ], [ "RISE OF THE ZOMBIES", "has_genre", "HORROR" ], [ "RISE OF THE ZOMBIES", "release_year", "2012" ], [ "SADAKO 3D", "has_genre", "HORROR" ], [ "SADAKO 3D", "release_year", "2012" ], [ "SCARY OR DIE", "has_genre", "HORROR" ], [ "SCARY OR DIE", "release_year", "2012" ], [ "SILENT NIGHT", "has_genre", "HORROR" ], [ "SILENT NIGHT", "release_year", "2012" ], [ "SINISTER", "has_genre", "HORROR" ], [ "SINISTER", "has_tags", "HORROR" ], [ "SINISTER", "release_year", "2012" ], [ "STITCHES", "has_genre", "HORROR" ], [ "STITCHES", "release_year", "2012" ], [ "STORAGE 24", "has_genre", "HORROR" ], [ "STORAGE 24", "release_year", "2012" ], [ "TEDDY BEAR", "in_language", "DANISH" ], [ "TEDDY BEAR", "release_year", "2012" ], [ "TEDDY BEAR", "written_by", "MARTIN ZANDVLIET" ], [ "THE ABCS OF DEATH", "has_genre", "HORROR" ], [ "THE ABCS OF DEATH", "release_year", "2012" ], [ "THE APPARITION", "has_genre", "HORROR" ], [ "THE APPARITION", "release_year", "2012" ], [ "THE BARRENS", "has_genre", "HORROR" ], [ "THE BARRENS", "release_year", "2012" ], [ "THE BATTERY", "has_genre", "HORROR" ], [ "THE BATTERY", "release_year", "2012" ], [ "THE BAY", "has_genre", "HORROR" ], [ "THE BAY", "release_year", "2012" ], [ "THE CABIN IN THE WOODS", "has_genre", "HORROR" ], [ "THE CABIN IN THE WOODS", "has_tags", "HORROR" ], [ "THE CABIN IN THE WOODS", "release_year", "2012" ], [ "THE COLLECTION", "has_genre", "HORROR" ], [ "THE COLLECTION", "release_year", "2012" ], [ "THE DEVIL INSIDE", "has_genre", "HORROR" ], [ "THE DEVIL INSIDE", "release_year", "2012" ], [ "THE DEVIL'S CARNIVAL", "has_genre", "HORROR" ], [ "THE DEVIL'S CARNIVAL", "release_year", "2012" ], [ "THE HAUNTING OF HELENA", "has_genre", "HORROR" ], [ "THE HAUNTING OF HELENA", "release_year", "2012" ], [ "THE LORDS OF SALEM", "has_genre", "HORROR" ], [ "THE LORDS OF SALEM", "release_year", "2012" ], [ "THE MAN WHO LAUGHS", "has_genre", "HORROR" ], [ "THE MAN WHO LAUGHS", "release_year", "2012" ], [ "THE PACT", "has_genre", "HORROR" ], [ "THE PACT", "release_year", "2012" ], [ "THE POSSESSION", "has_genre", "HORROR" ], [ "THE POSSESSION", "has_tags", "HORROR" ], [ "THE POSSESSION", "release_year", "2012" ], [ "THE RAVEN", "has_genre", "HORROR" ], [ "THE RAVEN", "has_tags", "HORROR" ], [ "THE RAVEN", "release_year", "2012" ], [ "THE THOMPSONS", "has_genre", "HORROR" ], [ "THE THOMPSONS", "release_year", "2012" ], [ "THE WOMAN IN BLACK", "has_genre", "HORROR" ], [ "THE WOMAN IN BLACK", "has_tags", "HORROR" ], [ "THE WOMAN IN BLACK", "release_year", "2012" ], [ "V/H/S", "has_genre", "HORROR" ], [ "V/H/S", "has_tags", "HORROR" ], [ "V/H/S", "release_year", "2012" ], [ "VAMPS", "has_genre", "HORROR" ], [ "VAMPS", "release_year", "2012" ], [ "WOULD YOU RATHER", "has_genre", "HORROR" ], [ "WOULD YOU RATHER", "has_tags", "HORROR" ], [ "WOULD YOU RATHER", "release_year", "2012" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8486, 1999 1421, 2013 35260, A CASE OF YOU 18943, BRIAN BLESSED 35657, GLORIA 67, TARZAN 12439, THE INSIDER src, edge_attr, dst 35260, release_year, 1421 35657, release_year, 8486 35657, release_year, 1421 67, release_year, 8486 67, release_year, 1421 67, starred_actors, 18943 12439, release_year, 8486 Question: How are A CASE OF YOU, BRIAN BLESSED, and THE INSIDER related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "A CASE OF YOU", "BRIAN BLESSED", "THE INSIDER" ], "valid_edges": [ [ "A CASE OF YOU", "release_year", "2013" ], [ "GLORIA", "release_year", "1999" ], [ "GLORIA", "release_year", "2013" ], [ "TARZAN", "release_year", "1999" ], [ "TARZAN", "release_year", "2013" ], [ "TARZAN", "starred_actors", "BRIAN BLESSED" ], [ "THE INSIDER", "release_year", "1999" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6776, 2000 33688, ABERDEEN 11856, COLE PORTER 17606, DAMIEN CHAZELLE 33486, DE-LOVELY 36212, DRAMA 32380, GOSSIP 16354, GUY AND MADELINE ON A PARK BENCH 15017, LENA HEADEY 22845, MUSIC 24593, MUSICAL 8546, NIGHT AND DAY 38804, WHIPLASH src, edge_attr, dst 33688, has_genre, 36212 33688, release_year, 6776 33688, starred_actors, 15017 33486, has_genre, 22845 33486, has_tags, 11856 32380, has_genre, 36212 32380, has_tags, 32380 32380, release_year, 6776 32380, starred_actors, 15017 16354, directed_by, 17606 16354, has_genre, 24593 16354, has_tags, 17606 16354, written_by, 17606 8546, has_genre, 24593 8546, has_tags, 11856 38804, directed_by, 17606 38804, has_genre, 36212 38804, has_genre, 22845 38804, has_tags, 17606 38804, has_tags, 22845 38804, written_by, 17606 Question: In what context are COLE PORTER, DAMIEN CHAZELLE, and LENA HEADEY connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "COLE PORTER", "DAMIEN CHAZELLE", "LENA HEADEY" ], "valid_edges": [ [ "ABERDEEN", "has_genre", "DRAMA" ], [ "ABERDEEN", "release_year", "2000" ], [ "ABERDEEN", "starred_actors", "LENA HEADEY" ], [ "DE-LOVELY", "has_genre", "MUSIC" ], [ "DE-LOVELY", "has_tags", "COLE PORTER" ], [ "GOSSIP", "has_genre", "DRAMA" ], [ "GOSSIP", "has_tags", "GOSSIP" ], [ "GOSSIP", "release_year", "2000" ], [ "GOSSIP", "starred_actors", "LENA HEADEY" ], [ "GUY AND MADELINE ON A PARK BENCH", "directed_by", "DAMIEN CHAZELLE" ], [ "GUY AND MADELINE ON A PARK BENCH", "has_genre", "MUSICAL" ], [ "GUY AND MADELINE ON A PARK BENCH", "has_tags", "DAMIEN CHAZELLE" ], [ "GUY AND MADELINE ON A PARK BENCH", "written_by", "DAMIEN CHAZELLE" ], [ "NIGHT AND DAY", "has_genre", "MUSICAL" ], [ "NIGHT AND DAY", "has_tags", "COLE PORTER" ], [ "WHIPLASH", "directed_by", "DAMIEN CHAZELLE" ], [ "WHIPLASH", "has_genre", "DRAMA" ], [ "WHIPLASH", "has_genre", "MUSIC" ], [ "WHIPLASH", "has_tags", "DAMIEN CHAZELLE" ], [ "WHIPLASH", "has_tags", "MUSIC" ], [ "WHIPLASH", "written_by", "DAMIEN CHAZELLE" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 658, 2012 34699, CHRONICLE 10377, CIVIC DUTY 30806, IMAGINAERUM 17791, JEFF RENFROE 22063, MAX LANDIS 32395, STOBE HARJU 24811, THRILLER src, edge_attr, dst 34699, has_genre, 24811 34699, release_year, 658 34699, written_by, 22063 10377, directed_by, 17791 10377, has_genre, 24811 30806, directed_by, 32395 30806, release_year, 658 30806, written_by, 32395 Question: How are JEFF RENFROE, MAX LANDIS, and STOBE HARJU related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "JEFF RENFROE", "MAX LANDIS", "STOBE HARJU" ], "valid_edges": [ [ "CHRONICLE", "has_genre", "THRILLER" ], [ "CHRONICLE", "release_year", "2012" ], [ "CHRONICLE", "written_by", "MAX LANDIS" ], [ "CIVIC DUTY", "directed_by", "JEFF RENFROE" ], [ "CIVIC DUTY", "has_genre", "THRILLER" ], [ "IMAGINAERUM", "directed_by", "STOBE HARJU" ], [ "IMAGINAERUM", "release_year", "2012" ], [ "IMAGINAERUM", "written_by", "STOBE HARJU" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6718, A FAREWELL TO ARMS 32006, ARCH OF TRIUMPH 25805, DOCTOR ZHIVAGO 24981, FOR THE MOMENT 20693, GONE WITH THE WIND 234, PEARL HARBOR 8379, ROMANCE 22580, SUITE FRANÇAISE 11696, THE CUCKOO 36390, THE ENGLISH PATIENT 7831, THE SHEIK 32898, THIS ABOVE ALL 19813, VERBOTEN! 22214, WAR src, edge_attr, dst 6718, has_genre, 8379 6718, has_genre, 22214 32006, has_genre, 8379 32006, has_genre, 22214 25805, has_genre, 8379 25805, has_genre, 22214 25805, has_tags, 22214 24981, has_genre, 8379 24981, has_genre, 22214 20693, has_genre, 8379 20693, has_genre, 22214 20693, has_tags, 8379 20693, has_tags, 22214 234, has_genre, 8379 234, has_tags, 8379 234, has_tags, 22214 22580, has_genre, 8379 22580, has_genre, 22214 11696, has_genre, 22214 36390, has_genre, 8379 36390, has_genre, 22214 36390, has_tags, 22214 7831, has_genre, 8379 32898, has_genre, 8379 32898, has_genre, 22214 19813, has_genre, 22214 Question: In what context are THE CUCKOO, THE SHEIK, and VERBOTEN! connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "THE CUCKOO", "THE SHEIK", "VERBOTEN!" ], "valid_edges": [ [ "A FAREWELL TO ARMS", "has_genre", "ROMANCE" ], [ "A FAREWELL TO ARMS", "has_genre", "WAR" ], [ "ARCH OF TRIUMPH", "has_genre", "ROMANCE" ], [ "ARCH OF TRIUMPH", "has_genre", "WAR" ], [ "DOCTOR ZHIVAGO", "has_genre", "ROMANCE" ], [ "DOCTOR ZHIVAGO", "has_genre", "WAR" ], [ "DOCTOR ZHIVAGO", "has_tags", "WAR" ], [ "FOR THE MOMENT", "has_genre", "ROMANCE" ], [ "FOR THE MOMENT", "has_genre", "WAR" ], [ "GONE WITH THE WIND", "has_genre", "ROMANCE" ], [ "GONE WITH THE WIND", "has_genre", "WAR" ], [ "GONE WITH THE WIND", "has_tags", "ROMANCE" ], [ "GONE WITH THE WIND", "has_tags", "WAR" ], [ "PEARL HARBOR", "has_genre", "ROMANCE" ], [ "PEARL HARBOR", "has_tags", "ROMANCE" ], [ "PEARL HARBOR", "has_tags", "WAR" ], [ "SUITE FRANÇAISE", "has_genre", "ROMANCE" ], [ "SUITE FRANÇAISE", "has_genre", "WAR" ], [ "THE CUCKOO", "has_genre", "WAR" ], [ "THE ENGLISH PATIENT", "has_genre", "ROMANCE" ], [ "THE ENGLISH PATIENT", "has_genre", "WAR" ], [ "THE ENGLISH PATIENT", "has_tags", "WAR" ], [ "THE SHEIK", "has_genre", "ROMANCE" ], [ "THIS ABOVE ALL", "has_genre", "ROMANCE" ], [ "THIS ABOVE ALL", "has_genre", "WAR" ], [ "VERBOTEN!", "has_genre", "WAR" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 30172, 1964 26257, 1994 4269, A HARD DAY'S NIGHT 17505, A LOW DOWN DIRTY SHAME 431, A MAN OF NO IMPORTANCE 36629, A MILLION TO JUAN 31850, A SHOT IN THE DARK 29838, A SIMPLE TWIST OF FATE 7687, ADVANCE TO THE REAR 8837, AIRHEADS 33883, ALL THESE WOMEN 27410, ANGELS IN THE OUTFIELD 24704, ANGIE 35639, BABY'S DAY OUT 1868, BARCELONA 37702, BEDTIME STORY 10890, BEVERLY HILLS COP III 29301, BLANK CHECK 24639, BLANKMAN 34157, BLINK 34318, CABIN BOY 22539, CAR 54, WHERE ARE YOU? 26883, CEMETERY MAN 16350, CHASERS 35468, CLEAN SLATE 35351, CLERKS 9387, CLIFFORD 30463, COMEDY 10092, CONTINENTAL DIVIDE 21391, CRACKERJACK 5277, CRITICAL CONDITION 25978, DEADLY ADVICE 19009, DEAR HEART 13424, DON'T DRINK THE WATER 3893, ED WOOD 26709, ERNEST GOES TO SCHOOL 20441, EXIT TO EDEN 22720, FATHER GOOSE 24028, FLOUNDERING 22371, FORREST GUMP 6215, FOUR WEDDINGS AND A FUNERAL 36927, FROM BEIJING WITH LOVE 19795, GET YOURSELF A COLLEGE GIRL 34775, GETTING EVEN WITH DAD 9142, GETTING IN 26124, GOOD NEIGHBOR SAM 12502, GOODBYE CHARLIE 5585, GREEDY 34489, GUARDING TESS 25670, HAIL CAESAR 20990, HOLY MATRIMONY 34412, I LIKE IT LIKE THAT 13163, I LOVE TROUBLE 18096, I.Q. 24832, IN THE ARMY NOW 14341, IT COULD HAPPEN TO YOU 39987, IT RUNS IN THE FAMILY 37202, IT'S PAT 29404, JUNIOR 14878, KABHI HAAN KABHI NAA 4285, KISS ME, STUPID 13469, KISSES FOR MY PRESIDENT 18648, LEPRECHAUN 2 19862, LIGHTNING JACK 16780, LITTLE GIANTS 5975, MAN'S FAVORITE SPORT? 25283, MAVERICK 35439, MICHAEL APTED 12620, MILK MONEY 12371, MIXED NUTS 24172, MO OGRODNIK 9817, MONKEY TROUBLE 25796, MURIEL'S WEDDING 28963, MY GIRL 2 10609, NELL 4398, NOBODY'S FOOL 32358, NORTH 36883, ONLY YOU 7364, PARIS WHEN IT SIZZLES 24732, PCU 11042, PRINCESS CARABOO 26174, PULP FICTION 22395, RADIOLAND MURDERS 26180, REALITY BITES 25577, RENAISSANCE MAN 14486, SANTA CLAUS CONQUERS THE MARTIANS 24232, SEND ME NO FLOWERS 24642, SERIAL MOM 7941, SEX AND THE SINGLE GIRL 1374, SLEEP WITH ME 6144, SPANKING THE MONKEY 7010, SPEECHLESS 35026, STAGGERED 20877, SWIMMING WITH SHARKS 2567, TAMMY AND THE T-REX 25270, THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT 30090, THE AIR UP THERE 36790, THE AMERICANIZATION OF EMILY 4157, THE BEST MAN 13685, THE CHASE 22164, THE COWBOY WAY 39794, THE DISORDERLY ORDERLY 9215, THE FAVOR 17956, THE FLINTSTONES 10878, THE HUDSUCKER PROXY 38210, THE INKWELL 38550, THE LITTLE RASCALS 23802, THE MASK 29233, THE MONSTER 33982, THE PAPER 2732, THE PATSY 26061, THE REF 13917, THE ROAD TO WELLVILLE 17314, THE SANTA CLAUSE 36289, THE SCOUT 33905, THE SEARCH FOR ONE-EYE JIMMY 4767, THE SUM OF US 29878, THE WORLD OF HENRY ORIENT 16762, THREESOME 18195, TRAPPED IN PARADISE 12860, TRUE LIES 33767, TWIN SITTERS 35371, UPTOWN GIRLS 12130, WELCOME, OR NO TRESPASSING 37603, WHAT A WAY TO GO! 39623, WITH HONORS src, edge_attr, dst 4269, has_genre, 30463 4269, has_tags, 30463 4269, release_year, 30172 17505, has_genre, 30463 17505, release_year, 26257 431, has_genre, 30463 431, release_year, 26257 36629, has_genre, 30463 36629, release_year, 26257 31850, has_genre, 30463 31850, release_year, 30172 29838, has_genre, 30463 29838, release_year, 26257 7687, has_genre, 30463 7687, release_year, 30172 8837, has_genre, 30463 8837, has_tags, 30463 8837, release_year, 26257 33883, has_genre, 30463 33883, release_year, 30172 27410, has_genre, 30463 27410, release_year, 26257 24704, has_genre, 30463 24704, release_year, 26257 35639, has_genre, 30463 35639, release_year, 26257 1868, has_genre, 30463 1868, release_year, 26257 37702, has_genre, 30463 37702, release_year, 30172 10890, has_genre, 30463 10890, has_tags, 30463 10890, release_year, 26257 29301, has_genre, 30463 29301, release_year, 26257 24639, has_genre, 30463 24639, release_year, 26257 34157, directed_by, 35439 34157, release_year, 26257 34318, has_genre, 30463 34318, release_year, 26257 22539, has_genre, 30463 22539, release_year, 26257 26883, has_genre, 30463 26883, release_year, 26257 16350, has_genre, 30463 16350, release_year, 26257 35468, has_genre, 30463 35468, release_year, 26257 35351, has_genre, 30463 35351, has_tags, 30463 35351, release_year, 26257 9387, has_genre, 30463 9387, release_year, 26257 10092, directed_by, 35439 10092, has_genre, 30463 21391, has_genre, 30463 21391, release_year, 26257 5277, directed_by, 35439 5277, has_genre, 30463 25978, has_genre, 30463 25978, release_year, 26257 19009, has_genre, 30463 19009, release_year, 30172 13424, has_genre, 30463 13424, release_year, 26257 3893, has_genre, 30463 3893, release_year, 26257 26709, has_genre, 30463 26709, release_year, 26257 20441, has_genre, 30463 20441, release_year, 26257 22720, has_genre, 30463 22720, has_tags, 30463 22720, release_year, 30172 24028, has_genre, 30463 24028, release_year, 26257 22371, has_tags, 30463 22371, release_year, 26257 6215, has_genre, 30463 6215, has_tags, 30463 6215, release_year, 26257 36927, has_genre, 30463 36927, release_year, 26257 19795, has_genre, 30463 19795, release_year, 30172 34775, has_genre, 30463 34775, release_year, 26257 9142, has_genre, 30463 9142, release_year, 26257 26124, has_genre, 30463 26124, release_year, 30172 12502, has_genre, 30463 12502, release_year, 30172 5585, has_genre, 30463 5585, release_year, 26257 34489, has_genre, 30463 34489, release_year, 26257 25670, has_genre, 30463 25670, release_year, 26257 20990, has_genre, 30463 20990, release_year, 26257 34412, has_genre, 30463 34412, release_year, 26257 13163, has_genre, 30463 13163, release_year, 26257 18096, has_genre, 30463 18096, release_year, 26257 24832, has_genre, 30463 24832, release_year, 26257 14341, has_genre, 30463 14341, has_tags, 30463 14341, release_year, 26257 39987, has_genre, 30463 39987, release_year, 26257 37202, has_genre, 30463 37202, release_year, 26257 29404, has_genre, 30463 29404, release_year, 26257 14878, has_genre, 30463 14878, release_year, 26257 4285, has_genre, 30463 4285, release_year, 30172 13469, has_genre, 30463 13469, release_year, 30172 18648, has_genre, 30463 18648, release_year, 26257 19862, has_genre, 30463 19862, release_year, 26257 16780, has_genre, 30463 16780, release_year, 26257 5975, has_genre, 30463 5975, release_year, 30172 25283, has_genre, 30463 25283, has_tags, 30463 25283, release_year, 26257 12620, has_genre, 30463 12620, release_year, 26257 12371, has_genre, 30463 12371, release_year, 26257 9817, has_genre, 30463 9817, release_year, 26257 25796, has_genre, 30463 25796, has_tags, 30463 25796, release_year, 26257 28963, has_genre, 30463 28963, release_year, 26257 10609, directed_by, 35439 10609, release_year, 26257 4398, has_genre, 30463 4398, release_year, 26257 32358, has_genre, 30463 32358, release_year, 26257 36883, has_genre, 30463 36883, release_year, 26257 7364, has_genre, 30463 7364, release_year, 30172 24732, has_genre, 30463 24732, release_year, 26257 11042, has_genre, 30463 11042, release_year, 26257 26174, has_tags, 30463 26174, release_year, 26257 22395, has_genre, 30463 22395, release_year, 26257 26180, has_genre, 30463 26180, release_year, 26257 25577, has_genre, 30463 25577, release_year, 26257 14486, has_genre, 30463 14486, release_year, 30172 24232, has_genre, 30463 24232, release_year, 30172 24642, has_genre, 30463 24642, has_tags, 30463 24642, release_year, 26257 7941, has_genre, 30463 7941, release_year, 30172 1374, has_genre, 30463 1374, release_year, 26257 6144, has_genre, 30463 6144, release_year, 26257 7010, has_genre, 30463 7010, release_year, 26257 35026, has_genre, 30463 35026, release_year, 26257 20877, has_genre, 30463 20877, release_year, 26257 2567, has_genre, 30463 2567, release_year, 26257 25270, has_genre, 30463 25270, release_year, 26257 30090, has_genre, 30463 30090, has_tags, 30463 30090, release_year, 26257 36790, has_genre, 30463 36790, release_year, 30172 4157, has_genre, 30463 4157, release_year, 30172 13685, has_genre, 30463 13685, release_year, 26257 22164, has_genre, 30463 22164, release_year, 26257 39794, has_genre, 30463 39794, release_year, 30172 9215, has_genre, 30463 9215, release_year, 26257 17956, has_genre, 30463 17956, has_tags, 30463 17956, release_year, 26257 10878, has_genre, 30463 10878, has_tags, 30463 10878, release_year, 26257 38210, has_genre, 30463 38210, release_year, 26257 38550, has_genre, 30463 38550, release_year, 26257 23802, has_genre, 30463 23802, has_tags, 30463 23802, release_year, 26257 29233, has_genre, 30463 29233, has_tags, 30463 29233, release_year, 26257 33982, has_genre, 30463 33982, release_year, 26257 2732, has_genre, 30463 2732, release_year, 30172 26061, has_genre, 30463 26061, has_tags, 30463 26061, release_year, 26257 13917, has_genre, 30463 13917, release_year, 26257 17314, has_genre, 30463 17314, release_year, 26257 36289, has_genre, 30463 36289, release_year, 26257 33905, has_genre, 30463 33905, release_year, 26257 4767, has_genre, 30463 4767, release_year, 26257 29878, has_genre, 30463 29878, has_tags, 30463 29878, release_year, 30172 16762, has_genre, 30463 16762, has_tags, 30463 16762, release_year, 26257 18195, has_genre, 30463 18195, release_year, 26257 12860, has_genre, 30463 12860, has_tags, 30463 12860, release_year, 26257 33767, has_genre, 30463 33767, release_year, 26257 35371, has_genre, 30463 35371, written_by, 24172 12130, has_genre, 30463 12130, release_year, 30172 37603, has_genre, 30463 37603, release_year, 30172 39623, has_genre, 30463 39623, release_year, 26257 Question: In what context are BLINK, MO OGRODNIK, and WELCOME, OR NO TRESPASSING connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BLINK", "MO OGRODNIK", "WELCOME, OR NO TRESPASSING" ], "valid_edges": [ [ "A HARD DAY'S NIGHT", "has_genre", "COMEDY" ], [ "A HARD DAY'S NIGHT", "has_tags", "COMEDY" ], [ "A HARD DAY'S NIGHT", "release_year", "1964" ], [ "A LOW DOWN DIRTY SHAME", "has_genre", "COMEDY" ], [ "A LOW DOWN DIRTY SHAME", "release_year", "1994" ], [ "A MAN OF NO IMPORTANCE", "has_genre", "COMEDY" ], [ "A MAN OF NO IMPORTANCE", "release_year", "1994" ], [ "A MILLION TO JUAN", "has_genre", "COMEDY" ], [ "A MILLION TO JUAN", "release_year", "1994" ], [ "A SHOT IN THE DARK", "has_genre", "COMEDY" ], [ "A SHOT IN THE DARK", "release_year", "1964" ], [ "A SIMPLE TWIST OF FATE", "has_genre", "COMEDY" ], [ "A SIMPLE TWIST OF FATE", "release_year", "1994" ], [ "ADVANCE TO THE REAR", "has_genre", "COMEDY" ], [ "ADVANCE TO THE REAR", "release_year", "1964" ], [ "AIRHEADS", "has_genre", "COMEDY" ], [ "AIRHEADS", "has_tags", "COMEDY" ], [ "AIRHEADS", "release_year", "1994" ], [ "ALL THESE WOMEN", "has_genre", "COMEDY" ], [ "ALL THESE WOMEN", "release_year", "1964" ], [ "ANGELS IN THE OUTFIELD", "has_genre", "COMEDY" ], [ "ANGELS IN THE OUTFIELD", "release_year", "1994" ], [ "ANGIE", "has_genre", "COMEDY" ], [ "ANGIE", "release_year", "1994" ], [ "BABY'S DAY OUT", "has_genre", "COMEDY" ], [ "BABY'S DAY OUT", "release_year", "1994" ], [ "BARCELONA", "has_genre", "COMEDY" ], [ "BARCELONA", "release_year", "1994" ], [ "BEDTIME STORY", "has_genre", "COMEDY" ], [ "BEDTIME STORY", "release_year", "1964" ], [ "BEVERLY HILLS COP III", "has_genre", "COMEDY" ], [ "BEVERLY HILLS COP III", "has_tags", "COMEDY" ], [ "BEVERLY HILLS COP III", "release_year", "1994" ], [ "BLANK CHECK", "has_genre", "COMEDY" ], [ "BLANK CHECK", "release_year", "1994" ], [ "BLANKMAN", "has_genre", "COMEDY" ], [ "BLANKMAN", "release_year", "1994" ], [ "BLINK", "directed_by", "MICHAEL APTED" ], [ "BLINK", "release_year", "1994" ], [ "CABIN BOY", "has_genre", "COMEDY" ], [ "CABIN BOY", "release_year", "1994" ], [ "CAR 54, WHERE ARE YOU?", "has_genre", "COMEDY" ], [ "CAR 54, WHERE ARE YOU?", "release_year", "1994" ], [ "CEMETERY MAN", "has_genre", "COMEDY" ], [ "CEMETERY MAN", "release_year", "1994" ], [ "CHASERS", "has_genre", "COMEDY" ], [ "CHASERS", "release_year", "1994" ], [ "CLEAN SLATE", "has_genre", "COMEDY" ], [ "CLEAN SLATE", "release_year", "1994" ], [ "CLERKS", "has_genre", "COMEDY" ], [ "CLERKS", "has_tags", "COMEDY" ], [ "CLERKS", "release_year", "1994" ], [ "CLIFFORD", "has_genre", "COMEDY" ], [ "CLIFFORD", "release_year", "1994" ], [ "CONTINENTAL DIVIDE", "directed_by", "MICHAEL APTED" ], [ "CONTINENTAL DIVIDE", "has_genre", "COMEDY" ], [ "CRACKERJACK", "has_genre", "COMEDY" ], [ "CRACKERJACK", "release_year", "1994" ], [ "CRITICAL CONDITION", "directed_by", "MICHAEL APTED" ], [ "CRITICAL CONDITION", "has_genre", "COMEDY" ], [ "DEADLY ADVICE", "has_genre", "COMEDY" ], [ "DEADLY ADVICE", "release_year", "1994" ], [ "DEAR HEART", "has_genre", "COMEDY" ], [ "DEAR HEART", "release_year", "1964" ], [ "DON'T DRINK THE WATER", "has_genre", "COMEDY" ], [ "DON'T DRINK THE WATER", "release_year", "1994" ], [ "ED WOOD", "has_genre", "COMEDY" ], [ "ED WOOD", "release_year", "1994" ], [ "ERNEST GOES TO SCHOOL", "has_genre", "COMEDY" ], [ "ERNEST GOES TO SCHOOL", "release_year", "1994" ], [ "EXIT TO EDEN", "has_genre", "COMEDY" ], [ "EXIT TO EDEN", "release_year", "1994" ], [ "FATHER GOOSE", "has_genre", "COMEDY" ], [ "FATHER GOOSE", "has_tags", "COMEDY" ], [ "FATHER GOOSE", "release_year", "1964" ], [ "FLOUNDERING", "has_genre", "COMEDY" ], [ "FLOUNDERING", "release_year", "1994" ], [ "FORREST GUMP", "has_tags", "COMEDY" ], [ "FORREST GUMP", "release_year", "1994" ], [ "FOUR WEDDINGS AND A FUNERAL", "has_genre", "COMEDY" ], [ "FOUR WEDDINGS AND A FUNERAL", "has_tags", "COMEDY" ], [ "FOUR WEDDINGS AND A FUNERAL", "release_year", "1994" ], [ "FROM BEIJING WITH LOVE", "has_genre", "COMEDY" ], [ "FROM BEIJING WITH LOVE", "release_year", "1994" ], [ "GET YOURSELF A COLLEGE GIRL", "has_genre", "COMEDY" ], [ "GET YOURSELF A COLLEGE GIRL", "release_year", "1964" ], [ "GETTING EVEN WITH DAD", "has_genre", "COMEDY" ], [ "GETTING EVEN WITH DAD", "release_year", "1994" ], [ "GETTING IN", "has_genre", "COMEDY" ], [ "GETTING IN", "release_year", "1994" ], [ "GOOD NEIGHBOR SAM", "has_genre", "COMEDY" ], [ "GOOD NEIGHBOR SAM", "release_year", "1964" ], [ "GOODBYE CHARLIE", "has_genre", "COMEDY" ], [ "GOODBYE CHARLIE", "release_year", "1964" ], [ "GREEDY", "has_genre", "COMEDY" ], [ "GREEDY", "release_year", "1994" ], [ "GUARDING TESS", "has_genre", "COMEDY" ], [ "GUARDING TESS", "release_year", "1994" ], [ "HAIL CAESAR", "has_genre", "COMEDY" ], [ "HAIL CAESAR", "release_year", "1994" ], [ "HOLY MATRIMONY", "has_genre", "COMEDY" ], [ "HOLY MATRIMONY", "release_year", "1994" ], [ "I LIKE IT LIKE THAT", "has_genre", "COMEDY" ], [ "I LIKE IT LIKE THAT", "release_year", "1994" ], [ "I LOVE TROUBLE", "has_genre", "COMEDY" ], [ "I LOVE TROUBLE", "release_year", "1994" ], [ "I.Q.", "has_genre", "COMEDY" ], [ "I.Q.", "release_year", "1994" ], [ "IN THE ARMY NOW", "has_genre", "COMEDY" ], [ "IN THE ARMY NOW", "release_year", "1994" ], [ "IT COULD HAPPEN TO YOU", "has_genre", "COMEDY" ], [ "IT COULD HAPPEN TO YOU", "has_tags", "COMEDY" ], [ "IT COULD HAPPEN TO YOU", "release_year", "1994" ], [ "IT RUNS IN THE FAMILY", "has_genre", "COMEDY" ], [ "IT RUNS IN THE FAMILY", "release_year", "1994" ], [ "IT'S PAT", "has_genre", "COMEDY" ], [ "IT'S PAT", "release_year", "1994" ], [ "JUNIOR", "has_genre", "COMEDY" ], [ "JUNIOR", "release_year", "1994" ], [ "KABHI HAAN KABHI NAA", "has_genre", "COMEDY" ], [ "KABHI HAAN KABHI NAA", "release_year", "1994" ], [ "KISS ME, STUPID", "has_genre", "COMEDY" ], [ "KISS ME, STUPID", "release_year", "1964" ], [ "KISSES FOR MY PRESIDENT", "has_genre", "COMEDY" ], [ "KISSES FOR MY PRESIDENT", "release_year", "1964" ], [ "LEPRECHAUN 2", "has_genre", "COMEDY" ], [ "LEPRECHAUN 2", "release_year", "1994" ], [ "LIGHTNING JACK", "has_genre", "COMEDY" ], [ "LIGHTNING JACK", "release_year", "1994" ], [ "LITTLE GIANTS", "has_genre", "COMEDY" ], [ "LITTLE GIANTS", "release_year", "1994" ], [ "MAN'S FAVORITE SPORT?", "has_genre", "COMEDY" ], [ "MAN'S FAVORITE SPORT?", "release_year", "1964" ], [ "MAVERICK", "has_genre", "COMEDY" ], [ "MAVERICK", "has_tags", "COMEDY" ], [ "MAVERICK", "release_year", "1994" ], [ "MILK MONEY", "has_genre", "COMEDY" ], [ "MILK MONEY", "release_year", "1994" ], [ "MIXED NUTS", "has_genre", "COMEDY" ], [ "MIXED NUTS", "release_year", "1994" ], [ "MONKEY TROUBLE", "has_genre", "COMEDY" ], [ "MONKEY TROUBLE", "release_year", "1994" ], [ "MURIEL'S WEDDING", "has_genre", "COMEDY" ], [ "MURIEL'S WEDDING", "has_tags", "COMEDY" ], [ "MURIEL'S WEDDING", "release_year", "1994" ], [ "MY GIRL 2", "has_genre", "COMEDY" ], [ "MY GIRL 2", "release_year", "1994" ], [ "NELL", "directed_by", "MICHAEL APTED" ], [ "NELL", "release_year", "1994" ], [ "NOBODY'S FOOL", "has_genre", "COMEDY" ], [ "NOBODY'S FOOL", "release_year", "1994" ], [ "NORTH", "has_genre", "COMEDY" ], [ "NORTH", "release_year", "1994" ], [ "ONLY YOU", "has_genre", "COMEDY" ], [ "ONLY YOU", "release_year", "1994" ], [ "PARIS WHEN IT SIZZLES", "has_genre", "COMEDY" ], [ "PARIS WHEN IT SIZZLES", "release_year", "1964" ], [ "PCU", "has_genre", "COMEDY" ], [ "PCU", "release_year", "1994" ], [ "PRINCESS CARABOO", "has_genre", "COMEDY" ], [ "PRINCESS CARABOO", "release_year", "1994" ], [ "PULP FICTION", "has_tags", "COMEDY" ], [ "PULP FICTION", "release_year", "1994" ], [ "RADIOLAND MURDERS", "has_genre", "COMEDY" ], [ "RADIOLAND MURDERS", "release_year", "1994" ], [ "REALITY BITES", "has_genre", "COMEDY" ], [ "REALITY BITES", "release_year", "1994" ], [ "RENAISSANCE MAN", "has_genre", "COMEDY" ], [ "RENAISSANCE MAN", "release_year", "1994" ], [ "SANTA CLAUS CONQUERS THE MARTIANS", "has_genre", "COMEDY" ], [ "SANTA CLAUS CONQUERS THE MARTIANS", "release_year", "1964" ], [ "SEND ME NO FLOWERS", "has_genre", "COMEDY" ], [ "SEND ME NO FLOWERS", "release_year", "1964" ], [ "SERIAL MOM", "has_genre", "COMEDY" ], [ "SERIAL MOM", "has_tags", "COMEDY" ], [ "SERIAL MOM", "release_year", "1994" ], [ "SEX AND THE SINGLE GIRL", "has_genre", "COMEDY" ], [ "SEX AND THE SINGLE GIRL", "release_year", "1964" ], [ "SLEEP WITH ME", "has_genre", "COMEDY" ], [ "SLEEP WITH ME", "release_year", "1994" ], [ "SPANKING THE MONKEY", "has_genre", "COMEDY" ], [ "SPANKING THE MONKEY", "release_year", "1994" ], [ "SPEECHLESS", "has_genre", "COMEDY" ], [ "SPEECHLESS", "release_year", "1994" ], [ "STAGGERED", "has_genre", "COMEDY" ], [ "STAGGERED", "release_year", "1994" ], [ "SWIMMING WITH SHARKS", "has_genre", "COMEDY" ], [ "SWIMMING WITH SHARKS", "release_year", "1994" ], [ "TAMMY AND THE T-REX", "has_genre", "COMEDY" ], [ "TAMMY AND THE T-REX", "release_year", "1994" ], [ "THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT", "has_genre", "COMEDY" ], [ "THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT", "release_year", "1994" ], [ "THE AIR UP THERE", "has_genre", "COMEDY" ], [ "THE AIR UP THERE", "has_tags", "COMEDY" ], [ "THE AIR UP THERE", "release_year", "1994" ], [ "THE AMERICANIZATION OF EMILY", "has_genre", "COMEDY" ], [ "THE AMERICANIZATION OF EMILY", "release_year", "1964" ], [ "THE BEST MAN", "has_genre", "COMEDY" ], [ "THE BEST MAN", "release_year", "1964" ], [ "THE CHASE", "has_genre", "COMEDY" ], [ "THE CHASE", "release_year", "1994" ], [ "THE COWBOY WAY", "has_genre", "COMEDY" ], [ "THE COWBOY WAY", "release_year", "1994" ], [ "THE DISORDERLY ORDERLY", "has_genre", "COMEDY" ], [ "THE DISORDERLY ORDERLY", "release_year", "1964" ], [ "THE FAVOR", "has_genre", "COMEDY" ], [ "THE FAVOR", "release_year", "1994" ], [ "THE FLINTSTONES", "has_genre", "COMEDY" ], [ "THE FLINTSTONES", "has_tags", "COMEDY" ], [ "THE FLINTSTONES", "release_year", "1994" ], [ "THE HUDSUCKER PROXY", "has_genre", "COMEDY" ], [ "THE HUDSUCKER PROXY", "has_tags", "COMEDY" ], [ "THE HUDSUCKER PROXY", "release_year", "1994" ], [ "THE INKWELL", "has_genre", "COMEDY" ], [ "THE INKWELL", "release_year", "1994" ], [ "THE LITTLE RASCALS", "has_genre", "COMEDY" ], [ "THE LITTLE RASCALS", "release_year", "1994" ], [ "THE MASK", "has_genre", "COMEDY" ], [ "THE MASK", "has_tags", "COMEDY" ], [ "THE MASK", "release_year", "1994" ], [ "THE MONSTER", "has_genre", "COMEDY" ], [ "THE MONSTER", "has_tags", "COMEDY" ], [ "THE MONSTER", "release_year", "1994" ], [ "THE PAPER", "has_genre", "COMEDY" ], [ "THE PAPER", "release_year", "1994" ], [ "THE PATSY", "has_genre", "COMEDY" ], [ "THE PATSY", "release_year", "1964" ], [ "THE REF", "has_genre", "COMEDY" ], [ "THE REF", "has_tags", "COMEDY" ], [ "THE REF", "release_year", "1994" ], [ "THE ROAD TO WELLVILLE", "has_genre", "COMEDY" ], [ "THE ROAD TO WELLVILLE", "release_year", "1994" ], [ "THE SANTA CLAUSE", "has_genre", "COMEDY" ], [ "THE SANTA CLAUSE", "release_year", "1994" ], [ "THE SCOUT", "has_genre", "COMEDY" ], [ "THE SCOUT", "release_year", "1994" ], [ "THE SEARCH FOR ONE-EYE JIMMY", "has_genre", "COMEDY" ], [ "THE SEARCH FOR ONE-EYE JIMMY", "release_year", "1994" ], [ "THE SUM OF US", "has_genre", "COMEDY" ], [ "THE SUM OF US", "release_year", "1994" ], [ "THE WORLD OF HENRY ORIENT", "has_genre", "COMEDY" ], [ "THE WORLD OF HENRY ORIENT", "has_tags", "COMEDY" ], [ "THE WORLD OF HENRY ORIENT", "release_year", "1964" ], [ "THREESOME", "has_genre", "COMEDY" ], [ "THREESOME", "has_tags", "COMEDY" ], [ "THREESOME", "release_year", "1994" ], [ "TRAPPED IN PARADISE", "has_genre", "COMEDY" ], [ "TRAPPED IN PARADISE", "release_year", "1994" ], [ "TRUE LIES", "has_genre", "COMEDY" ], [ "TRUE LIES", "has_tags", "COMEDY" ], [ "TRUE LIES", "release_year", "1994" ], [ "TWIN SITTERS", "has_genre", "COMEDY" ], [ "TWIN SITTERS", "release_year", "1994" ], [ "UPTOWN GIRLS", "has_genre", "COMEDY" ], [ "UPTOWN GIRLS", "written_by", "MO OGRODNIK" ], [ "WELCOME, OR NO TRESPASSING", "has_genre", "COMEDY" ], [ "WELCOME, OR NO TRESPASSING", "release_year", "1964" ], [ "WHAT A WAY TO GO!", "has_genre", "COMEDY" ], [ "WHAT A WAY TO GO!", "release_year", "1964" ], [ "WITH HONORS", "has_genre", "COMEDY" ], [ "WITH HONORS", "release_year", "1994" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 4210, A FINE MADNESS 30463, COMEDY 80, LABOR PAINS 33319, LARA SHAPIRO 24096, MARY WALSH 16645, NEW WATERFORD GIRL 36591, SEAN CONNERY src, edge_attr, dst 4210, has_genre, 30463 4210, starred_actors, 36591 80, directed_by, 33319 80, has_genre, 30463 80, written_by, 33319 16645, has_genre, 30463 16645, starred_actors, 24096 Question: How are LARA SHAPIRO, MARY WALSH, and SEAN CONNERY related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "LARA SHAPIRO", "MARY WALSH", "SEAN CONNERY" ], "valid_edges": [ [ "A FINE MADNESS", "has_genre", "COMEDY" ], [ "A FINE MADNESS", "starred_actors", "SEAN CONNERY" ], [ "LABOR PAINS", "directed_by", "LARA SHAPIRO" ], [ "LABOR PAINS", "has_genre", "COMEDY" ], [ "LABOR PAINS", "written_by", "LARA SHAPIRO" ], [ "NEW WATERFORD GIRL", "has_genre", "COMEDY" ], [ "NEW WATERFORD GIRL", "starred_actors", "MARY WALSH" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 38680, COMPULSION 952, HEATHER GRAHAM 14037, MARY 27490, PHILIPPE ARACTINGI 22652, SEBASTIAN SHAW 15966, THE SPY IN BLACK 24811, THRILLER 19779, UNDER THE BOMBS 22214, WAR src, edge_attr, dst 38680, has_genre, 24811 38680, starred_actors, 952 14037, has_genre, 24811 14037, starred_actors, 952 15966, has_genre, 24811 15966, has_genre, 22214 15966, starred_actors, 22652 19779, directed_by, 27490 19779, has_genre, 22214 19779, written_by, 27490 Question: In what context are HEATHER GRAHAM, PHILIPPE ARACTINGI, and SEBASTIAN SHAW connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "HEATHER GRAHAM", "PHILIPPE ARACTINGI", "SEBASTIAN SHAW" ], "valid_edges": [ [ "COMPULSION", "has_genre", "THRILLER" ], [ "COMPULSION", "starred_actors", "HEATHER GRAHAM" ], [ "MARY", "has_genre", "THRILLER" ], [ "MARY", "starred_actors", "HEATHER GRAHAM" ], [ "THE SPY IN BLACK", "has_genre", "THRILLER" ], [ "THE SPY IN BLACK", "has_genre", "WAR" ], [ "THE SPY IN BLACK", "starred_actors", "SEBASTIAN SHAW" ], [ "UNDER THE BOMBS", "directed_by", "PHILIPPE ARACTINGI" ], [ "UNDER THE BOMBS", "has_genre", "WAR" ], [ "UNDER THE BOMBS", "written_by", "PHILIPPE ARACTINGI" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 7034, CAN'T HELP SINGING 30615, CHRIS SANDERS 3461, IN OLD OKLAHOMA 10422, MARTHA SCOTT 23695, MULAN 24593, MUSICAL 35406, ROBERT PAIGE 36026, WESTERN src, edge_attr, dst 7034, has_genre, 24593 7034, has_genre, 36026 7034, starred_actors, 35406 3461, has_genre, 36026 3461, starred_actors, 10422 23695, has_tags, 24593 23695, written_by, 30615 Question: How are CHRIS SANDERS, MARTHA SCOTT, and ROBERT PAIGE related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "CHRIS SANDERS", "MARTHA SCOTT", "ROBERT PAIGE" ], "valid_edges": [ [ "CAN'T HELP SINGING", "has_genre", "MUSICAL" ], [ "CAN'T HELP SINGING", "has_genre", "WESTERN" ], [ "CAN'T HELP SINGING", "starred_actors", "ROBERT PAIGE" ], [ "IN OLD OKLAHOMA", "has_genre", "WESTERN" ], [ "IN OLD OKLAHOMA", "starred_actors", "MARTHA SCOTT" ], [ "MULAN", "has_tags", "MUSICAL" ], [ "MULAN", "written_by", "CHRIS SANDERS" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 20854, ALBERT BEICH 10045, BD-R 34664, BLOOD FROM THE MUMMY'S TOMB 29300, DEAD RINGER 21801, ROB EPSTEIN 9266, THE TIMES OF HARVEY MILK 12899, VALERIE LEON src, edge_attr, dst 34664, has_tags, 10045 34664, starred_actors, 12899 29300, has_tags, 10045 29300, written_by, 20854 9266, directed_by, 21801 9266, has_tags, 10045 9266, has_tags, 21801 Question: For what reason are ALBERT BEICH, ROB EPSTEIN, and VALERIE LEON associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ALBERT BEICH", "ROB EPSTEIN", "VALERIE LEON" ], "valid_edges": [ [ "BLOOD FROM THE MUMMY'S TOMB", "has_tags", "BD-R" ], [ "BLOOD FROM THE MUMMY'S TOMB", "starred_actors", "VALERIE LEON" ], [ "DEAD RINGER", "has_tags", "BD-R" ], [ "DEAD RINGER", "written_by", "ALBERT BEICH" ], [ "THE TIMES OF HARVEY MILK", "directed_by", "ROB EPSTEIN" ], [ "THE TIMES OF HARVEY MILK", "has_tags", "BD-R" ], [ "THE TIMES OF HARVEY MILK", "has_tags", "ROB EPSTEIN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 25221, 1981 24818, 1992 9005, 3 NINJAS 30146, A CHRISTMAS CAROL 16150, A CHRISTMAS STORY 9698, A CHRISTMAS TALE 31344, A LEAGUE OF THEIR OWN 10158, A PRINCESS FOR CHRISTMAS 28540, AN AMERICAN CAROL 26858, ARMY OF DARKNESS 14159, ARTHUR CHRISTMAS 34109, BAD SANTA 14271, BEETHOVEN 9127, BIG GIRLS DON'T CRY... THEY GET EVEN 32193, BLAME IT ON THE BELLBOY 14984, BLOW OUT 19829, BOOMERANG 8606, BRAIN DONORS 8065, BRIAN DE PALMA 23319, BUFFY THE VAMPIRE SLAYER 33773, BÉBÉ'S KIDS 10877, CAPTAIN RON 17280, CHRISTMAS 32407, CHRISTMAS IN CONNECTICUT 24598, CHRISTMAS WITH THE KRANKS 15721, CIAO, PROFESSORE! 12356, CLASS ACT 30463, COMEDY 11807, CRITTERS 4 27380, DEATH BECOMES HER 38345, DECK THE HALLS 771, DRESSED TO KILL 9505, ELF 9457, ENCINO MAN 29258, ERNEST SAVES CHRISTMAS 31630, EVIL TOONS 5636, FOLKS! 22005, FOUR CHRISTMASES 32553, FROZEN ASSETS 34177, GREMLINS 3829, HERO 18278, HOLIDAY IN HANDCUFFS 30886, HOME ALONE 18546, HONEYMOON IN VEGAS 25277, HUSBANDS AND WIVES 26137, I WANNA HOLD YOUR HAND 32168, ILLUSIVE TRACKS 4419, IN THE SOUP 19868, JACK FROST 4798, JINGLE ALL THE WAY 11719, JUST FRIENDS 25510, KING OF BEGGARS 13704, KUFFS 18950, LADYBUGS 191, LEAVING NORMAL 24500, LITTLE SISTER 3591, LIVE NUDE GIRLS 13672, LOVE ACTUALLY 19507, MAKE THE YULETIDE GAY 8268, MALEDETTO IL GIORNO CHE T'HO INCONTRATO 10560, MAN BITES DOG 24068, MAN TROUBLE 4561, MEMOIRS OF AN INVISIBLE MAN 34536, MISTRESS 12371, MIXED NUTS 39399, MO' MONEY 17135, MOM AND DAD SAVE THE WORLD 815, MR. BASEBALL 21919, MY COUSIN VINNY 2581, MY NEW GUN 27242, NANCY ALLEN 14917, OUT ON A LIMB 16910, PARENTI SERPENTI 25678, PETER'S FRIENDS 23220, REMEMBER THE NIGHT 4524, SANTA WITH MUSCLES 21320, SANTA'S SLAY 4187, SCROOGED 21512, SHERLOCK HOLMES 3358, SINGLES 8652, SISTER ACT 17370, STAY TUNED 2033, STOP! OR MY MOM WILL SHOOT 22993, STRAIGHT TALK 1432, STRICTLY BALLROOM 36928, THE BIKINI CARWASH COMPANY 13869, THE DISTINGUISHED GENTLEMAN 28948, THE FAMILY STONE 21831, THE GUN IN BETTY LOU'S HANDBAG 26955, THE HOLIDAY 24724, THE MIGHTY DUCKS 26128, THE MUPPET CHRISTMAS CAROL 24035, THE NORTHERNERS 28470, THE PERFECT HOLIDAY 10859, THE PREACHER'S WIFE 17314, THE SANTA CLAUSE 16918, THIS CHRISTMAS 24811, THRILLER 22911, TO GRANDMOTHER'S HOUSE WE GO 2281, TOYS 7279, TWIN DRAGONS 10376, USED PEOPLE 38839, WAYNE'S WORLD 22241, WE'RE NO ANGELS 20751, WHITE MEN CAN'T JUMP src, edge_attr, dst 25221, has_genre, 30463 9005, has_genre, 30463 9005, release_year, 24818 30146, has_genre, 30463 30146, has_tags, 17280 16150, has_genre, 30463 16150, has_tags, 17280 9698, has_genre, 30463 9698, has_tags, 17280 31344, has_genre, 30463 31344, release_year, 24818 10158, has_genre, 30463 10158, has_tags, 17280 28540, has_genre, 30463 28540, has_tags, 17280 26858, has_genre, 30463 26858, has_tags, 30463 26858, release_year, 24818 14159, has_genre, 30463 14159, has_tags, 17280 34109, has_genre, 30463 34109, has_tags, 17280 14271, has_genre, 30463 14271, has_tags, 30463 14271, release_year, 24818 9127, has_genre, 30463 9127, release_year, 24818 32193, has_genre, 30463 32193, release_year, 24818 14984, directed_by, 8065 14984, has_genre, 24811 14984, has_tags, 8065 14984, release_year, 25221 14984, starred_actors, 27242 14984, written_by, 8065 19829, has_genre, 30463 19829, release_year, 24818 8606, has_genre, 30463 8606, release_year, 24818 23319, has_genre, 30463 23319, has_tags, 30463 23319, release_year, 24818 33773, has_genre, 30463 33773, release_year, 24818 10877, has_genre, 30463 10877, has_tags, 30463 10877, release_year, 24818 32407, has_genre, 30463 32407, has_tags, 17280 24598, has_genre, 30463 24598, has_tags, 17280 15721, has_genre, 30463 15721, release_year, 24818 12356, has_genre, 30463 12356, release_year, 24818 11807, has_genre, 30463 11807, release_year, 24818 27380, has_genre, 30463 27380, release_year, 24818 38345, has_genre, 30463 38345, has_tags, 17280 771, directed_by, 8065 771, has_genre, 24811 771, has_tags, 8065 771, has_tags, 21512 771, starred_actors, 27242 771, written_by, 8065 9505, has_genre, 30463 9505, has_tags, 17280 9505, has_tags, 30463 9457, has_genre, 30463 9457, release_year, 24818 29258, has_genre, 30463 29258, has_tags, 17280 31630, has_genre, 30463 31630, release_year, 24818 5636, has_genre, 30463 5636, release_year, 24818 22005, has_genre, 30463 22005, has_tags, 17280 32553, has_genre, 30463 32553, release_year, 24818 34177, has_genre, 30463 34177, has_tags, 17280 3829, has_genre, 30463 3829, release_year, 24818 18278, has_genre, 30463 18278, has_tags, 17280 30886, has_genre, 30463 30886, has_tags, 17280 30886, has_tags, 30463 18546, has_genre, 30463 18546, release_year, 24818 25277, has_genre, 30463 25277, release_year, 24818 26137, has_genre, 30463 26137, starred_actors, 27242 32168, has_genre, 30463 32168, has_tags, 17280 4419, has_genre, 30463 4419, release_year, 24818 19868, has_genre, 30463 19868, has_tags, 17280 4798, has_genre, 30463 4798, has_tags, 17280 11719, has_genre, 30463 11719, has_tags, 17280 25510, has_genre, 30463 25510, release_year, 24818 13704, has_genre, 30463 13704, release_year, 24818 18950, has_genre, 30463 18950, release_year, 24818 191, has_genre, 30463 191, release_year, 24818 24500, has_genre, 30463 24500, release_year, 24818 3591, has_genre, 30463 13672, has_genre, 30463 13672, has_tags, 17280 13672, has_tags, 30463 19507, has_genre, 30463 19507, has_tags, 17280 8268, has_genre, 30463 8268, release_year, 24818 10560, has_genre, 30463 10560, release_year, 24818 24068, has_genre, 30463 24068, release_year, 24818 4561, has_genre, 30463 4561, release_year, 24818 34536, has_genre, 30463 34536, release_year, 24818 12371, has_genre, 30463 12371, has_tags, 17280 39399, has_genre, 30463 39399, release_year, 24818 17135, has_genre, 30463 17135, release_year, 24818 815, has_genre, 30463 815, release_year, 24818 21919, has_genre, 30463 21919, has_tags, 30463 21919, release_year, 24818 2581, has_genre, 30463 2581, release_year, 24818 14917, has_genre, 30463 14917, release_year, 24818 16910, has_genre, 30463 16910, release_year, 24818 25678, has_genre, 30463 25678, release_year, 24818 23220, has_genre, 30463 23220, has_tags, 17280 4524, has_genre, 30463 4524, has_tags, 17280 21320, has_genre, 30463 21320, has_tags, 17280 4187, has_genre, 30463 4187, has_tags, 17280 4187, has_tags, 30463 21512, has_tags, 30463 3358, has_genre, 30463 3358, release_year, 24818 8652, has_genre, 30463 8652, release_year, 24818 17370, has_genre, 30463 17370, release_year, 24818 2033, has_genre, 30463 2033, has_tags, 30463 2033, release_year, 24818 22993, has_genre, 30463 22993, release_year, 24818 1432, has_genre, 30463 1432, release_year, 24818 36928, has_genre, 30463 36928, release_year, 24818 13869, has_genre, 30463 13869, release_year, 24818 28948, has_genre, 30463 28948, has_tags, 17280 28948, has_tags, 30463 21831, has_genre, 30463 21831, release_year, 24818 26955, has_genre, 30463 26955, has_tags, 17280 24724, has_genre, 30463 24724, release_year, 24818 26128, has_genre, 30463 26128, has_tags, 17280 26128, release_year, 24818 24035, has_genre, 30463 24035, release_year, 24818 28470, has_genre, 30463 28470, has_tags, 17280 10859, has_genre, 30463 10859, has_tags, 17280 17314, has_genre, 30463 17314, has_tags, 17280 16918, has_genre, 30463 16918, has_tags, 17280 22911, has_tags, 17280 22911, release_year, 24818 2281, has_genre, 30463 2281, release_year, 24818 7279, has_genre, 30463 7279, release_year, 24818 10376, has_genre, 30463 10376, release_year, 24818 38839, has_genre, 30463 38839, has_tags, 30463 38839, release_year, 24818 22241, has_genre, 30463 22241, has_tags, 17280 22241, has_tags, 30463 20751, has_genre, 30463 20751, has_tags, 30463 20751, release_year, 24818 Question: In what context are LIVE NUDE GIRLS, NANCY ALLEN, and TO GRANDMOTHER'S HOUSE WE GO connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "LIVE NUDE GIRLS", "NANCY ALLEN", "TO GRANDMOTHER'S HOUSE WE GO" ], "valid_edges": [ [ "1981", "has_genre", "COMEDY" ], [ "3 NINJAS", "has_genre", "COMEDY" ], [ "3 NINJAS", "release_year", "1992" ], [ "A CHRISTMAS CAROL", "has_genre", "COMEDY" ], [ "A CHRISTMAS CAROL", "has_tags", "CHRISTMAS" ], [ "A CHRISTMAS STORY", "has_genre", "COMEDY" ], [ "A CHRISTMAS STORY", "has_tags", "CHRISTMAS" ], [ "A CHRISTMAS TALE", "has_genre", "COMEDY" ], [ "A CHRISTMAS TALE", "has_tags", "CHRISTMAS" ], [ "A LEAGUE OF THEIR OWN", "has_genre", "COMEDY" ], [ "A LEAGUE OF THEIR OWN", "release_year", "1992" ], [ "A PRINCESS FOR CHRISTMAS", "has_genre", "COMEDY" ], [ "A PRINCESS FOR CHRISTMAS", "has_tags", "CHRISTMAS" ], [ "AN AMERICAN CAROL", "has_genre", "COMEDY" ], [ "AN AMERICAN CAROL", "has_tags", "CHRISTMAS" ], [ "ARMY OF DARKNESS", "has_genre", "COMEDY" ], [ "ARMY OF DARKNESS", "has_tags", "COMEDY" ], [ "ARMY OF DARKNESS", "release_year", "1992" ], [ "ARTHUR CHRISTMAS", "has_genre", "COMEDY" ], [ "ARTHUR CHRISTMAS", "has_tags", "CHRISTMAS" ], [ "BAD SANTA", "has_genre", "COMEDY" ], [ "BAD SANTA", "has_tags", "CHRISTMAS" ], [ "BEETHOVEN", "has_genre", "COMEDY" ], [ "BEETHOVEN", "has_tags", "COMEDY" ], [ "BEETHOVEN", "release_year", "1992" ], [ "BIG GIRLS DON'T CRY... THEY GET EVEN", "has_genre", "COMEDY" ], [ "BIG GIRLS DON'T CRY... THEY GET EVEN", "release_year", "1992" ], [ "BLAME IT ON THE BELLBOY", "has_genre", "COMEDY" ], [ "BLAME IT ON THE BELLBOY", "release_year", "1992" ], [ "BLOW OUT", "directed_by", "BRIAN DE PALMA" ], [ "BLOW OUT", "has_genre", "THRILLER" ], [ "BLOW OUT", "has_tags", "BRIAN DE PALMA" ], [ "BLOW OUT", "release_year", "1981" ], [ "BLOW OUT", "starred_actors", "NANCY ALLEN" ], [ "BLOW OUT", "written_by", "BRIAN DE PALMA" ], [ "BOOMERANG", "has_genre", "COMEDY" ], [ "BOOMERANG", "release_year", "1992" ], [ "BRAIN DONORS", "has_genre", "COMEDY" ], [ "BRAIN DONORS", "release_year", "1992" ], [ "BUFFY THE VAMPIRE SLAYER", "has_genre", "COMEDY" ], [ "BUFFY THE VAMPIRE SLAYER", "has_tags", "COMEDY" ], [ "BUFFY THE VAMPIRE SLAYER", "release_year", "1992" ], [ "BÉBÉ'S KIDS", "has_genre", "COMEDY" ], [ "BÉBÉ'S KIDS", "release_year", "1992" ], [ "CAPTAIN RON", "has_genre", "COMEDY" ], [ "CAPTAIN RON", "has_tags", "COMEDY" ], [ "CAPTAIN RON", "release_year", "1992" ], [ "CHRISTMAS IN CONNECTICUT", "has_genre", "COMEDY" ], [ "CHRISTMAS IN CONNECTICUT", "has_tags", "CHRISTMAS" ], [ "CHRISTMAS WITH THE KRANKS", "has_genre", "COMEDY" ], [ "CHRISTMAS WITH THE KRANKS", "has_tags", "CHRISTMAS" ], [ "CIAO, PROFESSORE!", "has_genre", "COMEDY" ], [ "CIAO, PROFESSORE!", "release_year", "1992" ], [ "CLASS ACT", "has_genre", "COMEDY" ], [ "CLASS ACT", "release_year", "1992" ], [ "CRITTERS 4", "has_genre", "COMEDY" ], [ "CRITTERS 4", "release_year", "1992" ], [ "DEATH BECOMES HER", "has_genre", "COMEDY" ], [ "DEATH BECOMES HER", "release_year", "1992" ], [ "DECK THE HALLS", "has_genre", "COMEDY" ], [ "DECK THE HALLS", "has_tags", "CHRISTMAS" ], [ "DRESSED TO KILL", "directed_by", "BRIAN DE PALMA" ], [ "DRESSED TO KILL", "has_genre", "THRILLER" ], [ "DRESSED TO KILL", "has_tags", "BRIAN DE PALMA" ], [ "DRESSED TO KILL", "has_tags", "SHERLOCK HOLMES" ], [ "DRESSED TO KILL", "starred_actors", "NANCY ALLEN" ], [ "DRESSED TO KILL", "written_by", "BRIAN DE PALMA" ], [ "ELF", "has_genre", "COMEDY" ], [ "ELF", "has_tags", "CHRISTMAS" ], [ "ELF", "has_tags", "COMEDY" ], [ "ENCINO MAN", "has_genre", "COMEDY" ], [ "ENCINO MAN", "release_year", "1992" ], [ "ERNEST SAVES CHRISTMAS", "has_genre", "COMEDY" ], [ "ERNEST SAVES CHRISTMAS", "has_tags", "CHRISTMAS" ], [ "EVIL TOONS", "has_genre", "COMEDY" ], [ "EVIL TOONS", "release_year", "1992" ], [ "FOLKS!", "has_genre", "COMEDY" ], [ "FOLKS!", "release_year", "1992" ], [ "FOUR CHRISTMASES", "has_genre", "COMEDY" ], [ "FOUR CHRISTMASES", "has_tags", "CHRISTMAS" ], [ "FROZEN ASSETS", "has_genre", "COMEDY" ], [ "FROZEN ASSETS", "release_year", "1992" ], [ "GREMLINS", "has_genre", "COMEDY" ], [ "GREMLINS", "has_tags", "CHRISTMAS" ], [ "HERO", "has_genre", "COMEDY" ], [ "HERO", "release_year", "1992" ], [ "HOLIDAY IN HANDCUFFS", "has_genre", "COMEDY" ], [ "HOLIDAY IN HANDCUFFS", "has_tags", "CHRISTMAS" ], [ "HOME ALONE", "has_genre", "COMEDY" ], [ "HOME ALONE", "has_tags", "CHRISTMAS" ], [ "HOME ALONE", "has_tags", "COMEDY" ], [ "HONEYMOON IN VEGAS", "has_genre", "COMEDY" ], [ "HONEYMOON IN VEGAS", "release_year", "1992" ], [ "HUSBANDS AND WIVES", "has_genre", "COMEDY" ], [ "HUSBANDS AND WIVES", "release_year", "1992" ], [ "I WANNA HOLD YOUR HAND", "has_genre", "COMEDY" ], [ "I WANNA HOLD YOUR HAND", "starred_actors", "NANCY ALLEN" ], [ "ILLUSIVE TRACKS", "has_genre", "COMEDY" ], [ "ILLUSIVE TRACKS", "has_tags", "CHRISTMAS" ], [ "IN THE SOUP", "has_genre", "COMEDY" ], [ "IN THE SOUP", "release_year", "1992" ], [ "JACK FROST", "has_genre", "COMEDY" ], [ "JACK FROST", "has_tags", "CHRISTMAS" ], [ "JINGLE ALL THE WAY", "has_genre", "COMEDY" ], [ "JINGLE ALL THE WAY", "has_tags", "CHRISTMAS" ], [ "JUST FRIENDS", "has_genre", "COMEDY" ], [ "JUST FRIENDS", "has_tags", "CHRISTMAS" ], [ "KING OF BEGGARS", "has_genre", "COMEDY" ], [ "KING OF BEGGARS", "release_year", "1992" ], [ "KUFFS", "has_genre", "COMEDY" ], [ "KUFFS", "release_year", "1992" ], [ "LADYBUGS", "has_genre", "COMEDY" ], [ "LADYBUGS", "release_year", "1992" ], [ "LEAVING NORMAL", "has_genre", "COMEDY" ], [ "LEAVING NORMAL", "release_year", "1992" ], [ "LITTLE SISTER", "has_genre", "COMEDY" ], [ "LITTLE SISTER", "release_year", "1992" ], [ "LIVE NUDE GIRLS", "has_genre", "COMEDY" ], [ "LOVE ACTUALLY", "has_genre", "COMEDY" ], [ "LOVE ACTUALLY", "has_tags", "CHRISTMAS" ], [ "LOVE ACTUALLY", "has_tags", "COMEDY" ], [ "MAKE THE YULETIDE GAY", "has_genre", "COMEDY" ], [ "MAKE THE YULETIDE GAY", "has_tags", "CHRISTMAS" ], [ "MALEDETTO IL GIORNO CHE T'HO INCONTRATO", "has_genre", "COMEDY" ], [ "MALEDETTO IL GIORNO CHE T'HO INCONTRATO", "release_year", "1992" ], [ "MAN BITES DOG", "has_genre", "COMEDY" ], [ "MAN BITES DOG", "release_year", "1992" ], [ "MAN TROUBLE", "has_genre", "COMEDY" ], [ "MAN TROUBLE", "release_year", "1992" ], [ "MEMOIRS OF AN INVISIBLE MAN", "has_genre", "COMEDY" ], [ "MEMOIRS OF AN INVISIBLE MAN", "release_year", "1992" ], [ "MISTRESS", "has_genre", "COMEDY" ], [ "MISTRESS", "release_year", "1992" ], [ "MIXED NUTS", "has_genre", "COMEDY" ], [ "MIXED NUTS", "has_tags", "CHRISTMAS" ], [ "MO' MONEY", "has_genre", "COMEDY" ], [ "MO' MONEY", "release_year", "1992" ], [ "MOM AND DAD SAVE THE WORLD", "has_genre", "COMEDY" ], [ "MOM AND DAD SAVE THE WORLD", "release_year", "1992" ], [ "MR. BASEBALL", "has_genre", "COMEDY" ], [ "MR. BASEBALL", "release_year", "1992" ], [ "MY COUSIN VINNY", "has_genre", "COMEDY" ], [ "MY COUSIN VINNY", "has_tags", "COMEDY" ], [ "MY COUSIN VINNY", "release_year", "1992" ], [ "MY NEW GUN", "has_genre", "COMEDY" ], [ "MY NEW GUN", "release_year", "1992" ], [ "OUT ON A LIMB", "has_genre", "COMEDY" ], [ "OUT ON A LIMB", "release_year", "1992" ], [ "PARENTI SERPENTI", "has_genre", "COMEDY" ], [ "PARENTI SERPENTI", "release_year", "1992" ], [ "PETER'S FRIENDS", "has_genre", "COMEDY" ], [ "PETER'S FRIENDS", "release_year", "1992" ], [ "REMEMBER THE NIGHT", "has_genre", "COMEDY" ], [ "REMEMBER THE NIGHT", "has_tags", "CHRISTMAS" ], [ "SANTA WITH MUSCLES", "has_genre", "COMEDY" ], [ "SANTA WITH MUSCLES", "has_tags", "CHRISTMAS" ], [ "SANTA'S SLAY", "has_genre", "COMEDY" ], [ "SANTA'S SLAY", "has_tags", "CHRISTMAS" ], [ "SCROOGED", "has_genre", "COMEDY" ], [ "SCROOGED", "has_tags", "CHRISTMAS" ], [ "SCROOGED", "has_tags", "COMEDY" ], [ "SHERLOCK HOLMES", "has_tags", "COMEDY" ], [ "SINGLES", "has_genre", "COMEDY" ], [ "SINGLES", "release_year", "1992" ], [ "SISTER ACT", "has_genre", "COMEDY" ], [ "SISTER ACT", "release_year", "1992" ], [ "STAY TUNED", "has_genre", "COMEDY" ], [ "STAY TUNED", "release_year", "1992" ], [ "STOP! OR MY MOM WILL SHOOT", "has_genre", "COMEDY" ], [ "STOP! OR MY MOM WILL SHOOT", "has_tags", "COMEDY" ], [ "STOP! OR MY MOM WILL SHOOT", "release_year", "1992" ], [ "STRAIGHT TALK", "has_genre", "COMEDY" ], [ "STRAIGHT TALK", "release_year", "1992" ], [ "STRICTLY BALLROOM", "has_genre", "COMEDY" ], [ "STRICTLY BALLROOM", "release_year", "1992" ], [ "THE BIKINI CARWASH COMPANY", "has_genre", "COMEDY" ], [ "THE BIKINI CARWASH COMPANY", "release_year", "1992" ], [ "THE DISTINGUISHED GENTLEMAN", "has_genre", "COMEDY" ], [ "THE DISTINGUISHED GENTLEMAN", "release_year", "1992" ], [ "THE FAMILY STONE", "has_genre", "COMEDY" ], [ "THE FAMILY STONE", "has_tags", "CHRISTMAS" ], [ "THE FAMILY STONE", "has_tags", "COMEDY" ], [ "THE GUN IN BETTY LOU'S HANDBAG", "has_genre", "COMEDY" ], [ "THE GUN IN BETTY LOU'S HANDBAG", "release_year", "1992" ], [ "THE HOLIDAY", "has_genre", "COMEDY" ], [ "THE HOLIDAY", "has_tags", "CHRISTMAS" ], [ "THE MIGHTY DUCKS", "has_genre", "COMEDY" ], [ "THE MIGHTY DUCKS", "release_year", "1992" ], [ "THE MUPPET CHRISTMAS CAROL", "has_genre", "COMEDY" ], [ "THE MUPPET CHRISTMAS CAROL", "has_tags", "CHRISTMAS" ], [ "THE MUPPET CHRISTMAS CAROL", "release_year", "1992" ], [ "THE NORTHERNERS", "has_genre", "COMEDY" ], [ "THE NORTHERNERS", "release_year", "1992" ], [ "THE PERFECT HOLIDAY", "has_genre", "COMEDY" ], [ "THE PERFECT HOLIDAY", "has_tags", "CHRISTMAS" ], [ "THE PREACHER'S WIFE", "has_genre", "COMEDY" ], [ "THE PREACHER'S WIFE", "has_tags", "CHRISTMAS" ], [ "THE SANTA CLAUSE", "has_genre", "COMEDY" ], [ "THE SANTA CLAUSE", "has_tags", "CHRISTMAS" ], [ "THIS CHRISTMAS", "has_genre", "COMEDY" ], [ "THIS CHRISTMAS", "has_tags", "CHRISTMAS" ], [ "TO GRANDMOTHER'S HOUSE WE GO", "has_tags", "CHRISTMAS" ], [ "TO GRANDMOTHER'S HOUSE WE GO", "release_year", "1992" ], [ "TOYS", "has_genre", "COMEDY" ], [ "TOYS", "release_year", "1992" ], [ "TWIN DRAGONS", "has_genre", "COMEDY" ], [ "TWIN DRAGONS", "release_year", "1992" ], [ "USED PEOPLE", "has_genre", "COMEDY" ], [ "USED PEOPLE", "release_year", "1992" ], [ "WAYNE'S WORLD", "has_genre", "COMEDY" ], [ "WAYNE'S WORLD", "has_tags", "COMEDY" ], [ "WAYNE'S WORLD", "release_year", "1992" ], [ "WE'RE NO ANGELS", "has_genre", "COMEDY" ], [ "WE'RE NO ANGELS", "has_tags", "CHRISTMAS" ], [ "WE'RE NO ANGELS", "has_tags", "COMEDY" ], [ "WHITE MEN CAN'T JUMP", "has_genre", "COMEDY" ], [ "WHITE MEN CAN'T JUMP", "has_tags", "COMEDY" ], [ "WHITE MEN CAN'T JUMP", "release_year", "1992" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15271, ARIA 3957, BLUTZBRÜDAZ 33088, CARMEN 19500, FIRST SNOW 6480, GERMAN 7930, GUY PEARCE 1292, HEDWIG AND THE ANGRY INCH 18279, METROPOLIS 22845, MUSIC 21918, SIMON MACCORKINDALE 10522, TAKING SIDES 25270, THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT 34277, THE APPLE 25230, THE RIDDLE OF THE SANDS 37901, THE SOUND OF MUSIC src, edge_attr, dst 15271, has_genre, 22845 15271, in_language, 6480 3957, has_genre, 22845 3957, in_language, 6480 33088, has_genre, 22845 33088, in_language, 6480 19500, starred_actors, 7930 1292, has_genre, 22845 1292, has_tags, 22845 1292, in_language, 6480 18279, has_tags, 22845 18279, in_language, 6480 10522, has_genre, 22845 10522, in_language, 6480 25270, has_genre, 22845 25270, has_tags, 7930 25270, starred_actors, 7930 34277, has_genre, 22845 34277, has_tags, 22845 34277, in_language, 6480 25230, in_language, 6480 25230, starred_actors, 21918 37901, has_tags, 22845 37901, in_language, 6480 Question: For what reason are BLUTZBRÜDAZ, FIRST SNOW, and SIMON MACCORKINDALE associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BLUTZBRÜDAZ", "FIRST SNOW", "SIMON MACCORKINDALE" ], "valid_edges": [ [ "ARIA", "has_genre", "MUSIC" ], [ "ARIA", "in_language", "GERMAN" ], [ "BLUTZBRÜDAZ", "has_genre", "MUSIC" ], [ "BLUTZBRÜDAZ", "in_language", "GERMAN" ], [ "CARMEN", "has_genre", "MUSIC" ], [ "CARMEN", "in_language", "GERMAN" ], [ "FIRST SNOW", "starred_actors", "GUY PEARCE" ], [ "HEDWIG AND THE ANGRY INCH", "has_genre", "MUSIC" ], [ "HEDWIG AND THE ANGRY INCH", "has_tags", "MUSIC" ], [ "HEDWIG AND THE ANGRY INCH", "in_language", "GERMAN" ], [ "METROPOLIS", "has_tags", "MUSIC" ], [ "METROPOLIS", "in_language", "GERMAN" ], [ "TAKING SIDES", "has_genre", "MUSIC" ], [ "TAKING SIDES", "in_language", "GERMAN" ], [ "THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT", "has_genre", "MUSIC" ], [ "THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT", "has_tags", "GUY PEARCE" ], [ "THE ADVENTURES OF PRISCILLA, QUEEN OF THE DESERT", "starred_actors", "GUY PEARCE" ], [ "THE APPLE", "has_genre", "MUSIC" ], [ "THE APPLE", "has_tags", "MUSIC" ], [ "THE APPLE", "in_language", "GERMAN" ], [ "THE RIDDLE OF THE SANDS", "in_language", "GERMAN" ], [ "THE RIDDLE OF THE SANDS", "starred_actors", "SIMON MACCORKINDALE" ], [ "THE SOUND OF MUSIC", "has_tags", "MUSIC" ], [ "THE SOUND OF MUSIC", "in_language", "GERMAN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 4177, 2 DAYS IN PARIS 13747, 25 WATTS 31344, A LEAGUE OF THEIR OWN 37090, A LITTLE HELP 20033, ANGEL 28344, ANGUS 16052, ANNE OF THE THOUSAND DAYS 5593, BABY BOY 23952, BANDITS 27714, BARNEY'S VERSION 13418, BARTLEBY 28846, BEGINNERS 31957, BOY 17892, CAMOUFLAGE 18996, CEMETERY JUNCTION 13257, CHARLIE BARTLETT 13901, CHARLIE WILSON'S WAR 10349, CHICAGO 30463, COMEDY 23734, COUPE DE VILLE 656, CRUSH 16600, CYRUS 22663, DAN IN REAL LIFE 22667, DAYS OF DARKNESS 1915, DIL CHAHTA HAI 3172, DIRTY GIRL 36212, DRAMA 25651, DUMMY 14240, EVERYTHING MUST GO 15188, EXPIRED 2969, FATHER OF INVENTION 34555, FLAWLESS 24880, FLIPPED 6915, FOCUS 3079, FREEDOM WRITERS 5287, FROZEN 2155, GEORGIA RULE 12664, GHOST WORLD 27638, GREENBERG 19912, GRIFF THE INVISIBLE 33701, HAPPYTHANKYOUMOREPLEASE 21060, HARVARD MAN 1292, HEDWIG AND THE ANGRY INCH 3829, HERO 19039, HESHER 38901, HIGH HEELS AND LOW LIFES 37827, HIGH SCHOOL 4931, HOW DO YOU KNOW 35625, HUMAN NATURE 25277, HUSBANDS AND WIVES 34856, I THINK I LOVE MY WIFE 9359, IMELDA STAUNTON 21788, IT'S KIND OF A FUNNY STORY 23526, JACKPOT 12610, JOE SOMEBODY 37372, JUNO 4619, KING OF CALIFORNIA 7699, KINGDOM COME 37238, KNOCKED UP 39069, LARS AND THE REAL GIRL 191, LEAVING NORMAL 9452, LIFE AS WE KNOW IT 29734, LITTLE SECRETS 16814, LITTLE WHITE LIES 33238, MARTIAN CHILD 33714, MEAN MACHINE 22924, MEET MONICA VELOUR 2990, MERMAID 18214, MISTER LONELY 34536, MISTRESS 16662, MORNING GLORY 24616, MOSTLY MARTHA 9519, NO RESERVATIONS 33677, NOISE 32186, ONE MAN UP 28044, PAULINE AND PAULETTE 11129, PEEP WORLD 25678, PETER'S FRIENDS 31652, PRETTY IN PINK 28182, RARE BIRDS 18177, REIGN OVER ME 38381, ROCK STAR 15095, ROCKET SCIENCE 12153, RUSHMORE 22312, SAY ANYTHING... 26699, SHORT CUTS 37133, SOMEWHERE 2407, SON OF THE BRIDE 19971, SPIN 7949, STORYTELLING 1432, STRICTLY BALLROOM 25002, STRUCK BY LIGHTNING 30733, SUBMARINE 25865, SUBURBIA 27762, SWEET NOVEMBER 17992, SWING VOTE 34308, TEACHERS 15795, THE ANNIVERSARY PARTY 17931, THE BREAKFAST CLUB 25674, THE BROTHERS 13582, THE BUCKET LIST 32654, THE CHOSEN ONE 8512, THE DUKES 36722, THE FOUR-FACED LIAR 2507, THE KIDS ARE ALL RIGHT 28107, THE LAST KISS 25623, THE MATRIARCH 1345, THE NANNY DIARIES 20728, THE ROYAL TENENBAUMS 27067, THEN SHE FOUND ME 16918, THIS CHRISTMAS 1903, TINY FURNITURE 35988, TORTILLA SOUP 11987, TRUST 26275, TRUST ME 9436, VIVA 35443, VIZONTELE 4460, WAITRESS 33258, WEST IS WEST 29999, WHY DID I GET MARRIED TOO? 6287, WHY DID I GET MARRIED? 1287, WILDER NAPALM 29421, YEAR OF THE DOG 6279, YOU WILL MEET A TALL DARK STRANGER src, edge_attr, dst 4177, has_genre, 30463 4177, has_genre, 36212 13747, has_genre, 30463 13747, has_genre, 36212 31344, has_genre, 30463 31344, has_genre, 36212 31344, has_tags, 36212 37090, has_genre, 30463 37090, has_genre, 36212 20033, has_genre, 30463 20033, has_genre, 36212 28344, has_genre, 30463 28344, has_genre, 36212 16052, has_genre, 36212 5593, has_genre, 30463 5593, has_genre, 36212 23952, has_genre, 30463 23952, has_genre, 36212 27714, has_genre, 30463 27714, has_genre, 36212 13418, has_genre, 30463 13418, has_genre, 36212 28846, has_genre, 30463 28846, has_genre, 36212 28846, has_tags, 36212 31957, has_genre, 30463 31957, has_genre, 36212 17892, has_genre, 30463 17892, has_genre, 36212 18996, has_genre, 30463 18996, has_genre, 36212 18996, has_tags, 30463 13257, has_genre, 30463 13257, has_genre, 36212 13901, has_genre, 30463 13901, has_genre, 36212 10349, has_genre, 30463 10349, has_genre, 36212 23734, has_genre, 30463 23734, has_genre, 36212 656, has_genre, 36212 656, starred_actors, 9359 16600, has_genre, 30463 16600, has_genre, 36212 22663, has_genre, 30463 22663, has_genre, 36212 22667, has_genre, 30463 22667, has_genre, 36212 1915, has_genre, 30463 1915, has_genre, 36212 1915, has_tags, 30463 3172, has_genre, 30463 3172, has_genre, 36212 25651, has_genre, 30463 25651, has_genre, 36212 14240, has_genre, 30463 14240, has_genre, 36212 15188, has_genre, 30463 15188, has_genre, 36212 2969, has_genre, 30463 2969, has_genre, 36212 34555, has_genre, 30463 34555, has_genre, 36212 24880, has_genre, 30463 24880, has_genre, 36212 6915, has_genre, 30463 6915, has_genre, 36212 3079, has_genre, 36212 3079, has_tags, 37827 3079, starred_actors, 9359 3079, written_by, 3079 5287, has_genre, 30463 5287, has_genre, 36212 2155, has_genre, 30463 2155, has_genre, 36212 12664, has_genre, 30463 12664, has_genre, 36212 27638, has_genre, 30463 27638, has_genre, 36212 19912, has_genre, 30463 19912, has_genre, 36212 33701, has_genre, 30463 33701, has_genre, 36212 21060, has_genre, 30463 21060, has_genre, 36212 1292, has_genre, 30463 1292, has_genre, 36212 3829, has_genre, 30463 3829, has_genre, 36212 19039, has_genre, 30463 19039, has_genre, 36212 38901, has_genre, 30463 38901, has_genre, 36212 37827, has_genre, 30463 4931, has_genre, 30463 4931, has_genre, 36212 35625, has_genre, 30463 35625, has_genre, 36212 25277, has_genre, 30463 25277, has_genre, 36212 34856, has_genre, 30463 34856, has_genre, 36212 21788, has_genre, 30463 21788, has_genre, 36212 23526, has_genre, 30463 23526, has_genre, 36212 12610, has_genre, 30463 12610, has_genre, 36212 37372, has_genre, 30463 37372, has_genre, 36212 37372, has_tags, 30463 4619, has_genre, 30463 4619, has_genre, 36212 7699, has_genre, 30463 7699, has_genre, 36212 37238, has_genre, 30463 37238, has_genre, 36212 39069, has_genre, 30463 39069, has_genre, 36212 191, has_genre, 30463 191, has_genre, 36212 9452, has_genre, 30463 9452, has_genre, 36212 9452, has_tags, 30463 9452, has_tags, 36212 29734, has_genre, 30463 29734, has_genre, 36212 16814, has_genre, 30463 16814, has_genre, 36212 33238, has_genre, 30463 33238, has_genre, 36212 33714, has_genre, 30463 33714, has_genre, 36212 22924, has_genre, 30463 22924, has_genre, 36212 2990, has_genre, 30463 2990, has_genre, 36212 18214, has_genre, 30463 18214, has_genre, 36212 34536, has_genre, 30463 34536, has_genre, 36212 16662, has_genre, 30463 16662, has_genre, 36212 24616, has_genre, 30463 24616, has_genre, 36212 9519, has_genre, 30463 9519, has_genre, 36212 33677, has_genre, 30463 33677, has_genre, 36212 33677, has_tags, 30463 32186, has_genre, 30463 32186, has_genre, 36212 28044, has_genre, 30463 28044, has_genre, 36212 11129, has_genre, 30463 11129, has_genre, 36212 25678, has_genre, 30463 25678, has_genre, 36212 31652, has_genre, 30463 31652, has_genre, 36212 31652, has_tags, 30463 28182, has_genre, 30463 28182, has_genre, 36212 18177, has_genre, 36212 18177, has_tags, 30463 18177, has_tags, 36212 38381, has_genre, 30463 38381, has_genre, 36212 15095, has_genre, 30463 15095, has_genre, 36212 12153, has_genre, 30463 12153, has_genre, 36212 12153, has_tags, 30463 22312, has_genre, 30463 22312, has_genre, 36212 26699, has_genre, 30463 26699, has_genre, 36212 37133, has_genre, 30463 37133, has_genre, 36212 2407, has_genre, 30463 2407, has_genre, 36212 19971, has_genre, 30463 19971, has_genre, 36212 7949, has_genre, 30463 7949, has_genre, 36212 1432, has_genre, 30463 1432, has_genre, 36212 25002, has_genre, 30463 25002, has_genre, 36212 30733, has_genre, 30463 30733, has_genre, 36212 25865, has_genre, 30463 25865, has_genre, 36212 27762, has_genre, 30463 27762, has_genre, 36212 17992, has_genre, 30463 17992, has_genre, 36212 34308, has_genre, 30463 34308, has_genre, 36212 15795, has_genre, 30463 15795, has_genre, 36212 17931, has_genre, 30463 17931, has_genre, 36212 17931, has_tags, 30463 17931, has_tags, 36212 25674, has_genre, 30463 25674, has_genre, 36212 13582, has_genre, 30463 13582, has_genre, 36212 32654, has_genre, 30463 32654, has_genre, 36212 8512, has_genre, 30463 8512, has_genre, 36212 36722, has_genre, 30463 36722, has_genre, 36212 2507, has_genre, 30463 2507, has_genre, 36212 2507, has_tags, 30463 2507, has_tags, 36212 28107, has_genre, 30463 28107, has_genre, 36212 25623, has_genre, 30463 25623, has_genre, 36212 25623, has_tags, 36212 1345, has_genre, 30463 1345, has_genre, 36212 20728, has_genre, 30463 20728, has_genre, 36212 20728, has_tags, 30463 27067, has_genre, 30463 27067, has_genre, 36212 16918, has_genre, 30463 16918, has_genre, 36212 1903, has_genre, 30463 1903, has_genre, 36212 35988, has_genre, 30463 35988, has_genre, 36212 11987, has_genre, 30463 11987, has_genre, 36212 26275, has_genre, 30463 26275, has_genre, 36212 9436, has_genre, 30463 9436, has_genre, 36212 35443, has_genre, 30463 35443, has_genre, 36212 35443, has_tags, 30463 4460, has_genre, 30463 4460, has_genre, 36212 33258, has_genre, 30463 33258, has_genre, 36212 29999, has_genre, 30463 29999, has_genre, 36212 6287, has_genre, 30463 6287, has_genre, 36212 1287, has_genre, 30463 29421, has_genre, 30463 29421, has_genre, 36212 6279, has_genre, 30463 6279, has_genre, 36212 6279, has_tags, 30463 6279, has_tags, 36212 Question: In what context are ANNE OF THE THOUSAND DAYS, IMELDA STAUNTON, and WILDER NAPALM connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ANNE OF THE THOUSAND DAYS", "IMELDA STAUNTON", "WILDER NAPALM" ], "valid_edges": [ [ "2 DAYS IN PARIS", "has_genre", "COMEDY" ], [ "2 DAYS IN PARIS", "has_genre", "DRAMA" ], [ "25 WATTS", "has_genre", "COMEDY" ], [ "25 WATTS", "has_genre", "DRAMA" ], [ "A LEAGUE OF THEIR OWN", "has_genre", "COMEDY" ], [ "A LEAGUE OF THEIR OWN", "has_genre", "DRAMA" ], [ "A LEAGUE OF THEIR OWN", "has_tags", "DRAMA" ], [ "A LITTLE HELP", "has_genre", "COMEDY" ], [ "A LITTLE HELP", "has_genre", "DRAMA" ], [ "ANGEL", "has_genre", "COMEDY" ], [ "ANGEL", "has_genre", "DRAMA" ], [ "ANGUS", "has_genre", "COMEDY" ], [ "ANGUS", "has_genre", "DRAMA" ], [ "ANNE OF THE THOUSAND DAYS", "has_genre", "DRAMA" ], [ "BABY BOY", "has_genre", "COMEDY" ], [ "BABY BOY", "has_genre", "DRAMA" ], [ "BANDITS", "has_genre", "COMEDY" ], [ "BANDITS", "has_genre", "DRAMA" ], [ "BARNEY'S VERSION", "has_genre", "COMEDY" ], [ "BARNEY'S VERSION", "has_genre", "DRAMA" ], [ "BARTLEBY", "has_genre", "COMEDY" ], [ "BARTLEBY", "has_genre", "DRAMA" ], [ "BEGINNERS", "has_genre", "COMEDY" ], [ "BEGINNERS", "has_genre", "DRAMA" ], [ "BEGINNERS", "has_tags", "DRAMA" ], [ "BOY", "has_genre", "COMEDY" ], [ "BOY", "has_genre", "DRAMA" ], [ "CAMOUFLAGE", "has_genre", "COMEDY" ], [ "CAMOUFLAGE", "has_genre", "DRAMA" ], [ "CEMETERY JUNCTION", "has_genre", "COMEDY" ], [ "CEMETERY JUNCTION", "has_genre", "DRAMA" ], [ "CEMETERY JUNCTION", "has_tags", "COMEDY" ], [ "CHARLIE BARTLETT", "has_genre", "COMEDY" ], [ "CHARLIE BARTLETT", "has_genre", "DRAMA" ], [ "CHARLIE WILSON'S WAR", "has_genre", "COMEDY" ], [ "CHARLIE WILSON'S WAR", "has_genre", "DRAMA" ], [ "CHICAGO", "has_genre", "COMEDY" ], [ "CHICAGO", "has_genre", "DRAMA" ], [ "COUPE DE VILLE", "has_genre", "COMEDY" ], [ "COUPE DE VILLE", "has_genre", "DRAMA" ], [ "CRUSH", "has_genre", "DRAMA" ], [ "CRUSH", "starred_actors", "IMELDA STAUNTON" ], [ "CYRUS", "has_genre", "COMEDY" ], [ "CYRUS", "has_genre", "DRAMA" ], [ "DAN IN REAL LIFE", "has_genre", "COMEDY" ], [ "DAN IN REAL LIFE", "has_genre", "DRAMA" ], [ "DAYS OF DARKNESS", "has_genre", "COMEDY" ], [ "DAYS OF DARKNESS", "has_genre", "DRAMA" ], [ "DIL CHAHTA HAI", "has_genre", "COMEDY" ], [ "DIL CHAHTA HAI", "has_genre", "DRAMA" ], [ "DIL CHAHTA HAI", "has_tags", "COMEDY" ], [ "DIRTY GIRL", "has_genre", "COMEDY" ], [ "DIRTY GIRL", "has_genre", "DRAMA" ], [ "DUMMY", "has_genre", "COMEDY" ], [ "DUMMY", "has_genre", "DRAMA" ], [ "EVERYTHING MUST GO", "has_genre", "COMEDY" ], [ "EVERYTHING MUST GO", "has_genre", "DRAMA" ], [ "EXPIRED", "has_genre", "COMEDY" ], [ "EXPIRED", "has_genre", "DRAMA" ], [ "FATHER OF INVENTION", "has_genre", "COMEDY" ], [ "FATHER OF INVENTION", "has_genre", "DRAMA" ], [ "FLAWLESS", "has_genre", "COMEDY" ], [ "FLAWLESS", "has_genre", "DRAMA" ], [ "FLIPPED", "has_genre", "COMEDY" ], [ "FLIPPED", "has_genre", "DRAMA" ], [ "FOCUS", "has_genre", "COMEDY" ], [ "FOCUS", "has_genre", "DRAMA" ], [ "FREEDOM WRITERS", "has_genre", "DRAMA" ], [ "FREEDOM WRITERS", "has_tags", "HIGH SCHOOL" ], [ "FREEDOM WRITERS", "starred_actors", "IMELDA STAUNTON" ], [ "FREEDOM WRITERS", "written_by", "FREEDOM WRITERS" ], [ "FROZEN", "has_genre", "COMEDY" ], [ "FROZEN", "has_genre", "DRAMA" ], [ "GEORGIA RULE", "has_genre", "COMEDY" ], [ "GEORGIA RULE", "has_genre", "DRAMA" ], [ "GHOST WORLD", "has_genre", "COMEDY" ], [ "GHOST WORLD", "has_genre", "DRAMA" ], [ "GREENBERG", "has_genre", "COMEDY" ], [ "GREENBERG", "has_genre", "DRAMA" ], [ "GRIFF THE INVISIBLE", "has_genre", "COMEDY" ], [ "GRIFF THE INVISIBLE", "has_genre", "DRAMA" ], [ "HAPPYTHANKYOUMOREPLEASE", "has_genre", "COMEDY" ], [ "HAPPYTHANKYOUMOREPLEASE", "has_genre", "DRAMA" ], [ "HARVARD MAN", "has_genre", "COMEDY" ], [ "HARVARD MAN", "has_genre", "DRAMA" ], [ "HEDWIG AND THE ANGRY INCH", "has_genre", "COMEDY" ], [ "HEDWIG AND THE ANGRY INCH", "has_genre", "DRAMA" ], [ "HERO", "has_genre", "COMEDY" ], [ "HERO", "has_genre", "DRAMA" ], [ "HESHER", "has_genre", "COMEDY" ], [ "HESHER", "has_genre", "DRAMA" ], [ "HIGH HEELS AND LOW LIFES", "has_genre", "COMEDY" ], [ "HIGH HEELS AND LOW LIFES", "has_genre", "DRAMA" ], [ "HIGH SCHOOL", "has_genre", "COMEDY" ], [ "HOW DO YOU KNOW", "has_genre", "COMEDY" ], [ "HOW DO YOU KNOW", "has_genre", "DRAMA" ], [ "HUMAN NATURE", "has_genre", "COMEDY" ], [ "HUMAN NATURE", "has_genre", "DRAMA" ], [ "HUSBANDS AND WIVES", "has_genre", "COMEDY" ], [ "HUSBANDS AND WIVES", "has_genre", "DRAMA" ], [ "I THINK I LOVE MY WIFE", "has_genre", "COMEDY" ], [ "I THINK I LOVE MY WIFE", "has_genre", "DRAMA" ], [ "IT'S KIND OF A FUNNY STORY", "has_genre", "COMEDY" ], [ "IT'S KIND OF A FUNNY STORY", "has_genre", "DRAMA" ], [ "JACKPOT", "has_genre", "COMEDY" ], [ "JACKPOT", "has_genre", "DRAMA" ], [ "JOE SOMEBODY", "has_genre", "COMEDY" ], [ "JOE SOMEBODY", "has_genre", "DRAMA" ], [ "JUNO", "has_genre", "COMEDY" ], [ "JUNO", "has_genre", "DRAMA" ], [ "JUNO", "has_tags", "COMEDY" ], [ "KING OF CALIFORNIA", "has_genre", "COMEDY" ], [ "KING OF CALIFORNIA", "has_genre", "DRAMA" ], [ "KINGDOM COME", "has_genre", "COMEDY" ], [ "KINGDOM COME", "has_genre", "DRAMA" ], [ "KNOCKED UP", "has_genre", "COMEDY" ], [ "KNOCKED UP", "has_genre", "DRAMA" ], [ "LARS AND THE REAL GIRL", "has_genre", "COMEDY" ], [ "LARS AND THE REAL GIRL", "has_genre", "DRAMA" ], [ "LEAVING NORMAL", "has_genre", "COMEDY" ], [ "LEAVING NORMAL", "has_genre", "DRAMA" ], [ "LIFE AS WE KNOW IT", "has_genre", "COMEDY" ], [ "LIFE AS WE KNOW IT", "has_genre", "DRAMA" ], [ "LIFE AS WE KNOW IT", "has_tags", "COMEDY" ], [ "LIFE AS WE KNOW IT", "has_tags", "DRAMA" ], [ "LITTLE SECRETS", "has_genre", "COMEDY" ], [ "LITTLE SECRETS", "has_genre", "DRAMA" ], [ "LITTLE WHITE LIES", "has_genre", "COMEDY" ], [ "LITTLE WHITE LIES", "has_genre", "DRAMA" ], [ "MARTIAN CHILD", "has_genre", "COMEDY" ], [ "MARTIAN CHILD", "has_genre", "DRAMA" ], [ "MEAN MACHINE", "has_genre", "COMEDY" ], [ "MEAN MACHINE", "has_genre", "DRAMA" ], [ "MEET MONICA VELOUR", "has_genre", "COMEDY" ], [ "MEET MONICA VELOUR", "has_genre", "DRAMA" ], [ "MERMAID", "has_genre", "COMEDY" ], [ "MERMAID", "has_genre", "DRAMA" ], [ "MISTER LONELY", "has_genre", "COMEDY" ], [ "MISTER LONELY", "has_genre", "DRAMA" ], [ "MISTRESS", "has_genre", "COMEDY" ], [ "MISTRESS", "has_genre", "DRAMA" ], [ "MORNING GLORY", "has_genre", "COMEDY" ], [ "MORNING GLORY", "has_genre", "DRAMA" ], [ "MOSTLY MARTHA", "has_genre", "COMEDY" ], [ "MOSTLY MARTHA", "has_genre", "DRAMA" ], [ "NO RESERVATIONS", "has_genre", "COMEDY" ], [ "NO RESERVATIONS", "has_genre", "DRAMA" ], [ "NOISE", "has_genre", "COMEDY" ], [ "NOISE", "has_genre", "DRAMA" ], [ "NOISE", "has_tags", "COMEDY" ], [ "ONE MAN UP", "has_genre", "COMEDY" ], [ "ONE MAN UP", "has_genre", "DRAMA" ], [ "PAULINE AND PAULETTE", "has_genre", "COMEDY" ], [ "PAULINE AND PAULETTE", "has_genre", "DRAMA" ], [ "PEEP WORLD", "has_genre", "COMEDY" ], [ "PEEP WORLD", "has_genre", "DRAMA" ], [ "PETER'S FRIENDS", "has_genre", "COMEDY" ], [ "PETER'S FRIENDS", "has_genre", "DRAMA" ], [ "PRETTY IN PINK", "has_genre", "COMEDY" ], [ "PRETTY IN PINK", "has_genre", "DRAMA" ], [ "PRETTY IN PINK", "has_tags", "COMEDY" ], [ "RARE BIRDS", "has_genre", "COMEDY" ], [ "RARE BIRDS", "has_genre", "DRAMA" ], [ "REIGN OVER ME", "has_genre", "DRAMA" ], [ "REIGN OVER ME", "has_tags", "COMEDY" ], [ "REIGN OVER ME", "has_tags", "DRAMA" ], [ "ROCK STAR", "has_genre", "COMEDY" ], [ "ROCK STAR", "has_genre", "DRAMA" ], [ "ROCKET SCIENCE", "has_genre", "COMEDY" ], [ "ROCKET SCIENCE", "has_genre", "DRAMA" ], [ "RUSHMORE", "has_genre", "COMEDY" ], [ "RUSHMORE", "has_genre", "DRAMA" ], [ "RUSHMORE", "has_tags", "COMEDY" ], [ "SAY ANYTHING...", "has_genre", "COMEDY" ], [ "SAY ANYTHING...", "has_genre", "DRAMA" ], [ "SHORT CUTS", "has_genre", "COMEDY" ], [ "SHORT CUTS", "has_genre", "DRAMA" ], [ "SOMEWHERE", "has_genre", "COMEDY" ], [ "SOMEWHERE", "has_genre", "DRAMA" ], [ "SON OF THE BRIDE", "has_genre", "COMEDY" ], [ "SON OF THE BRIDE", "has_genre", "DRAMA" ], [ "SPIN", "has_genre", "COMEDY" ], [ "SPIN", "has_genre", "DRAMA" ], [ "STORYTELLING", "has_genre", "COMEDY" ], [ "STORYTELLING", "has_genre", "DRAMA" ], [ "STRICTLY BALLROOM", "has_genre", "COMEDY" ], [ "STRICTLY BALLROOM", "has_genre", "DRAMA" ], [ "STRUCK BY LIGHTNING", "has_genre", "COMEDY" ], [ "STRUCK BY LIGHTNING", "has_genre", "DRAMA" ], [ "SUBMARINE", "has_genre", "COMEDY" ], [ "SUBMARINE", "has_genre", "DRAMA" ], [ "SUBURBIA", "has_genre", "COMEDY" ], [ "SUBURBIA", "has_genre", "DRAMA" ], [ "SWEET NOVEMBER", "has_genre", "COMEDY" ], [ "SWEET NOVEMBER", "has_genre", "DRAMA" ], [ "SWING VOTE", "has_genre", "COMEDY" ], [ "SWING VOTE", "has_genre", "DRAMA" ], [ "TEACHERS", "has_genre", "COMEDY" ], [ "TEACHERS", "has_genre", "DRAMA" ], [ "THE ANNIVERSARY PARTY", "has_genre", "COMEDY" ], [ "THE ANNIVERSARY PARTY", "has_genre", "DRAMA" ], [ "THE BREAKFAST CLUB", "has_genre", "COMEDY" ], [ "THE BREAKFAST CLUB", "has_genre", "DRAMA" ], [ "THE BREAKFAST CLUB", "has_tags", "COMEDY" ], [ "THE BREAKFAST CLUB", "has_tags", "DRAMA" ], [ "THE BROTHERS", "has_genre", "COMEDY" ], [ "THE BROTHERS", "has_genre", "DRAMA" ], [ "THE BUCKET LIST", "has_genre", "COMEDY" ], [ "THE BUCKET LIST", "has_genre", "DRAMA" ], [ "THE CHOSEN ONE", "has_genre", "COMEDY" ], [ "THE CHOSEN ONE", "has_genre", "DRAMA" ], [ "THE DUKES", "has_genre", "COMEDY" ], [ "THE DUKES", "has_genre", "DRAMA" ], [ "THE FOUR-FACED LIAR", "has_genre", "COMEDY" ], [ "THE FOUR-FACED LIAR", "has_genre", "DRAMA" ], [ "THE KIDS ARE ALL RIGHT", "has_genre", "COMEDY" ], [ "THE KIDS ARE ALL RIGHT", "has_genre", "DRAMA" ], [ "THE KIDS ARE ALL RIGHT", "has_tags", "COMEDY" ], [ "THE KIDS ARE ALL RIGHT", "has_tags", "DRAMA" ], [ "THE LAST KISS", "has_genre", "COMEDY" ], [ "THE LAST KISS", "has_genre", "DRAMA" ], [ "THE MATRIARCH", "has_genre", "COMEDY" ], [ "THE MATRIARCH", "has_genre", "DRAMA" ], [ "THE MATRIARCH", "has_tags", "DRAMA" ], [ "THE NANNY DIARIES", "has_genre", "COMEDY" ], [ "THE NANNY DIARIES", "has_genre", "DRAMA" ], [ "THE ROYAL TENENBAUMS", "has_genre", "COMEDY" ], [ "THE ROYAL TENENBAUMS", "has_genre", "DRAMA" ], [ "THE ROYAL TENENBAUMS", "has_tags", "COMEDY" ], [ "THEN SHE FOUND ME", "has_genre", "COMEDY" ], [ "THEN SHE FOUND ME", "has_genre", "DRAMA" ], [ "THIS CHRISTMAS", "has_genre", "COMEDY" ], [ "THIS CHRISTMAS", "has_genre", "DRAMA" ], [ "TINY FURNITURE", "has_genre", "COMEDY" ], [ "TINY FURNITURE", "has_genre", "DRAMA" ], [ "TORTILLA SOUP", "has_genre", "COMEDY" ], [ "TORTILLA SOUP", "has_genre", "DRAMA" ], [ "TRUST", "has_genre", "COMEDY" ], [ "TRUST", "has_genre", "DRAMA" ], [ "TRUST ME", "has_genre", "COMEDY" ], [ "TRUST ME", "has_genre", "DRAMA" ], [ "VIVA", "has_genre", "COMEDY" ], [ "VIVA", "has_genre", "DRAMA" ], [ "VIZONTELE", "has_genre", "COMEDY" ], [ "VIZONTELE", "has_genre", "DRAMA" ], [ "VIZONTELE", "has_tags", "COMEDY" ], [ "WAITRESS", "has_genre", "COMEDY" ], [ "WAITRESS", "has_genre", "DRAMA" ], [ "WEST IS WEST", "has_genre", "COMEDY" ], [ "WEST IS WEST", "has_genre", "DRAMA" ], [ "WHY DID I GET MARRIED TOO?", "has_genre", "COMEDY" ], [ "WHY DID I GET MARRIED TOO?", "has_genre", "DRAMA" ], [ "WHY DID I GET MARRIED?", "has_genre", "COMEDY" ], [ "WHY DID I GET MARRIED?", "has_genre", "DRAMA" ], [ "WILDER NAPALM", "has_genre", "COMEDY" ], [ "YEAR OF THE DOG", "has_genre", "COMEDY" ], [ "YEAR OF THE DOG", "has_genre", "DRAMA" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_genre", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_genre", "DRAMA" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_tags", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_tags", "DRAMA" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35935, 2002 29424, 2011 28374, ANTWONE FISHER 23286, CARNAGE 12903, DISNEY 2670, FOOTLOOSE 3859, I AM NUMBER FOUR 3429, MISS BALA 14026, PINOCCHIO 21773, POLLYANNA 729, SLEEPING BEAUTY 3578, SMALL TOWN 19102, SUPER 8 7816, THE THREE MUSKETEERS 10921, TREASURE PLANET 37351, WINNIE THE POOH src, edge_attr, dst 28374, release_year, 35935 28374, written_by, 28374 23286, release_year, 35935 23286, release_year, 29424 2670, has_tags, 3578 2670, release_year, 29424 3859, has_tags, 12903 3859, release_year, 29424 3429, release_year, 29424 14026, has_tags, 12903 14026, release_year, 35935 21773, has_tags, 12903 21773, has_tags, 3578 729, has_tags, 12903 729, release_year, 29424 19102, has_tags, 3578 19102, release_year, 29424 7816, has_tags, 12903 7816, release_year, 29424 10921, has_tags, 12903 10921, release_year, 35935 37351, has_tags, 12903 37351, release_year, 29424 Question: For what reason are ANTWONE FISHER, MISS BALA, and POLLYANNA associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ANTWONE FISHER", "MISS BALA", "POLLYANNA" ], "valid_edges": [ [ "ANTWONE FISHER", "release_year", "2002" ], [ "ANTWONE FISHER", "written_by", "ANTWONE FISHER" ], [ "CARNAGE", "release_year", "2002" ], [ "CARNAGE", "release_year", "2011" ], [ "FOOTLOOSE", "has_tags", "SMALL TOWN" ], [ "FOOTLOOSE", "release_year", "2011" ], [ "I AM NUMBER FOUR", "has_tags", "DISNEY" ], [ "I AM NUMBER FOUR", "release_year", "2011" ], [ "MISS BALA", "release_year", "2011" ], [ "PINOCCHIO", "has_tags", "DISNEY" ], [ "PINOCCHIO", "release_year", "2002" ], [ "POLLYANNA", "has_tags", "DISNEY" ], [ "POLLYANNA", "has_tags", "SMALL TOWN" ], [ "SLEEPING BEAUTY", "has_tags", "DISNEY" ], [ "SLEEPING BEAUTY", "release_year", "2011" ], [ "SUPER 8", "has_tags", "SMALL TOWN" ], [ "SUPER 8", "release_year", "2011" ], [ "THE THREE MUSKETEERS", "has_tags", "DISNEY" ], [ "THE THREE MUSKETEERS", "release_year", "2011" ], [ "TREASURE PLANET", "has_tags", "DISNEY" ], [ "TREASURE PLANET", "release_year", "2002" ], [ "WINNIE THE POOH", "has_tags", "DISNEY" ], [ "WINNIE THE POOH", "release_year", "2011" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 10702, 1991 15407, 29TH STREET 39705, A LITTLE STIFF 18051, AFTER HOURS 23683, ALL I WANT FOR CHRISTMAS 27592, ANIMAL HOUSE 30578, ANOTHER YOU 24301, ART SCHOOL CONFIDENTIAL 35639, BABY'S DAY OUT 26205, BEING JOHN MALKOVICH 9414, BINGO 35599, BURN AFTER READING 29437, CAN'T BUY ME LOVE 28295, CAREER OPPORTUNITIES 15585, CITY SLICKERS 30463, COMEDY 29148, CRAZY SAFARI 8100, CURLY SUE 31214, DEFENDING YOUR LIFE 29485, DELIRIOUS 37395, DEN OFRIVILLIGE GOLFAREN 3160, DOC HOLLYWOOD 21407, DOGMA 16978, DON'T TELL MOM THE BABYSITTER'S DEAD 20846, DROP DEAD FRED 31008, DUTCH 10757, EDDIE 36202, ERNEST SCARED STUPID 31630, EVIL TOONS 38250, FATHER OF THE BRIDE 25551, FAVORITE DEADLY SINS 3010, FORGET PARIS 30356, FRED OLEN RAY 35150, FRIED GREEN TOMATOES 28778, GOTCHA! 18119, HE SAID, SHE SAID 34320, HEAR MY SONG 25610, HIGH STRUNG 6546, HOT SHOTS! 39726, HUDSON HAWK 3909, IF LOOKS COULD KILL 25363, JERRY AND TOM 1841, JOE MANTEGNA 28149, JOHN MALKOVICH 37963, JOHNNY ENGLISH 528, JOHNNY STECCHINO 29911, KEVIN BACON 30015, KING RALPH 7439, L.A. STORY 29323, LIFE STINKS 23416, LINDA FIORENTINO 11652, LONELY STREET 22842, MAKING MR. RIGHT 32627, MYSTERY DATE 25620, NECESSARY ROUGHNESS 40059, ONCE AROUND 23735, ONLY THE LONELY 16225, ORDINARY DECENT CRIMINAL 30662, OSCAR 17667, OTHER PEOPLE'S MONEY 25824, PARADISE 32423, PERFECTLY NORMAL 10910, PICTURE PERFECT 23297, PROBLEM CHILD 2 39, PURE LUCK 25772, PYRATES 29601, QUEENS LOGIC 31731, R.I.P.D. 31600, RUBIN AND ED 28824, SENTA BERGER 33607, SEX AND ZEN 32127, SHE'S HAVING A BABY 8932, SLACKER 11883, SOAPDISH 38762, SPEAKING OF THE DEVIL 22375, STEVE RASH 29906, SUBURBAN COMMANDO 5700, SUPER 35064, SWITCH 30090, THE AIR UP THERE 35152, THE AMBUSHERS 32454, THE BIG PICTURE 8210, THE BUTCHER'S WIFE 4345, THE COMMITMENTS 252, THE DARK BACKWARD 20229, THE FISHER KING 25305, THE GREAT BUCK HOWARD 32102, THE HARD WAY 37565, THE LAST BOY SCOUT 12947, THE MARRYING MAN 1545, THE SUPER 21375, THINGS CHANGE 10238, UNDERWORLD 7593, WHAT ABOUT BOB? src, edge_attr, dst 15407, has_genre, 30463 15407, release_year, 10702 39705, has_genre, 30463 39705, release_year, 10702 18051, has_genre, 30463 18051, has_tags, 23416 23683, has_genre, 30463 23683, release_year, 10702 27592, has_genre, 30463 27592, has_tags, 30463 27592, has_tags, 29911 30578, has_genre, 30463 30578, release_year, 10702 24301, has_genre, 30463 24301, has_tags, 28149 24301, starred_actors, 28149 35639, has_genre, 30463 35639, starred_actors, 1841 26205, has_genre, 30463 26205, has_tags, 30463 26205, has_tags, 28149 9414, has_genre, 30463 9414, release_year, 10702 35599, has_genre, 30463 35599, has_tags, 30463 35599, has_tags, 28149 35599, starred_actors, 28149 29437, directed_by, 22375 29437, has_genre, 30463 29437, has_tags, 22375 28295, has_genre, 30463 28295, release_year, 10702 15585, has_genre, 30463 15585, release_year, 10702 29148, has_genre, 30463 29148, release_year, 10702 8100, has_genre, 30463 8100, release_year, 10702 31214, has_genre, 30463 31214, release_year, 10702 29485, has_genre, 30463 29485, release_year, 10702 37395, has_genre, 30463 37395, release_year, 10702 3160, has_genre, 30463 3160, has_tags, 30463 3160, release_year, 10702 21407, has_genre, 30463 21407, has_tags, 30463 21407, has_tags, 23416 16978, has_genre, 30463 16978, release_year, 10702 20846, has_genre, 30463 20846, release_year, 10702 31008, has_genre, 30463 31008, release_year, 10702 10757, directed_by, 22375 10757, has_genre, 30463 10757, has_tags, 22375 36202, has_genre, 30463 36202, release_year, 10702 31630, directed_by, 30356 31630, has_genre, 30463 31630, written_by, 30356 38250, has_genre, 30463 38250, has_tags, 30463 38250, release_year, 10702 25551, has_genre, 30463 25551, starred_actors, 1841 3010, has_genre, 30463 3010, starred_actors, 1841 35150, has_genre, 30463 35150, release_year, 10702 28778, has_genre, 30463 28778, starred_actors, 23416 18119, has_genre, 30463 18119, release_year, 10702 18119, starred_actors, 29911 34320, has_genre, 30463 34320, release_year, 10702 25610, has_genre, 30463 25610, release_year, 10702 6546, has_genre, 30463 6546, has_tags, 30463 6546, release_year, 10702 39726, has_genre, 30463 39726, has_tags, 30463 39726, release_year, 10702 3909, has_genre, 30463 3909, release_year, 10702 25363, has_genre, 30463 25363, starred_actors, 1841 37963, has_genre, 30463 37963, has_tags, 30463 37963, has_tags, 28149 528, has_genre, 30463 528, release_year, 10702 30015, has_genre, 30463 30015, has_tags, 30463 30015, release_year, 10702 7439, has_genre, 30463 7439, release_year, 10702 29323, has_genre, 30463 29323, release_year, 10702 11652, has_genre, 30463 11652, starred_actors, 1841 22842, has_genre, 30463 22842, has_tags, 28149 22842, starred_actors, 28149 32627, has_genre, 30463 32627, release_year, 10702 25620, has_genre, 30463 25620, release_year, 10702 40059, has_genre, 30463 40059, release_year, 10702 23735, has_genre, 30463 23735, release_year, 10702 16225, has_genre, 30463 16225, starred_actors, 23416 30662, has_genre, 30463 30662, release_year, 10702 17667, has_genre, 30463 17667, release_year, 10702 25824, has_genre, 30463 25824, release_year, 10702 32423, has_genre, 30463 32423, release_year, 10702 10910, has_genre, 30463 10910, starred_actors, 29911 23297, has_genre, 30463 23297, release_year, 10702 39, has_genre, 30463 39, release_year, 10702 25772, has_genre, 30463 25772, release_year, 10702 25772, starred_actors, 29911 29601, directed_by, 22375 29601, has_genre, 30463 29601, has_tags, 28149 29601, release_year, 10702 29601, starred_actors, 1841 29601, starred_actors, 28149 29601, starred_actors, 29911 29601, starred_actors, 23416 31731, has_genre, 30463 31731, starred_actors, 29911 31600, has_genre, 30463 31600, release_year, 10702 33607, has_genre, 30463 33607, release_year, 10702 32127, has_genre, 30463 32127, starred_actors, 29911 8932, has_genre, 30463 8932, release_year, 10702 11883, has_genre, 30463 11883, has_tags, 30463 11883, release_year, 10702 38762, has_genre, 30463 38762, release_year, 10702 29906, has_genre, 30463 29906, release_year, 10702 5700, has_genre, 30463 5700, has_tags, 29911 5700, starred_actors, 29911 35064, has_genre, 30463 35064, release_year, 10702 30090, has_genre, 30463 30090, has_tags, 30463 30090, has_tags, 29911 30090, starred_actors, 29911 35152, has_genre, 30463 35152, starred_actors, 28824 32454, has_genre, 30463 32454, starred_actors, 29911 8210, has_genre, 30463 8210, release_year, 10702 4345, has_genre, 30463 4345, release_year, 10702 252, has_genre, 30463 252, release_year, 10702 20229, has_genre, 30463 20229, release_year, 10702 25305, has_genre, 30463 25305, starred_actors, 28149 32102, has_genre, 30463 32102, release_year, 10702 37565, has_genre, 30463 37565, release_year, 10702 12947, has_genre, 30463 12947, release_year, 10702 1545, has_genre, 30463 1545, release_year, 10702 21375, has_genre, 30463 21375, starred_actors, 1841 10238, has_genre, 30463 10238, starred_actors, 1841 7593, has_genre, 30463 7593, release_year, 10702 Question: How are FRED OLEN RAY, QUEENS LOGIC, and SENTA BERGER related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "FRED OLEN RAY", "QUEENS LOGIC", "SENTA BERGER" ], "valid_edges": [ [ "29TH STREET", "has_genre", "COMEDY" ], [ "29TH STREET", "release_year", "1991" ], [ "A LITTLE STIFF", "has_genre", "COMEDY" ], [ "A LITTLE STIFF", "release_year", "1991" ], [ "AFTER HOURS", "has_genre", "COMEDY" ], [ "AFTER HOURS", "has_tags", "LINDA FIORENTINO" ], [ "ALL I WANT FOR CHRISTMAS", "has_genre", "COMEDY" ], [ "ALL I WANT FOR CHRISTMAS", "release_year", "1991" ], [ "ANIMAL HOUSE", "has_genre", "COMEDY" ], [ "ANIMAL HOUSE", "has_tags", "COMEDY" ], [ "ANIMAL HOUSE", "has_tags", "KEVIN BACON" ], [ "ANOTHER YOU", "has_genre", "COMEDY" ], [ "ANOTHER YOU", "release_year", "1991" ], [ "ART SCHOOL CONFIDENTIAL", "has_genre", "COMEDY" ], [ "ART SCHOOL CONFIDENTIAL", "has_tags", "JOHN MALKOVICH" ], [ "ART SCHOOL CONFIDENTIAL", "starred_actors", "JOHN MALKOVICH" ], [ "BABY'S DAY OUT", "has_genre", "COMEDY" ], [ "BABY'S DAY OUT", "starred_actors", "JOE MANTEGNA" ], [ "BEING JOHN MALKOVICH", "has_genre", "COMEDY" ], [ "BEING JOHN MALKOVICH", "has_tags", "COMEDY" ], [ "BEING JOHN MALKOVICH", "has_tags", "JOHN MALKOVICH" ], [ "BINGO", "has_genre", "COMEDY" ], [ "BINGO", "release_year", "1991" ], [ "BURN AFTER READING", "has_genre", "COMEDY" ], [ "BURN AFTER READING", "has_tags", "COMEDY" ], [ "BURN AFTER READING", "has_tags", "JOHN MALKOVICH" ], [ "BURN AFTER READING", "starred_actors", "JOHN MALKOVICH" ], [ "CAN'T BUY ME LOVE", "directed_by", "STEVE RASH" ], [ "CAN'T BUY ME LOVE", "has_genre", "COMEDY" ], [ "CAN'T BUY ME LOVE", "has_tags", "STEVE RASH" ], [ "CAREER OPPORTUNITIES", "has_genre", "COMEDY" ], [ "CAREER OPPORTUNITIES", "release_year", "1991" ], [ "CITY SLICKERS", "has_genre", "COMEDY" ], [ "CITY SLICKERS", "release_year", "1991" ], [ "CRAZY SAFARI", "has_genre", "COMEDY" ], [ "CRAZY SAFARI", "release_year", "1991" ], [ "CURLY SUE", "has_genre", "COMEDY" ], [ "CURLY SUE", "release_year", "1991" ], [ "DEFENDING YOUR LIFE", "has_genre", "COMEDY" ], [ "DEFENDING YOUR LIFE", "release_year", "1991" ], [ "DELIRIOUS", "has_genre", "COMEDY" ], [ "DELIRIOUS", "release_year", "1991" ], [ "DEN OFRIVILLIGE GOLFAREN", "has_genre", "COMEDY" ], [ "DEN OFRIVILLIGE GOLFAREN", "release_year", "1991" ], [ "DOC HOLLYWOOD", "has_genre", "COMEDY" ], [ "DOC HOLLYWOOD", "has_tags", "COMEDY" ], [ "DOC HOLLYWOOD", "release_year", "1991" ], [ "DOGMA", "has_genre", "COMEDY" ], [ "DOGMA", "has_tags", "COMEDY" ], [ "DOGMA", "has_tags", "LINDA FIORENTINO" ], [ "DON'T TELL MOM THE BABYSITTER'S DEAD", "has_genre", "COMEDY" ], [ "DON'T TELL MOM THE BABYSITTER'S DEAD", "release_year", "1991" ], [ "DROP DEAD FRED", "has_genre", "COMEDY" ], [ "DROP DEAD FRED", "release_year", "1991" ], [ "DUTCH", "has_genre", "COMEDY" ], [ "DUTCH", "release_year", "1991" ], [ "EDDIE", "directed_by", "STEVE RASH" ], [ "EDDIE", "has_genre", "COMEDY" ], [ "EDDIE", "has_tags", "STEVE RASH" ], [ "ERNEST SCARED STUPID", "has_genre", "COMEDY" ], [ "ERNEST SCARED STUPID", "release_year", "1991" ], [ "EVIL TOONS", "directed_by", "FRED OLEN RAY" ], [ "EVIL TOONS", "has_genre", "COMEDY" ], [ "EVIL TOONS", "written_by", "FRED OLEN RAY" ], [ "FATHER OF THE BRIDE", "has_genre", "COMEDY" ], [ "FATHER OF THE BRIDE", "has_tags", "COMEDY" ], [ "FATHER OF THE BRIDE", "release_year", "1991" ], [ "FAVORITE DEADLY SINS", "has_genre", "COMEDY" ], [ "FAVORITE DEADLY SINS", "starred_actors", "JOE MANTEGNA" ], [ "FORGET PARIS", "has_genre", "COMEDY" ], [ "FORGET PARIS", "starred_actors", "JOE MANTEGNA" ], [ "FRIED GREEN TOMATOES", "has_genre", "COMEDY" ], [ "FRIED GREEN TOMATOES", "release_year", "1991" ], [ "GOTCHA!", "has_genre", "COMEDY" ], [ "GOTCHA!", "starred_actors", "LINDA FIORENTINO" ], [ "HE SAID, SHE SAID", "has_genre", "COMEDY" ], [ "HE SAID, SHE SAID", "release_year", "1991" ], [ "HE SAID, SHE SAID", "starred_actors", "KEVIN BACON" ], [ "HEAR MY SONG", "has_genre", "COMEDY" ], [ "HEAR MY SONG", "release_year", "1991" ], [ "HIGH STRUNG", "has_genre", "COMEDY" ], [ "HIGH STRUNG", "release_year", "1991" ], [ "HOT SHOTS!", "has_genre", "COMEDY" ], [ "HOT SHOTS!", "has_tags", "COMEDY" ], [ "HOT SHOTS!", "release_year", "1991" ], [ "HUDSON HAWK", "has_genre", "COMEDY" ], [ "HUDSON HAWK", "has_tags", "COMEDY" ], [ "HUDSON HAWK", "release_year", "1991" ], [ "IF LOOKS COULD KILL", "has_genre", "COMEDY" ], [ "IF LOOKS COULD KILL", "release_year", "1991" ], [ "JERRY AND TOM", "has_genre", "COMEDY" ], [ "JERRY AND TOM", "starred_actors", "JOE MANTEGNA" ], [ "JOHNNY ENGLISH", "has_genre", "COMEDY" ], [ "JOHNNY ENGLISH", "has_tags", "COMEDY" ], [ "JOHNNY ENGLISH", "has_tags", "JOHN MALKOVICH" ], [ "JOHNNY STECCHINO", "has_genre", "COMEDY" ], [ "JOHNNY STECCHINO", "release_year", "1991" ], [ "KING RALPH", "has_genre", "COMEDY" ], [ "KING RALPH", "has_tags", "COMEDY" ], [ "KING RALPH", "release_year", "1991" ], [ "L.A. STORY", "has_genre", "COMEDY" ], [ "L.A. STORY", "release_year", "1991" ], [ "LIFE STINKS", "has_genre", "COMEDY" ], [ "LIFE STINKS", "release_year", "1991" ], [ "LONELY STREET", "has_genre", "COMEDY" ], [ "LONELY STREET", "starred_actors", "JOE MANTEGNA" ], [ "MAKING MR. RIGHT", "has_genre", "COMEDY" ], [ "MAKING MR. RIGHT", "has_tags", "JOHN MALKOVICH" ], [ "MAKING MR. RIGHT", "starred_actors", "JOHN MALKOVICH" ], [ "MYSTERY DATE", "has_genre", "COMEDY" ], [ "MYSTERY DATE", "release_year", "1991" ], [ "NECESSARY ROUGHNESS", "has_genre", "COMEDY" ], [ "NECESSARY ROUGHNESS", "release_year", "1991" ], [ "ONCE AROUND", "has_genre", "COMEDY" ], [ "ONCE AROUND", "release_year", "1991" ], [ "ONLY THE LONELY", "has_genre", "COMEDY" ], [ "ONLY THE LONELY", "release_year", "1991" ], [ "ORDINARY DECENT CRIMINAL", "has_genre", "COMEDY" ], [ "ORDINARY DECENT CRIMINAL", "starred_actors", "LINDA FIORENTINO" ], [ "OSCAR", "has_genre", "COMEDY" ], [ "OSCAR", "release_year", "1991" ], [ "OTHER PEOPLE'S MONEY", "has_genre", "COMEDY" ], [ "OTHER PEOPLE'S MONEY", "release_year", "1991" ], [ "PARADISE", "has_genre", "COMEDY" ], [ "PARADISE", "release_year", "1991" ], [ "PERFECTLY NORMAL", "has_genre", "COMEDY" ], [ "PERFECTLY NORMAL", "release_year", "1991" ], [ "PICTURE PERFECT", "has_genre", "COMEDY" ], [ "PICTURE PERFECT", "starred_actors", "KEVIN BACON" ], [ "PROBLEM CHILD 2", "has_genre", "COMEDY" ], [ "PROBLEM CHILD 2", "release_year", "1991" ], [ "PURE LUCK", "has_genre", "COMEDY" ], [ "PURE LUCK", "release_year", "1991" ], [ "PYRATES", "has_genre", "COMEDY" ], [ "PYRATES", "release_year", "1991" ], [ "PYRATES", "starred_actors", "KEVIN BACON" ], [ "QUEENS LOGIC", "directed_by", "STEVE RASH" ], [ "QUEENS LOGIC", "has_genre", "COMEDY" ], [ "QUEENS LOGIC", "has_tags", "JOHN MALKOVICH" ], [ "QUEENS LOGIC", "release_year", "1991" ], [ "QUEENS LOGIC", "starred_actors", "JOE MANTEGNA" ], [ "QUEENS LOGIC", "starred_actors", "JOHN MALKOVICH" ], [ "QUEENS LOGIC", "starred_actors", "KEVIN BACON" ], [ "QUEENS LOGIC", "starred_actors", "LINDA FIORENTINO" ], [ "R.I.P.D.", "has_genre", "COMEDY" ], [ "R.I.P.D.", "starred_actors", "KEVIN BACON" ], [ "RUBIN AND ED", "has_genre", "COMEDY" ], [ "RUBIN AND ED", "release_year", "1991" ], [ "SEX AND ZEN", "has_genre", "COMEDY" ], [ "SEX AND ZEN", "release_year", "1991" ], [ "SHE'S HAVING A BABY", "has_genre", "COMEDY" ], [ "SHE'S HAVING A BABY", "starred_actors", "KEVIN BACON" ], [ "SLACKER", "has_genre", "COMEDY" ], [ "SLACKER", "release_year", "1991" ], [ "SOAPDISH", "has_genre", "COMEDY" ], [ "SOAPDISH", "has_tags", "COMEDY" ], [ "SOAPDISH", "release_year", "1991" ], [ "SPEAKING OF THE DEVIL", "has_genre", "COMEDY" ], [ "SPEAKING OF THE DEVIL", "release_year", "1991" ], [ "SUBURBAN COMMANDO", "has_genre", "COMEDY" ], [ "SUBURBAN COMMANDO", "release_year", "1991" ], [ "SUPER", "has_genre", "COMEDY" ], [ "SUPER", "has_tags", "KEVIN BACON" ], [ "SUPER", "starred_actors", "KEVIN BACON" ], [ "SWITCH", "has_genre", "COMEDY" ], [ "SWITCH", "release_year", "1991" ], [ "THE AIR UP THERE", "has_genre", "COMEDY" ], [ "THE AIR UP THERE", "has_tags", "COMEDY" ], [ "THE AIR UP THERE", "has_tags", "KEVIN BACON" ], [ "THE AIR UP THERE", "starred_actors", "KEVIN BACON" ], [ "THE AMBUSHERS", "has_genre", "COMEDY" ], [ "THE AMBUSHERS", "starred_actors", "SENTA BERGER" ], [ "THE BIG PICTURE", "has_genre", "COMEDY" ], [ "THE BIG PICTURE", "starred_actors", "KEVIN BACON" ], [ "THE BUTCHER'S WIFE", "has_genre", "COMEDY" ], [ "THE BUTCHER'S WIFE", "release_year", "1991" ], [ "THE COMMITMENTS", "has_genre", "COMEDY" ], [ "THE COMMITMENTS", "release_year", "1991" ], [ "THE DARK BACKWARD", "has_genre", "COMEDY" ], [ "THE DARK BACKWARD", "release_year", "1991" ], [ "THE FISHER KING", "has_genre", "COMEDY" ], [ "THE FISHER KING", "release_year", "1991" ], [ "THE GREAT BUCK HOWARD", "has_genre", "COMEDY" ], [ "THE GREAT BUCK HOWARD", "starred_actors", "JOHN MALKOVICH" ], [ "THE HARD WAY", "has_genre", "COMEDY" ], [ "THE HARD WAY", "release_year", "1991" ], [ "THE LAST BOY SCOUT", "has_genre", "COMEDY" ], [ "THE LAST BOY SCOUT", "release_year", "1991" ], [ "THE MARRYING MAN", "has_genre", "COMEDY" ], [ "THE MARRYING MAN", "release_year", "1991" ], [ "THE SUPER", "has_genre", "COMEDY" ], [ "THE SUPER", "release_year", "1991" ], [ "THINGS CHANGE", "has_genre", "COMEDY" ], [ "THINGS CHANGE", "starred_actors", "JOE MANTEGNA" ], [ "UNDERWORLD", "has_genre", "COMEDY" ], [ "UNDERWORLD", "starred_actors", "JOE MANTEGNA" ], [ "WHAT ABOUT BOB?", "has_genre", "COMEDY" ], [ "WHAT ABOUT BOB?", "release_year", "1991" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 1421, 2013 20629, A SINGLE SHOT 30769, AIN'T THEM BODIES SAINTS 1824, ALL GOOD THINGS 26048, AMERICAN HUSTLE 18880, BANGKOK 8402, BANGKOK DANGEROUS 30870, BEAUTIFUL CREATURES 6809, BLOOD OF REDEMPTION 7483, BLOOD TIES 31203, BRAZILIAN WESTERN 38211, BROKEN CITY 12604, CIRCUS OF HORRORS 33784, CLOSED CIRCUIT 25131, COLD COMES THE NIGHT 38680, COMPULSION 8511, COTTAGE COUNTRY 14724, CRIME 17284, DEAD MAN DOWN 20056, DEBRA HILL 31232, DEVIL'S KNOT 33382, DOM HEMINGWAY 14268, DONALD PLEASENCE 30262, DRACULA 21021, DRIVE 13986, EMPIRE STATE 28256, FILTH 4372, FORCE OF EXECUTION 1033, FRACTURE 24481, FRANCHISE 35657, GLORIA 4830, HALLOWEEN 15753, HALLOWEEN II 12915, HELI 5870, HORROR 39037, HUNTING ELEPHANTS 8667, IDENTITY THIEF 4472, JAMIE LEE CURTIS 24988, JOHN CARPENTER 23224, KRISTIN SCOTT THOMAS 29765, LIFE OF CRIME 9792, LOVE CRIME 31769, MALCOLM MCDOWELL 16074, METRO MANILA 25918, MICHAEL MYERS 7925, MYSTERY ROAD 34844, NICOLAS WINDING REFN 24110, ONLY GOD FORGIVES 32279, PARKER 29586, PAWN SHOP CHRONICLES 38043, PUSHER 35405, ROB ZOMBIE 22512, RUNNER RUNNER 11468, RUSH 20331, RYAN GOSLING 5409, SLASHER 13712, TAMMY LAUREN 13090, THAI 13431, THE BLING RING 16900, THE CALL 38918, THE FAMILY 30294, THE NIGHT OF THE GENERALS 40086, THE PLACE BEYOND THE PINES 12738, TRAFFIC DEPARTMENT 5750, VAMPIRE 919, VAMPIRE IN VENICE 13357, WISHMASTER 17777, ZULU src, edge_attr, dst 20629, has_genre, 14724 20629, release_year, 1421 30769, has_genre, 14724 30769, release_year, 1421 1824, has_genre, 14724 1824, has_tags, 20331 1824, starred_actors, 20331 26048, has_genre, 14724 26048, release_year, 1421 8402, has_genre, 14724 8402, has_tags, 18880 8402, has_tags, 14724 8402, in_language, 13090 30870, has_genre, 14724 30870, release_year, 1421 6809, has_genre, 14724 6809, release_year, 1421 7483, has_genre, 14724 7483, release_year, 1421 31203, has_genre, 14724 31203, release_year, 1421 38211, has_genre, 14724 38211, release_year, 1421 12604, has_genre, 5870 12604, starred_actors, 14268 33784, has_genre, 14724 33784, release_year, 1421 25131, has_genre, 14724 25131, release_year, 1421 38680, has_genre, 14724 38680, release_year, 1421 8511, has_genre, 14724 8511, release_year, 1421 17284, has_genre, 14724 17284, release_year, 1421 31232, has_genre, 14724 31232, release_year, 1421 33382, has_genre, 14724 33382, release_year, 1421 30262, has_genre, 5870 30262, has_tags, 30262 30262, has_tags, 5870 30262, has_tags, 5750 30262, starred_actors, 14268 21021, directed_by, 34844 21021, has_genre, 14724 21021, has_tags, 14724 21021, has_tags, 34844 21021, has_tags, 20331 21021, starred_actors, 20331 13986, has_genre, 14724 13986, release_year, 1421 28256, has_genre, 14724 28256, release_year, 1421 4372, has_genre, 14724 4372, release_year, 1421 1033, has_genre, 14724 1033, has_tags, 20331 1033, starred_actors, 20331 35657, has_genre, 14724 35657, release_year, 1421 4830, directed_by, 24988 4830, directed_by, 35405 4830, has_genre, 5870 4830, has_tags, 14268 4830, has_tags, 24481 4830, has_tags, 4830 4830, has_tags, 5870 4830, has_tags, 4472 4830, has_tags, 24988 4830, has_tags, 31769 4830, has_tags, 25918 4830, has_tags, 35405 4830, has_tags, 5409 4830, starred_actors, 14268 4830, starred_actors, 4472 4830, starred_actors, 31769 4830, written_by, 20056 4830, written_by, 24988 4830, written_by, 35405 15753, directed_by, 35405 15753, has_genre, 5870 15753, has_tags, 14268 15753, has_tags, 24481 15753, has_tags, 4830 15753, has_tags, 5870 15753, has_tags, 4472 15753, has_tags, 31769 15753, has_tags, 25918 15753, has_tags, 5409 15753, starred_actors, 14268 15753, starred_actors, 4472 15753, written_by, 20056 15753, written_by, 24988 15753, written_by, 35405 12915, has_genre, 14724 12915, release_year, 1421 39037, has_genre, 14724 39037, release_year, 1421 8667, has_genre, 14724 8667, has_tags, 14724 8667, release_year, 1421 29765, has_genre, 14724 29765, release_year, 1421 9792, has_genre, 14724 9792, starred_actors, 23224 16074, has_genre, 14724 16074, release_year, 1421 7925, has_genre, 14724 7925, release_year, 1421 24110, directed_by, 34844 24110, has_genre, 14724 24110, has_tags, 18880 24110, has_tags, 34844 24110, has_tags, 20331 24110, in_language, 13090 24110, release_year, 1421 24110, starred_actors, 23224 24110, starred_actors, 20331 24110, written_by, 34844 32279, has_genre, 14724 32279, has_tags, 14724 32279, release_year, 1421 29586, has_genre, 14724 29586, release_year, 1421 38043, directed_by, 34844 38043, has_genre, 14724 38043, has_tags, 34844 38043, written_by, 34844 22512, has_genre, 14724 22512, release_year, 1421 11468, has_genre, 14724 11468, release_year, 1421 13431, has_genre, 14724 13431, has_tags, 14724 13431, release_year, 1421 16900, has_genre, 14724 16900, release_year, 1421 38918, has_genre, 14724 38918, release_year, 1421 30294, has_genre, 14724 30294, starred_actors, 14268 40086, has_genre, 14724 40086, has_tags, 20331 40086, starred_actors, 20331 12738, has_genre, 14724 12738, release_year, 1421 919, has_genre, 5870 919, has_tags, 5750 919, starred_actors, 14268 13357, has_genre, 5870 13357, has_tags, 5870 13357, starred_actors, 13712 17777, has_genre, 14724 17777, release_year, 1421 Question: In what context are DONALD PLEASENCE, ONLY GOD FORGIVES, and TAMMY LAUREN connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "DONALD PLEASENCE", "ONLY GOD FORGIVES", "TAMMY LAUREN" ], "valid_edges": [ [ "A SINGLE SHOT", "has_genre", "CRIME" ], [ "A SINGLE SHOT", "release_year", "2013" ], [ "AIN'T THEM BODIES SAINTS", "has_genre", "CRIME" ], [ "AIN'T THEM BODIES SAINTS", "release_year", "2013" ], [ "ALL GOOD THINGS", "has_genre", "CRIME" ], [ "ALL GOOD THINGS", "has_tags", "RYAN GOSLING" ], [ "ALL GOOD THINGS", "starred_actors", "RYAN GOSLING" ], [ "AMERICAN HUSTLE", "has_genre", "CRIME" ], [ "AMERICAN HUSTLE", "release_year", "2013" ], [ "BANGKOK DANGEROUS", "has_genre", "CRIME" ], [ "BANGKOK DANGEROUS", "has_tags", "BANGKOK" ], [ "BANGKOK DANGEROUS", "has_tags", "CRIME" ], [ "BANGKOK DANGEROUS", "in_language", "THAI" ], [ "BEAUTIFUL CREATURES", "has_genre", "CRIME" ], [ "BEAUTIFUL CREATURES", "release_year", "2013" ], [ "BLOOD OF REDEMPTION", "has_genre", "CRIME" ], [ "BLOOD OF REDEMPTION", "release_year", "2013" ], [ "BLOOD TIES", "has_genre", "CRIME" ], [ "BLOOD TIES", "release_year", "2013" ], [ "BRAZILIAN WESTERN", "has_genre", "CRIME" ], [ "BRAZILIAN WESTERN", "release_year", "2013" ], [ "BROKEN CITY", "has_genre", "CRIME" ], [ "BROKEN CITY", "release_year", "2013" ], [ "CIRCUS OF HORRORS", "has_genre", "HORROR" ], [ "CIRCUS OF HORRORS", "starred_actors", "DONALD PLEASENCE" ], [ "CLOSED CIRCUIT", "has_genre", "CRIME" ], [ "CLOSED CIRCUIT", "release_year", "2013" ], [ "COLD COMES THE NIGHT", "has_genre", "CRIME" ], [ "COLD COMES THE NIGHT", "release_year", "2013" ], [ "COMPULSION", "has_genre", "CRIME" ], [ "COMPULSION", "release_year", "2013" ], [ "COTTAGE COUNTRY", "has_genre", "CRIME" ], [ "COTTAGE COUNTRY", "release_year", "2013" ], [ "DEAD MAN DOWN", "has_genre", "CRIME" ], [ "DEAD MAN DOWN", "release_year", "2013" ], [ "DEVIL'S KNOT", "has_genre", "CRIME" ], [ "DEVIL'S KNOT", "release_year", "2013" ], [ "DOM HEMINGWAY", "has_genre", "CRIME" ], [ "DOM HEMINGWAY", "release_year", "2013" ], [ "DRACULA", "has_genre", "HORROR" ], [ "DRACULA", "has_tags", "DRACULA" ], [ "DRACULA", "has_tags", "HORROR" ], [ "DRACULA", "has_tags", "VAMPIRE" ], [ "DRACULA", "starred_actors", "DONALD PLEASENCE" ], [ "DRIVE", "directed_by", "NICOLAS WINDING REFN" ], [ "DRIVE", "has_genre", "CRIME" ], [ "DRIVE", "has_tags", "CRIME" ], [ "DRIVE", "has_tags", "NICOLAS WINDING REFN" ], [ "DRIVE", "has_tags", "RYAN GOSLING" ], [ "DRIVE", "starred_actors", "RYAN GOSLING" ], [ "EMPIRE STATE", "has_genre", "CRIME" ], [ "EMPIRE STATE", "release_year", "2013" ], [ "FILTH", "has_genre", "CRIME" ], [ "FILTH", "release_year", "2013" ], [ "FORCE OF EXECUTION", "has_genre", "CRIME" ], [ "FORCE OF EXECUTION", "release_year", "2013" ], [ "FRACTURE", "has_genre", "CRIME" ], [ "FRACTURE", "has_tags", "RYAN GOSLING" ], [ "FRACTURE", "starred_actors", "RYAN GOSLING" ], [ "GLORIA", "has_genre", "CRIME" ], [ "GLORIA", "release_year", "2013" ], [ "HALLOWEEN", "directed_by", "JOHN CARPENTER" ], [ "HALLOWEEN", "directed_by", "ROB ZOMBIE" ], [ "HALLOWEEN", "has_genre", "HORROR" ], [ "HALLOWEEN", "has_tags", "DONALD PLEASENCE" ], [ "HALLOWEEN", "has_tags", "FRANCHISE" ], [ "HALLOWEEN", "has_tags", "HALLOWEEN" ], [ "HALLOWEEN", "has_tags", "HORROR" ], [ "HALLOWEEN", "has_tags", "JAMIE LEE CURTIS" ], [ "HALLOWEEN", "has_tags", "JOHN CARPENTER" ], [ "HALLOWEEN", "has_tags", "MALCOLM MCDOWELL" ], [ "HALLOWEEN", "has_tags", "MICHAEL MYERS" ], [ "HALLOWEEN", "has_tags", "ROB ZOMBIE" ], [ "HALLOWEEN", "has_tags", "SLASHER" ], [ "HALLOWEEN", "starred_actors", "DONALD PLEASENCE" ], [ "HALLOWEEN", "starred_actors", "JAMIE LEE CURTIS" ], [ "HALLOWEEN", "starred_actors", "MALCOLM MCDOWELL" ], [ "HALLOWEEN", "written_by", "DEBRA HILL" ], [ "HALLOWEEN", "written_by", "JOHN CARPENTER" ], [ "HALLOWEEN", "written_by", "ROB ZOMBIE" ], [ "HALLOWEEN II", "directed_by", "ROB ZOMBIE" ], [ "HALLOWEEN II", "has_genre", "HORROR" ], [ "HALLOWEEN II", "has_tags", "DONALD PLEASENCE" ], [ "HALLOWEEN II", "has_tags", "FRANCHISE" ], [ "HALLOWEEN II", "has_tags", "HALLOWEEN" ], [ "HALLOWEEN II", "has_tags", "HORROR" ], [ "HALLOWEEN II", "has_tags", "JAMIE LEE CURTIS" ], [ "HALLOWEEN II", "has_tags", "MALCOLM MCDOWELL" ], [ "HALLOWEEN II", "has_tags", "MICHAEL MYERS" ], [ "HALLOWEEN II", "has_tags", "SLASHER" ], [ "HALLOWEEN II", "starred_actors", "DONALD PLEASENCE" ], [ "HALLOWEEN II", "starred_actors", "JAMIE LEE CURTIS" ], [ "HALLOWEEN II", "written_by", "DEBRA HILL" ], [ "HALLOWEEN II", "written_by", "JOHN CARPENTER" ], [ "HALLOWEEN II", "written_by", "ROB ZOMBIE" ], [ "HELI", "has_genre", "CRIME" ], [ "HELI", "release_year", "2013" ], [ "HUNTING ELEPHANTS", "has_genre", "CRIME" ], [ "HUNTING ELEPHANTS", "release_year", "2013" ], [ "IDENTITY THIEF", "has_genre", "CRIME" ], [ "IDENTITY THIEF", "has_tags", "CRIME" ], [ "IDENTITY THIEF", "release_year", "2013" ], [ "LIFE OF CRIME", "has_genre", "CRIME" ], [ "LIFE OF CRIME", "release_year", "2013" ], [ "LOVE CRIME", "has_genre", "CRIME" ], [ "LOVE CRIME", "starred_actors", "KRISTIN SCOTT THOMAS" ], [ "METRO MANILA", "has_genre", "CRIME" ], [ "METRO MANILA", "release_year", "2013" ], [ "MYSTERY ROAD", "has_genre", "CRIME" ], [ "MYSTERY ROAD", "release_year", "2013" ], [ "ONLY GOD FORGIVES", "directed_by", "NICOLAS WINDING REFN" ], [ "ONLY GOD FORGIVES", "has_genre", "CRIME" ], [ "ONLY GOD FORGIVES", "has_tags", "BANGKOK" ], [ "ONLY GOD FORGIVES", "has_tags", "NICOLAS WINDING REFN" ], [ "ONLY GOD FORGIVES", "has_tags", "RYAN GOSLING" ], [ "ONLY GOD FORGIVES", "in_language", "THAI" ], [ "ONLY GOD FORGIVES", "release_year", "2013" ], [ "ONLY GOD FORGIVES", "starred_actors", "KRISTIN SCOTT THOMAS" ], [ "ONLY GOD FORGIVES", "starred_actors", "RYAN GOSLING" ], [ "ONLY GOD FORGIVES", "written_by", "NICOLAS WINDING REFN" ], [ "PARKER", "has_genre", "CRIME" ], [ "PARKER", "has_tags", "CRIME" ], [ "PARKER", "release_year", "2013" ], [ "PAWN SHOP CHRONICLES", "has_genre", "CRIME" ], [ "PAWN SHOP CHRONICLES", "release_year", "2013" ], [ "PUSHER", "directed_by", "NICOLAS WINDING REFN" ], [ "PUSHER", "has_genre", "CRIME" ], [ "PUSHER", "has_tags", "NICOLAS WINDING REFN" ], [ "PUSHER", "written_by", "NICOLAS WINDING REFN" ], [ "RUNNER RUNNER", "has_genre", "CRIME" ], [ "RUNNER RUNNER", "release_year", "2013" ], [ "RUSH", "has_genre", "CRIME" ], [ "RUSH", "release_year", "2013" ], [ "THE BLING RING", "has_genre", "CRIME" ], [ "THE BLING RING", "has_tags", "CRIME" ], [ "THE BLING RING", "release_year", "2013" ], [ "THE CALL", "has_genre", "CRIME" ], [ "THE CALL", "release_year", "2013" ], [ "THE FAMILY", "has_genre", "CRIME" ], [ "THE FAMILY", "release_year", "2013" ], [ "THE NIGHT OF THE GENERALS", "has_genre", "CRIME" ], [ "THE NIGHT OF THE GENERALS", "starred_actors", "DONALD PLEASENCE" ], [ "THE PLACE BEYOND THE PINES", "has_genre", "CRIME" ], [ "THE PLACE BEYOND THE PINES", "has_tags", "RYAN GOSLING" ], [ "THE PLACE BEYOND THE PINES", "starred_actors", "RYAN GOSLING" ], [ "TRAFFIC DEPARTMENT", "has_genre", "CRIME" ], [ "TRAFFIC DEPARTMENT", "release_year", "2013" ], [ "VAMPIRE IN VENICE", "has_genre", "HORROR" ], [ "VAMPIRE IN VENICE", "has_tags", "VAMPIRE" ], [ "VAMPIRE IN VENICE", "starred_actors", "DONALD PLEASENCE" ], [ "WISHMASTER", "has_genre", "HORROR" ], [ "WISHMASTER", "has_tags", "HORROR" ], [ "WISHMASTER", "starred_actors", "TAMMY LAUREN" ], [ "ZULU", "has_genre", "CRIME" ], [ "ZULU", "release_year", "2013" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 2452, 13 ASSASSINS 11, 1940 19543, 2001 MANIACS 37484, 2004 6314, A DAMSEL IN DISTRESS 24480, A NIGHTMARE ON ELM STREET 25465, APARTMENT 1303 7908, AUDITION 23324, BATTLEFIELD BASEBALL 4445, BITTER SWEET 36593, BLACK CHRISTMAS 3506, BLUE SKIES 25126, BODY SNATCHERS 16011, BROADWAY MELODY OF 1940 7381, CALVAIRE 8079, CAREFREE 38311, CHAOS 1551, CLUB DREAD 32408, CREEP 26635, CROWS ZERO 12155, DADDY LONG LEGS 24400, DARK WATER 21942, DAWN OF THE DEAD 5547, DEAD BIRDS 1353, DECOYS 25528, DEVIL'S PASS 23612, DOLLS 13272, DON'T BE AFRAID OF THE DARK 15286, DOWN ARGENTINE WAY 25662, DR. CYCLOPS 6662, DUMPLINGS 5799, EASTER PARADE 23129, EVIL DEAD 19728, EVILENKO 5038, FINIAN'S RAINBOW 16485, FOLLOW THE FLEET 29924, FRED ASTAIRE 22215, FRIDAY THE 13TH 31648, FRIGHT NIGHT 1578, FUNNY FACE 32864, GHOST STORY 3553, GODZILLA 4830, HALLOWEEN 11997, HELLBENT 23534, HOLIDAY INN 14279, HOLLY GOSS 5870, HORROR 10147, HOUSE 9686, HOUSE OF WAX 31551, HOUSE ON HAUNTED HILL 15439, I SPIT ON YOUR GRAVE 13174, INFECTION 25576, INNOCENCE 13070, INVADERS FROM MARS 14910, IT'S ALIVE 8164, IZO 8248, JAPAN 36874, JAPANESE 4418, KWAIDAN 29987, LET ME IN 8851, LITTLE SHOP OF HORRORS 20189, LOFT 26370, MADHOUSE 2461, MEMOIRS OF A GEISHA 35621, MILLENNIUM ACTRESS 21686, MOONLIGHT SERENADE 24593, MUSICAL 32988, NIGHT OF THE LIVING DEAD 11340, NIGHT OF THE LIVING DEAD 3D 18722, NOSFERATU THE VAMPYRE 3089, NOT OF THIS EARTH 36326, ONE MISSED CALL 6302, ONIBABA 36532, OVER YOUR DEAD BODY 234, PEARL HARBOR 14026, PINOCCHIO 6035, PRINCESS RACCOON 28091, PSYCHO 13237, PULSE 199, PUPPET MASTER VS DEMONIC TOYS 6175, QUARANTINE 28729, REMAKE 30416, REPO! THE GENETIC OPERA 32894, RIDING THE BULLET 28, RING 35249, RING 2 38241, RING OF DARKNESS 27308, ROBERTA 38307, ROYAL WEDDING 34022, SATAN'S LITTLE HELPER 36247, SAW 12071, SECOND CHORUS 2186, SEED OF CHUCKY 30996, SHUTTER 16848, SILK STOCKINGS 6260, SPIRITED AWAY 32367, STAGE FRIGHT 1584, STEPHEN SUSCO 13333, STRIKE UP THE BAND 7170, SWEET HOME 8998, SWING TIME 18903, TAKASHI SHIMIZU 20069, THAT'S ENTERTAINMENT! 31138, THAT'S ENTERTAINMENT, PART II 9015, THE AMITYVILLE HORROR 16069, THE APE 32851, THE BAND WAGON 3223, THE BARKLEYS OF BROADWAY 3543, THE BLOB 25463, THE CRAZIES 4694, THE CREATURE WASN'T NICE 3273, THE DEVIL BAT 7153, THE DEVIL'S CARNIVAL 20931, THE EYE 38052, THE GAY DIVORCEE 28048, THE GHOST BREAKERS 786, THE GRUDGE 9129, THE GRUDGE 2 19342, THE GRUDGE 3 17778, THE HAPPINESS OF THE KATAKURIS 11787, THE HAUNTING 15462, THE HILLS HAVE EYES 26776, THE INVISIBLE MAN RETURNS 15202, THE LAST HOUSE ON THE LEFT 29109, THE LODGER 17910, THE OMEN 2492, THE RING 12654, THE RING TWO 9923, THE SKY'S THE LIMIT 23847, THE STEPFORD WIVES 30082, THE STORY OF VERNON AND IRENE CASTLE 21548, THE SUN 9665, THE TEXAS CHAINSAW MASSACRE 22751, THE WICKER MAN 24040, THREE LITTLE WORDS 29819, TIN PAN ALLEY 19710, TOKYO ZOMBIE 12856, TOOLBOX MURDERS 32505, TOP HAT 19088, VAN HELSING 36374, VILLAGE OF THE DAMNED 29077, WASABI 8589, WICKED CITY 12029, WILD ZERO 38538, WILLARD 15472, YOLANDA AND THE THIEF 19677, YOU WERE NEVER LOVELIER 38863, YOU'LL NEVER GET RICH 14841, ZIEGFELD FOLLIES 38978, ZOMBIE HONEYMOON src, edge_attr, dst 2452, has_tags, 8248 2452, in_language, 36874 19543, has_genre, 5870 19543, has_tags, 28729 6314, has_genre, 24593 6314, starred_actors, 29924 24480, has_genre, 5870 24480, has_tags, 5870 24480, has_tags, 28729 25465, has_genre, 5870 25465, in_language, 36874 7908, has_genre, 5870 7908, in_language, 36874 23324, has_genre, 5870 23324, in_language, 36874 4445, has_genre, 24593 4445, release_year, 11 36593, has_genre, 5870 36593, has_tags, 5870 36593, has_tags, 28729 3506, has_genre, 24593 3506, starred_actors, 29924 25126, has_genre, 5870 25126, has_tags, 28729 16011, has_genre, 24593 16011, release_year, 11 16011, starred_actors, 29924 7381, has_genre, 5870 7381, has_tags, 5870 7381, release_year, 37484 8079, has_genre, 24593 8079, starred_actors, 29924 38311, has_genre, 5870 38311, in_language, 36874 1551, has_genre, 5870 1551, release_year, 37484 32408, has_genre, 5870 32408, release_year, 37484 26635, has_tags, 8248 26635, in_language, 36874 12155, has_genre, 24593 12155, has_tags, 29924 12155, has_tags, 24593 12155, starred_actors, 29924 24400, has_genre, 5870 24400, has_tags, 5870 24400, has_tags, 28729 24400, in_language, 36874 21942, has_genre, 5870 21942, has_tags, 5870 21942, has_tags, 28729 21942, release_year, 37484 5547, has_genre, 5870 5547, release_year, 37484 1353, has_genre, 5870 1353, release_year, 37484 25528, has_genre, 5870 25528, starred_actors, 14279 23612, has_genre, 5870 23612, has_tags, 8248 23612, in_language, 36874 13272, has_genre, 5870 13272, has_tags, 28729 15286, has_genre, 24593 15286, release_year, 11 25662, has_genre, 5870 25662, release_year, 11 6662, has_genre, 5870 6662, release_year, 37484 5799, has_genre, 24593 5799, has_tags, 29924 5799, starred_actors, 29924 23129, has_genre, 5870 23129, has_tags, 5870 23129, has_tags, 28729 19728, has_genre, 5870 19728, release_year, 37484 5038, has_genre, 24593 5038, has_tags, 29924 5038, has_tags, 24593 5038, starred_actors, 29924 16485, has_genre, 24593 16485, starred_actors, 29924 22215, has_genre, 5870 22215, has_tags, 28729 31648, has_genre, 5870 31648, has_tags, 28729 1578, has_genre, 24593 1578, has_tags, 29924 1578, has_tags, 24593 1578, starred_actors, 29924 32864, has_genre, 5870 32864, starred_actors, 29924 3553, has_genre, 5870 3553, has_tags, 8248 3553, in_language, 36874 4830, has_genre, 5870 4830, has_tags, 5870 4830, has_tags, 28729 11997, has_genre, 5870 11997, has_tags, 5870 11997, release_year, 37484 23534, has_genre, 24593 23534, has_tags, 29924 23534, starred_actors, 29924 10147, has_genre, 5870 10147, has_tags, 36874 10147, in_language, 36874 9686, has_genre, 5870 9686, has_tags, 28729 31551, has_genre, 5870 31551, has_tags, 28729 15439, has_genre, 5870 15439, has_tags, 28729 13174, has_genre, 5870 13174, in_language, 36874 13174, release_year, 37484 25576, has_genre, 5870 25576, release_year, 37484 13070, has_genre, 5870 13070, has_tags, 28729 14910, has_genre, 5870 14910, has_tags, 28729 8164, in_language, 36874 8164, release_year, 37484 4418, has_genre, 5870 4418, has_tags, 8248 4418, in_language, 36874 29987, has_genre, 5870 29987, has_tags, 5870 29987, has_tags, 28729 8851, has_genre, 5870 8851, has_genre, 24593 8851, has_tags, 24593 20189, has_genre, 5870 20189, in_language, 36874 26370, has_genre, 5870 26370, release_year, 37484 2461, has_tags, 8248 2461, has_tags, 36874 2461, in_language, 36874 35621, has_tags, 8248 35621, in_language, 36874 21686, has_genre, 24593 21686, in_language, 36874 32988, has_genre, 5870 32988, has_tags, 5870 32988, has_tags, 28729 11340, has_genre, 5870 11340, has_tags, 28729 18722, has_genre, 5870 18722, has_tags, 28729 3089, has_genre, 5870 3089, has_tags, 28729 36326, has_genre, 5870 36326, in_language, 36874 6302, has_genre, 5870 6302, in_language, 36874 36532, has_genre, 5870 36532, has_tags, 5870 36532, in_language, 36874 234, has_tags, 8248 234, in_language, 36874 14026, has_tags, 24593 14026, release_year, 11 6035, has_genre, 24593 6035, in_language, 36874 28091, has_genre, 5870 28091, has_tags, 5870 28091, has_tags, 28729 13237, has_genre, 5870 13237, has_tags, 5870 13237, has_tags, 36874 13237, in_language, 36874 199, has_genre, 5870 199, release_year, 37484 6175, has_genre, 5870 6175, has_tags, 28729 30416, has_genre, 5870 30416, has_genre, 24593 30416, has_tags, 24593 32894, has_genre, 5870 32894, release_year, 37484 28, has_genre, 5870 28, in_language, 36874 35249, has_genre, 5870 35249, has_tags, 5870 35249, in_language, 36874 38241, has_genre, 5870 38241, release_year, 37484 27308, has_genre, 24593 27308, has_tags, 29924 27308, starred_actors, 29924 38307, has_genre, 24593 38307, has_tags, 29924 38307, starred_actors, 29924 34022, has_genre, 5870 34022, release_year, 37484 36247, has_genre, 5870 36247, has_tags, 5870 36247, release_year, 37484 12071, has_genre, 24593 12071, release_year, 11 12071, starred_actors, 29924 2186, has_genre, 5870 2186, release_year, 37484 30996, has_genre, 5870 30996, has_tags, 5870 30996, has_tags, 28729 30996, release_year, 37484 16848, has_genre, 24593 16848, starred_actors, 29924 6260, has_tags, 8248 6260, has_tags, 36874 6260, in_language, 36874 32367, has_genre, 5870 32367, has_genre, 24593 13333, has_genre, 24593 13333, release_year, 11 7170, has_genre, 5870 7170, in_language, 36874 8998, has_genre, 24593 8998, starred_actors, 29924 20069, has_genre, 24593 20069, starred_actors, 29924 31138, has_genre, 24593 31138, starred_actors, 29924 9015, has_genre, 5870 9015, has_tags, 28729 16069, has_genre, 5870 16069, release_year, 11 32851, has_genre, 24593 32851, starred_actors, 29924 3223, has_genre, 24593 3223, has_tags, 29924 3223, starred_actors, 29924 3543, has_genre, 5870 3543, has_tags, 28729 25463, has_genre, 5870 25463, has_tags, 28729 4694, has_genre, 5870 4694, has_genre, 24593 3273, has_genre, 5870 3273, release_year, 11 7153, has_genre, 5870 7153, has_genre, 24593 20931, has_genre, 5870 20931, has_tags, 5870 20931, has_tags, 28729 38052, has_genre, 24593 38052, starred_actors, 29924 28048, has_genre, 5870 28048, release_year, 11 786, directed_by, 18903 786, has_genre, 5870 786, has_tags, 8248 786, has_tags, 28729 786, in_language, 36874 786, release_year, 37484 786, written_by, 1584 786, written_by, 18903 9129, directed_by, 18903 9129, has_genre, 5870 9129, written_by, 1584 9129, written_by, 18903 19342, has_genre, 5870 19342, written_by, 18903 17778, has_genre, 5870 17778, has_genre, 24593 17778, has_tags, 24593 11787, has_genre, 5870 11787, has_tags, 5870 11787, has_tags, 28729 15462, has_genre, 5870 15462, has_tags, 5870 15462, has_tags, 28729 26776, has_genre, 5870 26776, release_year, 11 15202, has_genre, 5870 15202, has_tags, 28729 29109, has_genre, 5870 29109, has_tags, 28729 17910, has_genre, 5870 17910, has_tags, 5870 17910, has_tags, 28729 2492, has_genre, 5870 2492, has_tags, 5870 2492, has_tags, 36874 2492, has_tags, 28729 12654, has_genre, 5870 12654, has_tags, 28729 9923, has_genre, 24593 9923, starred_actors, 29924 23847, has_genre, 5870 23847, has_tags, 28729 23847, release_year, 37484 30082, has_genre, 24593 30082, starred_actors, 29924 21548, has_tags, 8248 21548, in_language, 36874 9665, has_genre, 5870 9665, has_tags, 28729 22751, has_genre, 5870 22751, has_tags, 28729 24040, has_genre, 24593 24040, starred_actors, 29924 29819, has_genre, 24593 29819, release_year, 11 19710, has_tags, 8248 19710, in_language, 36874 12856, has_genre, 5870 12856, has_tags, 5870 12856, release_year, 37484 32505, has_genre, 24593 32505, has_tags, 29924 32505, starred_actors, 29924 19088, has_tags, 5870 19088, release_year, 37484 36374, has_genre, 5870 36374, has_tags, 28729 29077, has_tags, 8248 29077, in_language, 36874 8589, has_genre, 5870 8589, in_language, 36874 12029, has_genre, 5870 12029, in_language, 36874 38538, has_genre, 5870 38538, has_tags, 28729 15472, has_genre, 24593 15472, starred_actors, 29924 19677, has_genre, 24593 19677, has_tags, 29924 19677, starred_actors, 29924 38863, has_genre, 24593 38863, starred_actors, 29924 14841, has_genre, 24593 14841, starred_actors, 29924 38978, has_genre, 5870 38978, release_year, 37484 Question: How are BROADWAY MELODY OF 1940, HOLLY GOSS, and THE GRUDGE related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BROADWAY MELODY OF 1940", "HOLLY GOSS", "THE GRUDGE" ], "valid_edges": [ [ "13 ASSASSINS", "has_tags", "JAPAN" ], [ "13 ASSASSINS", "in_language", "JAPANESE" ], [ "2001 MANIACS", "has_genre", "HORROR" ], [ "2001 MANIACS", "has_tags", "REMAKE" ], [ "A DAMSEL IN DISTRESS", "has_genre", "MUSICAL" ], [ "A DAMSEL IN DISTRESS", "starred_actors", "FRED ASTAIRE" ], [ "A NIGHTMARE ON ELM STREET", "has_genre", "HORROR" ], [ "A NIGHTMARE ON ELM STREET", "has_tags", "HORROR" ], [ "A NIGHTMARE ON ELM STREET", "has_tags", "REMAKE" ], [ "APARTMENT 1303", "has_genre", "HORROR" ], [ "APARTMENT 1303", "in_language", "JAPANESE" ], [ "AUDITION", "has_genre", "HORROR" ], [ "AUDITION", "in_language", "JAPANESE" ], [ "BATTLEFIELD BASEBALL", "has_genre", "HORROR" ], [ "BATTLEFIELD BASEBALL", "in_language", "JAPANESE" ], [ "BITTER SWEET", "has_genre", "MUSICAL" ], [ "BITTER SWEET", "release_year", "1940" ], [ "BLACK CHRISTMAS", "has_genre", "HORROR" ], [ "BLACK CHRISTMAS", "has_tags", "HORROR" ], [ "BLACK CHRISTMAS", "has_tags", "REMAKE" ], [ "BLUE SKIES", "has_genre", "MUSICAL" ], [ "BLUE SKIES", "starred_actors", "FRED ASTAIRE" ], [ "BODY SNATCHERS", "has_genre", "HORROR" ], [ "BODY SNATCHERS", "has_tags", "REMAKE" ], [ "BROADWAY MELODY OF 1940", "has_genre", "MUSICAL" ], [ "BROADWAY MELODY OF 1940", "release_year", "1940" ], [ "BROADWAY MELODY OF 1940", "starred_actors", "FRED ASTAIRE" ], [ "CALVAIRE", "has_genre", "HORROR" ], [ "CALVAIRE", "has_tags", "HORROR" ], [ "CALVAIRE", "release_year", "2004" ], [ "CAREFREE", "has_genre", "MUSICAL" ], [ "CAREFREE", "starred_actors", "FRED ASTAIRE" ], [ "CHAOS", "has_genre", "HORROR" ], [ "CHAOS", "in_language", "JAPANESE" ], [ "CLUB DREAD", "has_genre", "HORROR" ], [ "CLUB DREAD", "release_year", "2004" ], [ "CREEP", "has_genre", "HORROR" ], [ "CREEP", "release_year", "2004" ], [ "CROWS ZERO", "has_tags", "JAPAN" ], [ "CROWS ZERO", "in_language", "JAPANESE" ], [ "DADDY LONG LEGS", "has_genre", "MUSICAL" ], [ "DADDY LONG LEGS", "has_tags", "FRED ASTAIRE" ], [ "DADDY LONG LEGS", "has_tags", "MUSICAL" ], [ "DADDY LONG LEGS", "starred_actors", "FRED ASTAIRE" ], [ "DARK WATER", "has_genre", "HORROR" ], [ "DARK WATER", "has_tags", "HORROR" ], [ "DARK WATER", "has_tags", "REMAKE" ], [ "DARK WATER", "in_language", "JAPANESE" ], [ "DAWN OF THE DEAD", "has_genre", "HORROR" ], [ "DAWN OF THE DEAD", "has_tags", "HORROR" ], [ "DAWN OF THE DEAD", "has_tags", "REMAKE" ], [ "DAWN OF THE DEAD", "release_year", "2004" ], [ "DEAD BIRDS", "has_genre", "HORROR" ], [ "DEAD BIRDS", "release_year", "2004" ], [ "DECOYS", "has_genre", "HORROR" ], [ "DECOYS", "release_year", "2004" ], [ "DEVIL'S PASS", "has_genre", "HORROR" ], [ "DEVIL'S PASS", "starred_actors", "HOLLY GOSS" ], [ "DOLLS", "has_genre", "HORROR" ], [ "DOLLS", "has_tags", "JAPAN" ], [ "DOLLS", "in_language", "JAPANESE" ], [ "DON'T BE AFRAID OF THE DARK", "has_genre", "HORROR" ], [ "DON'T BE AFRAID OF THE DARK", "has_tags", "REMAKE" ], [ "DOWN ARGENTINE WAY", "has_genre", "MUSICAL" ], [ "DOWN ARGENTINE WAY", "release_year", "1940" ], [ "DR. CYCLOPS", "has_genre", "HORROR" ], [ "DR. CYCLOPS", "release_year", "1940" ], [ "DUMPLINGS", "has_genre", "HORROR" ], [ "DUMPLINGS", "release_year", "2004" ], [ "EASTER PARADE", "has_genre", "MUSICAL" ], [ "EASTER PARADE", "has_tags", "FRED ASTAIRE" ], [ "EASTER PARADE", "starred_actors", "FRED ASTAIRE" ], [ "EVIL DEAD", "has_genre", "HORROR" ], [ "EVIL DEAD", "has_tags", "HORROR" ], [ "EVIL DEAD", "has_tags", "REMAKE" ], [ "EVILENKO", "has_genre", "HORROR" ], [ "EVILENKO", "release_year", "2004" ], [ "FINIAN'S RAINBOW", "has_genre", "MUSICAL" ], [ "FINIAN'S RAINBOW", "has_tags", "FRED ASTAIRE" ], [ "FINIAN'S RAINBOW", "has_tags", "MUSICAL" ], [ "FINIAN'S RAINBOW", "starred_actors", "FRED ASTAIRE" ], [ "FOLLOW THE FLEET", "has_genre", "MUSICAL" ], [ "FOLLOW THE FLEET", "starred_actors", "FRED ASTAIRE" ], [ "FRIDAY THE 13TH", "has_genre", "HORROR" ], [ "FRIDAY THE 13TH", "has_tags", "REMAKE" ], [ "FRIGHT NIGHT", "has_genre", "HORROR" ], [ "FRIGHT NIGHT", "has_tags", "REMAKE" ], [ "FUNNY FACE", "has_genre", "MUSICAL" ], [ "FUNNY FACE", "has_tags", "FRED ASTAIRE" ], [ "FUNNY FACE", "has_tags", "MUSICAL" ], [ "FUNNY FACE", "starred_actors", "FRED ASTAIRE" ], [ "GHOST STORY", "has_genre", "HORROR" ], [ "GHOST STORY", "starred_actors", "FRED ASTAIRE" ], [ "GODZILLA", "has_genre", "HORROR" ], [ "GODZILLA", "has_tags", "JAPAN" ], [ "GODZILLA", "in_language", "JAPANESE" ], [ "HALLOWEEN", "has_genre", "HORROR" ], [ "HALLOWEEN", "has_tags", "HORROR" ], [ "HALLOWEEN", "has_tags", "REMAKE" ], [ "HELLBENT", "has_genre", "HORROR" ], [ "HELLBENT", "has_tags", "HORROR" ], [ "HELLBENT", "release_year", "2004" ], [ "HOLIDAY INN", "has_genre", "MUSICAL" ], [ "HOLIDAY INN", "has_tags", "FRED ASTAIRE" ], [ "HOLIDAY INN", "starred_actors", "FRED ASTAIRE" ], [ "HOUSE", "has_genre", "HORROR" ], [ "HOUSE", "has_tags", "JAPANESE" ], [ "HOUSE", "in_language", "JAPANESE" ], [ "HOUSE OF WAX", "has_genre", "HORROR" ], [ "HOUSE OF WAX", "has_tags", "REMAKE" ], [ "HOUSE ON HAUNTED HILL", "has_genre", "HORROR" ], [ "HOUSE ON HAUNTED HILL", "has_tags", "REMAKE" ], [ "I SPIT ON YOUR GRAVE", "has_genre", "HORROR" ], [ "I SPIT ON YOUR GRAVE", "has_tags", "REMAKE" ], [ "INFECTION", "has_genre", "HORROR" ], [ "INFECTION", "in_language", "JAPANESE" ], [ "INFECTION", "release_year", "2004" ], [ "INNOCENCE", "has_genre", "HORROR" ], [ "INNOCENCE", "release_year", "2004" ], [ "INVADERS FROM MARS", "has_genre", "HORROR" ], [ "INVADERS FROM MARS", "has_tags", "REMAKE" ], [ "IT'S ALIVE", "has_genre", "HORROR" ], [ "IT'S ALIVE", "has_tags", "REMAKE" ], [ "IZO", "in_language", "JAPANESE" ], [ "IZO", "release_year", "2004" ], [ "KWAIDAN", "has_genre", "HORROR" ], [ "KWAIDAN", "has_tags", "JAPAN" ], [ "KWAIDAN", "in_language", "JAPANESE" ], [ "LET ME IN", "has_genre", "HORROR" ], [ "LET ME IN", "has_tags", "HORROR" ], [ "LET ME IN", "has_tags", "REMAKE" ], [ "LITTLE SHOP OF HORRORS", "has_genre", "HORROR" ], [ "LITTLE SHOP OF HORRORS", "has_genre", "MUSICAL" ], [ "LITTLE SHOP OF HORRORS", "has_tags", "MUSICAL" ], [ "LOFT", "has_genre", "HORROR" ], [ "LOFT", "in_language", "JAPANESE" ], [ "MADHOUSE", "has_genre", "HORROR" ], [ "MADHOUSE", "release_year", "2004" ], [ "MEMOIRS OF A GEISHA", "has_tags", "JAPAN" ], [ "MEMOIRS OF A GEISHA", "has_tags", "JAPANESE" ], [ "MEMOIRS OF A GEISHA", "in_language", "JAPANESE" ], [ "MILLENNIUM ACTRESS", "has_tags", "JAPAN" ], [ "MILLENNIUM ACTRESS", "in_language", "JAPANESE" ], [ "MOONLIGHT SERENADE", "has_genre", "MUSICAL" ], [ "MOONLIGHT SERENADE", "in_language", "JAPANESE" ], [ "NIGHT OF THE LIVING DEAD", "has_genre", "HORROR" ], [ "NIGHT OF THE LIVING DEAD", "has_tags", "HORROR" ], [ "NIGHT OF THE LIVING DEAD", "has_tags", "REMAKE" ], [ "NIGHT OF THE LIVING DEAD 3D", "has_genre", "HORROR" ], [ "NIGHT OF THE LIVING DEAD 3D", "has_tags", "REMAKE" ], [ "NOSFERATU THE VAMPYRE", "has_genre", "HORROR" ], [ "NOSFERATU THE VAMPYRE", "has_tags", "REMAKE" ], [ "NOT OF THIS EARTH", "has_genre", "HORROR" ], [ "NOT OF THIS EARTH", "has_tags", "REMAKE" ], [ "ONE MISSED CALL", "has_genre", "HORROR" ], [ "ONE MISSED CALL", "in_language", "JAPANESE" ], [ "ONIBABA", "has_genre", "HORROR" ], [ "ONIBABA", "in_language", "JAPANESE" ], [ "OVER YOUR DEAD BODY", "has_genre", "HORROR" ], [ "OVER YOUR DEAD BODY", "has_tags", "HORROR" ], [ "OVER YOUR DEAD BODY", "in_language", "JAPANESE" ], [ "PEARL HARBOR", "has_tags", "JAPAN" ], [ "PEARL HARBOR", "in_language", "JAPANESE" ], [ "PINOCCHIO", "has_tags", "MUSICAL" ], [ "PINOCCHIO", "release_year", "1940" ], [ "PRINCESS RACCOON", "has_genre", "MUSICAL" ], [ "PRINCESS RACCOON", "in_language", "JAPANESE" ], [ "PSYCHO", "has_genre", "HORROR" ], [ "PSYCHO", "has_tags", "HORROR" ], [ "PSYCHO", "has_tags", "REMAKE" ], [ "PULSE", "has_genre", "HORROR" ], [ "PULSE", "has_tags", "HORROR" ], [ "PULSE", "has_tags", "JAPANESE" ], [ "PULSE", "in_language", "JAPANESE" ], [ "PUPPET MASTER VS DEMONIC TOYS", "has_genre", "HORROR" ], [ "PUPPET MASTER VS DEMONIC TOYS", "release_year", "2004" ], [ "QUARANTINE", "has_genre", "HORROR" ], [ "QUARANTINE", "has_tags", "REMAKE" ], [ "REPO! THE GENETIC OPERA", "has_genre", "HORROR" ], [ "REPO! THE GENETIC OPERA", "has_genre", "MUSICAL" ], [ "REPO! THE GENETIC OPERA", "has_tags", "MUSICAL" ], [ "RIDING THE BULLET", "has_genre", "HORROR" ], [ "RIDING THE BULLET", "release_year", "2004" ], [ "RING", "has_genre", "HORROR" ], [ "RING", "in_language", "JAPANESE" ], [ "RING 2", "has_genre", "HORROR" ], [ "RING 2", "has_tags", "HORROR" ], [ "RING 2", "in_language", "JAPANESE" ], [ "RING OF DARKNESS", "has_genre", "HORROR" ], [ "RING OF DARKNESS", "release_year", "2004" ], [ "ROBERTA", "has_genre", "MUSICAL" ], [ "ROBERTA", "has_tags", "FRED ASTAIRE" ], [ "ROBERTA", "starred_actors", "FRED ASTAIRE" ], [ "ROYAL WEDDING", "has_genre", "MUSICAL" ], [ "ROYAL WEDDING", "has_tags", "FRED ASTAIRE" ], [ "ROYAL WEDDING", "starred_actors", "FRED ASTAIRE" ], [ "SATAN'S LITTLE HELPER", "has_genre", "HORROR" ], [ "SATAN'S LITTLE HELPER", "release_year", "2004" ], [ "SAW", "has_genre", "HORROR" ], [ "SAW", "has_tags", "HORROR" ], [ "SAW", "release_year", "2004" ], [ "SECOND CHORUS", "has_genre", "MUSICAL" ], [ "SECOND CHORUS", "release_year", "1940" ], [ "SECOND CHORUS", "starred_actors", "FRED ASTAIRE" ], [ "SEED OF CHUCKY", "has_genre", "HORROR" ], [ "SEED OF CHUCKY", "release_year", "2004" ], [ "SHUTTER", "has_genre", "HORROR" ], [ "SHUTTER", "has_tags", "HORROR" ], [ "SHUTTER", "has_tags", "REMAKE" ], [ "SHUTTER", "release_year", "2004" ], [ "SILK STOCKINGS", "has_genre", "MUSICAL" ], [ "SILK STOCKINGS", "starred_actors", "FRED ASTAIRE" ], [ "SPIRITED AWAY", "has_tags", "JAPAN" ], [ "SPIRITED AWAY", "has_tags", "JAPANESE" ], [ "SPIRITED AWAY", "in_language", "JAPANESE" ], [ "STAGE FRIGHT", "has_genre", "HORROR" ], [ "STAGE FRIGHT", "has_genre", "MUSICAL" ], [ "STRIKE UP THE BAND", "has_genre", "MUSICAL" ], [ "STRIKE UP THE BAND", "release_year", "1940" ], [ "SWEET HOME", "has_genre", "HORROR" ], [ "SWEET HOME", "in_language", "JAPANESE" ], [ "SWING TIME", "has_genre", "MUSICAL" ], [ "SWING TIME", "starred_actors", "FRED ASTAIRE" ], [ "THAT'S ENTERTAINMENT!", "has_genre", "MUSICAL" ], [ "THAT'S ENTERTAINMENT!", "starred_actors", "FRED ASTAIRE" ], [ "THAT'S ENTERTAINMENT, PART II", "has_genre", "MUSICAL" ], [ "THAT'S ENTERTAINMENT, PART II", "starred_actors", "FRED ASTAIRE" ], [ "THE AMITYVILLE HORROR", "has_genre", "HORROR" ], [ "THE AMITYVILLE HORROR", "has_tags", "REMAKE" ], [ "THE APE", "has_genre", "HORROR" ], [ "THE APE", "release_year", "1940" ], [ "THE BAND WAGON", "has_genre", "MUSICAL" ], [ "THE BAND WAGON", "starred_actors", "FRED ASTAIRE" ], [ "THE BARKLEYS OF BROADWAY", "has_genre", "MUSICAL" ], [ "THE BARKLEYS OF BROADWAY", "has_tags", "FRED ASTAIRE" ], [ "THE BARKLEYS OF BROADWAY", "starred_actors", "FRED ASTAIRE" ], [ "THE BLOB", "has_genre", "HORROR" ], [ "THE BLOB", "has_tags", "REMAKE" ], [ "THE CRAZIES", "has_genre", "HORROR" ], [ "THE CRAZIES", "has_tags", "REMAKE" ], [ "THE CREATURE WASN'T NICE", "has_genre", "HORROR" ], [ "THE CREATURE WASN'T NICE", "has_genre", "MUSICAL" ], [ "THE DEVIL BAT", "has_genre", "HORROR" ], [ "THE DEVIL BAT", "release_year", "1940" ], [ "THE DEVIL'S CARNIVAL", "has_genre", "HORROR" ], [ "THE DEVIL'S CARNIVAL", "has_genre", "MUSICAL" ], [ "THE EYE", "has_genre", "HORROR" ], [ "THE EYE", "has_tags", "HORROR" ], [ "THE EYE", "has_tags", "REMAKE" ], [ "THE GAY DIVORCEE", "has_genre", "MUSICAL" ], [ "THE GAY DIVORCEE", "starred_actors", "FRED ASTAIRE" ], [ "THE GHOST BREAKERS", "has_genre", "HORROR" ], [ "THE GHOST BREAKERS", "release_year", "1940" ], [ "THE GRUDGE", "directed_by", "TAKASHI SHIMIZU" ], [ "THE GRUDGE", "has_genre", "HORROR" ], [ "THE GRUDGE", "has_tags", "JAPAN" ], [ "THE GRUDGE", "has_tags", "REMAKE" ], [ "THE GRUDGE", "in_language", "JAPANESE" ], [ "THE GRUDGE", "release_year", "2004" ], [ "THE GRUDGE", "written_by", "STEPHEN SUSCO" ], [ "THE GRUDGE", "written_by", "TAKASHI SHIMIZU" ], [ "THE GRUDGE 2", "directed_by", "TAKASHI SHIMIZU" ], [ "THE GRUDGE 2", "has_genre", "HORROR" ], [ "THE GRUDGE 2", "written_by", "STEPHEN SUSCO" ], [ "THE GRUDGE 2", "written_by", "TAKASHI SHIMIZU" ], [ "THE GRUDGE 3", "has_genre", "HORROR" ], [ "THE GRUDGE 3", "written_by", "TAKASHI SHIMIZU" ], [ "THE HAPPINESS OF THE KATAKURIS", "has_genre", "HORROR" ], [ "THE HAPPINESS OF THE KATAKURIS", "has_genre", "MUSICAL" ], [ "THE HAPPINESS OF THE KATAKURIS", "has_tags", "MUSICAL" ], [ "THE HAUNTING", "has_genre", "HORROR" ], [ "THE HAUNTING", "has_tags", "HORROR" ], [ "THE HAUNTING", "has_tags", "REMAKE" ], [ "THE HILLS HAVE EYES", "has_genre", "HORROR" ], [ "THE HILLS HAVE EYES", "has_tags", "HORROR" ], [ "THE HILLS HAVE EYES", "has_tags", "REMAKE" ], [ "THE INVISIBLE MAN RETURNS", "has_genre", "HORROR" ], [ "THE INVISIBLE MAN RETURNS", "release_year", "1940" ], [ "THE LAST HOUSE ON THE LEFT", "has_genre", "HORROR" ], [ "THE LAST HOUSE ON THE LEFT", "has_tags", "REMAKE" ], [ "THE LODGER", "has_genre", "HORROR" ], [ "THE LODGER", "has_tags", "REMAKE" ], [ "THE OMEN", "has_genre", "HORROR" ], [ "THE OMEN", "has_tags", "HORROR" ], [ "THE OMEN", "has_tags", "REMAKE" ], [ "THE RING", "has_genre", "HORROR" ], [ "THE RING", "has_tags", "HORROR" ], [ "THE RING", "has_tags", "JAPANESE" ], [ "THE RING", "has_tags", "REMAKE" ], [ "THE RING TWO", "has_genre", "HORROR" ], [ "THE RING TWO", "has_tags", "REMAKE" ], [ "THE SKY'S THE LIMIT", "has_genre", "MUSICAL" ], [ "THE SKY'S THE LIMIT", "starred_actors", "FRED ASTAIRE" ], [ "THE STEPFORD WIVES", "has_genre", "HORROR" ], [ "THE STEPFORD WIVES", "has_tags", "REMAKE" ], [ "THE STEPFORD WIVES", "release_year", "2004" ], [ "THE STORY OF VERNON AND IRENE CASTLE", "has_genre", "MUSICAL" ], [ "THE STORY OF VERNON AND IRENE CASTLE", "starred_actors", "FRED ASTAIRE" ], [ "THE SUN", "has_tags", "JAPAN" ], [ "THE SUN", "in_language", "JAPANESE" ], [ "THE TEXAS CHAINSAW MASSACRE", "has_genre", "HORROR" ], [ "THE TEXAS CHAINSAW MASSACRE", "has_tags", "REMAKE" ], [ "THE WICKER MAN", "has_genre", "HORROR" ], [ "THE WICKER MAN", "has_tags", "REMAKE" ], [ "THREE LITTLE WORDS", "has_genre", "MUSICAL" ], [ "THREE LITTLE WORDS", "starred_actors", "FRED ASTAIRE" ], [ "TIN PAN ALLEY", "has_genre", "MUSICAL" ], [ "TIN PAN ALLEY", "release_year", "1940" ], [ "TOKYO ZOMBIE", "has_tags", "JAPAN" ], [ "TOKYO ZOMBIE", "in_language", "JAPANESE" ], [ "TOOLBOX MURDERS", "has_genre", "HORROR" ], [ "TOOLBOX MURDERS", "has_tags", "HORROR" ], [ "TOOLBOX MURDERS", "release_year", "2004" ], [ "TOP HAT", "has_genre", "MUSICAL" ], [ "TOP HAT", "has_tags", "FRED ASTAIRE" ], [ "TOP HAT", "starred_actors", "FRED ASTAIRE" ], [ "VAN HELSING", "has_tags", "HORROR" ], [ "VAN HELSING", "release_year", "2004" ], [ "VILLAGE OF THE DAMNED", "has_genre", "HORROR" ], [ "VILLAGE OF THE DAMNED", "has_tags", "REMAKE" ], [ "WASABI", "has_tags", "JAPAN" ], [ "WASABI", "in_language", "JAPANESE" ], [ "WICKED CITY", "has_genre", "HORROR" ], [ "WICKED CITY", "in_language", "JAPANESE" ], [ "WILD ZERO", "has_genre", "HORROR" ], [ "WILD ZERO", "in_language", "JAPANESE" ], [ "WILLARD", "has_genre", "HORROR" ], [ "WILLARD", "has_tags", "REMAKE" ], [ "YOLANDA AND THE THIEF", "has_genre", "MUSICAL" ], [ "YOLANDA AND THE THIEF", "starred_actors", "FRED ASTAIRE" ], [ "YOU WERE NEVER LOVELIER", "has_genre", "MUSICAL" ], [ "YOU WERE NEVER LOVELIER", "has_tags", "FRED ASTAIRE" ], [ "YOU WERE NEVER LOVELIER", "starred_actors", "FRED ASTAIRE" ], [ "YOU'LL NEVER GET RICH", "has_genre", "MUSICAL" ], [ "YOU'LL NEVER GET RICH", "starred_actors", "FRED ASTAIRE" ], [ "ZIEGFELD FOLLIES", "has_genre", "MUSICAL" ], [ "ZIEGFELD FOLLIES", "starred_actors", "FRED ASTAIRE" ], [ "ZOMBIE HONEYMOON", "has_genre", "HORROR" ], [ "ZOMBIE HONEYMOON", "release_year", "2004" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 2133, 1998 6776, 2000 4476, CHOCOLAT 7091, FRANCE 32434, GEORGE WASHINGTON 22006, I WANT YOU 14601, LES MISÉRABLES 4182, THE MAN IN THE IRON MASK 12691, THE PATRIOT 23150, THE WOMEN ON THE 6TH FLOOR src, edge_attr, dst 4476, has_tags, 7091 4476, release_year, 6776 32434, release_year, 6776 22006, release_year, 2133 14601, has_tags, 7091 14601, release_year, 2133 4182, has_tags, 7091 4182, release_year, 2133 12691, release_year, 2133 12691, release_year, 6776 23150, has_tags, 7091 Question: For what reason are GEORGE WASHINGTON, I WANT YOU, and THE WOMEN ON THE 6TH FLOOR associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "GEORGE WASHINGTON", "I WANT YOU", "THE WOMEN ON THE 6TH FLOOR" ], "valid_edges": [ [ "CHOCOLAT", "has_tags", "FRANCE" ], [ "CHOCOLAT", "release_year", "2000" ], [ "GEORGE WASHINGTON", "release_year", "2000" ], [ "I WANT YOU", "release_year", "1998" ], [ "LES MISÉRABLES", "has_tags", "FRANCE" ], [ "LES MISÉRABLES", "release_year", "1998" ], [ "THE MAN IN THE IRON MASK", "has_tags", "FRANCE" ], [ "THE MAN IN THE IRON MASK", "release_year", "1998" ], [ "THE PATRIOT", "release_year", "1998" ], [ "THE PATRIOT", "release_year", "2000" ], [ "THE WOMEN ON THE 6TH FLOOR", "has_tags", "FRANCE" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 14588, CHRIS COLFER 14129, DIRTY DANCING 36212, DRAMA 14520, JENNIFER GREY 29961, PARKLAND 25002, STRUCK BY LIGHTNING 30391, VINCENT BUGLIOSI src, edge_attr, dst 14129, has_genre, 36212 14129, has_tags, 14520 14129, starred_actors, 14520 29961, has_genre, 36212 29961, written_by, 30391 25002, has_genre, 36212 25002, starred_actors, 14588 25002, written_by, 14588 Question: How are CHRIS COLFER, JENNIFER GREY, and VINCENT BUGLIOSI related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "CHRIS COLFER", "JENNIFER GREY", "VINCENT BUGLIOSI" ], "valid_edges": [ [ "DIRTY DANCING", "has_genre", "DRAMA" ], [ "DIRTY DANCING", "has_tags", "JENNIFER GREY" ], [ "DIRTY DANCING", "starred_actors", "JENNIFER GREY" ], [ "PARKLAND", "has_genre", "DRAMA" ], [ "PARKLAND", "written_by", "VINCENT BUGLIOSI" ], [ "STRUCK BY LIGHTNING", "has_genre", "DRAMA" ], [ "STRUCK BY LIGHTNING", "starred_actors", "CHRIS COLFER" ], [ "STRUCK BY LIGHTNING", "written_by", "CHRIS COLFER" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 25354, $9.99 26762, 2008 16060, 27 DRESSES 37783, A BUNCH OF AMATEURS 9698, A CHRISTMAS TALE 955, A FILM WITH ME IN IT 6602, A MATTER OF LOAF AND DEATH 38059, ABSURDISTAN 30643, ACROSS THE UNIVERSE 37010, AGE OF CONSENT 28540, AN AMERICAN CAROL 26658, ANOTHER CINDERELLA STORY 6336, ANTHONY LAPAGLIA 18169, APRIL FOOL'S DAY 28274, ASSASSINATION OF A HIGH SCHOOL PRESIDENT 37608, AUSTRALIA 9340, BABY MAMA 24827, BAGHEAD 10673, BART GOT A ROOM 26783, BE KIND REWIND 25639, BEDTIME STORIES 19159, BEER FOR MY HORSES 13412, BERLIN CALLING 25846, BEVERLY HILLS CHIHUAHUA 4902, BIRDS OF AMERICA 7314, BOLT 10081, BOTTLE SHOCK 35599, BURN AFTER READING 33360, CANDY 15437, CHAOS THEORY 33927, CHOKE 24116, COLLEGE 21862, COLLEGE ROAD TRIP 30463, COMEDY 22452, COMMANDMENTS 24877, DANCE OF THE DEAD 9709, DE L'AUTRE CÔTÉ DU LIT 35428, DEAD FURY 12702, DEFINITELY, MAYBE 18106, DIMINISHED CAPACITY 36212, DRAMA 19245, DRILLBIT TAYLOR 29521, EASY VIRTUE 25559, EXTREME MOVIE 22541, FATSO 14303, FINDING AMANDA 17653, FINE, TOTALLY FINE 17405, FIRST SUNDAY 6219, FORGETTING SARAH MARSHALL 22005, FOUR CHRISTMASES 25086, FRENCH FILM 21839, GEOFFREY RUSH 17416, GET SMART 2159, GHOST TOWN 31060, GIGANTIC 19912, GRIFF THE INVISIBLE 13778, HAMLET 2 9852, HANK AND MIKE 36942, HAPPY-GO-LUCKY 31330, HOME 7811, HORTON HEARS A WHO! 10147, HOUSE 31549, HOW TO BE 33499, HOW TO BE A SERIAL KILLER 3508, HUMBOLDT COUNTY 17763, I SELL THE DEAD 21265, IGOR 33279, IN BRUGES 25088, JUST ADD WATER 11089, KILLER MOVIE 35095, KILLER PAD 19602, KUNG FU PANDA 39347, LEATHERHEADS 31011, LYMELIFE 16378, MAD MONEY 24627, MADE OF HONOR 5163, MAMMA MIA! 3844, MANAGEMENT 7813, MARY AND MAX 20844, MEET DAVE 9678, MEET THE BROWNS 19216, MID-AUGUST LUNCH 31735, MIDDLE OF NOWHERE 20418, MISS PETTIGREW LIVES FOR A DAY 25796, MURIEL'S WEDDING 15428, MY BEST FRIEND'S GIRL 27683, MY SASSY GIRL 33751, NEW YORK, I LOVE YOU 34872, NINJA CHEERLEADERS 27725, ONE-EYED MONSTER 16554, OPEN SEASON 2 15037, OTIS 292, OVER HER DEAD BODY 1697, PINEAPPLE EXPRESS 30478, PRIVATE LESSONS 17627, RAB NE BANA DI JODI 34313, REMARKABLE POWER 9633, ROLE MODELS 28099, RUDO Y CURSI 37850, SEMI-PRO 7015, SEX AND THE CITY 12676, SEX DRIVE 2024, SHINE 10415, SILENT WEDDING 11074, SINGH IS KINNG 30145, SMART PEOPLE 25151, SNOW DOGS 22068, SOUL MEN 24506, SPACE CHIMPS 15449, STEP BROTHERS 31154, STRANGE WILDERNESS 1432, STRICTLY BALLROOM 13322, STRICTLY SEXUAL 29231, SUNSHINE CLEANING 21445, SUPERHERO MOVIE 19488, SURFER, DUDE 17992, SWING VOTE 10020, THE ACCIDENTAL HUSBAND 34206, THE ADVENTURES OF FOOD BOY 33436, THE BANK 11158, THE BEATLES 39238, THE BROTHERS BLOOM 33457, THE DEAL 25305, THE GREAT BUCK HOWARD 25624, THE HOUSE BUNNY 31058, THE LONGSHOTS 14591, THE LOVE GURU 7303, THE LUCKY ONES 39053, THE ONION MOVIE 3558, THE OTHER END OF THE LINE 6703, THE RAMEN GIRL 5156, THE ROCKER 2739, THE SAPPHIRES 20210, THE WOMEN 8393, TROPIC THUNDER 9639, UNDEAD 20713, VISIONEERS 10231, WHAT HAPPENS IN VEGAS 27612, WHAT JUST HAPPENED 1547, WIENERS 32981, WILD CHILD 33110, WITLESS PROTECTION 1945, YES MAN 23774, YOU DON'T MESS WITH THE ZOHAN 16999, ZACK AND MIRI MAKE A PORNO src, edge_attr, dst 25354, has_tags, 37608 25354, release_year, 26762 25354, starred_actors, 6336 25354, starred_actors, 21839 16060, has_genre, 30463 16060, release_year, 26762 37783, has_genre, 30463 37783, release_year, 26762 9698, has_genre, 30463 9698, release_year, 26762 955, has_genre, 30463 955, release_year, 26762 6602, has_genre, 30463 6602, has_tags, 30463 6602, release_year, 26762 38059, has_genre, 30463 38059, release_year, 26762 30643, has_genre, 36212 30643, has_tags, 11158 37010, has_genre, 30463 37010, has_tags, 37608 28540, has_genre, 30463 28540, release_year, 26762 26658, has_genre, 30463 26658, release_year, 26762 18169, has_genre, 30463 18169, release_year, 26762 28274, has_genre, 30463 28274, release_year, 26762 37608, has_genre, 36212 37608, has_tags, 37608 37608, release_year, 26762 9340, has_genre, 30463 9340, release_year, 26762 24827, has_genre, 30463 24827, release_year, 26762 10673, has_genre, 30463 10673, release_year, 26762 26783, has_genre, 30463 26783, release_year, 26762 25639, has_genre, 30463 25639, release_year, 26762 19159, has_genre, 30463 19159, release_year, 26762 13412, has_genre, 30463 13412, release_year, 26762 25846, has_genre, 30463 25846, has_tags, 30463 25846, release_year, 26762 4902, has_genre, 30463 4902, release_year, 26762 7314, has_genre, 30463 7314, release_year, 26762 10081, has_genre, 30463 10081, release_year, 26762 35599, has_genre, 30463 35599, has_tags, 30463 35599, release_year, 26762 33360, has_tags, 37608 33360, has_tags, 21839 33360, starred_actors, 21839 15437, has_genre, 30463 15437, release_year, 26762 33927, has_genre, 30463 33927, has_tags, 30463 33927, release_year, 26762 24116, has_genre, 30463 24116, release_year, 26762 21862, has_genre, 30463 21862, has_tags, 30463 21862, release_year, 26762 22452, has_genre, 30463 22452, starred_actors, 6336 24877, has_genre, 30463 24877, release_year, 26762 9709, has_genre, 30463 9709, release_year, 26762 35428, has_genre, 30463 35428, release_year, 26762 12702, has_genre, 30463 12702, release_year, 26762 18106, has_genre, 30463 18106, release_year, 26762 19245, has_genre, 30463 19245, release_year, 26762 29521, has_genre, 30463 29521, has_tags, 30463 29521, release_year, 26762 25559, has_genre, 30463 25559, release_year, 26762 22541, has_genre, 30463 22541, release_year, 26762 14303, has_genre, 30463 14303, release_year, 26762 17653, has_genre, 30463 17653, release_year, 26762 17405, has_genre, 30463 17405, release_year, 26762 6219, has_genre, 30463 6219, has_tags, 30463 6219, release_year, 26762 22005, has_genre, 30463 22005, release_year, 26762 25086, has_genre, 30463 25086, release_year, 26762 17416, has_genre, 30463 17416, release_year, 26762 2159, has_genre, 30463 2159, release_year, 26762 31060, has_genre, 30463 31060, release_year, 26762 19912, has_genre, 30463 19912, has_tags, 37608 13778, has_genre, 30463 13778, release_year, 26762 9852, has_genre, 30463 9852, release_year, 26762 36942, has_genre, 30463 36942, release_year, 26762 31330, has_genre, 30463 31330, release_year, 26762 7811, has_genre, 30463 7811, has_tags, 30463 7811, release_year, 26762 10147, has_genre, 30463 10147, release_year, 26762 31549, has_genre, 30463 31549, release_year, 26762 33499, has_genre, 30463 33499, release_year, 26762 3508, has_genre, 30463 3508, release_year, 26762 17763, has_genre, 30463 17763, release_year, 26762 21265, has_genre, 30463 21265, release_year, 26762 33279, has_genre, 30463 33279, has_tags, 30463 33279, release_year, 26762 25088, has_genre, 30463 25088, release_year, 26762 11089, has_genre, 30463 11089, release_year, 26762 35095, has_genre, 30463 35095, release_year, 26762 19602, has_tags, 30463 19602, release_year, 26762 39347, has_genre, 30463 39347, has_tags, 30463 39347, release_year, 26762 31011, has_genre, 30463 31011, release_year, 26762 16378, has_genre, 30463 16378, release_year, 26762 24627, has_genre, 30463 24627, release_year, 26762 5163, has_genre, 30463 5163, release_year, 26762 3844, has_genre, 30463 3844, release_year, 26762 7813, has_genre, 30463 7813, has_tags, 37608 20844, has_genre, 30463 20844, release_year, 26762 9678, has_genre, 30463 9678, release_year, 26762 19216, has_genre, 30463 19216, release_year, 26762 31735, has_genre, 30463 31735, release_year, 26762 20418, has_genre, 30463 20418, release_year, 26762 25796, has_genre, 30463 25796, has_tags, 37608 25796, has_tags, 30463 15428, has_genre, 30463 15428, release_year, 26762 27683, has_genre, 30463 27683, release_year, 26762 33751, has_genre, 30463 33751, release_year, 26762 34872, has_genre, 30463 34872, release_year, 26762 27725, has_genre, 30463 27725, release_year, 26762 16554, has_genre, 30463 16554, release_year, 26762 15037, has_genre, 30463 15037, release_year, 26762 292, has_genre, 30463 292, release_year, 26762 1697, has_genre, 30463 1697, has_tags, 30463 1697, release_year, 26762 30478, has_genre, 30463 30478, release_year, 26762 17627, has_genre, 30463 17627, release_year, 26762 34313, has_genre, 30463 34313, release_year, 26762 9633, has_genre, 30463 9633, has_tags, 30463 9633, release_year, 26762 28099, has_genre, 30463 28099, release_year, 26762 37850, has_genre, 30463 37850, release_year, 26762 7015, has_genre, 30463 7015, release_year, 26762 12676, has_genre, 30463 12676, has_tags, 30463 12676, release_year, 26762 2024, has_tags, 37608 2024, has_tags, 21839 2024, starred_actors, 21839 10415, has_genre, 30463 10415, release_year, 26762 11074, has_genre, 30463 11074, release_year, 26762 30145, has_genre, 30463 30145, release_year, 26762 25151, has_genre, 30463 22068, has_genre, 30463 22068, release_year, 26762 24506, has_genre, 30463 24506, release_year, 26762 15449, has_genre, 30463 15449, has_tags, 30463 15449, release_year, 26762 31154, has_genre, 30463 31154, release_year, 26762 1432, has_genre, 30463 1432, has_tags, 37608 13322, has_genre, 30463 13322, release_year, 26762 29231, has_genre, 30463 29231, release_year, 26762 21445, has_genre, 30463 21445, release_year, 26762 19488, has_genre, 30463 19488, release_year, 26762 17992, has_genre, 30463 17992, release_year, 26762 10020, has_genre, 30463 10020, release_year, 26762 34206, has_genre, 30463 34206, release_year, 26762 33436, has_tags, 37608 33436, starred_actors, 6336 39238, has_genre, 30463 39238, release_year, 26762 33457, has_genre, 30463 33457, release_year, 26762 25305, has_genre, 30463 25305, release_year, 26762 25624, has_genre, 30463 25624, has_tags, 30463 25624, release_year, 26762 31058, has_genre, 30463 31058, release_year, 26762 14591, has_genre, 30463 14591, release_year, 26762 7303, has_genre, 30463 7303, release_year, 26762 39053, has_genre, 30463 39053, has_tags, 30463 39053, release_year, 26762 3558, has_genre, 30463 3558, release_year, 26762 6703, has_genre, 30463 6703, release_year, 26762 5156, has_genre, 30463 5156, release_year, 26762 2739, has_genre, 30463 2739, has_tags, 37608 20210, has_genre, 30463 20210, release_year, 26762 8393, has_genre, 30463 8393, release_year, 26762 9639, has_genre, 30463 9639, has_tags, 37608 20713, has_genre, 30463 20713, release_year, 26762 10231, has_genre, 30463 10231, has_tags, 30463 10231, release_year, 26762 27612, has_genre, 30463 27612, release_year, 26762 1547, has_genre, 30463 1547, release_year, 26762 32981, has_genre, 30463 32981, release_year, 26762 33110, has_genre, 30463 33110, release_year, 26762 1945, has_genre, 30463 1945, release_year, 26762 23774, has_genre, 30463 23774, has_tags, 30463 23774, release_year, 26762 16999, has_genre, 30463 16999, release_year, 26762 Question: How are $9.99, SNOW DOGS, and THE BEATLES related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "$9.99", "SNOW DOGS", "THE BEATLES" ], "valid_edges": [ [ "$9.99", "has_tags", "AUSTRALIA" ], [ "$9.99", "release_year", "2008" ], [ "$9.99", "starred_actors", "ANTHONY LAPAGLIA" ], [ "$9.99", "starred_actors", "GEOFFREY RUSH" ], [ "27 DRESSES", "has_genre", "COMEDY" ], [ "27 DRESSES", "release_year", "2008" ], [ "A BUNCH OF AMATEURS", "has_genre", "COMEDY" ], [ "A BUNCH OF AMATEURS", "release_year", "2008" ], [ "A CHRISTMAS TALE", "has_genre", "COMEDY" ], [ "A CHRISTMAS TALE", "release_year", "2008" ], [ "A FILM WITH ME IN IT", "has_genre", "COMEDY" ], [ "A FILM WITH ME IN IT", "release_year", "2008" ], [ "A MATTER OF LOAF AND DEATH", "has_genre", "COMEDY" ], [ "A MATTER OF LOAF AND DEATH", "has_tags", "COMEDY" ], [ "A MATTER OF LOAF AND DEATH", "release_year", "2008" ], [ "ABSURDISTAN", "has_genre", "COMEDY" ], [ "ABSURDISTAN", "release_year", "2008" ], [ "ACROSS THE UNIVERSE", "has_genre", "DRAMA" ], [ "ACROSS THE UNIVERSE", "has_tags", "THE BEATLES" ], [ "AGE OF CONSENT", "has_genre", "COMEDY" ], [ "AGE OF CONSENT", "has_tags", "AUSTRALIA" ], [ "AN AMERICAN CAROL", "has_genre", "COMEDY" ], [ "AN AMERICAN CAROL", "release_year", "2008" ], [ "ANOTHER CINDERELLA STORY", "has_genre", "COMEDY" ], [ "ANOTHER CINDERELLA STORY", "release_year", "2008" ], [ "APRIL FOOL'S DAY", "has_genre", "COMEDY" ], [ "APRIL FOOL'S DAY", "release_year", "2008" ], [ "ASSASSINATION OF A HIGH SCHOOL PRESIDENT", "has_genre", "COMEDY" ], [ "ASSASSINATION OF A HIGH SCHOOL PRESIDENT", "release_year", "2008" ], [ "AUSTRALIA", "has_genre", "DRAMA" ], [ "AUSTRALIA", "has_tags", "AUSTRALIA" ], [ "AUSTRALIA", "release_year", "2008" ], [ "BABY MAMA", "has_genre", "COMEDY" ], [ "BABY MAMA", "release_year", "2008" ], [ "BAGHEAD", "has_genre", "COMEDY" ], [ "BAGHEAD", "release_year", "2008" ], [ "BART GOT A ROOM", "has_genre", "COMEDY" ], [ "BART GOT A ROOM", "release_year", "2008" ], [ "BE KIND REWIND", "has_genre", "COMEDY" ], [ "BE KIND REWIND", "release_year", "2008" ], [ "BEDTIME STORIES", "has_genre", "COMEDY" ], [ "BEDTIME STORIES", "release_year", "2008" ], [ "BEER FOR MY HORSES", "has_genre", "COMEDY" ], [ "BEER FOR MY HORSES", "release_year", "2008" ], [ "BERLIN CALLING", "has_genre", "COMEDY" ], [ "BERLIN CALLING", "release_year", "2008" ], [ "BEVERLY HILLS CHIHUAHUA", "has_genre", "COMEDY" ], [ "BEVERLY HILLS CHIHUAHUA", "has_tags", "COMEDY" ], [ "BEVERLY HILLS CHIHUAHUA", "release_year", "2008" ], [ "BIRDS OF AMERICA", "has_genre", "COMEDY" ], [ "BIRDS OF AMERICA", "release_year", "2008" ], [ "BOLT", "has_genre", "COMEDY" ], [ "BOLT", "release_year", "2008" ], [ "BOTTLE SHOCK", "has_genre", "COMEDY" ], [ "BOTTLE SHOCK", "release_year", "2008" ], [ "BURN AFTER READING", "has_genre", "COMEDY" ], [ "BURN AFTER READING", "has_tags", "COMEDY" ], [ "BURN AFTER READING", "release_year", "2008" ], [ "CANDY", "has_tags", "AUSTRALIA" ], [ "CANDY", "has_tags", "GEOFFREY RUSH" ], [ "CANDY", "starred_actors", "GEOFFREY RUSH" ], [ "CHAOS THEORY", "has_genre", "COMEDY" ], [ "CHAOS THEORY", "release_year", "2008" ], [ "CHOKE", "has_genre", "COMEDY" ], [ "CHOKE", "has_tags", "COMEDY" ], [ "CHOKE", "release_year", "2008" ], [ "COLLEGE", "has_genre", "COMEDY" ], [ "COLLEGE", "release_year", "2008" ], [ "COLLEGE ROAD TRIP", "has_genre", "COMEDY" ], [ "COLLEGE ROAD TRIP", "has_tags", "COMEDY" ], [ "COLLEGE ROAD TRIP", "release_year", "2008" ], [ "COMMANDMENTS", "has_genre", "COMEDY" ], [ "COMMANDMENTS", "starred_actors", "ANTHONY LAPAGLIA" ], [ "DANCE OF THE DEAD", "has_genre", "COMEDY" ], [ "DANCE OF THE DEAD", "release_year", "2008" ], [ "DE L'AUTRE CÔTÉ DU LIT", "has_genre", "COMEDY" ], [ "DE L'AUTRE CÔTÉ DU LIT", "release_year", "2008" ], [ "DEAD FURY", "has_genre", "COMEDY" ], [ "DEAD FURY", "release_year", "2008" ], [ "DEFINITELY, MAYBE", "has_genre", "COMEDY" ], [ "DEFINITELY, MAYBE", "release_year", "2008" ], [ "DIMINISHED CAPACITY", "has_genre", "COMEDY" ], [ "DIMINISHED CAPACITY", "release_year", "2008" ], [ "DRILLBIT TAYLOR", "has_genre", "COMEDY" ], [ "DRILLBIT TAYLOR", "release_year", "2008" ], [ "EASY VIRTUE", "has_genre", "COMEDY" ], [ "EASY VIRTUE", "has_tags", "COMEDY" ], [ "EASY VIRTUE", "release_year", "2008" ], [ "EXTREME MOVIE", "has_genre", "COMEDY" ], [ "EXTREME MOVIE", "release_year", "2008" ], [ "FATSO", "has_genre", "COMEDY" ], [ "FATSO", "release_year", "2008" ], [ "FINDING AMANDA", "has_genre", "COMEDY" ], [ "FINDING AMANDA", "release_year", "2008" ], [ "FINE, TOTALLY FINE", "has_genre", "COMEDY" ], [ "FINE, TOTALLY FINE", "release_year", "2008" ], [ "FIRST SUNDAY", "has_genre", "COMEDY" ], [ "FIRST SUNDAY", "release_year", "2008" ], [ "FORGETTING SARAH MARSHALL", "has_genre", "COMEDY" ], [ "FORGETTING SARAH MARSHALL", "has_tags", "COMEDY" ], [ "FORGETTING SARAH MARSHALL", "release_year", "2008" ], [ "FOUR CHRISTMASES", "has_genre", "COMEDY" ], [ "FOUR CHRISTMASES", "release_year", "2008" ], [ "FRENCH FILM", "has_genre", "COMEDY" ], [ "FRENCH FILM", "release_year", "2008" ], [ "GET SMART", "has_genre", "COMEDY" ], [ "GET SMART", "release_year", "2008" ], [ "GHOST TOWN", "has_genre", "COMEDY" ], [ "GHOST TOWN", "release_year", "2008" ], [ "GIGANTIC", "has_genre", "COMEDY" ], [ "GIGANTIC", "release_year", "2008" ], [ "GRIFF THE INVISIBLE", "has_genre", "COMEDY" ], [ "GRIFF THE INVISIBLE", "has_tags", "AUSTRALIA" ], [ "HAMLET 2", "has_genre", "COMEDY" ], [ "HAMLET 2", "release_year", "2008" ], [ "HANK AND MIKE", "has_genre", "COMEDY" ], [ "HANK AND MIKE", "release_year", "2008" ], [ "HAPPY-GO-LUCKY", "has_genre", "COMEDY" ], [ "HAPPY-GO-LUCKY", "release_year", "2008" ], [ "HOME", "has_genre", "COMEDY" ], [ "HOME", "release_year", "2008" ], [ "HORTON HEARS A WHO!", "has_genre", "COMEDY" ], [ "HORTON HEARS A WHO!", "has_tags", "COMEDY" ], [ "HORTON HEARS A WHO!", "release_year", "2008" ], [ "HOUSE", "has_genre", "COMEDY" ], [ "HOUSE", "release_year", "2008" ], [ "HOW TO BE", "has_genre", "COMEDY" ], [ "HOW TO BE", "release_year", "2008" ], [ "HOW TO BE A SERIAL KILLER", "has_genre", "COMEDY" ], [ "HOW TO BE A SERIAL KILLER", "release_year", "2008" ], [ "HUMBOLDT COUNTY", "has_genre", "COMEDY" ], [ "HUMBOLDT COUNTY", "release_year", "2008" ], [ "I SELL THE DEAD", "has_genre", "COMEDY" ], [ "I SELL THE DEAD", "release_year", "2008" ], [ "IGOR", "has_genre", "COMEDY" ], [ "IGOR", "release_year", "2008" ], [ "IN BRUGES", "has_genre", "COMEDY" ], [ "IN BRUGES", "has_tags", "COMEDY" ], [ "IN BRUGES", "release_year", "2008" ], [ "JUST ADD WATER", "has_genre", "COMEDY" ], [ "JUST ADD WATER", "release_year", "2008" ], [ "KILLER MOVIE", "has_genre", "COMEDY" ], [ "KILLER MOVIE", "release_year", "2008" ], [ "KILLER PAD", "has_genre", "COMEDY" ], [ "KILLER PAD", "release_year", "2008" ], [ "KUNG FU PANDA", "has_tags", "COMEDY" ], [ "KUNG FU PANDA", "release_year", "2008" ], [ "LEATHERHEADS", "has_genre", "COMEDY" ], [ "LEATHERHEADS", "has_tags", "COMEDY" ], [ "LEATHERHEADS", "release_year", "2008" ], [ "LYMELIFE", "has_genre", "COMEDY" ], [ "LYMELIFE", "release_year", "2008" ], [ "MAD MONEY", "has_genre", "COMEDY" ], [ "MAD MONEY", "release_year", "2008" ], [ "MADE OF HONOR", "has_genre", "COMEDY" ], [ "MADE OF HONOR", "release_year", "2008" ], [ "MAMMA MIA!", "has_genre", "COMEDY" ], [ "MAMMA MIA!", "release_year", "2008" ], [ "MANAGEMENT", "has_genre", "COMEDY" ], [ "MANAGEMENT", "release_year", "2008" ], [ "MARY AND MAX", "has_genre", "COMEDY" ], [ "MARY AND MAX", "has_tags", "AUSTRALIA" ], [ "MEET DAVE", "has_genre", "COMEDY" ], [ "MEET DAVE", "release_year", "2008" ], [ "MEET THE BROWNS", "has_genre", "COMEDY" ], [ "MEET THE BROWNS", "release_year", "2008" ], [ "MID-AUGUST LUNCH", "has_genre", "COMEDY" ], [ "MID-AUGUST LUNCH", "release_year", "2008" ], [ "MIDDLE OF NOWHERE", "has_genre", "COMEDY" ], [ "MIDDLE OF NOWHERE", "release_year", "2008" ], [ "MISS PETTIGREW LIVES FOR A DAY", "has_genre", "COMEDY" ], [ "MISS PETTIGREW LIVES FOR A DAY", "release_year", "2008" ], [ "MURIEL'S WEDDING", "has_genre", "COMEDY" ], [ "MURIEL'S WEDDING", "has_tags", "AUSTRALIA" ], [ "MURIEL'S WEDDING", "has_tags", "COMEDY" ], [ "MY BEST FRIEND'S GIRL", "has_genre", "COMEDY" ], [ "MY BEST FRIEND'S GIRL", "release_year", "2008" ], [ "MY SASSY GIRL", "has_genre", "COMEDY" ], [ "MY SASSY GIRL", "release_year", "2008" ], [ "NEW YORK, I LOVE YOU", "has_genre", "COMEDY" ], [ "NEW YORK, I LOVE YOU", "release_year", "2008" ], [ "NINJA CHEERLEADERS", "has_genre", "COMEDY" ], [ "NINJA CHEERLEADERS", "release_year", "2008" ], [ "ONE-EYED MONSTER", "has_genre", "COMEDY" ], [ "ONE-EYED MONSTER", "release_year", "2008" ], [ "OPEN SEASON 2", "has_genre", "COMEDY" ], [ "OPEN SEASON 2", "release_year", "2008" ], [ "OTIS", "has_genre", "COMEDY" ], [ "OTIS", "release_year", "2008" ], [ "OVER HER DEAD BODY", "has_genre", "COMEDY" ], [ "OVER HER DEAD BODY", "release_year", "2008" ], [ "PINEAPPLE EXPRESS", "has_genre", "COMEDY" ], [ "PINEAPPLE EXPRESS", "has_tags", "COMEDY" ], [ "PINEAPPLE EXPRESS", "release_year", "2008" ], [ "PRIVATE LESSONS", "has_genre", "COMEDY" ], [ "PRIVATE LESSONS", "release_year", "2008" ], [ "RAB NE BANA DI JODI", "has_genre", "COMEDY" ], [ "RAB NE BANA DI JODI", "release_year", "2008" ], [ "REMARKABLE POWER", "has_genre", "COMEDY" ], [ "REMARKABLE POWER", "release_year", "2008" ], [ "ROLE MODELS", "has_genre", "COMEDY" ], [ "ROLE MODELS", "has_tags", "COMEDY" ], [ "ROLE MODELS", "release_year", "2008" ], [ "RUDO Y CURSI", "has_genre", "COMEDY" ], [ "RUDO Y CURSI", "release_year", "2008" ], [ "SEMI-PRO", "has_genre", "COMEDY" ], [ "SEMI-PRO", "release_year", "2008" ], [ "SEX AND THE CITY", "has_genre", "COMEDY" ], [ "SEX AND THE CITY", "release_year", "2008" ], [ "SEX DRIVE", "has_genre", "COMEDY" ], [ "SEX DRIVE", "has_tags", "COMEDY" ], [ "SEX DRIVE", "release_year", "2008" ], [ "SHINE", "has_tags", "AUSTRALIA" ], [ "SHINE", "has_tags", "GEOFFREY RUSH" ], [ "SHINE", "starred_actors", "GEOFFREY RUSH" ], [ "SILENT WEDDING", "has_genre", "COMEDY" ], [ "SILENT WEDDING", "release_year", "2008" ], [ "SINGH IS KINNG", "has_genre", "COMEDY" ], [ "SINGH IS KINNG", "release_year", "2008" ], [ "SMART PEOPLE", "has_genre", "COMEDY" ], [ "SMART PEOPLE", "release_year", "2008" ], [ "SNOW DOGS", "has_genre", "COMEDY" ], [ "SOUL MEN", "has_genre", "COMEDY" ], [ "SOUL MEN", "release_year", "2008" ], [ "SPACE CHIMPS", "has_genre", "COMEDY" ], [ "SPACE CHIMPS", "release_year", "2008" ], [ "STEP BROTHERS", "has_genre", "COMEDY" ], [ "STEP BROTHERS", "has_tags", "COMEDY" ], [ "STEP BROTHERS", "release_year", "2008" ], [ "STRANGE WILDERNESS", "has_genre", "COMEDY" ], [ "STRANGE WILDERNESS", "release_year", "2008" ], [ "STRICTLY BALLROOM", "has_genre", "COMEDY" ], [ "STRICTLY BALLROOM", "has_tags", "AUSTRALIA" ], [ "STRICTLY SEXUAL", "has_genre", "COMEDY" ], [ "STRICTLY SEXUAL", "release_year", "2008" ], [ "SUNSHINE CLEANING", "has_genre", "COMEDY" ], [ "SUNSHINE CLEANING", "release_year", "2008" ], [ "SUPERHERO MOVIE", "has_genre", "COMEDY" ], [ "SUPERHERO MOVIE", "release_year", "2008" ], [ "SURFER, DUDE", "has_genre", "COMEDY" ], [ "SURFER, DUDE", "release_year", "2008" ], [ "SWING VOTE", "has_genre", "COMEDY" ], [ "SWING VOTE", "release_year", "2008" ], [ "THE ACCIDENTAL HUSBAND", "has_genre", "COMEDY" ], [ "THE ACCIDENTAL HUSBAND", "release_year", "2008" ], [ "THE ADVENTURES OF FOOD BOY", "has_genre", "COMEDY" ], [ "THE ADVENTURES OF FOOD BOY", "release_year", "2008" ], [ "THE BANK", "has_tags", "AUSTRALIA" ], [ "THE BANK", "starred_actors", "ANTHONY LAPAGLIA" ], [ "THE BROTHERS BLOOM", "has_genre", "COMEDY" ], [ "THE BROTHERS BLOOM", "release_year", "2008" ], [ "THE DEAL", "has_genre", "COMEDY" ], [ "THE DEAL", "release_year", "2008" ], [ "THE GREAT BUCK HOWARD", "has_genre", "COMEDY" ], [ "THE GREAT BUCK HOWARD", "release_year", "2008" ], [ "THE HOUSE BUNNY", "has_genre", "COMEDY" ], [ "THE HOUSE BUNNY", "has_tags", "COMEDY" ], [ "THE HOUSE BUNNY", "release_year", "2008" ], [ "THE LONGSHOTS", "has_genre", "COMEDY" ], [ "THE LONGSHOTS", "release_year", "2008" ], [ "THE LOVE GURU", "has_genre", "COMEDY" ], [ "THE LOVE GURU", "release_year", "2008" ], [ "THE LUCKY ONES", "has_genre", "COMEDY" ], [ "THE LUCKY ONES", "release_year", "2008" ], [ "THE ONION MOVIE", "has_genre", "COMEDY" ], [ "THE ONION MOVIE", "has_tags", "COMEDY" ], [ "THE ONION MOVIE", "release_year", "2008" ], [ "THE OTHER END OF THE LINE", "has_genre", "COMEDY" ], [ "THE OTHER END OF THE LINE", "release_year", "2008" ], [ "THE RAMEN GIRL", "has_genre", "COMEDY" ], [ "THE RAMEN GIRL", "release_year", "2008" ], [ "THE ROCKER", "has_genre", "COMEDY" ], [ "THE ROCKER", "release_year", "2008" ], [ "THE SAPPHIRES", "has_genre", "COMEDY" ], [ "THE SAPPHIRES", "has_tags", "AUSTRALIA" ], [ "THE WOMEN", "has_genre", "COMEDY" ], [ "THE WOMEN", "release_year", "2008" ], [ "TROPIC THUNDER", "has_genre", "COMEDY" ], [ "TROPIC THUNDER", "release_year", "2008" ], [ "UNDEAD", "has_genre", "COMEDY" ], [ "UNDEAD", "has_tags", "AUSTRALIA" ], [ "VISIONEERS", "has_genre", "COMEDY" ], [ "VISIONEERS", "release_year", "2008" ], [ "WHAT HAPPENS IN VEGAS", "has_genre", "COMEDY" ], [ "WHAT HAPPENS IN VEGAS", "has_tags", "COMEDY" ], [ "WHAT HAPPENS IN VEGAS", "release_year", "2008" ], [ "WHAT JUST HAPPENED", "has_genre", "COMEDY" ], [ "WHAT JUST HAPPENED", "release_year", "2008" ], [ "WIENERS", "has_genre", "COMEDY" ], [ "WIENERS", "release_year", "2008" ], [ "WILD CHILD", "has_genre", "COMEDY" ], [ "WILD CHILD", "release_year", "2008" ], [ "WITLESS PROTECTION", "has_genre", "COMEDY" ], [ "WITLESS PROTECTION", "release_year", "2008" ], [ "YES MAN", "has_genre", "COMEDY" ], [ "YES MAN", "release_year", "2008" ], [ "YOU DON'T MESS WITH THE ZOHAN", "has_genre", "COMEDY" ], [ "YOU DON'T MESS WITH THE ZOHAN", "has_tags", "COMEDY" ], [ "YOU DON'T MESS WITH THE ZOHAN", "release_year", "2008" ], [ "ZACK AND MIRI MAKE A PORNO", "has_genre", "COMEDY" ], [ "ZACK AND MIRI MAKE A PORNO", "release_year", "2008" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 32043, BROADCAST NEWS 30463, COMEDY 36212, DRAMA 24940, HOPE AND GLORY 23849, HOUSEKEEPING 19498, MERMAIDS 40064, MY OLD LADY 30044, PETER NELSON 1687, PURELY BELTER 25023, QUARTET 23599, THE BEST EXOTIC MARIGOLD HOTEL 33050, THE CAMPAIGN 1371, THE LONELY PASSION OF JUDITH HEARNE 8791, THE SECOND BEST EXOTIC MARIGOLD HOTEL 36468, TOUGH GUYS DON'T DANCE 6981, WISH YOU WERE HERE src, edge_attr, dst 32043, has_genre, 30463 32043, has_genre, 36212 24940, has_genre, 30463 24940, has_genre, 36212 23849, has_genre, 30463 23849, has_genre, 36212 19498, has_genre, 30463 19498, has_genre, 36212 19498, has_tags, 30463 19498, has_tags, 36212 40064, has_genre, 30463 40064, has_genre, 36212 1687, has_genre, 30463 1687, has_genre, 36212 25023, has_genre, 30463 25023, has_genre, 36212 25023, has_tags, 30463 23599, has_genre, 30463 23599, has_genre, 36212 33050, has_genre, 30463 1371, has_genre, 36212 1371, written_by, 30044 8791, has_genre, 30463 8791, has_genre, 36212 36468, has_genre, 30463 36468, has_genre, 36212 6981, has_genre, 30463 6981, has_genre, 36212 Question: For what reason are PETER NELSON, PURELY BELTER, and THE CAMPAIGN associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "PETER NELSON", "PURELY BELTER", "THE CAMPAIGN" ], "valid_edges": [ [ "BROADCAST NEWS", "has_genre", "COMEDY" ], [ "BROADCAST NEWS", "has_genre", "DRAMA" ], [ "HOPE AND GLORY", "has_genre", "COMEDY" ], [ "HOPE AND GLORY", "has_genre", "DRAMA" ], [ "HOUSEKEEPING", "has_genre", "COMEDY" ], [ "HOUSEKEEPING", "has_genre", "DRAMA" ], [ "MERMAIDS", "has_genre", "COMEDY" ], [ "MERMAIDS", "has_genre", "DRAMA" ], [ "MERMAIDS", "has_tags", "COMEDY" ], [ "MERMAIDS", "has_tags", "DRAMA" ], [ "MY OLD LADY", "has_genre", "COMEDY" ], [ "MY OLD LADY", "has_genre", "DRAMA" ], [ "PURELY BELTER", "has_genre", "COMEDY" ], [ "PURELY BELTER", "has_genre", "DRAMA" ], [ "QUARTET", "has_genre", "COMEDY" ], [ "QUARTET", "has_genre", "DRAMA" ], [ "QUARTET", "has_tags", "COMEDY" ], [ "THE BEST EXOTIC MARIGOLD HOTEL", "has_genre", "COMEDY" ], [ "THE BEST EXOTIC MARIGOLD HOTEL", "has_genre", "DRAMA" ], [ "THE CAMPAIGN", "has_genre", "COMEDY" ], [ "THE LONELY PASSION OF JUDITH HEARNE", "has_genre", "DRAMA" ], [ "THE LONELY PASSION OF JUDITH HEARNE", "written_by", "PETER NELSON" ], [ "THE SECOND BEST EXOTIC MARIGOLD HOTEL", "has_genre", "COMEDY" ], [ "THE SECOND BEST EXOTIC MARIGOLD HOTEL", "has_genre", "DRAMA" ], [ "TOUGH GUYS DON'T DANCE", "has_genre", "COMEDY" ], [ "TOUGH GUYS DON'T DANCE", "has_genre", "DRAMA" ], [ "WISH YOU WERE HERE", "has_genre", "COMEDY" ], [ "WISH YOU WERE HERE", "has_genre", "DRAMA" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 2452, 13 ASSASSINS 33013, A LETTER TO MOMO 29800, ACE ATTORNEY 29816, AIR DOLL 31491, AN AUTUMN AFTERNOON 35497, ANTARCTICA 7421, APART FROM YOU 7908, AUDITION 30907, BAREFOOT GEN 39878, BRIGHT FUTURE 5376, CAPE NO. 7 21907, COME SEE THE PARADISE 1148, CONFESSIONS 24410, DEMONLOVER 2710, DEPARTURES 38714, DOUBLE SUICIDE 36212, DRAMA 16970, EUREKA 18312, FABIÁN BIELINSKY 5931, FLOATING CLOUDS 1612, FROM UP ON POPPY HILL 38222, FUNERAL PARADE OF ROSES 14853, GO FOR BROKE! 22091, GRAVE OF THE FIREFLIES 12994, HARAKIRI 20207, HELENA BERGSTRÖM 22180, HIMIZU 9849, HOUSE OF ANGELS 11051, I AM WAITING 36874, JAPANESE 21614, JAPANESE STORY 28036, KAN SHIMOZAWA 21224, KIKUJIRO 1859, LATE AUTUMN 19809, LATE SPRING 25019, LIKE FATHER, LIKE SON 36020, LIKE SOMEONE IN LOVE 39818, MACHIBUSE 21686, MOONLIGHT SERENADE 21030, NANA 1922, NINE QUEENS 25709, NOBODY KNOWS 16592, NORWEGIAN WOOD 12009, ODD OBSESSION 34288, ONLY YESTERDAY 234, PEARL HARBOR 34639, POSTMAN BLUES 18584, PRINCESS MONONOKE 11570, RASHOMON 27361, RIDING ALONE FOR THOUSANDS OF MILES 35944, SAMURAI 33216, SAMURAI BANNERS 20932, SEVEN SAMURAI 4027, SHARA 9448, SILENCE 26949, SISTERS OF THE GION 32584, SUSPECT X 12462, THE ADVENTURES OF MILO AND OTIS 14688, THE FLOATING CASTLE 34462, THE FLOWERS OF WAR 24493, THE FUNERAL 31571, THE LOYAL 47 RONIN 25054, THE PILLOW BOOK 21548, THE SUN 8873, THE TALE OF ZATOICHI 18076, THE WIND RISES 13374, THRONE OF BLOOD 34804, TOKYO FIST 16940, TORA! TORA! TORA! 4527, UGETSU 14956, VOICES OF A DISTANT STAR 22783, WHEN A WOMAN ASCENDS THE STAIRS 24623, WHISPER OF THE HEART 54, YOJIMBO 29857, ZATOICHI MEETS YOJIMBO src, edge_attr, dst 2452, has_genre, 36212 2452, has_tags, 35944 2452, in_language, 36874 33013, has_genre, 36212 33013, in_language, 36874 29800, has_genre, 36212 29800, in_language, 36874 29816, has_genre, 36212 29816, in_language, 36874 31491, has_genre, 36212 31491, has_tags, 36874 31491, in_language, 36874 35497, has_genre, 36212 35497, in_language, 36874 7421, has_genre, 36212 7421, in_language, 36874 7908, has_genre, 36212 7908, in_language, 36874 30907, has_genre, 36212 30907, in_language, 36874 39878, has_genre, 36212 39878, in_language, 36874 5376, has_genre, 36212 5376, in_language, 36874 21907, has_genre, 36212 21907, in_language, 36874 1148, has_genre, 36212 1148, has_tags, 36874 1148, in_language, 36874 24410, has_genre, 36212 24410, in_language, 36874 2710, has_genre, 36212 2710, in_language, 36874 38714, has_genre, 36212 38714, in_language, 36874 16970, has_genre, 36212 16970, in_language, 36874 5931, has_genre, 36212 5931, in_language, 36874 1612, has_genre, 36212 1612, in_language, 36874 38222, has_genre, 36212 38222, in_language, 36874 14853, has_genre, 36212 14853, in_language, 36874 22091, has_genre, 36212 22091, in_language, 36874 12994, has_genre, 36212 12994, has_tags, 36874 12994, in_language, 36874 22180, has_genre, 36212 22180, in_language, 36874 9849, has_genre, 36212 9849, starred_actors, 20207 11051, has_genre, 36212 11051, in_language, 36874 21614, has_genre, 36212 21614, in_language, 36874 21224, has_genre, 36212 21224, in_language, 36874 1859, has_genre, 36212 1859, in_language, 36874 19809, has_genre, 36212 19809, in_language, 36874 25019, has_genre, 36212 25019, in_language, 36874 36020, has_genre, 36212 36020, in_language, 36874 39818, has_genre, 36212 39818, in_language, 36874 21686, has_genre, 36212 21686, in_language, 36874 21030, has_genre, 36212 21030, in_language, 36874 1922, directed_by, 18312 1922, has_genre, 36212 1922, written_by, 18312 25709, has_genre, 36212 25709, in_language, 36874 16592, has_genre, 36212 16592, has_tags, 36874 16592, in_language, 36874 12009, has_genre, 36212 12009, in_language, 36874 34288, has_genre, 36212 34288, in_language, 36874 234, has_genre, 36212 234, has_tags, 36212 234, in_language, 36874 34639, has_genre, 36212 34639, in_language, 36874 18584, has_tags, 36212 18584, has_tags, 36874 18584, in_language, 36874 11570, has_genre, 36212 11570, in_language, 36874 27361, has_genre, 36212 27361, in_language, 36874 33216, has_genre, 36212 33216, in_language, 36874 20932, has_genre, 36212 20932, has_tags, 36212 20932, has_tags, 35944 20932, in_language, 36874 4027, has_genre, 36212 4027, in_language, 36874 9448, has_genre, 36212 9448, in_language, 36874 26949, has_genre, 36212 26949, in_language, 36874 32584, has_genre, 36212 32584, in_language, 36874 12462, has_genre, 36212 12462, in_language, 36874 14688, has_genre, 36212 14688, in_language, 36874 34462, has_genre, 36212 34462, in_language, 36874 24493, has_genre, 36212 24493, in_language, 36874 31571, has_genre, 36212 31571, in_language, 36874 25054, has_genre, 36212 25054, in_language, 36874 21548, has_genre, 36212 21548, in_language, 36874 8873, has_genre, 36212 8873, has_tags, 35944 8873, in_language, 36874 8873, written_by, 28036 18076, has_genre, 36212 18076, in_language, 36874 13374, has_genre, 36212 13374, has_tags, 36874 13374, in_language, 36874 34804, has_genre, 36212 34804, in_language, 36874 16940, has_genre, 36212 16940, has_tags, 36874 16940, in_language, 36874 4527, has_genre, 36212 4527, in_language, 36874 14956, has_genre, 36212 14956, in_language, 36874 22783, has_genre, 36212 22783, in_language, 36874 24623, has_genre, 36212 24623, in_language, 36874 54, has_genre, 36212 54, in_language, 36874 29857, has_genre, 36212 29857, in_language, 36874 29857, written_by, 28036 Question: How are FABIÁN BIELINSKY, HELENA BERGSTRÖM, and THE TALE OF ZATOICHI related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "FABIÁN BIELINSKY", "HELENA BERGSTRÖM", "THE TALE OF ZATOICHI" ], "valid_edges": [ [ "13 ASSASSINS", "has_genre", "DRAMA" ], [ "13 ASSASSINS", "has_tags", "SAMURAI" ], [ "13 ASSASSINS", "in_language", "JAPANESE" ], [ "A LETTER TO MOMO", "has_genre", "DRAMA" ], [ "A LETTER TO MOMO", "in_language", "JAPANESE" ], [ "ACE ATTORNEY", "has_genre", "DRAMA" ], [ "ACE ATTORNEY", "in_language", "JAPANESE" ], [ "AIR DOLL", "has_genre", "DRAMA" ], [ "AIR DOLL", "in_language", "JAPANESE" ], [ "AN AUTUMN AFTERNOON", "has_genre", "DRAMA" ], [ "AN AUTUMN AFTERNOON", "has_tags", "JAPANESE" ], [ "AN AUTUMN AFTERNOON", "in_language", "JAPANESE" ], [ "ANTARCTICA", "has_genre", "DRAMA" ], [ "ANTARCTICA", "in_language", "JAPANESE" ], [ "APART FROM YOU", "has_genre", "DRAMA" ], [ "APART FROM YOU", "in_language", "JAPANESE" ], [ "AUDITION", "has_genre", "DRAMA" ], [ "AUDITION", "in_language", "JAPANESE" ], [ "BAREFOOT GEN", "has_genre", "DRAMA" ], [ "BAREFOOT GEN", "in_language", "JAPANESE" ], [ "BRIGHT FUTURE", "has_genre", "DRAMA" ], [ "BRIGHT FUTURE", "in_language", "JAPANESE" ], [ "CAPE NO. 7", "has_genre", "DRAMA" ], [ "CAPE NO. 7", "in_language", "JAPANESE" ], [ "COME SEE THE PARADISE", "has_genre", "DRAMA" ], [ "COME SEE THE PARADISE", "in_language", "JAPANESE" ], [ "CONFESSIONS", "has_genre", "DRAMA" ], [ "CONFESSIONS", "has_tags", "JAPANESE" ], [ "CONFESSIONS", "in_language", "JAPANESE" ], [ "DEMONLOVER", "has_genre", "DRAMA" ], [ "DEMONLOVER", "in_language", "JAPANESE" ], [ "DEPARTURES", "has_genre", "DRAMA" ], [ "DEPARTURES", "in_language", "JAPANESE" ], [ "DOUBLE SUICIDE", "has_genre", "DRAMA" ], [ "DOUBLE SUICIDE", "in_language", "JAPANESE" ], [ "EUREKA", "has_genre", "DRAMA" ], [ "EUREKA", "in_language", "JAPANESE" ], [ "FLOATING CLOUDS", "has_genre", "DRAMA" ], [ "FLOATING CLOUDS", "in_language", "JAPANESE" ], [ "FROM UP ON POPPY HILL", "has_genre", "DRAMA" ], [ "FROM UP ON POPPY HILL", "in_language", "JAPANESE" ], [ "FUNERAL PARADE OF ROSES", "has_genre", "DRAMA" ], [ "FUNERAL PARADE OF ROSES", "in_language", "JAPANESE" ], [ "GO FOR BROKE!", "has_genre", "DRAMA" ], [ "GO FOR BROKE!", "in_language", "JAPANESE" ], [ "GRAVE OF THE FIREFLIES", "has_genre", "DRAMA" ], [ "GRAVE OF THE FIREFLIES", "in_language", "JAPANESE" ], [ "HARAKIRI", "has_genre", "DRAMA" ], [ "HARAKIRI", "has_tags", "JAPANESE" ], [ "HARAKIRI", "in_language", "JAPANESE" ], [ "HIMIZU", "has_genre", "DRAMA" ], [ "HIMIZU", "in_language", "JAPANESE" ], [ "HOUSE OF ANGELS", "has_genre", "DRAMA" ], [ "HOUSE OF ANGELS", "starred_actors", "HELENA BERGSTRÖM" ], [ "I AM WAITING", "has_genre", "DRAMA" ], [ "I AM WAITING", "in_language", "JAPANESE" ], [ "JAPANESE STORY", "has_genre", "DRAMA" ], [ "JAPANESE STORY", "in_language", "JAPANESE" ], [ "KIKUJIRO", "has_genre", "DRAMA" ], [ "KIKUJIRO", "in_language", "JAPANESE" ], [ "LATE AUTUMN", "has_genre", "DRAMA" ], [ "LATE AUTUMN", "in_language", "JAPANESE" ], [ "LATE SPRING", "has_genre", "DRAMA" ], [ "LATE SPRING", "in_language", "JAPANESE" ], [ "LIKE FATHER, LIKE SON", "has_genre", "DRAMA" ], [ "LIKE FATHER, LIKE SON", "in_language", "JAPANESE" ], [ "LIKE SOMEONE IN LOVE", "has_genre", "DRAMA" ], [ "LIKE SOMEONE IN LOVE", "in_language", "JAPANESE" ], [ "MACHIBUSE", "has_genre", "DRAMA" ], [ "MACHIBUSE", "in_language", "JAPANESE" ], [ "MOONLIGHT SERENADE", "has_genre", "DRAMA" ], [ "MOONLIGHT SERENADE", "in_language", "JAPANESE" ], [ "NANA", "has_genre", "DRAMA" ], [ "NANA", "in_language", "JAPANESE" ], [ "NINE QUEENS", "directed_by", "FABIÁN BIELINSKY" ], [ "NINE QUEENS", "has_genre", "DRAMA" ], [ "NINE QUEENS", "written_by", "FABIÁN BIELINSKY" ], [ "NOBODY KNOWS", "has_genre", "DRAMA" ], [ "NOBODY KNOWS", "in_language", "JAPANESE" ], [ "NORWEGIAN WOOD", "has_genre", "DRAMA" ], [ "NORWEGIAN WOOD", "has_tags", "JAPANESE" ], [ "NORWEGIAN WOOD", "in_language", "JAPANESE" ], [ "ODD OBSESSION", "has_genre", "DRAMA" ], [ "ODD OBSESSION", "in_language", "JAPANESE" ], [ "ONLY YESTERDAY", "has_genre", "DRAMA" ], [ "ONLY YESTERDAY", "in_language", "JAPANESE" ], [ "PEARL HARBOR", "has_genre", "DRAMA" ], [ "PEARL HARBOR", "has_tags", "DRAMA" ], [ "PEARL HARBOR", "in_language", "JAPANESE" ], [ "POSTMAN BLUES", "has_genre", "DRAMA" ], [ "POSTMAN BLUES", "in_language", "JAPANESE" ], [ "PRINCESS MONONOKE", "has_tags", "DRAMA" ], [ "PRINCESS MONONOKE", "has_tags", "JAPANESE" ], [ "PRINCESS MONONOKE", "in_language", "JAPANESE" ], [ "RASHOMON", "has_genre", "DRAMA" ], [ "RASHOMON", "in_language", "JAPANESE" ], [ "RIDING ALONE FOR THOUSANDS OF MILES", "has_genre", "DRAMA" ], [ "RIDING ALONE FOR THOUSANDS OF MILES", "in_language", "JAPANESE" ], [ "SAMURAI BANNERS", "has_genre", "DRAMA" ], [ "SAMURAI BANNERS", "in_language", "JAPANESE" ], [ "SEVEN SAMURAI", "has_genre", "DRAMA" ], [ "SEVEN SAMURAI", "has_tags", "DRAMA" ], [ "SEVEN SAMURAI", "has_tags", "SAMURAI" ], [ "SEVEN SAMURAI", "in_language", "JAPANESE" ], [ "SHARA", "has_genre", "DRAMA" ], [ "SHARA", "in_language", "JAPANESE" ], [ "SILENCE", "has_genre", "DRAMA" ], [ "SILENCE", "in_language", "JAPANESE" ], [ "SISTERS OF THE GION", "has_genre", "DRAMA" ], [ "SISTERS OF THE GION", "in_language", "JAPANESE" ], [ "SUSPECT X", "has_genre", "DRAMA" ], [ "SUSPECT X", "in_language", "JAPANESE" ], [ "THE ADVENTURES OF MILO AND OTIS", "has_genre", "DRAMA" ], [ "THE ADVENTURES OF MILO AND OTIS", "in_language", "JAPANESE" ], [ "THE FLOATING CASTLE", "has_genre", "DRAMA" ], [ "THE FLOATING CASTLE", "in_language", "JAPANESE" ], [ "THE FLOWERS OF WAR", "has_genre", "DRAMA" ], [ "THE FLOWERS OF WAR", "in_language", "JAPANESE" ], [ "THE FUNERAL", "has_genre", "DRAMA" ], [ "THE FUNERAL", "in_language", "JAPANESE" ], [ "THE LOYAL 47 RONIN", "has_genre", "DRAMA" ], [ "THE LOYAL 47 RONIN", "in_language", "JAPANESE" ], [ "THE PILLOW BOOK", "has_genre", "DRAMA" ], [ "THE PILLOW BOOK", "in_language", "JAPANESE" ], [ "THE SUN", "has_genre", "DRAMA" ], [ "THE SUN", "in_language", "JAPANESE" ], [ "THE TALE OF ZATOICHI", "has_genre", "DRAMA" ], [ "THE TALE OF ZATOICHI", "has_tags", "SAMURAI" ], [ "THE TALE OF ZATOICHI", "in_language", "JAPANESE" ], [ "THE TALE OF ZATOICHI", "written_by", "KAN SHIMOZAWA" ], [ "THE WIND RISES", "has_genre", "DRAMA" ], [ "THE WIND RISES", "in_language", "JAPANESE" ], [ "THRONE OF BLOOD", "has_genre", "DRAMA" ], [ "THRONE OF BLOOD", "has_tags", "JAPANESE" ], [ "THRONE OF BLOOD", "in_language", "JAPANESE" ], [ "TOKYO FIST", "has_genre", "DRAMA" ], [ "TOKYO FIST", "in_language", "JAPANESE" ], [ "TORA! TORA! TORA!", "has_genre", "DRAMA" ], [ "TORA! TORA! TORA!", "has_tags", "JAPANESE" ], [ "TORA! TORA! TORA!", "in_language", "JAPANESE" ], [ "UGETSU", "has_genre", "DRAMA" ], [ "UGETSU", "in_language", "JAPANESE" ], [ "VOICES OF A DISTANT STAR", "has_genre", "DRAMA" ], [ "VOICES OF A DISTANT STAR", "in_language", "JAPANESE" ], [ "WHEN A WOMAN ASCENDS THE STAIRS", "has_genre", "DRAMA" ], [ "WHEN A WOMAN ASCENDS THE STAIRS", "in_language", "JAPANESE" ], [ "WHISPER OF THE HEART", "has_genre", "DRAMA" ], [ "WHISPER OF THE HEART", "in_language", "JAPANESE" ], [ "YOJIMBO", "has_genre", "DRAMA" ], [ "YOJIMBO", "in_language", "JAPANESE" ], [ "ZATOICHI MEETS YOJIMBO", "has_genre", "DRAMA" ], [ "ZATOICHI MEETS YOJIMBO", "in_language", "JAPANESE" ], [ "ZATOICHI MEETS YOJIMBO", "written_by", "KAN SHIMOZAWA" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 24438, 1993 1097, 2003 15174, AND STARRING PANCHO VILLA AS HIMSELF 21382, ANTONIO BANDERAS 37088, GLENN CLOSE 2957, ONCE UPON A TIME IN MEXICO 18917, SKYLARK 31584, SLIM SUSIE 34001, THE HOUSE OF THE SPIRITS 18569, ULF MALMROS src, edge_attr, dst 15174, release_year, 1097 15174, starred_actors, 21382 2957, has_tags, 21382 2957, release_year, 1097 2957, starred_actors, 21382 18917, release_year, 24438 18917, starred_actors, 37088 31584, directed_by, 18569 31584, release_year, 1097 31584, written_by, 18569 34001, has_tags, 21382 34001, has_tags, 37088 34001, release_year, 24438 Question: How are ANTONIO BANDERAS, SKYLARK, and ULF MALMROS related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ANTONIO BANDERAS", "SKYLARK", "ULF MALMROS" ], "valid_edges": [ [ "AND STARRING PANCHO VILLA AS HIMSELF", "release_year", "2003" ], [ "AND STARRING PANCHO VILLA AS HIMSELF", "starred_actors", "ANTONIO BANDERAS" ], [ "ONCE UPON A TIME IN MEXICO", "has_tags", "ANTONIO BANDERAS" ], [ "ONCE UPON A TIME IN MEXICO", "release_year", "2003" ], [ "ONCE UPON A TIME IN MEXICO", "starred_actors", "ANTONIO BANDERAS" ], [ "SKYLARK", "release_year", "1993" ], [ "SKYLARK", "starred_actors", "GLENN CLOSE" ], [ "SLIM SUSIE", "directed_by", "ULF MALMROS" ], [ "SLIM SUSIE", "release_year", "2003" ], [ "SLIM SUSIE", "written_by", "ULF MALMROS" ], [ "THE HOUSE OF THE SPIRITS", "has_tags", "ANTONIO BANDERAS" ], [ "THE HOUSE OF THE SPIRITS", "has_tags", "GLENN CLOSE" ], [ "THE HOUSE OF THE SPIRITS", "release_year", "1993" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 27261, 2009 1421, 2013 26267, CINEMATOGRAPHY 6480, GERMAN 21573, GIRL ON A BICYCLE 34974, GOLD 31326, GRAVITY 20941, HAMLET 33460, HANS ZIMMER 2722, INSIDE LLEWYN DAVIS 848, OCULUS 13471, PRISONERS 11124, STALINGRAD 19442, THE BOOK THIEF 13153, THE GERMAN DOCTOR 2872, THE GREAT GATSBY 14478, THE LION KING 3104, THE WHITE RIBBON 37916, WETLANDS src, edge_attr, dst 21573, in_language, 6480 21573, release_year, 1421 34974, in_language, 6480 34974, release_year, 1421 31326, has_tags, 26267 31326, release_year, 1421 20941, release_year, 27261 2722, has_tags, 26267 2722, release_year, 1421 848, release_year, 1421 13471, has_tags, 26267 13471, release_year, 1421 11124, has_tags, 6480 11124, in_language, 6480 11124, release_year, 1421 19442, in_language, 6480 19442, release_year, 1421 13153, in_language, 6480 13153, release_year, 1421 2872, has_tags, 26267 2872, release_year, 1421 14478, has_tags, 20941 14478, has_tags, 33460 3104, has_tags, 26267 3104, has_tags, 6480 3104, in_language, 6480 3104, release_year, 27261 37916, in_language, 6480 37916, release_year, 1421 Question: In what context are HANS ZIMMER, OCULUS, and THE WHITE RIBBON connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "HANS ZIMMER", "OCULUS", "THE WHITE RIBBON" ], "valid_edges": [ [ "GIRL ON A BICYCLE", "in_language", "GERMAN" ], [ "GIRL ON A BICYCLE", "release_year", "2013" ], [ "GOLD", "in_language", "GERMAN" ], [ "GOLD", "release_year", "2013" ], [ "GRAVITY", "has_tags", "CINEMATOGRAPHY" ], [ "GRAVITY", "release_year", "2013" ], [ "HAMLET", "release_year", "2009" ], [ "INSIDE LLEWYN DAVIS", "has_tags", "CINEMATOGRAPHY" ], [ "INSIDE LLEWYN DAVIS", "release_year", "2013" ], [ "OCULUS", "release_year", "2013" ], [ "PRISONERS", "has_tags", "CINEMATOGRAPHY" ], [ "PRISONERS", "release_year", "2013" ], [ "STALINGRAD", "has_tags", "GERMAN" ], [ "STALINGRAD", "in_language", "GERMAN" ], [ "STALINGRAD", "release_year", "2013" ], [ "THE BOOK THIEF", "in_language", "GERMAN" ], [ "THE BOOK THIEF", "release_year", "2013" ], [ "THE GERMAN DOCTOR", "in_language", "GERMAN" ], [ "THE GERMAN DOCTOR", "release_year", "2013" ], [ "THE GREAT GATSBY", "has_tags", "CINEMATOGRAPHY" ], [ "THE GREAT GATSBY", "release_year", "2013" ], [ "THE LION KING", "has_tags", "HAMLET" ], [ "THE LION KING", "has_tags", "HANS ZIMMER" ], [ "THE WHITE RIBBON", "has_tags", "CINEMATOGRAPHY" ], [ "THE WHITE RIBBON", "has_tags", "GERMAN" ], [ "THE WHITE RIBBON", "in_language", "GERMAN" ], [ "THE WHITE RIBBON", "release_year", "2009" ], [ "WETLANDS", "in_language", "GERMAN" ], [ "WETLANDS", "release_year", "2013" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15421, 100 BLOODY ACRES 24580, 2-HEADED SHARK ATTACK 658, 2012 38712, 30 DAYS OF NIGHT 18899, A BUCKET OF BLOOD 30586, A FANTASTIC FEAR OF EVERYTHING 25327, AFTERSHOCK 12146, ANNE BOBBY 34229, ANTIVIRAL 35584, ANY QUESTIONS FOR BEN? 3449, APARTMENT 1303 3D 22078, ARBITRAGE 10277, ARGO 36593, BLACK CHRISTMAS 13736, BLACK ROCK 2570, BLACK SUNDAY 31909, BLACK SWAN 5946, BLOODY BLOODY BIBLE CAMP 17899, BUG 21399, CABIN 27929, CABIN FEVER 38311, CHAOS 30191, CHERNOBYL DIARIES 6286, CHRIS HEMSWORTH 30339, CITADEL 38925, CLOUD ATLAS 38598, COME OUT AND PLAY 22349, CRAWLSPACE 30529, DARK SHADOWS 33477, DARKEST NIGHT 21942, DAWN OF THE DEAD 17466, DETENTION OF THE DEAD 31163, DIRECTORIAL DEBUT 16041, DJANGO UNCHAINED 28770, DRACULA 3D 32596, DREW GODDARD 6394, EXCISION 35384, FAT KID RULES THE WORLD 3785, FRAN KRANZ 1041, GALLOWWALKERS 24098, GRAVE ENCOUNTERS 2 2486, GRINDHOUSE 5870, HORROR 4823, HOUSE AT THE END OF THE STREET 36133, JOHN DIES AT THE END 8192, JOSS WHEDON 1000, LET THE RIGHT ONE IN 35217, LOOPER 24437, MANIAC 31377, MUCH ADO ABOUT NOTHING 11484, MUTANT CHRONICLES 33156, MY NAME IS BRUCE 22011, NIGHTBREED 25269, NINE LIVES 10573, NO ONE LIVES 36239, PARANORMAL ACTIVITY 4 35289, PARANORMAN 17916, PIRANHA 3DD 23588, PROMETHEUS 6175, QUARANTINE 25023, QUARTET 13081, R 27373, RED DAWN 38226, RESOLUTION 31339, RISE OF THE ZOMBIES 20664, ROB SITCH 38815, SADAKO 3D 33912, SANTO CILAURO 38572, SATIRE 24992, SCARY OR DIE 9373, SEVERANCE 15926, SILENT NIGHT 34584, SILVER LININGS PLAYBOOK 8359, SINISTER 13768, SLEEPY HOLLOW 26930, SLITHER 21148, SNOW WHITE AND THE HUNTSMAN 9247, STITCHES 39265, STORAGE 24 273, TED 31162, THE ABCS OF DEATH 39296, THE APPARITION 16103, THE AVENGERS 25250, THE BARRENS 19623, THE BATTERY 16753, THE BAY 19938, THE CABIN IN THE WOODS 13392, THE CASTLE 2974, THE COLLECTION 18592, THE DEVIL INSIDE 7153, THE DEVIL'S CARNIVAL 19064, THE HAUNTING OF HELENA 11861, THE LORDS OF SALEM 24652, THE MAN WHO LAUGHS 33919, THE MIST 956, THE ORPHANAGE 29525, THE PACT 2023, THE POSSESSION 22294, THE PROPHECY 18162, THE RAVEN 27427, THE STRANGERS 27003, THE THOMPSONS 3437, THE WOMAN IN BLACK 939, THIS IS 40 24359, TOM GLEISNER 31252, V/H/S 30621, VACANCY 24570, VAMPS 20105, WORLD WAR Z 39464, WOULD YOU RATHER src, edge_attr, dst 15421, has_genre, 5870 15421, release_year, 658 24580, has_genre, 5870 24580, release_year, 658 38712, has_genre, 5870 38712, has_tags, 13081 18899, has_genre, 5870 18899, has_tags, 38572 30586, has_genre, 5870 30586, has_tags, 5870 30586, release_year, 658 25327, has_genre, 5870 25327, release_year, 658 34229, has_genre, 5870 34229, release_year, 658 35584, directed_by, 20664 35584, release_year, 658 35584, written_by, 20664 35584, written_by, 33912 35584, written_by, 24359 3449, has_genre, 5870 3449, release_year, 658 22078, has_tags, 13081 22078, release_year, 658 10277, has_tags, 13081 10277, release_year, 658 36593, has_genre, 5870 36593, has_tags, 5870 36593, has_tags, 13081 13736, has_genre, 5870 13736, release_year, 658 2570, has_genre, 5870 2570, has_tags, 31163 31909, has_tags, 5870 31909, has_tags, 13081 5946, has_genre, 5870 5946, release_year, 658 17899, has_genre, 5870 17899, has_tags, 13081 27929, has_genre, 5870 27929, has_tags, 21399 27929, has_tags, 5870 38311, has_genre, 5870 38311, has_tags, 13081 30191, has_genre, 5870 30191, release_year, 658 30339, has_genre, 5870 30339, release_year, 658 38925, has_tags, 13081 38925, release_year, 658 38598, has_genre, 5870 38598, release_year, 658 22349, has_genre, 5870 22349, release_year, 658 30529, has_genre, 5870 30529, release_year, 658 33477, has_genre, 5870 33477, has_tags, 5870 33477, release_year, 658 21942, has_genre, 5870 21942, has_tags, 31163 21942, has_tags, 5870 21942, has_tags, 13081 17466, has_genre, 5870 17466, release_year, 658 16041, has_tags, 13081 16041, release_year, 658 28770, has_genre, 5870 28770, release_year, 658 6394, has_genre, 5870 6394, release_year, 658 35384, has_tags, 31163 35384, release_year, 658 1041, has_genre, 5870 1041, release_year, 658 24098, has_genre, 5870 24098, release_year, 658 2486, has_genre, 5870 2486, has_tags, 13081 4823, has_genre, 5870 4823, release_year, 658 36133, has_genre, 5870 36133, release_year, 658 1000, has_genre, 5870 1000, has_tags, 5870 1000, has_tags, 13081 35217, has_tags, 13081 35217, release_year, 658 24437, has_genre, 5870 24437, release_year, 658 31377, directed_by, 8192 31377, has_tags, 3785 31377, has_tags, 8192 31377, release_year, 658 31377, written_by, 8192 11484, has_genre, 5870 11484, has_tags, 13081 33156, has_genre, 5870 33156, has_tags, 13081 22011, has_genre, 5870 22011, starred_actors, 12146 25269, has_genre, 5870 25269, has_tags, 13081 10573, has_genre, 5870 10573, release_year, 658 36239, has_genre, 5870 36239, release_year, 658 35289, has_tags, 5870 35289, release_year, 658 17916, has_genre, 5870 17916, release_year, 658 23588, has_tags, 13081 23588, release_year, 658 6175, has_genre, 5870 6175, has_tags, 13081 25023, has_tags, 31163 25023, release_year, 658 27373, has_tags, 6286 27373, release_year, 658 27373, starred_actors, 6286 38226, has_genre, 5870 38226, release_year, 658 31339, has_genre, 5870 31339, release_year, 658 38815, has_genre, 5870 38815, release_year, 658 24992, has_genre, 5870 24992, release_year, 658 9373, has_genre, 5870 9373, has_tags, 5870 9373, has_tags, 13081 15926, has_genre, 5870 15926, release_year, 658 34584, has_tags, 13081 34584, release_year, 658 8359, has_genre, 5870 8359, has_tags, 5870 8359, release_year, 658 13768, has_genre, 5870 13768, has_tags, 5870 13768, has_tags, 13081 26930, has_genre, 5870 26930, has_tags, 5870 26930, has_tags, 13081 21148, has_tags, 6286 21148, release_year, 658 21148, starred_actors, 6286 9247, has_genre, 5870 9247, release_year, 658 39265, has_genre, 5870 39265, release_year, 658 273, has_tags, 31163 273, release_year, 658 31162, has_genre, 5870 31162, release_year, 658 39296, has_genre, 5870 39296, release_year, 658 16103, directed_by, 8192 16103, has_tags, 6286 16103, has_tags, 8192 16103, release_year, 658 16103, starred_actors, 6286 16103, written_by, 8192 25250, has_genre, 5870 25250, release_year, 658 19623, has_genre, 5870 19623, release_year, 658 16753, has_genre, 5870 16753, release_year, 658 19938, directed_by, 32596 19938, has_genre, 5870 19938, has_tags, 21399 19938, has_tags, 6286 19938, has_tags, 31163 19938, has_tags, 32596 19938, has_tags, 3785 19938, has_tags, 5870 19938, has_tags, 8192 19938, has_tags, 13081 19938, has_tags, 38572 19938, release_year, 658 19938, starred_actors, 6286 19938, starred_actors, 3785 19938, written_by, 32596 19938, written_by, 8192 13392, directed_by, 20664 13392, has_tags, 20664 13392, written_by, 20664 13392, written_by, 33912 13392, written_by, 24359 2974, has_genre, 5870 2974, release_year, 658 18592, has_genre, 5870 18592, release_year, 658 7153, has_genre, 5870 7153, release_year, 658 19064, has_genre, 5870 19064, release_year, 658 11861, has_genre, 5870 11861, release_year, 658 24652, has_genre, 5870 24652, release_year, 658 33919, has_genre, 5870 33919, has_tags, 13081 956, has_tags, 5870 956, has_tags, 13081 29525, has_genre, 5870 29525, release_year, 658 2023, has_genre, 5870 2023, has_tags, 5870 2023, release_year, 658 22294, has_genre, 5870 22294, has_tags, 13081 18162, has_genre, 5870 18162, has_tags, 5870 18162, release_year, 658 27427, has_genre, 5870 27427, has_tags, 13081 27003, has_genre, 5870 27003, release_year, 658 3437, has_genre, 5870 3437, has_tags, 5870 3437, release_year, 658 939, has_tags, 13081 939, release_year, 658 31252, has_genre, 5870 31252, has_tags, 5870 31252, release_year, 658 30621, has_genre, 5870 30621, has_tags, 13081 24570, has_genre, 5870 24570, release_year, 658 20105, has_genre, 5870 20105, written_by, 32596 39464, has_genre, 5870 39464, has_tags, 5870 39464, release_year, 658 Question: In what context are ANNE BOBBY, THE CABIN IN THE WOODS, and TOM GLEISNER connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ANNE BOBBY", "THE CABIN IN THE WOODS", "TOM GLEISNER" ], "valid_edges": [ [ "100 BLOODY ACRES", "has_genre", "HORROR" ], [ "100 BLOODY ACRES", "release_year", "2012" ], [ "2-HEADED SHARK ATTACK", "has_genre", "HORROR" ], [ "2-HEADED SHARK ATTACK", "release_year", "2012" ], [ "30 DAYS OF NIGHT", "has_genre", "HORROR" ], [ "30 DAYS OF NIGHT", "has_tags", "R" ], [ "A BUCKET OF BLOOD", "has_genre", "HORROR" ], [ "A BUCKET OF BLOOD", "has_tags", "SATIRE" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_genre", "HORROR" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_tags", "HORROR" ], [ "A FANTASTIC FEAR OF EVERYTHING", "release_year", "2012" ], [ "AFTERSHOCK", "has_genre", "HORROR" ], [ "AFTERSHOCK", "release_year", "2012" ], [ "ANTIVIRAL", "has_genre", "HORROR" ], [ "ANTIVIRAL", "release_year", "2012" ], [ "ANY QUESTIONS FOR BEN?", "directed_by", "ROB SITCH" ], [ "ANY QUESTIONS FOR BEN?", "release_year", "2012" ], [ "ANY QUESTIONS FOR BEN?", "written_by", "ROB SITCH" ], [ "ANY QUESTIONS FOR BEN?", "written_by", "SANTO CILAURO" ], [ "ANY QUESTIONS FOR BEN?", "written_by", "TOM GLEISNER" ], [ "APARTMENT 1303 3D", "has_genre", "HORROR" ], [ "APARTMENT 1303 3D", "release_year", "2012" ], [ "ARBITRAGE", "has_tags", "R" ], [ "ARBITRAGE", "release_year", "2012" ], [ "ARGO", "has_tags", "R" ], [ "ARGO", "release_year", "2012" ], [ "BLACK CHRISTMAS", "has_genre", "HORROR" ], [ "BLACK CHRISTMAS", "has_tags", "HORROR" ], [ "BLACK CHRISTMAS", "has_tags", "R" ], [ "BLACK ROCK", "has_genre", "HORROR" ], [ "BLACK ROCK", "release_year", "2012" ], [ "BLACK SUNDAY", "has_genre", "HORROR" ], [ "BLACK SUNDAY", "has_tags", "DIRECTORIAL DEBUT" ], [ "BLACK SWAN", "has_tags", "HORROR" ], [ "BLACK SWAN", "has_tags", "R" ], [ "BLOODY BLOODY BIBLE CAMP", "has_genre", "HORROR" ], [ "BLOODY BLOODY BIBLE CAMP", "release_year", "2012" ], [ "BUG", "has_genre", "HORROR" ], [ "BUG", "has_tags", "R" ], [ "CABIN FEVER", "has_genre", "HORROR" ], [ "CABIN FEVER", "has_tags", "CABIN" ], [ "CABIN FEVER", "has_tags", "HORROR" ], [ "CHAOS", "has_genre", "HORROR" ], [ "CHAOS", "has_tags", "R" ], [ "CHERNOBYL DIARIES", "has_genre", "HORROR" ], [ "CHERNOBYL DIARIES", "release_year", "2012" ], [ "CITADEL", "has_genre", "HORROR" ], [ "CITADEL", "release_year", "2012" ], [ "CLOUD ATLAS", "has_tags", "R" ], [ "CLOUD ATLAS", "release_year", "2012" ], [ "COME OUT AND PLAY", "has_genre", "HORROR" ], [ "COME OUT AND PLAY", "release_year", "2012" ], [ "CRAWLSPACE", "has_genre", "HORROR" ], [ "CRAWLSPACE", "release_year", "2012" ], [ "DARK SHADOWS", "has_genre", "HORROR" ], [ "DARK SHADOWS", "release_year", "2012" ], [ "DARKEST NIGHT", "has_genre", "HORROR" ], [ "DARKEST NIGHT", "has_tags", "HORROR" ], [ "DARKEST NIGHT", "release_year", "2012" ], [ "DAWN OF THE DEAD", "has_genre", "HORROR" ], [ "DAWN OF THE DEAD", "has_tags", "DIRECTORIAL DEBUT" ], [ "DAWN OF THE DEAD", "has_tags", "HORROR" ], [ "DAWN OF THE DEAD", "has_tags", "R" ], [ "DETENTION OF THE DEAD", "has_genre", "HORROR" ], [ "DETENTION OF THE DEAD", "release_year", "2012" ], [ "DJANGO UNCHAINED", "has_tags", "R" ], [ "DJANGO UNCHAINED", "release_year", "2012" ], [ "DRACULA 3D", "has_genre", "HORROR" ], [ "DRACULA 3D", "release_year", "2012" ], [ "EXCISION", "has_genre", "HORROR" ], [ "EXCISION", "release_year", "2012" ], [ "FAT KID RULES THE WORLD", "has_tags", "DIRECTORIAL DEBUT" ], [ "FAT KID RULES THE WORLD", "release_year", "2012" ], [ "GALLOWWALKERS", "has_genre", "HORROR" ], [ "GALLOWWALKERS", "release_year", "2012" ], [ "GRAVE ENCOUNTERS 2", "has_genre", "HORROR" ], [ "GRAVE ENCOUNTERS 2", "release_year", "2012" ], [ "GRINDHOUSE", "has_genre", "HORROR" ], [ "GRINDHOUSE", "has_tags", "R" ], [ "HOUSE AT THE END OF THE STREET", "has_genre", "HORROR" ], [ "HOUSE AT THE END OF THE STREET", "release_year", "2012" ], [ "JOHN DIES AT THE END", "has_genre", "HORROR" ], [ "JOHN DIES AT THE END", "release_year", "2012" ], [ "LET THE RIGHT ONE IN", "has_genre", "HORROR" ], [ "LET THE RIGHT ONE IN", "has_tags", "HORROR" ], [ "LET THE RIGHT ONE IN", "has_tags", "R" ], [ "LOOPER", "has_tags", "R" ], [ "LOOPER", "release_year", "2012" ], [ "MANIAC", "has_genre", "HORROR" ], [ "MANIAC", "release_year", "2012" ], [ "MUCH ADO ABOUT NOTHING", "directed_by", "JOSS WHEDON" ], [ "MUCH ADO ABOUT NOTHING", "has_tags", "FRAN KRANZ" ], [ "MUCH ADO ABOUT NOTHING", "has_tags", "JOSS WHEDON" ], [ "MUCH ADO ABOUT NOTHING", "release_year", "2012" ], [ "MUCH ADO ABOUT NOTHING", "written_by", "JOSS WHEDON" ], [ "MUTANT CHRONICLES", "has_genre", "HORROR" ], [ "MUTANT CHRONICLES", "has_tags", "R" ], [ "MY NAME IS BRUCE", "has_genre", "HORROR" ], [ "MY NAME IS BRUCE", "has_tags", "R" ], [ "NIGHTBREED", "has_genre", "HORROR" ], [ "NIGHTBREED", "starred_actors", "ANNE BOBBY" ], [ "NINE LIVES", "has_genre", "HORROR" ], [ "NINE LIVES", "has_tags", "R" ], [ "NO ONE LIVES", "has_genre", "HORROR" ], [ "NO ONE LIVES", "release_year", "2012" ], [ "PARANORMAL ACTIVITY 4", "has_genre", "HORROR" ], [ "PARANORMAL ACTIVITY 4", "release_year", "2012" ], [ "PARANORMAN", "has_tags", "HORROR" ], [ "PARANORMAN", "release_year", "2012" ], [ "PIRANHA 3DD", "has_genre", "HORROR" ], [ "PIRANHA 3DD", "release_year", "2012" ], [ "PROMETHEUS", "has_tags", "R" ], [ "PROMETHEUS", "release_year", "2012" ], [ "QUARANTINE", "has_genre", "HORROR" ], [ "QUARANTINE", "has_tags", "R" ], [ "QUARTET", "has_tags", "DIRECTORIAL DEBUT" ], [ "QUARTET", "release_year", "2012" ], [ "RED DAWN", "has_tags", "CHRIS HEMSWORTH" ], [ "RED DAWN", "release_year", "2012" ], [ "RED DAWN", "starred_actors", "CHRIS HEMSWORTH" ], [ "RESOLUTION", "has_genre", "HORROR" ], [ "RESOLUTION", "release_year", "2012" ], [ "RISE OF THE ZOMBIES", "has_genre", "HORROR" ], [ "RISE OF THE ZOMBIES", "release_year", "2012" ], [ "SADAKO 3D", "has_genre", "HORROR" ], [ "SADAKO 3D", "release_year", "2012" ], [ "SCARY OR DIE", "has_genre", "HORROR" ], [ "SCARY OR DIE", "release_year", "2012" ], [ "SEVERANCE", "has_genre", "HORROR" ], [ "SEVERANCE", "has_tags", "HORROR" ], [ "SEVERANCE", "has_tags", "R" ], [ "SILENT NIGHT", "has_genre", "HORROR" ], [ "SILENT NIGHT", "release_year", "2012" ], [ "SILVER LININGS PLAYBOOK", "has_tags", "R" ], [ "SILVER LININGS PLAYBOOK", "release_year", "2012" ], [ "SINISTER", "has_genre", "HORROR" ], [ "SINISTER", "has_tags", "HORROR" ], [ "SINISTER", "release_year", "2012" ], [ "SLEEPY HOLLOW", "has_genre", "HORROR" ], [ "SLEEPY HOLLOW", "has_tags", "HORROR" ], [ "SLEEPY HOLLOW", "has_tags", "R" ], [ "SLITHER", "has_genre", "HORROR" ], [ "SLITHER", "has_tags", "HORROR" ], [ "SLITHER", "has_tags", "R" ], [ "SNOW WHITE AND THE HUNTSMAN", "has_tags", "CHRIS HEMSWORTH" ], [ "SNOW WHITE AND THE HUNTSMAN", "release_year", "2012" ], [ "SNOW WHITE AND THE HUNTSMAN", "starred_actors", "CHRIS HEMSWORTH" ], [ "STITCHES", "has_genre", "HORROR" ], [ "STITCHES", "release_year", "2012" ], [ "STORAGE 24", "has_genre", "HORROR" ], [ "STORAGE 24", "release_year", "2012" ], [ "TED", "has_tags", "DIRECTORIAL DEBUT" ], [ "TED", "release_year", "2012" ], [ "THE ABCS OF DEATH", "has_genre", "HORROR" ], [ "THE ABCS OF DEATH", "release_year", "2012" ], [ "THE APPARITION", "has_genre", "HORROR" ], [ "THE APPARITION", "release_year", "2012" ], [ "THE AVENGERS", "directed_by", "JOSS WHEDON" ], [ "THE AVENGERS", "has_tags", "CHRIS HEMSWORTH" ], [ "THE AVENGERS", "has_tags", "JOSS WHEDON" ], [ "THE AVENGERS", "release_year", "2012" ], [ "THE AVENGERS", "starred_actors", "CHRIS HEMSWORTH" ], [ "THE AVENGERS", "written_by", "JOSS WHEDON" ], [ "THE BARRENS", "has_genre", "HORROR" ], [ "THE BARRENS", "release_year", "2012" ], [ "THE BATTERY", "has_genre", "HORROR" ], [ "THE BATTERY", "release_year", "2012" ], [ "THE BAY", "has_genre", "HORROR" ], [ "THE BAY", "release_year", "2012" ], [ "THE CABIN IN THE WOODS", "directed_by", "DREW GODDARD" ], [ "THE CABIN IN THE WOODS", "has_genre", "HORROR" ], [ "THE CABIN IN THE WOODS", "has_tags", "CABIN" ], [ "THE CABIN IN THE WOODS", "has_tags", "CHRIS HEMSWORTH" ], [ "THE CABIN IN THE WOODS", "has_tags", "DIRECTORIAL DEBUT" ], [ "THE CABIN IN THE WOODS", "has_tags", "DREW GODDARD" ], [ "THE CABIN IN THE WOODS", "has_tags", "FRAN KRANZ" ], [ "THE CABIN IN THE WOODS", "has_tags", "HORROR" ], [ "THE CABIN IN THE WOODS", "has_tags", "JOSS WHEDON" ], [ "THE CABIN IN THE WOODS", "has_tags", "R" ], [ "THE CABIN IN THE WOODS", "has_tags", "SATIRE" ], [ "THE CABIN IN THE WOODS", "release_year", "2012" ], [ "THE CABIN IN THE WOODS", "starred_actors", "CHRIS HEMSWORTH" ], [ "THE CABIN IN THE WOODS", "starred_actors", "FRAN KRANZ" ], [ "THE CABIN IN THE WOODS", "written_by", "DREW GODDARD" ], [ "THE CABIN IN THE WOODS", "written_by", "JOSS WHEDON" ], [ "THE CASTLE", "directed_by", "ROB SITCH" ], [ "THE CASTLE", "has_tags", "ROB SITCH" ], [ "THE CASTLE", "written_by", "ROB SITCH" ], [ "THE CASTLE", "written_by", "SANTO CILAURO" ], [ "THE CASTLE", "written_by", "TOM GLEISNER" ], [ "THE COLLECTION", "has_genre", "HORROR" ], [ "THE COLLECTION", "release_year", "2012" ], [ "THE DEVIL INSIDE", "has_genre", "HORROR" ], [ "THE DEVIL INSIDE", "release_year", "2012" ], [ "THE DEVIL'S CARNIVAL", "has_genre", "HORROR" ], [ "THE DEVIL'S CARNIVAL", "release_year", "2012" ], [ "THE HAUNTING OF HELENA", "has_genre", "HORROR" ], [ "THE HAUNTING OF HELENA", "release_year", "2012" ], [ "THE LORDS OF SALEM", "has_genre", "HORROR" ], [ "THE LORDS OF SALEM", "release_year", "2012" ], [ "THE MAN WHO LAUGHS", "has_genre", "HORROR" ], [ "THE MAN WHO LAUGHS", "release_year", "2012" ], [ "THE MIST", "has_genre", "HORROR" ], [ "THE MIST", "has_tags", "R" ], [ "THE ORPHANAGE", "has_tags", "HORROR" ], [ "THE ORPHANAGE", "has_tags", "R" ], [ "THE PACT", "has_genre", "HORROR" ], [ "THE PACT", "release_year", "2012" ], [ "THE POSSESSION", "has_genre", "HORROR" ], [ "THE POSSESSION", "has_tags", "HORROR" ], [ "THE POSSESSION", "release_year", "2012" ], [ "THE PROPHECY", "has_genre", "HORROR" ], [ "THE PROPHECY", "has_tags", "R" ], [ "THE RAVEN", "has_genre", "HORROR" ], [ "THE RAVEN", "has_tags", "HORROR" ], [ "THE RAVEN", "release_year", "2012" ], [ "THE STRANGERS", "has_genre", "HORROR" ], [ "THE STRANGERS", "has_tags", "R" ], [ "THE THOMPSONS", "has_genre", "HORROR" ], [ "THE THOMPSONS", "release_year", "2012" ], [ "THE WOMAN IN BLACK", "has_genre", "HORROR" ], [ "THE WOMAN IN BLACK", "has_tags", "HORROR" ], [ "THE WOMAN IN BLACK", "release_year", "2012" ], [ "THIS IS 40", "has_tags", "R" ], [ "THIS IS 40", "release_year", "2012" ], [ "V/H/S", "has_genre", "HORROR" ], [ "V/H/S", "has_tags", "HORROR" ], [ "V/H/S", "release_year", "2012" ], [ "VACANCY", "has_genre", "HORROR" ], [ "VACANCY", "has_tags", "R" ], [ "VAMPS", "has_genre", "HORROR" ], [ "VAMPS", "release_year", "2012" ], [ "WORLD WAR Z", "has_genre", "HORROR" ], [ "WORLD WAR Z", "written_by", "DREW GODDARD" ], [ "WOULD YOU RATHER", "has_genre", "HORROR" ], [ "WOULD YOU RATHER", "has_tags", "HORROR" ], [ "WOULD YOU RATHER", "release_year", "2012" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 33013, A LETTER TO MOMO 24177, ANIMATION 6294, ANIME 30907, BAREFOOT GEN 7106, BROKEN VESSELS 36212, DRAMA 21774, GUNBUSTER 8248, JAPAN 36874, JAPANESE 18529, JOHN MCMAHON 34288, ONLY YESTERDAY 18584, PRINCESS MONONOKE 38976, STUDIO GHIBLI 1277, THE COLOR OF MONEY 14956, VOICES OF A DISTANT STAR 16901, WALTER TEVIS src, edge_attr, dst 33013, has_genre, 24177 33013, has_genre, 36212 33013, has_tags, 6294 33013, in_language, 36874 30907, has_genre, 36212 30907, has_tags, 6294 30907, in_language, 36874 7106, has_genre, 36212 7106, written_by, 18529 21774, has_genre, 36212 21774, has_tags, 6294 34288, has_genre, 24177 34288, has_genre, 36212 34288, has_tags, 6294 34288, has_tags, 8248 34288, has_tags, 38976 34288, in_language, 36874 18584, has_tags, 6294 18584, has_tags, 36212 18584, has_tags, 8248 18584, has_tags, 36874 18584, has_tags, 38976 18584, in_language, 36874 1277, has_genre, 36212 1277, written_by, 16901 14956, has_genre, 24177 14956, has_genre, 36212 14956, has_tags, 6294 14956, in_language, 36874 Question: For what reason are ANIME, JOHN MCMAHON, and WALTER TEVIS associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ANIME", "JOHN MCMAHON", "WALTER TEVIS" ], "valid_edges": [ [ "A LETTER TO MOMO", "has_genre", "ANIMATION" ], [ "A LETTER TO MOMO", "has_genre", "DRAMA" ], [ "A LETTER TO MOMO", "has_tags", "ANIME" ], [ "A LETTER TO MOMO", "in_language", "JAPANESE" ], [ "BAREFOOT GEN", "has_genre", "DRAMA" ], [ "BAREFOOT GEN", "has_tags", "ANIME" ], [ "BAREFOOT GEN", "in_language", "JAPANESE" ], [ "BROKEN VESSELS", "has_genre", "DRAMA" ], [ "BROKEN VESSELS", "written_by", "JOHN MCMAHON" ], [ "GUNBUSTER", "has_genre", "DRAMA" ], [ "GUNBUSTER", "has_tags", "ANIME" ], [ "ONLY YESTERDAY", "has_genre", "ANIMATION" ], [ "ONLY YESTERDAY", "has_genre", "DRAMA" ], [ "ONLY YESTERDAY", "has_tags", "ANIME" ], [ "ONLY YESTERDAY", "has_tags", "JAPAN" ], [ "ONLY YESTERDAY", "has_tags", "STUDIO GHIBLI" ], [ "ONLY YESTERDAY", "in_language", "JAPANESE" ], [ "PRINCESS MONONOKE", "has_tags", "ANIME" ], [ "PRINCESS MONONOKE", "has_tags", "DRAMA" ], [ "PRINCESS MONONOKE", "has_tags", "JAPAN" ], [ "PRINCESS MONONOKE", "has_tags", "JAPANESE" ], [ "PRINCESS MONONOKE", "has_tags", "STUDIO GHIBLI" ], [ "PRINCESS MONONOKE", "in_language", "JAPANESE" ], [ "THE COLOR OF MONEY", "has_genre", "DRAMA" ], [ "THE COLOR OF MONEY", "written_by", "WALTER TEVIS" ], [ "VOICES OF A DISTANT STAR", "has_genre", "ANIMATION" ], [ "VOICES OF A DISTANT STAR", "has_genre", "DRAMA" ], [ "VOICES OF A DISTANT STAR", "has_tags", "ANIME" ], [ "VOICES OF A DISTANT STAR", "in_language", "JAPANESE" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 16426, BELLE DE JOUR 14724, CRIME 38279, CYBERPUNK 9542, DIARY OF A CHAMBERMAID 36212, DRAMA 11389, EDDY MORETTI 6012, FRENCH 1003, HACKERS 35505, LUIS BUÑUEL 26605, THE CRIMINAL LIFE OF ARCHIBALDO DE LA CRUZ 2049, WHITE LIGHTNIN' src, edge_attr, dst 16426, directed_by, 35505 16426, has_genre, 36212 16426, has_tags, 36212 16426, has_tags, 35505 16426, in_language, 6012 16426, written_by, 35505 9542, directed_by, 35505 9542, has_genre, 36212 9542, has_tags, 35505 9542, in_language, 6012 9542, written_by, 35505 1003, has_genre, 14724 1003, has_tags, 38279 1003, has_tags, 1003 26605, directed_by, 35505 26605, has_genre, 14724 26605, has_tags, 35505 26605, written_by, 35505 2049, has_genre, 36212 2049, written_by, 11389 Question: How are CYBERPUNK, EDDY MORETTI, and LUIS BUÑUEL related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "CYBERPUNK", "EDDY MORETTI", "LUIS BUÑUEL" ], "valid_edges": [ [ "BELLE DE JOUR", "directed_by", "LUIS BUÑUEL" ], [ "BELLE DE JOUR", "has_genre", "DRAMA" ], [ "BELLE DE JOUR", "has_tags", "DRAMA" ], [ "BELLE DE JOUR", "has_tags", "LUIS BUÑUEL" ], [ "BELLE DE JOUR", "in_language", "FRENCH" ], [ "BELLE DE JOUR", "written_by", "LUIS BUÑUEL" ], [ "DIARY OF A CHAMBERMAID", "directed_by", "LUIS BUÑUEL" ], [ "DIARY OF A CHAMBERMAID", "has_genre", "DRAMA" ], [ "DIARY OF A CHAMBERMAID", "has_tags", "LUIS BUÑUEL" ], [ "DIARY OF A CHAMBERMAID", "in_language", "FRENCH" ], [ "DIARY OF A CHAMBERMAID", "written_by", "LUIS BUÑUEL" ], [ "HACKERS", "has_genre", "CRIME" ], [ "HACKERS", "has_tags", "CYBERPUNK" ], [ "HACKERS", "has_tags", "HACKERS" ], [ "THE CRIMINAL LIFE OF ARCHIBALDO DE LA CRUZ", "directed_by", "LUIS BUÑUEL" ], [ "THE CRIMINAL LIFE OF ARCHIBALDO DE LA CRUZ", "has_genre", "CRIME" ], [ "THE CRIMINAL LIFE OF ARCHIBALDO DE LA CRUZ", "has_tags", "LUIS BUÑUEL" ], [ "THE CRIMINAL LIFE OF ARCHIBALDO DE LA CRUZ", "written_by", "LUIS BUÑUEL" ], [ "WHITE LIGHTNIN'", "has_genre", "DRAMA" ], [ "WHITE LIGHTNIN'", "written_by", "EDDY MORETTI" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 7977, 1969 658, 2012 5423, A LATE QUARTET 32632, A ROYAL AFFAIR 20562, A THOUSAND WORDS 21800, ABOUT CHERRY 29800, ACE ATTORNEY 30332, AMOUR 36963, ANNA KARENINA 24708, ANY DAY NOW 22078, ARBITRAGE 10277, ARGO 13686, ARTHUR NEWMAN 9695, AT ANY PRICE 31449, BARABBAS 38263, BARBARA 23994, BARFI! 30581, BEASTS OF THE SOUTHERN WILD 37191, BEING FLYNN 28199, BEL AMI 32770, BEST MAN DOWN 14594, BEYOND THE HILLS 890, BIG MIRACLE 36071, BLACKBIRD 39848, BLUE LIKE JAZZ 18062, BOOTH TARKINGTON 22284, BOY EATING THE BIRD'S FOOD 27030, BROKEN 33711, CAESAR MUST DIE 32831, CALL GIRL 24707, CAMILLE REWINDS 12735, CHASING MAVERICKS 32450, CHEERFUL WEATHER FOR THE WEDDING 27338, CLIP 38925, CLOUD ATLAS 21016, COMPLIANCE 24757, CONSUMING SPIRITS 37589, COSMOPOLIS 19894, CRAVE 32797, DANGEROUS LIAISONS 10643, DARLING COMPANION 31694, DEADFALL 38977, DISCONNECT 36212, DRAMA 7480, EDEN 26106, END OF WATCH 32892, ENGLISH VINGLISH 6394, EXCISION 19570, FAREWELL, MY QUEEN 39636, FILL THE VOID 5563, FLIGHT 12314, FOR ELLEN 7740, FOREIGN LETTERS 15473, FORGETTING THE GIRL 6076, FRANCES HA 24535, GAME CHANGE 2427, GIRL IN PROGRESS 5647, HANNAH ARENDT 3917, HELLO HERMAN 22793, HEROINE 27350, HITCHCOCK 3030, HOLY MOTORS 35319, HOPE SPRINGS 37844, HYDE PARK ON HUDSON 19154, I BELONG 38589, I DO 32376, IMAGINE 26911, IN THE FOG 24025, INESCAPABLE 6301, JAYNE MANSFIELD'S CAR 5418, K-11 12835, KAUWBOY 26232, KEEP THE LIGHTS ON 2354, LADISLAV FUKS 3103, LAURENCE ANYWAYS 1418, LAWLESS 14601, LES MISÉRABLES 14931, LIBERAL ARTS 39727, LIFE OF PI 36020, LIKE SOMEONE IN LOVE 47, LINCOLN 17372, LOL 34517, LUV 35871, MAGIC MIKE 13801, MARFA GIRL 34611, ME AND YOU 31735, MIDDLE OF NOWHERE 39165, MUD 19118, MUSEUM HOURS 24915, MY WAY 26089, NAKED HARBOUR 25504, NEIGHBORING SOUNDS 8107, NO 20503, NOBODY WALKS 17532, NOT FADE AWAY 17263, NOW IS GOOD 21677, ON THE ROAD 38602, OUR CHILDREN 8523, OUT IN THE DARK 31312, PEOPLE LIKE US 15815, POST TENEBRAS LUX 11253, PROMISED LAND 11237, PURGE 25023, QUARTET 35746, REALITY 33501, RENOIR 11839, REVENGE FOR JOLLY! 12597, RUBY SPARKS 30316, RUST AND BONE 35808, SAFE 17168, SEEKING A FRIEND FOR THE END OF THE WORLD 25060, SEXUAL CHRONICLES OF A FRENCH FAMILY 28583, SHADOW DANCER 34584, SILVER LININGS PLAYBOOK 7174, SISTER 39426, SMASHED 28026, SOMEBODY UP THERE LIKES ME 33759, SOMETHING IN THE AIR 17346, SPRING BREAKERS 17608, STARLET 39286, STILL MINE 40067, STOLEN 25002, STRUCK BY LIGHTNING 3038, STUCK IN LOVE 18335, TABU 19501, THANKS FOR SHARING 3885, THE ARTIST AND THE MODEL 19623, THE BATTERY 24441, THE BROKEN CIRCLE BREAKDOWN 38175, THE CITIZEN 6644, THE COMEDY 24231, THE CREMATOR 2399, THE DEEP 10295, THE END OF LOVE 27103, THE FITZGERALD FAMILY CHRISTMAS 14688, THE FLOATING CASTLE 35267, THE FORGER 16929, THE GIRL 15980, THE GUILT TRIP 9328, THE HUNT 38614, THE IMPOSSIBLE 22800, THE LESSER BLESSED 33948, THE LETTER 4984, THE LUCKY ONE 23191, THE MAGIC OF BELLE ISLE 37370, THE MAGNIFICENT AMBERSONS 24652, THE MAN WHO LAUGHS 17964, THE MASTER 15798, THE ODD LIFE OF TIMOTHY GREEN 28735, THE OTHER SON 1195, THE PATIENCE STONE 7673, THE PERKS OF BEING A WALLFLOWER 2739, THE SAPPHIRES 27064, THE SCAPEGOAT 8650, THE SESSIONS 37915, THE STORY OF LUKE 14524, THE SWEENEY 12086, THE VOW 26797, THE WALL 4614, THE WE AND THE I 3437, THE WOMAN IN BLACK 15504, THE WORDS 266, THREE WORLDS 5352, THY WOMB 9918, TO THE WONDER 17451, TROUBLE WITH THE CURVE 13359, TWO LIVES 10305, UNCONDITIONAL 30056, WAR WITCH 7030, WHAT IF... 24333, WHAT MAISIE KNEW 34118, WHAT TO EXPECT WHEN YOU'RE EXPECTING 21183, WHITE ELEPHANT 18608, WHITE FROG 36174, WINNING STREAK 26337, WON'T BACK DOWN 31040, XINGU 9052, YOSSI 16849, ZERO DARK THIRTY src, edge_attr, dst 7977, has_genre, 36212 5423, has_genre, 36212 5423, has_tags, 36212 5423, release_year, 658 32632, has_genre, 36212 32632, release_year, 658 20562, has_genre, 36212 20562, release_year, 658 21800, has_genre, 36212 21800, release_year, 658 29800, has_genre, 36212 29800, release_year, 658 30332, has_genre, 36212 30332, release_year, 658 36963, has_genre, 36212 36963, has_tags, 36212 36963, release_year, 658 24708, has_genre, 36212 24708, release_year, 658 22078, has_genre, 36212 22078, release_year, 658 10277, has_genre, 36212 10277, has_tags, 36212 10277, release_year, 658 13686, has_genre, 36212 13686, release_year, 658 9695, has_genre, 36212 9695, release_year, 658 31449, has_genre, 36212 31449, release_year, 658 38263, has_genre, 36212 38263, release_year, 658 23994, has_genre, 36212 23994, release_year, 658 30581, has_genre, 36212 30581, release_year, 658 37191, has_genre, 36212 37191, release_year, 658 28199, has_genre, 36212 28199, release_year, 658 32770, has_genre, 36212 32770, release_year, 658 14594, has_genre, 36212 14594, release_year, 658 890, has_genre, 36212 890, release_year, 658 36071, has_genre, 36212 36071, release_year, 658 39848, has_genre, 36212 39848, release_year, 658 22284, has_genre, 36212 22284, release_year, 658 27030, has_genre, 36212 27030, release_year, 658 33711, has_genre, 36212 33711, release_year, 658 32831, has_genre, 36212 32831, release_year, 658 24707, has_genre, 36212 24707, release_year, 658 12735, has_genre, 36212 12735, release_year, 658 32450, has_genre, 36212 32450, release_year, 658 27338, has_genre, 36212 27338, release_year, 658 38925, has_genre, 36212 38925, has_tags, 36212 38925, release_year, 658 21016, has_genre, 36212 21016, release_year, 658 24757, has_genre, 36212 24757, release_year, 658 37589, has_genre, 36212 37589, release_year, 658 19894, has_genre, 36212 19894, release_year, 658 32797, has_genre, 36212 32797, release_year, 658 10643, has_genre, 36212 10643, release_year, 658 31694, has_genre, 36212 31694, release_year, 658 38977, has_genre, 36212 38977, has_tags, 36212 38977, release_year, 658 7480, has_genre, 36212 7480, release_year, 658 26106, has_genre, 36212 26106, release_year, 658 32892, has_genre, 36212 32892, release_year, 658 6394, has_genre, 36212 6394, release_year, 658 19570, has_genre, 36212 19570, release_year, 658 39636, has_genre, 36212 39636, release_year, 658 5563, has_genre, 36212 5563, release_year, 658 12314, has_genre, 36212 12314, release_year, 658 7740, has_genre, 36212 7740, release_year, 658 15473, has_genre, 36212 15473, release_year, 658 6076, has_genre, 36212 6076, release_year, 658 24535, has_genre, 36212 24535, release_year, 658 2427, has_genre, 36212 2427, release_year, 658 5647, has_genre, 36212 5647, release_year, 658 3917, has_genre, 36212 3917, release_year, 658 22793, has_genre, 36212 22793, release_year, 658 27350, has_genre, 36212 27350, release_year, 658 3030, has_genre, 36212 3030, release_year, 658 35319, has_genre, 36212 35319, release_year, 658 37844, has_genre, 36212 37844, release_year, 658 19154, has_genre, 36212 19154, release_year, 658 38589, has_genre, 36212 38589, release_year, 658 32376, has_genre, 36212 32376, release_year, 658 26911, has_genre, 36212 26911, release_year, 658 24025, has_genre, 36212 24025, release_year, 658 6301, has_genre, 36212 6301, release_year, 658 5418, has_genre, 36212 5418, release_year, 658 12835, has_genre, 36212 12835, release_year, 658 26232, has_genre, 36212 26232, release_year, 658 3103, has_genre, 36212 3103, release_year, 658 1418, has_genre, 36212 1418, has_tags, 36212 1418, release_year, 658 14601, has_genre, 36212 14601, release_year, 658 14931, has_genre, 36212 14931, release_year, 658 39727, has_genre, 36212 39727, release_year, 658 36020, has_genre, 36212 36020, release_year, 658 47, has_genre, 36212 47, has_tags, 36212 47, release_year, 658 17372, has_genre, 36212 17372, release_year, 658 34517, has_genre, 36212 34517, release_year, 658 35871, has_genre, 36212 35871, release_year, 658 13801, has_genre, 36212 13801, release_year, 658 34611, has_genre, 36212 34611, release_year, 658 31735, has_genre, 36212 31735, release_year, 658 39165, has_genre, 36212 39165, release_year, 658 19118, has_genre, 36212 19118, release_year, 658 24915, has_genre, 36212 24915, release_year, 658 26089, has_genre, 36212 26089, release_year, 658 25504, has_genre, 36212 25504, release_year, 658 8107, has_genre, 36212 8107, release_year, 658 20503, has_genre, 36212 20503, release_year, 658 17532, has_genre, 36212 17532, release_year, 658 17263, has_genre, 36212 17263, release_year, 658 21677, has_genre, 36212 21677, release_year, 658 38602, has_genre, 36212 38602, release_year, 658 8523, has_genre, 36212 8523, release_year, 658 31312, has_genre, 36212 31312, has_tags, 36212 31312, release_year, 658 15815, has_genre, 36212 15815, release_year, 658 11253, has_genre, 36212 11253, release_year, 658 11237, has_genre, 36212 11237, release_year, 658 25023, has_genre, 36212 25023, release_year, 658 35746, has_genre, 36212 35746, release_year, 658 33501, has_genre, 36212 33501, release_year, 658 11839, has_genre, 36212 11839, release_year, 658 12597, has_genre, 36212 12597, release_year, 658 30316, has_genre, 36212 30316, release_year, 658 35808, has_genre, 36212 35808, release_year, 658 17168, has_genre, 36212 17168, release_year, 658 25060, has_genre, 36212 25060, release_year, 658 28583, has_genre, 36212 28583, release_year, 658 34584, has_genre, 36212 34584, has_tags, 36212 34584, release_year, 658 7174, has_genre, 36212 7174, release_year, 658 39426, has_genre, 36212 39426, release_year, 658 28026, has_genre, 36212 28026, release_year, 658 33759, has_genre, 36212 33759, release_year, 658 17346, has_genre, 36212 17346, release_year, 658 17608, has_genre, 36212 17608, release_year, 658 39286, has_genre, 36212 39286, release_year, 658 40067, has_genre, 36212 40067, release_year, 658 25002, has_genre, 36212 25002, release_year, 658 3038, has_genre, 36212 3038, release_year, 658 18335, has_genre, 36212 18335, release_year, 658 19501, has_genre, 36212 19501, release_year, 658 3885, has_genre, 36212 3885, release_year, 658 19623, has_genre, 36212 19623, release_year, 658 24441, has_genre, 36212 24441, has_tags, 36212 24441, release_year, 658 38175, has_genre, 36212 38175, release_year, 658 6644, has_genre, 36212 6644, release_year, 658 24231, has_genre, 36212 24231, release_year, 7977 24231, written_by, 2354 2399, has_genre, 36212 2399, release_year, 658 10295, has_genre, 36212 10295, release_year, 658 27103, has_genre, 36212 27103, release_year, 658 14688, has_genre, 36212 14688, release_year, 658 35267, has_genre, 36212 35267, release_year, 658 16929, has_genre, 36212 16929, release_year, 658 15980, has_genre, 36212 15980, release_year, 658 9328, has_genre, 36212 9328, has_tags, 36212 9328, release_year, 658 38614, has_genre, 36212 38614, has_tags, 36212 38614, release_year, 658 22800, has_genre, 36212 22800, release_year, 658 33948, has_genre, 36212 33948, release_year, 658 4984, has_genre, 36212 4984, release_year, 658 23191, has_genre, 36212 23191, release_year, 658 37370, has_genre, 36212 37370, written_by, 18062 24652, has_genre, 36212 24652, release_year, 658 17964, has_genre, 36212 17964, release_year, 658 15798, has_genre, 36212 15798, release_year, 658 28735, has_genre, 36212 28735, release_year, 658 1195, has_genre, 36212 1195, release_year, 658 7673, has_genre, 36212 7673, has_tags, 36212 7673, release_year, 658 2739, has_genre, 36212 2739, release_year, 658 27064, has_genre, 36212 27064, release_year, 658 8650, has_genre, 36212 8650, has_tags, 36212 8650, release_year, 658 37915, has_genre, 36212 37915, release_year, 658 14524, has_genre, 36212 14524, release_year, 658 12086, has_genre, 36212 12086, release_year, 658 26797, has_genre, 36212 26797, release_year, 658 4614, has_genre, 36212 4614, release_year, 658 3437, has_genre, 36212 3437, release_year, 658 15504, has_genre, 36212 15504, release_year, 658 266, has_genre, 36212 266, release_year, 658 5352, has_genre, 36212 5352, release_year, 658 9918, has_genre, 36212 9918, release_year, 658 17451, has_genre, 36212 17451, release_year, 658 13359, has_genre, 36212 13359, release_year, 658 10305, has_genre, 36212 10305, release_year, 658 30056, has_genre, 36212 30056, release_year, 658 7030, has_genre, 36212 7030, release_year, 658 24333, has_genre, 36212 24333, release_year, 658 34118, has_genre, 36212 34118, release_year, 658 21183, has_genre, 36212 21183, release_year, 658 18608, has_genre, 36212 18608, release_year, 658 36174, has_genre, 36212 36174, release_year, 658 26337, has_genre, 36212 26337, release_year, 658 31040, has_genre, 36212 31040, release_year, 658 9052, has_genre, 36212 9052, release_year, 658 16849, has_genre, 36212 16849, release_year, 658 Question: For what reason are BOOTH TARKINGTON, FORGETTING THE GIRL, and LADISLAV FUKS associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BOOTH TARKINGTON", "FORGETTING THE GIRL", "LADISLAV FUKS" ], "valid_edges": [ [ "1969", "has_genre", "DRAMA" ], [ "A LATE QUARTET", "has_genre", "DRAMA" ], [ "A LATE QUARTET", "has_tags", "DRAMA" ], [ "A LATE QUARTET", "release_year", "2012" ], [ "A ROYAL AFFAIR", "has_genre", "DRAMA" ], [ "A ROYAL AFFAIR", "release_year", "2012" ], [ "A THOUSAND WORDS", "has_genre", "DRAMA" ], [ "A THOUSAND WORDS", "release_year", "2012" ], [ "ABOUT CHERRY", "has_genre", "DRAMA" ], [ "ABOUT CHERRY", "release_year", "2012" ], [ "ACE ATTORNEY", "has_genre", "DRAMA" ], [ "ACE ATTORNEY", "release_year", "2012" ], [ "AMOUR", "has_genre", "DRAMA" ], [ "AMOUR", "release_year", "2012" ], [ "ANNA KARENINA", "has_genre", "DRAMA" ], [ "ANNA KARENINA", "has_tags", "DRAMA" ], [ "ANNA KARENINA", "release_year", "2012" ], [ "ANY DAY NOW", "has_genre", "DRAMA" ], [ "ANY DAY NOW", "release_year", "2012" ], [ "ARBITRAGE", "has_genre", "DRAMA" ], [ "ARBITRAGE", "release_year", "2012" ], [ "ARGO", "has_genre", "DRAMA" ], [ "ARGO", "has_tags", "DRAMA" ], [ "ARGO", "release_year", "2012" ], [ "ARTHUR NEWMAN", "has_genre", "DRAMA" ], [ "ARTHUR NEWMAN", "release_year", "2012" ], [ "AT ANY PRICE", "has_genre", "DRAMA" ], [ "AT ANY PRICE", "release_year", "2012" ], [ "BARABBAS", "has_genre", "DRAMA" ], [ "BARABBAS", "release_year", "2012" ], [ "BARBARA", "has_genre", "DRAMA" ], [ "BARBARA", "release_year", "2012" ], [ "BARFI!", "has_genre", "DRAMA" ], [ "BARFI!", "release_year", "2012" ], [ "BEASTS OF THE SOUTHERN WILD", "has_genre", "DRAMA" ], [ "BEASTS OF THE SOUTHERN WILD", "release_year", "2012" ], [ "BEING FLYNN", "has_genre", "DRAMA" ], [ "BEING FLYNN", "release_year", "2012" ], [ "BEL AMI", "has_genre", "DRAMA" ], [ "BEL AMI", "release_year", "2012" ], [ "BEST MAN DOWN", "has_genre", "DRAMA" ], [ "BEST MAN DOWN", "release_year", "2012" ], [ "BEYOND THE HILLS", "has_genre", "DRAMA" ], [ "BEYOND THE HILLS", "release_year", "2012" ], [ "BIG MIRACLE", "has_genre", "DRAMA" ], [ "BIG MIRACLE", "release_year", "2012" ], [ "BLACKBIRD", "has_genre", "DRAMA" ], [ "BLACKBIRD", "release_year", "2012" ], [ "BLUE LIKE JAZZ", "has_genre", "DRAMA" ], [ "BLUE LIKE JAZZ", "release_year", "2012" ], [ "BOY EATING THE BIRD'S FOOD", "has_genre", "DRAMA" ], [ "BOY EATING THE BIRD'S FOOD", "release_year", "2012" ], [ "BROKEN", "has_genre", "DRAMA" ], [ "BROKEN", "release_year", "2012" ], [ "CAESAR MUST DIE", "has_genre", "DRAMA" ], [ "CAESAR MUST DIE", "release_year", "2012" ], [ "CALL GIRL", "has_genre", "DRAMA" ], [ "CALL GIRL", "release_year", "2012" ], [ "CAMILLE REWINDS", "has_genre", "DRAMA" ], [ "CAMILLE REWINDS", "release_year", "2012" ], [ "CHASING MAVERICKS", "has_genre", "DRAMA" ], [ "CHASING MAVERICKS", "release_year", "2012" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "has_genre", "DRAMA" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "release_year", "2012" ], [ "CLIP", "has_genre", "DRAMA" ], [ "CLIP", "release_year", "2012" ], [ "CLOUD ATLAS", "has_genre", "DRAMA" ], [ "CLOUD ATLAS", "has_tags", "DRAMA" ], [ "CLOUD ATLAS", "release_year", "2012" ], [ "COMPLIANCE", "has_genre", "DRAMA" ], [ "COMPLIANCE", "release_year", "2012" ], [ "CONSUMING SPIRITS", "has_genre", "DRAMA" ], [ "CONSUMING SPIRITS", "release_year", "2012" ], [ "COSMOPOLIS", "has_genre", "DRAMA" ], [ "COSMOPOLIS", "release_year", "2012" ], [ "CRAVE", "has_genre", "DRAMA" ], [ "CRAVE", "release_year", "2012" ], [ "DANGEROUS LIAISONS", "has_genre", "DRAMA" ], [ "DANGEROUS LIAISONS", "release_year", "2012" ], [ "DARLING COMPANION", "has_genre", "DRAMA" ], [ "DARLING COMPANION", "release_year", "2012" ], [ "DEADFALL", "has_genre", "DRAMA" ], [ "DEADFALL", "release_year", "2012" ], [ "DISCONNECT", "has_genre", "DRAMA" ], [ "DISCONNECT", "has_tags", "DRAMA" ], [ "DISCONNECT", "release_year", "2012" ], [ "EDEN", "has_genre", "DRAMA" ], [ "EDEN", "release_year", "2012" ], [ "END OF WATCH", "has_genre", "DRAMA" ], [ "END OF WATCH", "release_year", "2012" ], [ "ENGLISH VINGLISH", "has_genre", "DRAMA" ], [ "ENGLISH VINGLISH", "release_year", "2012" ], [ "EXCISION", "has_genre", "DRAMA" ], [ "EXCISION", "release_year", "2012" ], [ "FAREWELL, MY QUEEN", "has_genre", "DRAMA" ], [ "FAREWELL, MY QUEEN", "release_year", "2012" ], [ "FILL THE VOID", "has_genre", "DRAMA" ], [ "FILL THE VOID", "release_year", "2012" ], [ "FLIGHT", "has_genre", "DRAMA" ], [ "FLIGHT", "release_year", "2012" ], [ "FOR ELLEN", "has_genre", "DRAMA" ], [ "FOR ELLEN", "release_year", "2012" ], [ "FOREIGN LETTERS", "has_genre", "DRAMA" ], [ "FOREIGN LETTERS", "release_year", "2012" ], [ "FORGETTING THE GIRL", "has_genre", "DRAMA" ], [ "FORGETTING THE GIRL", "release_year", "2012" ], [ "FRANCES HA", "has_genre", "DRAMA" ], [ "FRANCES HA", "release_year", "2012" ], [ "GAME CHANGE", "has_genre", "DRAMA" ], [ "GAME CHANGE", "release_year", "2012" ], [ "GIRL IN PROGRESS", "has_genre", "DRAMA" ], [ "GIRL IN PROGRESS", "release_year", "2012" ], [ "HANNAH ARENDT", "has_genre", "DRAMA" ], [ "HANNAH ARENDT", "release_year", "2012" ], [ "HELLO HERMAN", "has_genre", "DRAMA" ], [ "HELLO HERMAN", "release_year", "2012" ], [ "HEROINE", "has_genre", "DRAMA" ], [ "HEROINE", "release_year", "2012" ], [ "HITCHCOCK", "has_genre", "DRAMA" ], [ "HITCHCOCK", "release_year", "2012" ], [ "HOLY MOTORS", "has_genre", "DRAMA" ], [ "HOLY MOTORS", "release_year", "2012" ], [ "HOPE SPRINGS", "has_genre", "DRAMA" ], [ "HOPE SPRINGS", "release_year", "2012" ], [ "HYDE PARK ON HUDSON", "has_genre", "DRAMA" ], [ "HYDE PARK ON HUDSON", "release_year", "2012" ], [ "I BELONG", "has_genre", "DRAMA" ], [ "I BELONG", "release_year", "2012" ], [ "I DO", "has_genre", "DRAMA" ], [ "I DO", "release_year", "2012" ], [ "IMAGINE", "has_genre", "DRAMA" ], [ "IMAGINE", "release_year", "2012" ], [ "IN THE FOG", "has_genre", "DRAMA" ], [ "IN THE FOG", "release_year", "2012" ], [ "INESCAPABLE", "has_genre", "DRAMA" ], [ "INESCAPABLE", "release_year", "2012" ], [ "JAYNE MANSFIELD'S CAR", "has_genre", "DRAMA" ], [ "JAYNE MANSFIELD'S CAR", "release_year", "2012" ], [ "K-11", "has_genre", "DRAMA" ], [ "K-11", "release_year", "2012" ], [ "KAUWBOY", "has_genre", "DRAMA" ], [ "KAUWBOY", "release_year", "2012" ], [ "KEEP THE LIGHTS ON", "has_genre", "DRAMA" ], [ "KEEP THE LIGHTS ON", "release_year", "2012" ], [ "LAURENCE ANYWAYS", "has_genre", "DRAMA" ], [ "LAURENCE ANYWAYS", "release_year", "2012" ], [ "LAWLESS", "has_genre", "DRAMA" ], [ "LAWLESS", "has_tags", "DRAMA" ], [ "LAWLESS", "release_year", "2012" ], [ "LES MISÉRABLES", "has_genre", "DRAMA" ], [ "LES MISÉRABLES", "release_year", "2012" ], [ "LIBERAL ARTS", "has_genre", "DRAMA" ], [ "LIBERAL ARTS", "release_year", "2012" ], [ "LIFE OF PI", "has_genre", "DRAMA" ], [ "LIFE OF PI", "release_year", "2012" ], [ "LIKE SOMEONE IN LOVE", "has_genre", "DRAMA" ], [ "LIKE SOMEONE IN LOVE", "release_year", "2012" ], [ "LINCOLN", "has_genre", "DRAMA" ], [ "LINCOLN", "has_tags", "DRAMA" ], [ "LINCOLN", "release_year", "2012" ], [ "LOL", "has_genre", "DRAMA" ], [ "LOL", "release_year", "2012" ], [ "LUV", "has_genre", "DRAMA" ], [ "LUV", "release_year", "2012" ], [ "MAGIC MIKE", "has_genre", "DRAMA" ], [ "MAGIC MIKE", "release_year", "2012" ], [ "MARFA GIRL", "has_genre", "DRAMA" ], [ "MARFA GIRL", "release_year", "2012" ], [ "ME AND YOU", "has_genre", "DRAMA" ], [ "ME AND YOU", "release_year", "2012" ], [ "MIDDLE OF NOWHERE", "has_genre", "DRAMA" ], [ "MIDDLE OF NOWHERE", "release_year", "2012" ], [ "MUD", "has_genre", "DRAMA" ], [ "MUD", "release_year", "2012" ], [ "MUSEUM HOURS", "has_genre", "DRAMA" ], [ "MUSEUM HOURS", "release_year", "2012" ], [ "MY WAY", "has_genre", "DRAMA" ], [ "MY WAY", "release_year", "2012" ], [ "NAKED HARBOUR", "has_genre", "DRAMA" ], [ "NAKED HARBOUR", "release_year", "2012" ], [ "NEIGHBORING SOUNDS", "has_genre", "DRAMA" ], [ "NEIGHBORING SOUNDS", "release_year", "2012" ], [ "NO", "has_genre", "DRAMA" ], [ "NO", "release_year", "2012" ], [ "NOBODY WALKS", "has_genre", "DRAMA" ], [ "NOBODY WALKS", "release_year", "2012" ], [ "NOT FADE AWAY", "has_genre", "DRAMA" ], [ "NOT FADE AWAY", "release_year", "2012" ], [ "NOW IS GOOD", "has_genre", "DRAMA" ], [ "NOW IS GOOD", "release_year", "2012" ], [ "ON THE ROAD", "has_genre", "DRAMA" ], [ "ON THE ROAD", "release_year", "2012" ], [ "OUR CHILDREN", "has_genre", "DRAMA" ], [ "OUR CHILDREN", "release_year", "2012" ], [ "OUT IN THE DARK", "has_genre", "DRAMA" ], [ "OUT IN THE DARK", "release_year", "2012" ], [ "PEOPLE LIKE US", "has_genre", "DRAMA" ], [ "PEOPLE LIKE US", "has_tags", "DRAMA" ], [ "PEOPLE LIKE US", "release_year", "2012" ], [ "POST TENEBRAS LUX", "has_genre", "DRAMA" ], [ "POST TENEBRAS LUX", "release_year", "2012" ], [ "PROMISED LAND", "has_genre", "DRAMA" ], [ "PROMISED LAND", "release_year", "2012" ], [ "PURGE", "has_genre", "DRAMA" ], [ "PURGE", "release_year", "2012" ], [ "QUARTET", "has_genre", "DRAMA" ], [ "QUARTET", "release_year", "2012" ], [ "REALITY", "has_genre", "DRAMA" ], [ "REALITY", "release_year", "2012" ], [ "RENOIR", "has_genre", "DRAMA" ], [ "RENOIR", "release_year", "2012" ], [ "REVENGE FOR JOLLY!", "has_genre", "DRAMA" ], [ "REVENGE FOR JOLLY!", "release_year", "2012" ], [ "RUBY SPARKS", "has_genre", "DRAMA" ], [ "RUBY SPARKS", "release_year", "2012" ], [ "RUST AND BONE", "has_genre", "DRAMA" ], [ "RUST AND BONE", "release_year", "2012" ], [ "SAFE", "has_genre", "DRAMA" ], [ "SAFE", "release_year", "2012" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "has_genre", "DRAMA" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "release_year", "2012" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "has_genre", "DRAMA" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "release_year", "2012" ], [ "SHADOW DANCER", "has_genre", "DRAMA" ], [ "SHADOW DANCER", "release_year", "2012" ], [ "SILVER LININGS PLAYBOOK", "has_genre", "DRAMA" ], [ "SILVER LININGS PLAYBOOK", "has_tags", "DRAMA" ], [ "SILVER LININGS PLAYBOOK", "release_year", "2012" ], [ "SISTER", "has_genre", "DRAMA" ], [ "SISTER", "release_year", "2012" ], [ "SMASHED", "has_genre", "DRAMA" ], [ "SMASHED", "release_year", "2012" ], [ "SOMEBODY UP THERE LIKES ME", "has_genre", "DRAMA" ], [ "SOMEBODY UP THERE LIKES ME", "release_year", "2012" ], [ "SOMETHING IN THE AIR", "has_genre", "DRAMA" ], [ "SOMETHING IN THE AIR", "release_year", "2012" ], [ "SPRING BREAKERS", "has_genre", "DRAMA" ], [ "SPRING BREAKERS", "release_year", "2012" ], [ "STARLET", "has_genre", "DRAMA" ], [ "STARLET", "release_year", "2012" ], [ "STILL MINE", "has_genre", "DRAMA" ], [ "STILL MINE", "release_year", "2012" ], [ "STOLEN", "has_genre", "DRAMA" ], [ "STOLEN", "release_year", "2012" ], [ "STRUCK BY LIGHTNING", "has_genre", "DRAMA" ], [ "STRUCK BY LIGHTNING", "release_year", "2012" ], [ "STUCK IN LOVE", "has_genre", "DRAMA" ], [ "STUCK IN LOVE", "release_year", "2012" ], [ "TABU", "has_genre", "DRAMA" ], [ "TABU", "release_year", "2012" ], [ "THANKS FOR SHARING", "has_genre", "DRAMA" ], [ "THANKS FOR SHARING", "release_year", "2012" ], [ "THE ARTIST AND THE MODEL", "has_genre", "DRAMA" ], [ "THE ARTIST AND THE MODEL", "release_year", "2012" ], [ "THE BATTERY", "has_genre", "DRAMA" ], [ "THE BATTERY", "release_year", "2012" ], [ "THE BROKEN CIRCLE BREAKDOWN", "has_genre", "DRAMA" ], [ "THE BROKEN CIRCLE BREAKDOWN", "has_tags", "DRAMA" ], [ "THE BROKEN CIRCLE BREAKDOWN", "release_year", "2012" ], [ "THE CITIZEN", "has_genre", "DRAMA" ], [ "THE CITIZEN", "release_year", "2012" ], [ "THE COMEDY", "has_genre", "DRAMA" ], [ "THE COMEDY", "release_year", "2012" ], [ "THE CREMATOR", "has_genre", "DRAMA" ], [ "THE CREMATOR", "release_year", "1969" ], [ "THE CREMATOR", "written_by", "LADISLAV FUKS" ], [ "THE DEEP", "has_genre", "DRAMA" ], [ "THE DEEP", "release_year", "2012" ], [ "THE END OF LOVE", "has_genre", "DRAMA" ], [ "THE END OF LOVE", "release_year", "2012" ], [ "THE FITZGERALD FAMILY CHRISTMAS", "has_genre", "DRAMA" ], [ "THE FITZGERALD FAMILY CHRISTMAS", "release_year", "2012" ], [ "THE FLOATING CASTLE", "has_genre", "DRAMA" ], [ "THE FLOATING CASTLE", "release_year", "2012" ], [ "THE FORGER", "has_genre", "DRAMA" ], [ "THE FORGER", "release_year", "2012" ], [ "THE GIRL", "has_genre", "DRAMA" ], [ "THE GIRL", "release_year", "2012" ], [ "THE GUILT TRIP", "has_genre", "DRAMA" ], [ "THE GUILT TRIP", "release_year", "2012" ], [ "THE HUNT", "has_genre", "DRAMA" ], [ "THE HUNT", "has_tags", "DRAMA" ], [ "THE HUNT", "release_year", "2012" ], [ "THE IMPOSSIBLE", "has_genre", "DRAMA" ], [ "THE IMPOSSIBLE", "has_tags", "DRAMA" ], [ "THE IMPOSSIBLE", "release_year", "2012" ], [ "THE LESSER BLESSED", "has_genre", "DRAMA" ], [ "THE LESSER BLESSED", "release_year", "2012" ], [ "THE LETTER", "has_genre", "DRAMA" ], [ "THE LETTER", "release_year", "2012" ], [ "THE LUCKY ONE", "has_genre", "DRAMA" ], [ "THE LUCKY ONE", "release_year", "2012" ], [ "THE MAGIC OF BELLE ISLE", "has_genre", "DRAMA" ], [ "THE MAGIC OF BELLE ISLE", "release_year", "2012" ], [ "THE MAGNIFICENT AMBERSONS", "has_genre", "DRAMA" ], [ "THE MAGNIFICENT AMBERSONS", "written_by", "BOOTH TARKINGTON" ], [ "THE MAN WHO LAUGHS", "has_genre", "DRAMA" ], [ "THE MAN WHO LAUGHS", "release_year", "2012" ], [ "THE MASTER", "has_genre", "DRAMA" ], [ "THE MASTER", "release_year", "2012" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "has_genre", "DRAMA" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "release_year", "2012" ], [ "THE OTHER SON", "has_genre", "DRAMA" ], [ "THE OTHER SON", "release_year", "2012" ], [ "THE PATIENCE STONE", "has_genre", "DRAMA" ], [ "THE PATIENCE STONE", "release_year", "2012" ], [ "THE PERKS OF BEING A WALLFLOWER", "has_genre", "DRAMA" ], [ "THE PERKS OF BEING A WALLFLOWER", "has_tags", "DRAMA" ], [ "THE PERKS OF BEING A WALLFLOWER", "release_year", "2012" ], [ "THE SAPPHIRES", "has_genre", "DRAMA" ], [ "THE SAPPHIRES", "release_year", "2012" ], [ "THE SCAPEGOAT", "has_genre", "DRAMA" ], [ "THE SCAPEGOAT", "release_year", "2012" ], [ "THE SESSIONS", "has_genre", "DRAMA" ], [ "THE SESSIONS", "has_tags", "DRAMA" ], [ "THE SESSIONS", "release_year", "2012" ], [ "THE STORY OF LUKE", "has_genre", "DRAMA" ], [ "THE STORY OF LUKE", "release_year", "2012" ], [ "THE SWEENEY", "has_genre", "DRAMA" ], [ "THE SWEENEY", "release_year", "2012" ], [ "THE VOW", "has_genre", "DRAMA" ], [ "THE VOW", "release_year", "2012" ], [ "THE WALL", "has_genre", "DRAMA" ], [ "THE WALL", "release_year", "2012" ], [ "THE WE AND THE I", "has_genre", "DRAMA" ], [ "THE WE AND THE I", "release_year", "2012" ], [ "THE WOMAN IN BLACK", "has_genre", "DRAMA" ], [ "THE WOMAN IN BLACK", "release_year", "2012" ], [ "THE WORDS", "has_genre", "DRAMA" ], [ "THE WORDS", "release_year", "2012" ], [ "THREE WORLDS", "has_genre", "DRAMA" ], [ "THREE WORLDS", "release_year", "2012" ], [ "THY WOMB", "has_genre", "DRAMA" ], [ "THY WOMB", "release_year", "2012" ], [ "TO THE WONDER", "has_genre", "DRAMA" ], [ "TO THE WONDER", "release_year", "2012" ], [ "TROUBLE WITH THE CURVE", "has_genre", "DRAMA" ], [ "TROUBLE WITH THE CURVE", "release_year", "2012" ], [ "TWO LIVES", "has_genre", "DRAMA" ], [ "TWO LIVES", "release_year", "2012" ], [ "UNCONDITIONAL", "has_genre", "DRAMA" ], [ "UNCONDITIONAL", "release_year", "2012" ], [ "WAR WITCH", "has_genre", "DRAMA" ], [ "WAR WITCH", "release_year", "2012" ], [ "WHAT IF...", "has_genre", "DRAMA" ], [ "WHAT IF...", "release_year", "2012" ], [ "WHAT MAISIE KNEW", "has_genre", "DRAMA" ], [ "WHAT MAISIE KNEW", "release_year", "2012" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "has_genre", "DRAMA" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "release_year", "2012" ], [ "WHITE ELEPHANT", "has_genre", "DRAMA" ], [ "WHITE ELEPHANT", "release_year", "2012" ], [ "WHITE FROG", "has_genre", "DRAMA" ], [ "WHITE FROG", "release_year", "2012" ], [ "WINNING STREAK", "has_genre", "DRAMA" ], [ "WINNING STREAK", "release_year", "2012" ], [ "WON'T BACK DOWN", "has_genre", "DRAMA" ], [ "WON'T BACK DOWN", "release_year", "2012" ], [ "XINGU", "has_genre", "DRAMA" ], [ "XINGU", "release_year", "2012" ], [ "YOSSI", "has_genre", "DRAMA" ], [ "YOSSI", "release_year", "2012" ], [ "ZERO DARK THIRTY", "has_genre", "DRAMA" ], [ "ZERO DARK THIRTY", "release_year", "2012" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35845, 2006 6378, ANDREA ARNOLD 24510, CANVAS 26721, DAVID KROSS 14586, RED ROAD 39122, TOUGH ENOUGH src, edge_attr, dst 24510, release_year, 35845 14586, directed_by, 6378 14586, has_tags, 6378 14586, release_year, 35845 14586, written_by, 6378 39122, release_year, 35845 39122, starred_actors, 26721 Question: In what context are ANDREA ARNOLD, CANVAS, and DAVID KROSS connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ANDREA ARNOLD", "CANVAS", "DAVID KROSS" ], "valid_edges": [ [ "CANVAS", "release_year", "2006" ], [ "RED ROAD", "directed_by", "ANDREA ARNOLD" ], [ "RED ROAD", "has_tags", "ANDREA ARNOLD" ], [ "RED ROAD", "release_year", "2006" ], [ "RED ROAD", "written_by", "ANDREA ARNOLD" ], [ "TOUGH ENOUGH", "release_year", "2006" ], [ "TOUGH ENOUGH", "starred_actors", "DAVID KROSS" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 36522, 1934 1006, 1996 10251, ARIZONA 24896, CLAUDETTE COLBERT 19510, CLEOPATRA 15436, EPIC 4803, FEAR 31865, FINAL DESTINATION 2 29375, GENERATION X 11565, GOOD 5921, IMITATION OF LIFE 7575, INTIMATE RELATIONS 802, IT'S MY PARTY 25290, JERRY MAGUIRE 5127, MOTHER NIGHT 19341, RACE 21462, RAISING ARIZONA 20393, THE LONG KISS GOODNIGHT 1993, WARREN WILLIAM src, edge_attr, dst 10251, starred_actors, 1993 19510, has_tags, 15436 19510, release_year, 36522 19510, starred_actors, 24896 19510, starred_actors, 1993 15436, has_imdb_rating, 11565 4803, has_imdb_rating, 11565 4803, release_year, 1006 31865, has_imdb_rating, 11565 29375, release_year, 1006 11565, has_imdb_rating, 11565 5921, has_tags, 19341 5921, release_year, 36522 5921, starred_actors, 24896 5921, starred_actors, 1993 7575, has_imdb_rating, 11565 7575, release_year, 1006 802, has_imdb_rating, 11565 802, release_year, 1006 25290, has_imdb_rating, 11565 25290, release_year, 1006 5127, has_imdb_rating, 11565 5127, release_year, 1006 19341, has_imdb_rating, 11565 21462, has_imdb_rating, 11565 21462, has_tags, 10251 20393, has_imdb_rating, 11565 20393, release_year, 1006 Question: For what reason are FINAL DESTINATION 2, GENERATION X, and WARREN WILLIAM associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "FINAL DESTINATION 2", "GENERATION X", "WARREN WILLIAM" ], "valid_edges": [ [ "ARIZONA", "starred_actors", "WARREN WILLIAM" ], [ "CLEOPATRA", "has_tags", "EPIC" ], [ "CLEOPATRA", "release_year", "1934" ], [ "CLEOPATRA", "starred_actors", "CLAUDETTE COLBERT" ], [ "CLEOPATRA", "starred_actors", "WARREN WILLIAM" ], [ "EPIC", "has_imdb_rating", "GOOD" ], [ "FEAR", "has_imdb_rating", "GOOD" ], [ "FEAR", "release_year", "1996" ], [ "FINAL DESTINATION 2", "has_imdb_rating", "GOOD" ], [ "GENERATION X", "release_year", "1996" ], [ "GOOD", "has_imdb_rating", "GOOD" ], [ "IMITATION OF LIFE", "has_tags", "RACE" ], [ "IMITATION OF LIFE", "release_year", "1934" ], [ "IMITATION OF LIFE", "starred_actors", "CLAUDETTE COLBERT" ], [ "IMITATION OF LIFE", "starred_actors", "WARREN WILLIAM" ], [ "INTIMATE RELATIONS", "has_imdb_rating", "GOOD" ], [ "INTIMATE RELATIONS", "release_year", "1996" ], [ "IT'S MY PARTY", "has_imdb_rating", "GOOD" ], [ "IT'S MY PARTY", "release_year", "1996" ], [ "JERRY MAGUIRE", "has_imdb_rating", "GOOD" ], [ "JERRY MAGUIRE", "release_year", "1996" ], [ "MOTHER NIGHT", "has_imdb_rating", "GOOD" ], [ "MOTHER NIGHT", "release_year", "1996" ], [ "RACE", "has_imdb_rating", "GOOD" ], [ "RAISING ARIZONA", "has_imdb_rating", "GOOD" ], [ "RAISING ARIZONA", "has_tags", "ARIZONA" ], [ "THE LONG KISS GOODNIGHT", "has_imdb_rating", "GOOD" ], [ "THE LONG KISS GOODNIGHT", "release_year", "1996" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 724, 1979 39289, ACTION 21803, CAPTAIN AMERICA 23921, CUBA 1544, KEVIN DRONEY 28310, METEOR 39027, MORTAL KOMBAT 6183, REB BROWN 36591, SEAN CONNERY 16103, THE AVENGERS 19052, UNCOMMON VALOR src, edge_attr, dst 21803, has_tags, 21803 21803, release_year, 724 21803, starred_actors, 6183 23921, release_year, 724 23921, starred_actors, 36591 28310, release_year, 724 28310, starred_actors, 36591 39027, has_genre, 39289 39027, written_by, 1544 16103, has_tags, 21803 16103, has_tags, 36591 16103, starred_actors, 36591 19052, has_genre, 39289 19052, starred_actors, 6183 Question: In what context are KEVIN DRONEY, METEOR, and REB BROWN connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "KEVIN DRONEY", "METEOR", "REB BROWN" ], "valid_edges": [ [ "CAPTAIN AMERICA", "has_tags", "CAPTAIN AMERICA" ], [ "CAPTAIN AMERICA", "release_year", "1979" ], [ "CAPTAIN AMERICA", "starred_actors", "REB BROWN" ], [ "CUBA", "release_year", "1979" ], [ "CUBA", "starred_actors", "SEAN CONNERY" ], [ "METEOR", "release_year", "1979" ], [ "METEOR", "starred_actors", "SEAN CONNERY" ], [ "MORTAL KOMBAT", "has_genre", "ACTION" ], [ "MORTAL KOMBAT", "written_by", "KEVIN DRONEY" ], [ "THE AVENGERS", "has_tags", "CAPTAIN AMERICA" ], [ "THE AVENGERS", "has_tags", "SEAN CONNERY" ], [ "THE AVENGERS", "starred_actors", "SEAN CONNERY" ], [ "UNCOMMON VALOR", "has_genre", "ACTION" ], [ "UNCOMMON VALOR", "starred_actors", "REB BROWN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8486, 1999 27123, ANGEL BABY 36212, DRAMA 27446, IN TOO DEEP 33911, INSTINCT 22200, MAURA TIERNEY 9509, MICHAEL RYMER 24816, OXYGEN 25336, RIDE THE PINK HORSE 183, THOMAS GOMEZ src, edge_attr, dst 27123, directed_by, 9509 27123, has_genre, 36212 27123, written_by, 9509 27446, directed_by, 9509 27446, has_genre, 36212 27446, release_year, 8486 33911, release_year, 8486 33911, starred_actors, 22200 24816, release_year, 8486 24816, starred_actors, 22200 25336, has_genre, 36212 25336, starred_actors, 183 Question: For what reason are MAURA TIERNEY, MICHAEL RYMER, and THOMAS GOMEZ associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "MAURA TIERNEY", "MICHAEL RYMER", "THOMAS GOMEZ" ], "valid_edges": [ [ "ANGEL BABY", "directed_by", "MICHAEL RYMER" ], [ "ANGEL BABY", "has_genre", "DRAMA" ], [ "ANGEL BABY", "written_by", "MICHAEL RYMER" ], [ "IN TOO DEEP", "directed_by", "MICHAEL RYMER" ], [ "IN TOO DEEP", "has_genre", "DRAMA" ], [ "IN TOO DEEP", "release_year", "1999" ], [ "INSTINCT", "release_year", "1999" ], [ "INSTINCT", "starred_actors", "MAURA TIERNEY" ], [ "OXYGEN", "release_year", "1999" ], [ "OXYGEN", "starred_actors", "MAURA TIERNEY" ], [ "RIDE THE PINK HORSE", "has_genre", "DRAMA" ], [ "RIDE THE PINK HORSE", "starred_actors", "THOMAS GOMEZ" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 1006, 1996 30219, BLONDE ICE 14724, CRIME 37290, DALTON JAMES 33345, DANIEL TOPOLSKI 18084, JACK BERNHARD 4294, THE SUBSTITUTE 3432, TRUE BLUE src, edge_attr, dst 30219, directed_by, 18084 30219, has_genre, 14724 4294, has_genre, 14724 4294, release_year, 1006 4294, starred_actors, 37290 3432, release_year, 1006 3432, written_by, 33345 Question: In what context are DALTON JAMES, DANIEL TOPOLSKI, and JACK BERNHARD connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "DALTON JAMES", "DANIEL TOPOLSKI", "JACK BERNHARD" ], "valid_edges": [ [ "BLONDE ICE", "directed_by", "JACK BERNHARD" ], [ "BLONDE ICE", "has_genre", "CRIME" ], [ "THE SUBSTITUTE", "has_genre", "CRIME" ], [ "THE SUBSTITUTE", "release_year", "1996" ], [ "THE SUBSTITUTE", "starred_actors", "DALTON JAMES" ], [ "TRUE BLUE", "release_year", "1996" ], [ "TRUE BLUE", "written_by", "DANIEL TOPOLSKI" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6181, ALL THE KING'S MEN 5044, AUTUMN SONATA 10045, BD-R 19826, BLACK DEATH 14530, COLM MEANEY 23112, CON AIR 26931, COOL HAND LUKE 23205, DARLING 36212, DRAMA 3099, EVENING 10210, LAJOS KOLTAI 19809, LATE SPRING 17728, MAD LOVE 31562, MY LIFE AS A DOG 27893, POSSESSED 30919, PRISON 15475, SHOTGUN STORIES 34349, SVENGALI 33978, THE BRIBE 15198, THE HUNCHBACK OF NOTRE DAME 35764, THE PASSIONATE FRIENDS 8727, THE PICTURE OF DORIAN GRAY 164, UNDER CAPRICORN src, edge_attr, dst 6181, has_genre, 36212 6181, has_tags, 10045 5044, has_genre, 36212 5044, has_tags, 10045 19826, has_genre, 36212 19826, has_tags, 10045 23112, has_tags, 30919 23112, starred_actors, 14530 26931, has_genre, 36212 26931, has_tags, 10045 26931, has_tags, 36212 23205, has_genre, 36212 23205, has_tags, 10045 3099, directed_by, 10210 3099, has_genre, 36212 19809, has_genre, 36212 19809, has_tags, 10045 17728, has_genre, 36212 17728, has_tags, 10045 31562, has_genre, 36212 31562, has_tags, 10045 27893, has_genre, 36212 27893, has_tags, 10045 30919, has_genre, 36212 15475, has_genre, 36212 15475, has_tags, 10045 34349, has_genre, 36212 34349, has_tags, 10045 33978, has_genre, 36212 33978, has_tags, 10045 15198, has_genre, 36212 15198, has_tags, 10045 35764, has_genre, 36212 35764, has_tags, 10045 8727, has_genre, 36212 8727, has_tags, 10045 164, has_genre, 36212 164, has_tags, 10045 Question: For what reason are COLM MEANEY, LAJOS KOLTAI, and POSSESSED associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "COLM MEANEY", "LAJOS KOLTAI", "POSSESSED" ], "valid_edges": [ [ "ALL THE KING'S MEN", "has_genre", "DRAMA" ], [ "ALL THE KING'S MEN", "has_tags", "BD-R" ], [ "AUTUMN SONATA", "has_genre", "DRAMA" ], [ "AUTUMN SONATA", "has_tags", "BD-R" ], [ "BLACK DEATH", "has_genre", "DRAMA" ], [ "BLACK DEATH", "has_tags", "BD-R" ], [ "CON AIR", "has_tags", "PRISON" ], [ "CON AIR", "starred_actors", "COLM MEANEY" ], [ "COOL HAND LUKE", "has_genre", "DRAMA" ], [ "COOL HAND LUKE", "has_tags", "BD-R" ], [ "COOL HAND LUKE", "has_tags", "DRAMA" ], [ "DARLING", "has_genre", "DRAMA" ], [ "DARLING", "has_tags", "BD-R" ], [ "EVENING", "directed_by", "LAJOS KOLTAI" ], [ "EVENING", "has_genre", "DRAMA" ], [ "LATE SPRING", "has_genre", "DRAMA" ], [ "LATE SPRING", "has_tags", "BD-R" ], [ "MAD LOVE", "has_genre", "DRAMA" ], [ "MAD LOVE", "has_tags", "BD-R" ], [ "MY LIFE AS A DOG", "has_genre", "DRAMA" ], [ "MY LIFE AS A DOG", "has_tags", "BD-R" ], [ "POSSESSED", "has_genre", "DRAMA" ], [ "POSSESSED", "has_tags", "BD-R" ], [ "PRISON", "has_genre", "DRAMA" ], [ "SHOTGUN STORIES", "has_genre", "DRAMA" ], [ "SHOTGUN STORIES", "has_tags", "BD-R" ], [ "SVENGALI", "has_genre", "DRAMA" ], [ "SVENGALI", "has_tags", "BD-R" ], [ "THE BRIBE", "has_genre", "DRAMA" ], [ "THE BRIBE", "has_tags", "BD-R" ], [ "THE HUNCHBACK OF NOTRE DAME", "has_genre", "DRAMA" ], [ "THE HUNCHBACK OF NOTRE DAME", "has_tags", "BD-R" ], [ "THE PASSIONATE FRIENDS", "has_genre", "DRAMA" ], [ "THE PASSIONATE FRIENDS", "has_tags", "BD-R" ], [ "THE PICTURE OF DORIAN GRAY", "has_genre", "DRAMA" ], [ "THE PICTURE OF DORIAN GRAY", "has_tags", "BD-R" ], [ "UNDER CAPRICORN", "has_genre", "DRAMA" ], [ "UNDER CAPRICORN", "has_tags", "BD-R" ] ] }

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You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 18366, 1960 37484, 2004 12102, BUSH'S BRAIN 1551, CLUB DREAD 40091, CONTROL ROOM 17267, CRAZY SEXY CANCER 8300, CZECH DREAM 19515, DARWIN'S NIGHTMARE 24831, DIG! 12841, DOCUMENTARY 30411, FAHRENHEIT 9/11 24923, FAHRENHYPE 9/11 23477, HAWKING 28027, ISLAND 25583, JOHN PILGER 27565, KRIS CARR 4832, L'AVVENTURA 13797, MONDOVINO 12983, PALESTINE IS STILL THE ISSUE 40022, PAPER CLIPS 18482, PRIMARY 32499, RIDING GIANTS 5274, STEALING A NATION 24109, SUPER SIZE ME 8061, SWISS FAMILY ROBINSON 3734, THE 3RD VOICE 28461, THE ALAMO 4420, THE HUNTING OF THE PRESIDENT 29242, THE NEW RULERS OF THE WORLD 30565, THE NOMI SONG 30297, THE WAR ON DEMOCRACY 4773, THE WHITE DIAMOND 19891, WATERMARKS src, edge_attr, dst 12102, has_genre, 12841 12102, release_year, 37484 1551, has_tags, 28027 1551, release_year, 37484 40091, has_genre, 12841 40091, has_tags, 12841 40091, release_year, 37484 17267, directed_by, 27565 17267, has_genre, 12841 17267, starred_actors, 27565 17267, written_by, 27565 8300, has_genre, 12841 8300, release_year, 37484 19515, has_genre, 12841 19515, release_year, 37484 24831, has_genre, 12841 24831, release_year, 37484 30411, has_genre, 12841 30411, has_tags, 12841 30411, release_year, 37484 24923, has_genre, 12841 24923, release_year, 37484 23477, has_genre, 12841 23477, release_year, 37484 4832, has_tags, 28027 4832, release_year, 18366 13797, has_genre, 12841 13797, release_year, 37484 12983, has_genre, 12841 12983, starred_actors, 25583 12983, written_by, 25583 40022, has_genre, 12841 40022, release_year, 37484 18482, has_genre, 12841 18482, release_year, 18366 32499, has_genre, 12841 32499, release_year, 37484 5274, directed_by, 25583 5274, has_genre, 12841 5274, has_tags, 28027 5274, has_tags, 25583 5274, release_year, 37484 5274, starred_actors, 25583 5274, written_by, 25583 24109, has_genre, 12841 24109, has_tags, 12841 24109, release_year, 37484 8061, has_tags, 28027 8061, release_year, 18366 3734, release_year, 18366 28461, release_year, 18366 28461, release_year, 37484 4420, has_genre, 12841 4420, has_tags, 12841 4420, release_year, 37484 29242, directed_by, 25583 29242, has_genre, 12841 29242, has_tags, 25583 29242, written_by, 25583 30565, has_genre, 12841 30565, release_year, 37484 30297, directed_by, 25583 30297, has_genre, 12841 30297, has_tags, 25583 30297, starred_actors, 25583 30297, written_by, 25583 4773, has_genre, 12841 4773, has_tags, 12841 4773, release_year, 37484 19891, has_genre, 12841 19891, release_year, 37484 Question: For what reason are KRIS CARR, STEALING A NATION, and THE 3RD VOICE associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "KRIS CARR", "STEALING A NATION", "THE 3RD VOICE" ], "valid_edges": [ [ "BUSH'S BRAIN", "has_genre", "DOCUMENTARY" ], [ "BUSH'S BRAIN", "release_year", "2004" ], [ "CLUB DREAD", "has_tags", "ISLAND" ], [ "CLUB DREAD", "release_year", "2004" ], [ "CONTROL ROOM", "has_genre", "DOCUMENTARY" ], [ "CONTROL ROOM", "has_tags", "DOCUMENTARY" ], [ "CONTROL ROOM", "release_year", "2004" ], [ "CRAZY SEXY CANCER", "directed_by", "KRIS CARR" ], [ "CRAZY SEXY CANCER", "has_genre", "DOCUMENTARY" ], [ "CRAZY SEXY CANCER", "starred_actors", "KRIS CARR" ], [ "CRAZY SEXY CANCER", "written_by", "KRIS CARR" ], [ "CZECH DREAM", "has_genre", "DOCUMENTARY" ], [ "CZECH DREAM", "release_year", "2004" ], [ "DARWIN'S NIGHTMARE", "has_genre", "DOCUMENTARY" ], [ "DARWIN'S NIGHTMARE", "release_year", "2004" ], [ "DIG!", "has_genre", "DOCUMENTARY" ], [ "DIG!", "release_year", "2004" ], [ "FAHRENHEIT 9/11", "has_genre", "DOCUMENTARY" ], [ "FAHRENHEIT 9/11", "has_tags", "DOCUMENTARY" ], [ "FAHRENHEIT 9/11", "release_year", "2004" ], [ "FAHRENHYPE 9/11", "has_genre", "DOCUMENTARY" ], [ "FAHRENHYPE 9/11", "release_year", "2004" ], [ "HAWKING", "has_genre", "DOCUMENTARY" ], [ "HAWKING", "release_year", "2004" ], [ "L'AVVENTURA", "has_tags", "ISLAND" ], [ "L'AVVENTURA", "release_year", "1960" ], [ "MONDOVINO", "has_genre", "DOCUMENTARY" ], [ "MONDOVINO", "release_year", "2004" ], [ "PALESTINE IS STILL THE ISSUE", "has_genre", "DOCUMENTARY" ], [ "PALESTINE IS STILL THE ISSUE", "starred_actors", "JOHN PILGER" ], [ "PALESTINE IS STILL THE ISSUE", "written_by", "JOHN PILGER" ], [ "PAPER CLIPS", "has_genre", "DOCUMENTARY" ], [ "PAPER CLIPS", "release_year", "2004" ], [ "PRIMARY", "has_genre", "DOCUMENTARY" ], [ "PRIMARY", "release_year", "1960" ], [ "RIDING GIANTS", "has_genre", "DOCUMENTARY" ], [ "RIDING GIANTS", "release_year", "2004" ], [ "STEALING A NATION", "directed_by", "JOHN PILGER" ], [ "STEALING A NATION", "has_genre", "DOCUMENTARY" ], [ "STEALING A NATION", "has_tags", "ISLAND" ], [ "STEALING A NATION", "has_tags", "JOHN PILGER" ], [ "STEALING A NATION", "release_year", "2004" ], [ "STEALING A NATION", "starred_actors", "JOHN PILGER" ], [ "STEALING A NATION", "written_by", "JOHN PILGER" ], [ "SUPER SIZE ME", "has_genre", "DOCUMENTARY" ], [ "SUPER SIZE ME", "has_tags", "DOCUMENTARY" ], [ "SUPER SIZE ME", "release_year", "2004" ], [ "SWISS FAMILY ROBINSON", "has_tags", "ISLAND" ], [ "SWISS FAMILY ROBINSON", "release_year", "1960" ], [ "THE 3RD VOICE", "release_year", "1960" ], [ "THE ALAMO", "release_year", "1960" ], [ "THE ALAMO", "release_year", "2004" ], [ "THE HUNTING OF THE PRESIDENT", "has_genre", "DOCUMENTARY" ], [ "THE HUNTING OF THE PRESIDENT", "has_tags", "DOCUMENTARY" ], [ "THE HUNTING OF THE PRESIDENT", "release_year", "2004" ], [ "THE NEW RULERS OF THE WORLD", "directed_by", "JOHN PILGER" ], [ "THE NEW RULERS OF THE WORLD", "has_genre", "DOCUMENTARY" ], [ "THE NEW RULERS OF THE WORLD", "has_tags", "JOHN PILGER" ], [ "THE NEW RULERS OF THE WORLD", "written_by", "JOHN PILGER" ], [ "THE NOMI SONG", "has_genre", "DOCUMENTARY" ], [ "THE NOMI SONG", "release_year", "2004" ], [ "THE WAR ON DEMOCRACY", "directed_by", "JOHN PILGER" ], [ "THE WAR ON DEMOCRACY", "has_genre", "DOCUMENTARY" ], [ "THE WAR ON DEMOCRACY", "has_tags", "JOHN PILGER" ], [ "THE WAR ON DEMOCRACY", "starred_actors", "JOHN PILGER" ], [ "THE WAR ON DEMOCRACY", "written_by", "JOHN PILGER" ], [ "THE WHITE DIAMOND", "has_genre", "DOCUMENTARY" ], [ "THE WHITE DIAMOND", "has_tags", "DOCUMENTARY" ], [ "THE WHITE DIAMOND", "release_year", "2004" ], [ "WATERMARKS", "has_genre", "DOCUMENTARY" ], [ "WATERMARKS", "release_year", "2004" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 39435, 1975 8503, BETTIE PAGE REVEALS ALL 17218, BULLE OGIER 12841, DOCUMENTARY 34808, GEORGE HICKENLOOPER 1205, MAYOR OF THE SUNSET STRIP 18415, MAÎTRESSE 5835, REBECCA ROMIJN 36518, ROLLERBALL src, edge_attr, dst 8503, has_genre, 12841 8503, starred_actors, 5835 1205, directed_by, 34808 1205, has_genre, 12841 1205, has_tags, 12841 1205, written_by, 34808 18415, release_year, 39435 18415, starred_actors, 17218 36518, release_year, 39435 36518, starred_actors, 5835 Question: In what context are BULLE OGIER, GEORGE HICKENLOOPER, and REBECCA ROMIJN connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BULLE OGIER", "GEORGE HICKENLOOPER", "REBECCA ROMIJN" ], "valid_edges": [ [ "BETTIE PAGE REVEALS ALL", "has_genre", "DOCUMENTARY" ], [ "BETTIE PAGE REVEALS ALL", "starred_actors", "REBECCA ROMIJN" ], [ "MAYOR OF THE SUNSET STRIP", "directed_by", "GEORGE HICKENLOOPER" ], [ "MAYOR OF THE SUNSET STRIP", "has_genre", "DOCUMENTARY" ], [ "MAYOR OF THE SUNSET STRIP", "has_tags", "DOCUMENTARY" ], [ "MAYOR OF THE SUNSET STRIP", "written_by", "GEORGE HICKENLOOPER" ], [ "MAÎTRESSE", "release_year", "1975" ], [ "MAÎTRESSE", "starred_actors", "BULLE OGIER" ], [ "ROLLERBALL", "release_year", "1975" ], [ "ROLLERBALL", "starred_actors", "REBECCA ROMIJN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 21136, 10 MINUTES 7977, 1969 35935, 2002 10349, CHICAGO 36212, DRAMA 18394, EMPIRE 19943, HOUSE OF FOOLS 30206, KEN PARK 39755, LIVE FROM BAGHDAD 26032, MR. FREEDOM 38585, NICHOLAS NICKLEBY 11696, THE CUCKOO 12614, THE PIANIST 8949, WE WERE SOLDIERS 14051, WILLIAM KLEIN src, edge_attr, dst 21136, has_genre, 36212 21136, release_year, 35935 7977, has_genre, 36212 10349, has_genre, 36212 10349, release_year, 35935 18394, release_year, 35935 19943, has_genre, 36212 19943, release_year, 35935 30206, has_genre, 36212 30206, release_year, 35935 39755, has_genre, 36212 39755, release_year, 35935 26032, directed_by, 14051 26032, has_tags, 14051 26032, release_year, 7977 26032, written_by, 14051 38585, has_genre, 36212 38585, release_year, 35935 11696, has_genre, 36212 11696, release_year, 35935 12614, has_genre, 36212 12614, has_tags, 36212 12614, release_year, 35935 8949, has_genre, 36212 8949, release_year, 35935 Question: How are EMPIRE, NICHOLAS NICKLEBY, and WILLIAM KLEIN related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "EMPIRE", "NICHOLAS NICKLEBY", "WILLIAM KLEIN" ], "valid_edges": [ [ "10 MINUTES", "has_genre", "DRAMA" ], [ "10 MINUTES", "release_year", "2002" ], [ "1969", "has_genre", "DRAMA" ], [ "CHICAGO", "has_genre", "DRAMA" ], [ "CHICAGO", "release_year", "2002" ], [ "EMPIRE", "release_year", "2002" ], [ "HOUSE OF FOOLS", "has_genre", "DRAMA" ], [ "HOUSE OF FOOLS", "release_year", "2002" ], [ "KEN PARK", "has_genre", "DRAMA" ], [ "KEN PARK", "release_year", "2002" ], [ "LIVE FROM BAGHDAD", "has_genre", "DRAMA" ], [ "LIVE FROM BAGHDAD", "release_year", "2002" ], [ "MR. FREEDOM", "directed_by", "WILLIAM KLEIN" ], [ "MR. FREEDOM", "has_tags", "WILLIAM KLEIN" ], [ "MR. FREEDOM", "release_year", "1969" ], [ "MR. FREEDOM", "written_by", "WILLIAM KLEIN" ], [ "NICHOLAS NICKLEBY", "has_genre", "DRAMA" ], [ "NICHOLAS NICKLEBY", "release_year", "2002" ], [ "THE CUCKOO", "has_genre", "DRAMA" ], [ "THE CUCKOO", "release_year", "2002" ], [ "THE PIANIST", "has_genre", "DRAMA" ], [ "THE PIANIST", "has_tags", "DRAMA" ], [ "THE PIANIST", "release_year", "2002" ], [ "WE WERE SOLDIERS", "has_genre", "DRAMA" ], [ "WE WERE SOLDIERS", "release_year", "2002" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35187, 1948 35063, 1976 6718, A FAREWELL TO ARMS 40009, A FOREIGN AFFAIR 33215, A NIGHT TO REMEMBER 24319, A PASSAGE TO INDIA 9152, A STAR IS BORN 10341, ADVENTURES OF DON JUAN 19904, ADVENTURES OF KITTY O'DAY 28463, AFTER THE FOX 36358, ALEC GUINNESS 29638, ALICE IN WONDERLAND 19293, ALL THE PRESIDENT'S MEN 6793, AND THEN THERE WERE NONE 13573, ANNA CHRISTIE 24104, ARSÈNE LUPIN 694, ASSAULT ON PRECINCT 13 30204, BAFFLED! 10045, BD-R 38657, BEAT THE DEVIL 34744, BEING THERE 25490, BERLIN EXPRESS 8139, BICYCLE THIEVES 18599, BILOXI BLUES 23878, BOUND FOR GLORY 12718, BREAKFAST AT TIFFANY'S 16659, CASINO ROYALE 13302, CHAPTER TWO 6903, CHARIOTS OF FIRE 14858, CHINATOWN 16536, CYRANO DE BERGERAC 981, DEATH ON THE NILE 2362, DENNIS PALUMBO 23396, DIAL M FOR MURDER 16041, DJANGO UNCHAINED 18025, DÉDÉE D'ANVERS 39812, EILEEN BRENNAN 31783, ENGLISH 819, FIST OF THE NORTH STAR 9003, FUNNY GAMES 19221, GASLIGHT 5527, GIGI 8046, GOSFORD PARK 4709, GREAT EXPECTATIONS 20941, HAMLET 36495, I REMEMBER MAMA 30468, I'M ALL RIGHT JACK 30184, IN COLD BLOOD 28169, IN THE CUT 847, JACK THE GIANT KILLER 37227, JAMES COCO 21922, JANE EYRE 38579, JOAN OF ARC 4929, KEY LARGO 19098, KING KONG 26649, KISMET 17954, LETTER FROM AN UNKNOWN WOMAN 33160, LITTLE DORRIT 34533, LOGAN'S RUN 32275, LORD OF THE FLIES 20992, MACBETH 27050, MAN OF LA MANCHA 20579, MARAT/SADE 22678, MARLOWE 16925, MAYERLING 20138, MIDWAY 36083, MIRANDA 6543, MODEL SHOP 7186, MR. BLANDINGS BUILDS HIS DREAM HOUSE 35137, MURDER BY DEATH 31661, MY FAIR LADY 10680, MY FAVORITE YEAR 15145, MYSTERY 35498, MYSTERY OF THE 13TH GUEST 16462, MYSTERY OF THE WAX MUSEUM 37159, NEIL SIMON 31566, NETWORK 30629, NIGHT NURSE 37989, OBSESSION 15644, OLIVER TWIST 5794, ONLY WHEN I LAUGH 36164, OUR MAN IN HAVANA 23755, PETER FALK 38529, PETER SELLERS 39459, RED RIVER 5897, REPULSION 8389, ROBERT MOORE 15873, ROBIN AND MARIAN 2738, ROMEO AND JULIET 32487, SAPPHIRE 6119, SLEUTH 31624, SPELLBOUND 8436, SPIRITS OF THE DEAD 39288, SUDDENLY, LAST SUMMER 29067, SUMMER HOLIDAY 24926, SWEET CHARITY 31138, THAT'S ENTERTAINMENT, PART II 18998, THE BAT 30757, THE BOY WITH GREEN HAIR 36857, THE BRIDGE ON THE RIVER KWAI 14222, THE CANTERVILLE GHOST 36282, THE CHEAP DETECTIVE 18651, THE CORSICAN BROTHERS 24990, THE CURSE OF FRANKENSTEIN 6442, THE FALLEN IDOL 22869, THE GHOST SHIP 6597, THE GOLDEN EYE 19595, THE HEARTBREAK KID 31283, THE HOUND OF THE BASKERVILLES 15198, THE HUNCHBACK OF NOTRE DAME 19104, THE IN-LAWS 4091, THE LADY VANISHES 30690, THE LADYKILLERS 5508, THE LAVENDER HILL MOB 29109, THE LODGER 5834, THE MOUSE THAT ROARED 7341, THE NAKED CITY 11902, THE OUTLAW JOSEY WALES 929, THE PARTY 31851, THE PRISONER OF ZENDA 18162, THE RAVEN 36665, THE RED SHOES 18274, THE ROSE TATTOO 8477, THE SCARLET PIMPERNEL 11265, THE SEA HAWK 30746, THE SECRET LIFE OF WALTER MITTY 8304, THE SILVER CHALICE 4784, THE SNAKE PIT 34579, THE SPY WHO CAME IN FROM THE COLD 26081, THE SUNSHINE BOYS 3516, THE SWAN 7816, THE THREE MUSKETEERS 16504, THE TOWN THAT DREADED SUNDOWN 983, THE TREASURE OF THE SIERRA MADRE 19255, THE VALACHI PAPERS 17568, THE VANISHING 14962, THE WATCHER IN THE WOODS 22751, THE WICKER MAN 14983, THE WINSLOW BOY 34254, THEY ONLY KILL THEIR MASTERS 14471, THIS HAPPY BREED 190, TRUMAN CAPOTE 36262, UNFAITHFULLY YOURS 4378, VICTIM 11659, VIVA MARIA! 22844, WENT THE DAY WELL? 8390, WHISTLING IN DIXIE 10142, WHISTLING IN THE DARK 35212, WHO'S AFRAID OF VIRGINIA WOOLF? 39738, WILLIAM BEAUDINE 6037, WITCHFINDER GENERAL src, edge_attr, dst 6718, has_tags, 10045 6718, in_language, 31783 40009, has_tags, 10045 40009, release_year, 35187 33215, has_genre, 15145 33215, has_tags, 10045 24319, has_tags, 10045 24319, in_language, 31783 9152, has_tags, 10045 9152, release_year, 35063 10341, has_tags, 10045 10341, release_year, 35187 19904, directed_by, 39738 19904, has_genre, 15145 28463, has_tags, 10045 28463, has_tags, 37159 28463, in_language, 31783 28463, starred_actors, 38529 28463, written_by, 37159 29638, has_tags, 10045 29638, in_language, 31783 19293, has_tags, 10045 19293, release_year, 35063 6793, has_genre, 15145 6793, has_tags, 10045 6793, has_tags, 15145 13573, has_tags, 10045 13573, in_language, 31783 24104, has_genre, 15145 24104, has_tags, 10045 694, has_tags, 10045 694, release_year, 35063 30204, has_genre, 15145 30204, in_language, 31783 38657, has_tags, 10045 38657, written_by, 190 34744, has_tags, 10045 34744, has_tags, 38529 34744, starred_actors, 38529 25490, has_tags, 10045 25490, release_year, 35187 8139, has_tags, 10045 8139, release_year, 35187 18599, has_tags, 10045 18599, has_tags, 37159 18599, written_by, 37159 23878, has_tags, 10045 23878, release_year, 35063 12718, has_tags, 10045 12718, has_tags, 190 12718, written_by, 190 16659, has_tags, 10045 16659, has_tags, 38529 16659, starred_actors, 38529 13302, directed_by, 8389 13302, has_tags, 10045 13302, written_by, 37159 6903, has_tags, 10045 6903, in_language, 31783 14858, has_genre, 15145 14858, has_tags, 10045 14858, has_tags, 15145 16536, has_tags, 10045 16536, in_language, 31783 981, has_genre, 15145 981, has_tags, 10045 981, has_tags, 15145 23396, has_tags, 10045 23396, in_language, 31783 16041, has_tags, 10045 16041, in_language, 31783 18025, in_language, 31783 18025, release_year, 35187 819, has_tags, 10045 819, in_language, 31783 9003, has_tags, 10045 9003, in_language, 31783 19221, has_genre, 15145 19221, has_tags, 10045 5527, has_tags, 10045 5527, in_language, 31783 8046, has_genre, 15145 8046, in_language, 31783 4709, has_tags, 36358 4709, has_tags, 10045 20941, has_tags, 10045 20941, in_language, 31783 20941, release_year, 35187 36495, has_tags, 10045 36495, release_year, 35187 30468, has_tags, 10045 30468, has_tags, 38529 30468, starred_actors, 38529 30184, has_tags, 10045 30184, has_tags, 190 30184, written_by, 190 28169, has_genre, 15145 28169, in_language, 31783 847, has_tags, 10045 847, in_language, 31783 21922, has_tags, 10045 21922, in_language, 31783 38579, has_tags, 10045 38579, release_year, 35187 4929, has_tags, 10045 4929, release_year, 35187 19098, has_tags, 10045 19098, release_year, 35063 26649, has_tags, 10045 26649, in_language, 31783 17954, has_tags, 10045 17954, release_year, 35187 33160, has_tags, 36358 33160, has_tags, 10045 34533, has_tags, 10045 34533, release_year, 35063 32275, has_tags, 10045 32275, in_language, 31783 20992, in_language, 31783 20992, release_year, 35187 27050, has_tags, 10045 27050, in_language, 31783 27050, starred_actors, 37227 20579, has_tags, 10045 20579, in_language, 31783 22678, has_genre, 15145 22678, has_tags, 10045 16925, has_tags, 10045 16925, in_language, 31783 20138, has_tags, 10045 20138, release_year, 35063 36083, has_tags, 10045 36083, release_year, 35187 6543, has_tags, 10045 6543, in_language, 31783 7186, has_tags, 10045 7186, release_year, 35187 35137, directed_by, 8389 35137, has_genre, 15145 35137, has_tags, 36358 35137, has_tags, 10045 35137, has_tags, 15145 35137, has_tags, 37159 35137, has_tags, 38529 35137, has_tags, 190 35137, release_year, 35063 35137, starred_actors, 39812 35137, starred_actors, 37227 35137, starred_actors, 23755 35137, starred_actors, 190 35137, written_by, 37159 31661, has_tags, 10045 31661, in_language, 31783 10680, has_tags, 10045 10680, written_by, 2362 35498, directed_by, 39738 35498, has_genre, 15145 16462, has_genre, 15145 16462, has_tags, 10045 31566, has_tags, 10045 31566, release_year, 35063 30629, has_genre, 15145 30629, has_tags, 10045 37989, has_genre, 15145 37989, has_tags, 10045 37989, release_year, 35063 15644, has_tags, 36358 15644, has_tags, 10045 15644, release_year, 35187 15644, starred_actors, 36358 5794, has_tags, 10045 5794, has_tags, 37159 5794, starred_actors, 37227 5794, written_by, 37159 36164, has_tags, 36358 36164, has_tags, 10045 36164, starred_actors, 36358 39459, has_tags, 10045 39459, release_year, 35187 5897, has_tags, 10045 5897, in_language, 31783 15873, has_tags, 10045 15873, release_year, 35063 2738, has_tags, 10045 2738, in_language, 31783 32487, has_genre, 15145 32487, in_language, 31783 6119, has_genre, 15145 6119, has_tags, 10045 6119, has_tags, 15145 31624, has_genre, 15145 31624, has_tags, 10045 31624, has_tags, 15145 8436, has_genre, 15145 8436, has_tags, 10045 8436, in_language, 31783 39288, has_genre, 15145 39288, has_tags, 10045 29067, has_tags, 10045 29067, release_year, 35187 24926, has_tags, 10045 24926, written_by, 37159 31138, has_tags, 10045 31138, release_year, 35063 18998, has_genre, 15145 18998, has_tags, 10045 30757, has_tags, 10045 30757, release_year, 35187 36857, has_tags, 36358 36857, has_tags, 10045 36857, starred_actors, 36358 14222, has_tags, 10045 14222, in_language, 31783 36282, directed_by, 8389 36282, has_tags, 10045 36282, has_tags, 37159 36282, starred_actors, 39812 36282, starred_actors, 23755 36282, written_by, 37159 18651, has_tags, 10045 18651, in_language, 31783 24990, has_tags, 10045 24990, in_language, 31783 6442, has_tags, 10045 6442, release_year, 35187 22869, has_genre, 15145 22869, has_tags, 10045 6597, directed_by, 39738 6597, in_language, 31783 6597, release_year, 35187 19595, has_tags, 10045 19595, has_tags, 37159 19595, written_by, 37159 31283, has_genre, 15145 31283, has_tags, 10045 15198, has_tags, 10045 15198, in_language, 31783 19104, has_tags, 10045 19104, starred_actors, 23755 4091, has_genre, 15145 4091, has_tags, 10045 4091, in_language, 31783 30690, has_tags, 36358 30690, has_tags, 10045 30690, has_tags, 38529 30690, starred_actors, 36358 30690, starred_actors, 38529 5508, has_tags, 36358 5508, has_tags, 10045 5508, starred_actors, 36358 29109, has_genre, 15145 29109, has_tags, 10045 5834, has_tags, 10045 5834, has_tags, 38529 5834, starred_actors, 38529 7341, has_tags, 10045 7341, release_year, 35187 11902, has_tags, 10045 11902, release_year, 35063 929, has_tags, 10045 929, has_tags, 38529 929, starred_actors, 38529 31851, has_tags, 10045 31851, starred_actors, 38529 18162, has_genre, 15145 18162, has_tags, 10045 36665, has_tags, 10045 36665, release_year, 35187 18274, has_tags, 10045 18274, in_language, 31783 8477, has_tags, 10045 8477, in_language, 31783 11265, has_tags, 10045 11265, in_language, 31783 30746, has_tags, 10045 30746, in_language, 31783 8304, has_tags, 10045 8304, in_language, 31783 4784, has_tags, 10045 4784, release_year, 35187 34579, has_tags, 10045 34579, in_language, 31783 26081, has_tags, 10045 26081, has_tags, 37159 26081, written_by, 37159 3516, has_tags, 10045 3516, starred_actors, 36358 7816, has_tags, 10045 7816, release_year, 35187 16504, has_tags, 10045 16504, release_year, 35063 983, has_tags, 10045 983, in_language, 31783 983, release_year, 35187 19255, has_tags, 10045 19255, in_language, 31783 17568, has_tags, 10045 17568, in_language, 31783 14962, has_genre, 15145 14962, in_language, 31783 22751, has_genre, 15145 22751, has_tags, 10045 14983, in_language, 31783 14983, release_year, 35187 34254, has_genre, 15145 34254, has_tags, 10045 14471, has_tags, 10045 14471, in_language, 31783 36262, has_tags, 10045 36262, release_year, 35187 4378, has_tags, 10045 4378, in_language, 31783 11659, has_tags, 10045 11659, in_language, 31783 22844, has_tags, 10045 22844, in_language, 31783 8390, has_genre, 15145 8390, has_tags, 10045 10142, has_genre, 15145 10142, has_tags, 10045 35212, has_tags, 10045 35212, in_language, 31783 6037, has_tags, 10045 6037, in_language, 31783 Question: For what reason are DENNIS PALUMBO, MURDER BY DEATH, and THE GOLDEN EYE associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "DENNIS PALUMBO", "MURDER BY DEATH", "THE GOLDEN EYE" ], "valid_edges": [ [ "A FAREWELL TO ARMS", "has_tags", "BD-R" ], [ "A FAREWELL TO ARMS", "in_language", "ENGLISH" ], [ "A FOREIGN AFFAIR", "has_tags", "BD-R" ], [ "A FOREIGN AFFAIR", "release_year", "1948" ], [ "A NIGHT TO REMEMBER", "has_genre", "MYSTERY" ], [ "A NIGHT TO REMEMBER", "has_tags", "BD-R" ], [ "A PASSAGE TO INDIA", "has_tags", "BD-R" ], [ "A PASSAGE TO INDIA", "in_language", "ENGLISH" ], [ "A STAR IS BORN", "has_tags", "BD-R" ], [ "A STAR IS BORN", "release_year", "1976" ], [ "ADVENTURES OF DON JUAN", "has_tags", "BD-R" ], [ "ADVENTURES OF DON JUAN", "release_year", "1948" ], [ "ADVENTURES OF KITTY O'DAY", "directed_by", "WILLIAM BEAUDINE" ], [ "ADVENTURES OF KITTY O'DAY", "has_genre", "MYSTERY" ], [ "AFTER THE FOX", "has_tags", "BD-R" ], [ "AFTER THE FOX", "has_tags", "NEIL SIMON" ], [ "AFTER THE FOX", "in_language", "ENGLISH" ], [ "AFTER THE FOX", "starred_actors", "PETER SELLERS" ], [ "AFTER THE FOX", "written_by", "NEIL SIMON" ], [ "ALICE IN WONDERLAND", "has_tags", "BD-R" ], [ "ALICE IN WONDERLAND", "in_language", "ENGLISH" ], [ "ALL THE PRESIDENT'S MEN", "has_tags", "BD-R" ], [ "ALL THE PRESIDENT'S MEN", "release_year", "1976" ], [ "AND THEN THERE WERE NONE", "has_genre", "MYSTERY" ], [ "AND THEN THERE WERE NONE", "has_tags", "BD-R" ], [ "AND THEN THERE WERE NONE", "has_tags", "MYSTERY" ], [ "ANNA CHRISTIE", "has_tags", "BD-R" ], [ "ANNA CHRISTIE", "in_language", "ENGLISH" ], [ "ARSÈNE LUPIN", "has_genre", "MYSTERY" ], [ "ARSÈNE LUPIN", "has_tags", "BD-R" ], [ "ASSAULT ON PRECINCT 13", "has_tags", "BD-R" ], [ "ASSAULT ON PRECINCT 13", "release_year", "1976" ], [ "BAFFLED!", "has_genre", "MYSTERY" ], [ "BAFFLED!", "in_language", "ENGLISH" ], [ "BEAT THE DEVIL", "has_tags", "BD-R" ], [ "BEAT THE DEVIL", "written_by", "TRUMAN CAPOTE" ], [ "BEING THERE", "has_tags", "BD-R" ], [ "BEING THERE", "has_tags", "PETER SELLERS" ], [ "BEING THERE", "starred_actors", "PETER SELLERS" ], [ "BERLIN EXPRESS", "has_tags", "BD-R" ], [ "BERLIN EXPRESS", "release_year", "1948" ], [ "BICYCLE THIEVES", "has_tags", "BD-R" ], [ "BICYCLE THIEVES", "release_year", "1948" ], [ "BILOXI BLUES", "has_tags", "BD-R" ], [ "BILOXI BLUES", "has_tags", "NEIL SIMON" ], [ "BILOXI BLUES", "written_by", "NEIL SIMON" ], [ "BOUND FOR GLORY", "has_tags", "BD-R" ], [ "BOUND FOR GLORY", "release_year", "1976" ], [ "BREAKFAST AT TIFFANY'S", "has_tags", "BD-R" ], [ "BREAKFAST AT TIFFANY'S", "has_tags", "TRUMAN CAPOTE" ], [ "BREAKFAST AT TIFFANY'S", "written_by", "TRUMAN CAPOTE" ], [ "CASINO ROYALE", "has_tags", "BD-R" ], [ "CASINO ROYALE", "has_tags", "PETER SELLERS" ], [ "CASINO ROYALE", "starred_actors", "PETER SELLERS" ], [ "CHAPTER TWO", "directed_by", "ROBERT MOORE" ], [ "CHAPTER TWO", "has_tags", "BD-R" ], [ "CHAPTER TWO", "written_by", "NEIL SIMON" ], [ "CHARIOTS OF FIRE", "has_tags", "BD-R" ], [ "CHARIOTS OF FIRE", "in_language", "ENGLISH" ], [ "CHINATOWN", "has_genre", "MYSTERY" ], [ "CHINATOWN", "has_tags", "BD-R" ], [ "CHINATOWN", "has_tags", "MYSTERY" ], [ "CYRANO DE BERGERAC", "has_tags", "BD-R" ], [ "CYRANO DE BERGERAC", "in_language", "ENGLISH" ], [ "DEATH ON THE NILE", "has_genre", "MYSTERY" ], [ "DEATH ON THE NILE", "has_tags", "BD-R" ], [ "DEATH ON THE NILE", "has_tags", "MYSTERY" ], [ "DIAL M FOR MURDER", "has_tags", "BD-R" ], [ "DIAL M FOR MURDER", "in_language", "ENGLISH" ], [ "DJANGO UNCHAINED", "has_tags", "BD-R" ], [ "DJANGO UNCHAINED", "in_language", "ENGLISH" ], [ "DÉDÉE D'ANVERS", "in_language", "ENGLISH" ], [ "DÉDÉE D'ANVERS", "release_year", "1948" ], [ "FIST OF THE NORTH STAR", "has_tags", "BD-R" ], [ "FIST OF THE NORTH STAR", "in_language", "ENGLISH" ], [ "FUNNY GAMES", "has_tags", "BD-R" ], [ "FUNNY GAMES", "in_language", "ENGLISH" ], [ "GASLIGHT", "has_genre", "MYSTERY" ], [ "GASLIGHT", "has_tags", "BD-R" ], [ "GIGI", "has_tags", "BD-R" ], [ "GIGI", "in_language", "ENGLISH" ], [ "GOSFORD PARK", "has_genre", "MYSTERY" ], [ "GOSFORD PARK", "in_language", "ENGLISH" ], [ "GREAT EXPECTATIONS", "has_tags", "ALEC GUINNESS" ], [ "GREAT EXPECTATIONS", "has_tags", "BD-R" ], [ "HAMLET", "has_tags", "BD-R" ], [ "HAMLET", "in_language", "ENGLISH" ], [ "HAMLET", "release_year", "1948" ], [ "I REMEMBER MAMA", "has_tags", "BD-R" ], [ "I REMEMBER MAMA", "release_year", "1948" ], [ "I'M ALL RIGHT JACK", "has_tags", "BD-R" ], [ "I'M ALL RIGHT JACK", "has_tags", "PETER SELLERS" ], [ "I'M ALL RIGHT JACK", "starred_actors", "PETER SELLERS" ], [ "IN COLD BLOOD", "has_tags", "BD-R" ], [ "IN COLD BLOOD", "has_tags", "TRUMAN CAPOTE" ], [ "IN COLD BLOOD", "written_by", "TRUMAN CAPOTE" ], [ "IN THE CUT", "has_genre", "MYSTERY" ], [ "IN THE CUT", "in_language", "ENGLISH" ], [ "JACK THE GIANT KILLER", "has_tags", "BD-R" ], [ "JACK THE GIANT KILLER", "in_language", "ENGLISH" ], [ "JANE EYRE", "has_tags", "BD-R" ], [ "JANE EYRE", "in_language", "ENGLISH" ], [ "JOAN OF ARC", "has_tags", "BD-R" ], [ "JOAN OF ARC", "release_year", "1948" ], [ "KEY LARGO", "has_tags", "BD-R" ], [ "KEY LARGO", "release_year", "1948" ], [ "KING KONG", "has_tags", "BD-R" ], [ "KING KONG", "release_year", "1976" ], [ "KISMET", "has_tags", "BD-R" ], [ "KISMET", "in_language", "ENGLISH" ], [ "LETTER FROM AN UNKNOWN WOMAN", "has_tags", "BD-R" ], [ "LETTER FROM AN UNKNOWN WOMAN", "release_year", "1948" ], [ "LITTLE DORRIT", "has_tags", "ALEC GUINNESS" ], [ "LITTLE DORRIT", "has_tags", "BD-R" ], [ "LOGAN'S RUN", "has_tags", "BD-R" ], [ "LOGAN'S RUN", "release_year", "1976" ], [ "LORD OF THE FLIES", "has_tags", "BD-R" ], [ "LORD OF THE FLIES", "in_language", "ENGLISH" ], [ "MACBETH", "in_language", "ENGLISH" ], [ "MACBETH", "release_year", "1948" ], [ "MAN OF LA MANCHA", "has_tags", "BD-R" ], [ "MAN OF LA MANCHA", "in_language", "ENGLISH" ], [ "MAN OF LA MANCHA", "starred_actors", "JAMES COCO" ], [ "MARAT/SADE", "has_tags", "BD-R" ], [ "MARAT/SADE", "in_language", "ENGLISH" ], [ "MARLOWE", "has_genre", "MYSTERY" ], [ "MARLOWE", "has_tags", "BD-R" ], [ "MAYERLING", "has_tags", "BD-R" ], [ "MAYERLING", "in_language", "ENGLISH" ], [ "MIDWAY", "has_tags", "BD-R" ], [ "MIDWAY", "release_year", "1976" ], [ "MIRANDA", "has_tags", "BD-R" ], [ "MIRANDA", "release_year", "1948" ], [ "MODEL SHOP", "has_tags", "BD-R" ], [ "MODEL SHOP", "in_language", "ENGLISH" ], [ "MR. BLANDINGS BUILDS HIS DREAM HOUSE", "has_tags", "BD-R" ], [ "MR. BLANDINGS BUILDS HIS DREAM HOUSE", "release_year", "1948" ], [ "MURDER BY DEATH", "directed_by", "ROBERT MOORE" ], [ "MURDER BY DEATH", "has_genre", "MYSTERY" ], [ "MURDER BY DEATH", "has_tags", "ALEC GUINNESS" ], [ "MURDER BY DEATH", "has_tags", "BD-R" ], [ "MURDER BY DEATH", "has_tags", "MYSTERY" ], [ "MURDER BY DEATH", "has_tags", "NEIL SIMON" ], [ "MURDER BY DEATH", "has_tags", "PETER SELLERS" ], [ "MURDER BY DEATH", "has_tags", "TRUMAN CAPOTE" ], [ "MURDER BY DEATH", "release_year", "1976" ], [ "MURDER BY DEATH", "starred_actors", "EILEEN BRENNAN" ], [ "MURDER BY DEATH", "starred_actors", "JAMES COCO" ], [ "MURDER BY DEATH", "starred_actors", "PETER FALK" ], [ "MURDER BY DEATH", "starred_actors", "TRUMAN CAPOTE" ], [ "MURDER BY DEATH", "written_by", "NEIL SIMON" ], [ "MY FAIR LADY", "has_tags", "BD-R" ], [ "MY FAIR LADY", "in_language", "ENGLISH" ], [ "MY FAVORITE YEAR", "has_tags", "BD-R" ], [ "MY FAVORITE YEAR", "written_by", "DENNIS PALUMBO" ], [ "MYSTERY OF THE 13TH GUEST", "directed_by", "WILLIAM BEAUDINE" ], [ "MYSTERY OF THE 13TH GUEST", "has_genre", "MYSTERY" ], [ "MYSTERY OF THE WAX MUSEUM", "has_genre", "MYSTERY" ], [ "MYSTERY OF THE WAX MUSEUM", "has_tags", "BD-R" ], [ "NETWORK", "has_tags", "BD-R" ], [ "NETWORK", "release_year", "1976" ], [ "NIGHT NURSE", "has_genre", "MYSTERY" ], [ "NIGHT NURSE", "has_tags", "BD-R" ], [ "OBSESSION", "has_genre", "MYSTERY" ], [ "OBSESSION", "has_tags", "BD-R" ], [ "OBSESSION", "release_year", "1976" ], [ "OLIVER TWIST", "has_tags", "ALEC GUINNESS" ], [ "OLIVER TWIST", "has_tags", "BD-R" ], [ "OLIVER TWIST", "release_year", "1948" ], [ "OLIVER TWIST", "starred_actors", "ALEC GUINNESS" ], [ "ONLY WHEN I LAUGH", "has_tags", "BD-R" ], [ "ONLY WHEN I LAUGH", "has_tags", "NEIL SIMON" ], [ "ONLY WHEN I LAUGH", "starred_actors", "JAMES COCO" ], [ "ONLY WHEN I LAUGH", "written_by", "NEIL SIMON" ], [ "OUR MAN IN HAVANA", "has_tags", "ALEC GUINNESS" ], [ "OUR MAN IN HAVANA", "has_tags", "BD-R" ], [ "OUR MAN IN HAVANA", "starred_actors", "ALEC GUINNESS" ], [ "RED RIVER", "has_tags", "BD-R" ], [ "RED RIVER", "release_year", "1948" ], [ "REPULSION", "has_tags", "BD-R" ], [ "REPULSION", "in_language", "ENGLISH" ], [ "ROBIN AND MARIAN", "has_tags", "BD-R" ], [ "ROBIN AND MARIAN", "release_year", "1976" ], [ "ROMEO AND JULIET", "has_tags", "BD-R" ], [ "ROMEO AND JULIET", "in_language", "ENGLISH" ], [ "SAPPHIRE", "has_genre", "MYSTERY" ], [ "SAPPHIRE", "in_language", "ENGLISH" ], [ "SLEUTH", "has_genre", "MYSTERY" ], [ "SLEUTH", "has_tags", "BD-R" ], [ "SLEUTH", "has_tags", "MYSTERY" ], [ "SPELLBOUND", "has_genre", "MYSTERY" ], [ "SPELLBOUND", "has_tags", "BD-R" ], [ "SPELLBOUND", "has_tags", "MYSTERY" ], [ "SPIRITS OF THE DEAD", "has_genre", "MYSTERY" ], [ "SPIRITS OF THE DEAD", "has_tags", "BD-R" ], [ "SPIRITS OF THE DEAD", "in_language", "ENGLISH" ], [ "SUDDENLY, LAST SUMMER", "has_genre", "MYSTERY" ], [ "SUDDENLY, LAST SUMMER", "has_tags", "BD-R" ], [ "SUMMER HOLIDAY", "has_tags", "BD-R" ], [ "SUMMER HOLIDAY", "release_year", "1948" ], [ "SWEET CHARITY", "has_tags", "BD-R" ], [ "SWEET CHARITY", "written_by", "NEIL SIMON" ], [ "THAT'S ENTERTAINMENT, PART II", "has_tags", "BD-R" ], [ "THAT'S ENTERTAINMENT, PART II", "release_year", "1976" ], [ "THE BAT", "has_genre", "MYSTERY" ], [ "THE BAT", "has_tags", "BD-R" ], [ "THE BOY WITH GREEN HAIR", "has_tags", "BD-R" ], [ "THE BOY WITH GREEN HAIR", "release_year", "1948" ], [ "THE BRIDGE ON THE RIVER KWAI", "has_tags", "ALEC GUINNESS" ], [ "THE BRIDGE ON THE RIVER KWAI", "has_tags", "BD-R" ], [ "THE BRIDGE ON THE RIVER KWAI", "starred_actors", "ALEC GUINNESS" ], [ "THE CANTERVILLE GHOST", "has_tags", "BD-R" ], [ "THE CANTERVILLE GHOST", "in_language", "ENGLISH" ], [ "THE CHEAP DETECTIVE", "directed_by", "ROBERT MOORE" ], [ "THE CHEAP DETECTIVE", "has_tags", "BD-R" ], [ "THE CHEAP DETECTIVE", "has_tags", "NEIL SIMON" ], [ "THE CHEAP DETECTIVE", "starred_actors", "EILEEN BRENNAN" ], [ "THE CHEAP DETECTIVE", "starred_actors", "PETER FALK" ], [ "THE CHEAP DETECTIVE", "written_by", "NEIL SIMON" ], [ "THE CORSICAN BROTHERS", "has_tags", "BD-R" ], [ "THE CORSICAN BROTHERS", "in_language", "ENGLISH" ], [ "THE CURSE OF FRANKENSTEIN", "has_tags", "BD-R" ], [ "THE CURSE OF FRANKENSTEIN", "in_language", "ENGLISH" ], [ "THE FALLEN IDOL", "has_tags", "BD-R" ], [ "THE FALLEN IDOL", "release_year", "1948" ], [ "THE GHOST SHIP", "has_genre", "MYSTERY" ], [ "THE GHOST SHIP", "has_tags", "BD-R" ], [ "THE GOLDEN EYE", "directed_by", "WILLIAM BEAUDINE" ], [ "THE GOLDEN EYE", "in_language", "ENGLISH" ], [ "THE GOLDEN EYE", "release_year", "1948" ], [ "THE HEARTBREAK KID", "has_tags", "BD-R" ], [ "THE HEARTBREAK KID", "has_tags", "NEIL SIMON" ], [ "THE HEARTBREAK KID", "written_by", "NEIL SIMON" ], [ "THE HOUND OF THE BASKERVILLES", "has_genre", "MYSTERY" ], [ "THE HOUND OF THE BASKERVILLES", "has_tags", "BD-R" ], [ "THE HUNCHBACK OF NOTRE DAME", "has_tags", "BD-R" ], [ "THE HUNCHBACK OF NOTRE DAME", "in_language", "ENGLISH" ], [ "THE IN-LAWS", "has_tags", "BD-R" ], [ "THE IN-LAWS", "starred_actors", "PETER FALK" ], [ "THE LADY VANISHES", "has_genre", "MYSTERY" ], [ "THE LADY VANISHES", "has_tags", "BD-R" ], [ "THE LADY VANISHES", "in_language", "ENGLISH" ], [ "THE LADYKILLERS", "has_tags", "ALEC GUINNESS" ], [ "THE LADYKILLERS", "has_tags", "BD-R" ], [ "THE LADYKILLERS", "has_tags", "PETER SELLERS" ], [ "THE LADYKILLERS", "starred_actors", "ALEC GUINNESS" ], [ "THE LADYKILLERS", "starred_actors", "PETER SELLERS" ], [ "THE LAVENDER HILL MOB", "has_tags", "ALEC GUINNESS" ], [ "THE LAVENDER HILL MOB", "has_tags", "BD-R" ], [ "THE LAVENDER HILL MOB", "starred_actors", "ALEC GUINNESS" ], [ "THE LODGER", "has_genre", "MYSTERY" ], [ "THE LODGER", "has_tags", "BD-R" ], [ "THE MOUSE THAT ROARED", "has_tags", "BD-R" ], [ "THE MOUSE THAT ROARED", "has_tags", "PETER SELLERS" ], [ "THE MOUSE THAT ROARED", "starred_actors", "PETER SELLERS" ], [ "THE NAKED CITY", "has_tags", "BD-R" ], [ "THE NAKED CITY", "release_year", "1948" ], [ "THE OUTLAW JOSEY WALES", "has_tags", "BD-R" ], [ "THE OUTLAW JOSEY WALES", "release_year", "1976" ], [ "THE PARTY", "has_tags", "BD-R" ], [ "THE PARTY", "has_tags", "PETER SELLERS" ], [ "THE PARTY", "starred_actors", "PETER SELLERS" ], [ "THE PRISONER OF ZENDA", "has_tags", "BD-R" ], [ "THE PRISONER OF ZENDA", "starred_actors", "PETER SELLERS" ], [ "THE RAVEN", "has_genre", "MYSTERY" ], [ "THE RAVEN", "has_tags", "BD-R" ], [ "THE RED SHOES", "has_tags", "BD-R" ], [ "THE RED SHOES", "release_year", "1948" ], [ "THE ROSE TATTOO", "has_tags", "BD-R" ], [ "THE ROSE TATTOO", "in_language", "ENGLISH" ], [ "THE SCARLET PIMPERNEL", "has_tags", "BD-R" ], [ "THE SCARLET PIMPERNEL", "in_language", "ENGLISH" ], [ "THE SEA HAWK", "has_tags", "BD-R" ], [ "THE SEA HAWK", "in_language", "ENGLISH" ], [ "THE SECRET LIFE OF WALTER MITTY", "has_tags", "BD-R" ], [ "THE SECRET LIFE OF WALTER MITTY", "in_language", "ENGLISH" ], [ "THE SILVER CHALICE", "has_tags", "BD-R" ], [ "THE SILVER CHALICE", "in_language", "ENGLISH" ], [ "THE SNAKE PIT", "has_tags", "BD-R" ], [ "THE SNAKE PIT", "release_year", "1948" ], [ "THE SPY WHO CAME IN FROM THE COLD", "has_tags", "BD-R" ], [ "THE SPY WHO CAME IN FROM THE COLD", "in_language", "ENGLISH" ], [ "THE SUNSHINE BOYS", "has_tags", "BD-R" ], [ "THE SUNSHINE BOYS", "has_tags", "NEIL SIMON" ], [ "THE SUNSHINE BOYS", "written_by", "NEIL SIMON" ], [ "THE SWAN", "has_tags", "BD-R" ], [ "THE SWAN", "starred_actors", "ALEC GUINNESS" ], [ "THE THREE MUSKETEERS", "has_tags", "BD-R" ], [ "THE THREE MUSKETEERS", "release_year", "1948" ], [ "THE TOWN THAT DREADED SUNDOWN", "has_tags", "BD-R" ], [ "THE TOWN THAT DREADED SUNDOWN", "release_year", "1976" ], [ "THE TREASURE OF THE SIERRA MADRE", "has_tags", "BD-R" ], [ "THE TREASURE OF THE SIERRA MADRE", "in_language", "ENGLISH" ], [ "THE TREASURE OF THE SIERRA MADRE", "release_year", "1948" ], [ "THE VALACHI PAPERS", "has_tags", "BD-R" ], [ "THE VALACHI PAPERS", "in_language", "ENGLISH" ], [ "THE VANISHING", "has_tags", "BD-R" ], [ "THE VANISHING", "in_language", "ENGLISH" ], [ "THE WATCHER IN THE WOODS", "has_genre", "MYSTERY" ], [ "THE WATCHER IN THE WOODS", "in_language", "ENGLISH" ], [ "THE WICKER MAN", "has_genre", "MYSTERY" ], [ "THE WICKER MAN", "has_tags", "BD-R" ], [ "THE WINSLOW BOY", "in_language", "ENGLISH" ], [ "THE WINSLOW BOY", "release_year", "1948" ], [ "THEY ONLY KILL THEIR MASTERS", "has_genre", "MYSTERY" ], [ "THEY ONLY KILL THEIR MASTERS", "has_tags", "BD-R" ], [ "THIS HAPPY BREED", "has_tags", "BD-R" ], [ "THIS HAPPY BREED", "in_language", "ENGLISH" ], [ "UNFAITHFULLY YOURS", "has_tags", "BD-R" ], [ "UNFAITHFULLY YOURS", "release_year", "1948" ], [ "VICTIM", "has_tags", "BD-R" ], [ "VICTIM", "in_language", "ENGLISH" ], [ "VIVA MARIA!", "has_tags", "BD-R" ], [ "VIVA MARIA!", "in_language", "ENGLISH" ], [ "WENT THE DAY WELL?", "has_tags", "BD-R" ], [ "WENT THE DAY WELL?", "in_language", "ENGLISH" ], [ "WHISTLING IN DIXIE", "has_genre", "MYSTERY" ], [ "WHISTLING IN DIXIE", "has_tags", "BD-R" ], [ "WHISTLING IN THE DARK", "has_genre", "MYSTERY" ], [ "WHISTLING IN THE DARK", "has_tags", "BD-R" ], [ "WHO'S AFRAID OF VIRGINIA WOOLF?", "has_tags", "BD-R" ], [ "WHO'S AFRAID OF VIRGINIA WOOLF?", "in_language", "ENGLISH" ], [ "WITCHFINDER GENERAL", "has_tags", "BD-R" ], [ "WITCHFINDER GENERAL", "in_language", "ENGLISH" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 28964, A PLACE OF ONE'S OWN 15359, ANTOINE BERTRAND 36388, BOURVIL 30463, COMEDY 36212, DRAMA 17468, LOUIS CYR 34047, STARBUCK 15930, THE SUCKER src, edge_attr, dst 28964, has_genre, 36212 17468, has_genre, 36212 17468, starred_actors, 15359 34047, has_genre, 30463 34047, starred_actors, 15359 15930, has_genre, 30463 15930, starred_actors, 36388 Question: In what context are A PLACE OF ONE'S OWN, ANTOINE BERTRAND, and BOURVIL connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "A PLACE OF ONE'S OWN", "ANTOINE BERTRAND", "BOURVIL" ], "valid_edges": [ [ "A PLACE OF ONE'S OWN", "has_genre", "DRAMA" ], [ "LOUIS CYR", "has_genre", "DRAMA" ], [ "LOUIS CYR", "starred_actors", "ANTOINE BERTRAND" ], [ "STARBUCK", "has_genre", "COMEDY" ], [ "STARBUCK", "starred_actors", "ANTOINE BERTRAND" ], [ "THE SUCKER", "has_genre", "COMEDY" ], [ "THE SUCKER", "starred_actors", "BOURVIL" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 37224, 1990 15374, 2005 1856, EUROPA EUROPA 7498, FLIGHTPLAN 36633, FOUR EYED MONSTERS 6480, GERMAN 34073, HELL 38312, MOUTH TO MOUTH 33684, SIBLING RIVALRY 3008, SUMMER IN BERLIN 3354, THE BROTHERS GRIMM 25829, THE CABINET OF DR. CALIGARI 39544, WE FEED THE WORLD src, edge_attr, dst 1856, has_tags, 6480 1856, in_language, 6480 1856, release_year, 37224 7498, in_language, 6480 7498, release_year, 15374 36633, release_year, 15374 34073, in_language, 6480 34073, release_year, 15374 38312, in_language, 6480 38312, release_year, 15374 33684, release_year, 37224 3008, in_language, 6480 3008, release_year, 15374 3354, in_language, 6480 3354, release_year, 15374 25829, in_language, 6480 39544, in_language, 6480 39544, release_year, 15374 Question: For what reason are FOUR EYED MONSTERS, SIBLING RIVALRY, and THE CABINET OF DR. CALIGARI associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "FOUR EYED MONSTERS", "SIBLING RIVALRY", "THE CABINET OF DR. CALIGARI" ], "valid_edges": [ [ "EUROPA EUROPA", "has_tags", "GERMAN" ], [ "EUROPA EUROPA", "in_language", "GERMAN" ], [ "EUROPA EUROPA", "release_year", "1990" ], [ "FLIGHTPLAN", "in_language", "GERMAN" ], [ "FLIGHTPLAN", "release_year", "2005" ], [ "FOUR EYED MONSTERS", "release_year", "2005" ], [ "HELL", "in_language", "GERMAN" ], [ "HELL", "release_year", "2005" ], [ "MOUTH TO MOUTH", "in_language", "GERMAN" ], [ "MOUTH TO MOUTH", "release_year", "2005" ], [ "SIBLING RIVALRY", "release_year", "1990" ], [ "SUMMER IN BERLIN", "in_language", "GERMAN" ], [ "SUMMER IN BERLIN", "release_year", "2005" ], [ "THE BROTHERS GRIMM", "in_language", "GERMAN" ], [ "THE BROTHERS GRIMM", "release_year", "2005" ], [ "THE CABINET OF DR. CALIGARI", "in_language", "GERMAN" ], [ "WE FEED THE WORLD", "in_language", "GERMAN" ], [ "WE FEED THE WORLD", "release_year", "2005" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 1006, 1996 26762, 2008 11565, GOOD 24425, HARRIET THE SPY 7575, INTIMATE RELATIONS 19855, PEYTON REED 1945, YES MAN src, edge_attr, dst 11565, has_imdb_rating, 11565 11565, release_year, 26762 24425, release_year, 1006 7575, has_imdb_rating, 11565 7575, release_year, 1006 1945, directed_by, 19855 1945, has_tags, 19855 1945, release_year, 26762 Question: In what context are HARRIET THE SPY, INTIMATE RELATIONS, and PEYTON REED connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "HARRIET THE SPY", "INTIMATE RELATIONS", "PEYTON REED" ], "valid_edges": [ [ "GOOD", "has_imdb_rating", "GOOD" ], [ "GOOD", "release_year", "2008" ], [ "HARRIET THE SPY", "release_year", "1996" ], [ "INTIMATE RELATIONS", "has_imdb_rating", "GOOD" ], [ "INTIMATE RELATIONS", "release_year", "1996" ], [ "YES MAN", "directed_by", "PEYTON REED" ], [ "YES MAN", "has_tags", "PEYTON REED" ], [ "YES MAN", "release_year", "2008" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 14259, 1997 14724, CRIME 13964, JOHN RIDLEY 15546, THE TUNNEL OF LOVE 19965, THIS WORLD, THEN THE FIREWORKS 21589, U TURN 10133, UNKNOWN src, edge_attr, dst 15546, has_imdb_votes, 10133 19965, release_year, 14259 21589, has_genre, 14724 21589, release_year, 14259 21589, written_by, 13964 10133, has_genre, 14724 Question: For what reason are JOHN RIDLEY, THE TUNNEL OF LOVE, and THIS WORLD, THEN THE FIREWORKS associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "JOHN RIDLEY", "THE TUNNEL OF LOVE", "THIS WORLD, THEN THE FIREWORKS" ], "valid_edges": [ [ "THE TUNNEL OF LOVE", "has_imdb_votes", "UNKNOWN" ], [ "THIS WORLD, THEN THE FIREWORKS", "release_year", "1997" ], [ "U TURN", "has_genre", "CRIME" ], [ "U TURN", "release_year", "1997" ], [ "U TURN", "written_by", "JOHN RIDLEY" ], [ "UNKNOWN", "has_genre", "CRIME" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 29424, 2011 658, 2012 20562, A THOUSAND WORDS 29800, ACE ATTORNEY 21413, AMARCORD 23994, BARFI! 32770, BEST MAN DOWN 39848, BLUE LIKE JAZZ 35916, BREAD AND CHOCOLATE 33711, CAESAR MUST DIE 26848, CELEBRITY 32450, CHEERFUL WEATHER FOR THE WEDDING 38745, CITY LIGHTS 30463, COMEDY 36212, DRAMA 32892, ENGLISH VINGLISH 6076, FRANCES HA 15135, GINGER AND FRED 1937, HANNAH AND HER SISTERS 39349, HONEYSUCKLE ROSE 35319, HOPE SPRINGS 25277, HUSBANDS AND WIVES 37844, HYDE PARK ON HUDSON 38589, I DO 17344, I VITELLONI 16200, ITALIAN 34634, JASON BUTLER HARNER 18699, JULIET OF THE SPIRITS 25104, LA DOLCE VITA 14931, LIBERAL ARTS 24419, LIFE IS BEAUTIFUL 17372, LOL 19802, LOVE 13672, LOVE ACTUALLY 34517, LUV 35871, MAGIC MIKE 33790, MANHATTAN 34611, ME AND YOU 831, MELINDA AND MELINDA 19216, MID-AUGUST LUNCH 31735, MIDDLE OF NOWHERE 32186, ONE MAN UP 34602, PUNCH-DRUNK LOVE 25023, QUARTET 35746, REALITY 11839, REVENGE FOR JOLLY! 12597, RUBY SPARKS 12153, RUSHMORE 35586, SAHARA 17168, SEEKING A FRIEND FOR THE END OF THE WORLD 25060, SEXUAL CHRONICLES OF A FRENCH FAMILY 34584, SILVER LININGS PLAYBOOK 28026, SOMEBODY UP THERE LIKES ME 7288, STARDUST MEMORIES 25002, STRUCK BY LIGHTNING 3038, STUCK IN LOVE 32984, SWEET AND LOWDOWN 26790, SWEPT AWAY 19501, THANKS FOR SHARING 4157, THE BEST MAN 36932, THE CAIMAN 6644, THE COMEDY 15373, THE FRONT 34673, THE GREEN 15980, THE GUILT TRIP 28107, THE LAST KISS 28217, THE LIFE AQUATIC WITH STEVE ZISSOU 15798, THE ODD LIFE OF TIMOTHY GREEN 20728, THE ROYAL TENENBAUMS 2739, THE SAPPHIRES 37915, THE STORY OF LUKE 6598, TO ROME WITH LOVE 4325, WE ALL LOVED EACH OTHER SO MUCH 35511, WE HAVE A POPE 34118, WHAT TO EXPECT WHEN YOU'RE EXPECTING 35681, WILLIE NELSON 24451, WONDER BOYS 21552, WOODY ALLEN 6279, YOU WILL MEET A TALL DARK STRANGER src, edge_attr, dst 20562, has_genre, 30463 20562, release_year, 658 29800, has_genre, 30463 29800, release_year, 658 21413, has_genre, 30463 21413, in_language, 16200 23994, has_genre, 30463 23994, release_year, 658 32770, has_genre, 30463 32770, release_year, 658 39848, has_genre, 30463 39848, release_year, 658 35916, has_genre, 30463 35916, in_language, 16200 33711, in_language, 16200 33711, release_year, 658 26848, directed_by, 21552 26848, has_genre, 30463 26848, has_tags, 21552 26848, written_by, 21552 32450, has_genre, 30463 32450, release_year, 658 38745, has_genre, 30463 38745, has_tags, 30463 38745, has_tags, 19802 32892, has_genre, 30463 32892, release_year, 658 6076, has_genre, 30463 6076, release_year, 658 15135, has_genre, 30463 15135, in_language, 16200 1937, directed_by, 21552 1937, has_genre, 30463 1937, has_tags, 30463 1937, has_tags, 21552 1937, written_by, 21552 39349, has_genre, 36212 39349, starred_actors, 35681 35319, has_genre, 30463 35319, release_year, 658 25277, directed_by, 21552 25277, has_genre, 30463 25277, has_tags, 21552 25277, starred_actors, 21552 25277, written_by, 21552 37844, has_genre, 30463 37844, release_year, 658 38589, has_genre, 30463 38589, release_year, 658 17344, has_genre, 30463 17344, has_tags, 16200 17344, in_language, 16200 18699, has_genre, 30463 18699, has_tags, 16200 18699, in_language, 16200 25104, has_genre, 30463 25104, has_tags, 16200 25104, in_language, 16200 14931, has_genre, 30463 14931, release_year, 658 24419, has_genre, 30463 24419, has_tags, 30463 24419, has_tags, 16200 24419, in_language, 16200 17372, has_genre, 30463 17372, release_year, 658 19802, has_genre, 36212 19802, release_year, 29424 13672, has_genre, 30463 13672, has_tags, 30463 13672, has_tags, 19802 34517, has_genre, 30463 34517, release_year, 658 35871, has_genre, 30463 35871, release_year, 658 33790, directed_by, 21552 33790, has_genre, 30463 33790, has_tags, 30463 33790, has_tags, 19802 33790, has_tags, 21552 33790, starred_actors, 21552 33790, written_by, 21552 34611, in_language, 16200 34611, release_year, 658 831, directed_by, 21552 831, has_genre, 30463 831, has_tags, 21552 831, written_by, 21552 19216, has_genre, 30463 19216, in_language, 16200 31735, has_genre, 30463 31735, release_year, 658 32186, has_genre, 30463 32186, in_language, 16200 34602, has_genre, 30463 34602, has_tags, 30463 34602, has_tags, 19802 25023, has_genre, 30463 25023, has_tags, 30463 25023, release_year, 658 35746, in_language, 16200 35746, release_year, 658 11839, has_genre, 30463 11839, release_year, 658 12597, has_genre, 30463 12597, release_year, 658 12153, has_genre, 30463 12153, has_tags, 30463 12153, has_tags, 19802 35586, has_genre, 30463 35586, in_language, 16200 17168, has_genre, 30463 17168, release_year, 658 25060, has_genre, 30463 25060, release_year, 658 34584, has_genre, 30463 34584, release_year, 658 28026, has_genre, 30463 28026, release_year, 658 7288, directed_by, 21552 7288, has_genre, 30463 7288, has_tags, 21552 7288, starred_actors, 21552 7288, written_by, 21552 25002, has_genre, 30463 25002, release_year, 658 3038, has_genre, 30463 3038, has_tags, 19802 3038, release_year, 658 32984, directed_by, 21552 32984, has_genre, 30463 32984, has_tags, 21552 32984, starred_actors, 21552 32984, written_by, 21552 26790, has_genre, 30463 26790, in_language, 16200 19501, has_genre, 30463 19501, release_year, 658 4157, has_genre, 30463 4157, in_language, 16200 36932, has_genre, 30463 36932, in_language, 16200 6644, has_tags, 30463 6644, release_year, 658 15373, has_genre, 30463 15373, has_tags, 21552 15373, starred_actors, 21552 34673, has_genre, 36212 34673, release_year, 29424 34673, starred_actors, 34634 15980, has_genre, 30463 15980, release_year, 658 28107, has_genre, 30463 28107, in_language, 16200 28217, has_genre, 30463 28217, has_tags, 30463 28217, in_language, 16200 15798, has_genre, 30463 15798, release_year, 658 20728, has_genre, 30463 20728, has_tags, 30463 20728, has_tags, 19802 2739, has_genre, 30463 2739, release_year, 658 37915, has_genre, 30463 37915, release_year, 658 6598, directed_by, 21552 6598, has_genre, 30463 6598, has_tags, 30463 6598, has_tags, 19802 6598, has_tags, 21552 6598, in_language, 16200 6598, release_year, 658 6598, written_by, 21552 4325, has_genre, 30463 4325, in_language, 16200 35511, has_genre, 30463 35511, has_tags, 30463 35511, has_tags, 16200 35511, in_language, 16200 34118, has_genre, 30463 34118, release_year, 658 24451, has_genre, 30463 24451, has_tags, 19802 6279, directed_by, 21552 6279, has_genre, 30463 6279, has_tags, 30463 6279, has_tags, 21552 6279, written_by, 21552 Question: How are JASON BUTLER HARNER, TO ROME WITH LOVE, and WILLIE NELSON related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "JASON BUTLER HARNER", "TO ROME WITH LOVE", "WILLIE NELSON" ], "valid_edges": [ [ "A THOUSAND WORDS", "has_genre", "COMEDY" ], [ "A THOUSAND WORDS", "release_year", "2012" ], [ "ACE ATTORNEY", "has_genre", "COMEDY" ], [ "ACE ATTORNEY", "release_year", "2012" ], [ "AMARCORD", "has_genre", "COMEDY" ], [ "AMARCORD", "in_language", "ITALIAN" ], [ "BARFI!", "has_genre", "COMEDY" ], [ "BARFI!", "release_year", "2012" ], [ "BEST MAN DOWN", "has_genre", "COMEDY" ], [ "BEST MAN DOWN", "release_year", "2012" ], [ "BLUE LIKE JAZZ", "has_genre", "COMEDY" ], [ "BLUE LIKE JAZZ", "release_year", "2012" ], [ "BREAD AND CHOCOLATE", "has_genre", "COMEDY" ], [ "BREAD AND CHOCOLATE", "in_language", "ITALIAN" ], [ "CAESAR MUST DIE", "in_language", "ITALIAN" ], [ "CAESAR MUST DIE", "release_year", "2012" ], [ "CELEBRITY", "directed_by", "WOODY ALLEN" ], [ "CELEBRITY", "has_genre", "COMEDY" ], [ "CELEBRITY", "has_tags", "WOODY ALLEN" ], [ "CELEBRITY", "written_by", "WOODY ALLEN" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "has_genre", "COMEDY" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "release_year", "2012" ], [ "CITY LIGHTS", "has_genre", "COMEDY" ], [ "CITY LIGHTS", "has_tags", "COMEDY" ], [ "CITY LIGHTS", "has_tags", "LOVE" ], [ "ENGLISH VINGLISH", "has_genre", "COMEDY" ], [ "ENGLISH VINGLISH", "release_year", "2012" ], [ "FRANCES HA", "has_genre", "COMEDY" ], [ "FRANCES HA", "release_year", "2012" ], [ "GINGER AND FRED", "has_genre", "COMEDY" ], [ "GINGER AND FRED", "in_language", "ITALIAN" ], [ "HANNAH AND HER SISTERS", "directed_by", "WOODY ALLEN" ], [ "HANNAH AND HER SISTERS", "has_genre", "COMEDY" ], [ "HANNAH AND HER SISTERS", "has_tags", "COMEDY" ], [ "HANNAH AND HER SISTERS", "has_tags", "WOODY ALLEN" ], [ "HANNAH AND HER SISTERS", "written_by", "WOODY ALLEN" ], [ "HONEYSUCKLE ROSE", "has_genre", "DRAMA" ], [ "HONEYSUCKLE ROSE", "starred_actors", "WILLIE NELSON" ], [ "HOPE SPRINGS", "has_genre", "COMEDY" ], [ "HOPE SPRINGS", "release_year", "2012" ], [ "HUSBANDS AND WIVES", "directed_by", "WOODY ALLEN" ], [ "HUSBANDS AND WIVES", "has_genre", "COMEDY" ], [ "HUSBANDS AND WIVES", "has_tags", "WOODY ALLEN" ], [ "HUSBANDS AND WIVES", "starred_actors", "WOODY ALLEN" ], [ "HUSBANDS AND WIVES", "written_by", "WOODY ALLEN" ], [ "HYDE PARK ON HUDSON", "has_genre", "COMEDY" ], [ "HYDE PARK ON HUDSON", "release_year", "2012" ], [ "I DO", "has_genre", "COMEDY" ], [ "I DO", "release_year", "2012" ], [ "I VITELLONI", "has_genre", "COMEDY" ], [ "I VITELLONI", "has_tags", "ITALIAN" ], [ "I VITELLONI", "in_language", "ITALIAN" ], [ "JULIET OF THE SPIRITS", "has_genre", "COMEDY" ], [ "JULIET OF THE SPIRITS", "has_tags", "ITALIAN" ], [ "JULIET OF THE SPIRITS", "in_language", "ITALIAN" ], [ "LA DOLCE VITA", "has_genre", "COMEDY" ], [ "LA DOLCE VITA", "has_tags", "ITALIAN" ], [ "LA DOLCE VITA", "in_language", "ITALIAN" ], [ "LIBERAL ARTS", "has_genre", "COMEDY" ], [ "LIBERAL ARTS", "release_year", "2012" ], [ "LIFE IS BEAUTIFUL", "has_genre", "COMEDY" ], [ "LIFE IS BEAUTIFUL", "has_tags", "COMEDY" ], [ "LIFE IS BEAUTIFUL", "has_tags", "ITALIAN" ], [ "LIFE IS BEAUTIFUL", "in_language", "ITALIAN" ], [ "LOL", "has_genre", "COMEDY" ], [ "LOL", "release_year", "2012" ], [ "LOVE", "has_genre", "DRAMA" ], [ "LOVE", "release_year", "2011" ], [ "LOVE ACTUALLY", "has_genre", "COMEDY" ], [ "LOVE ACTUALLY", "has_tags", "COMEDY" ], [ "LOVE ACTUALLY", "has_tags", "LOVE" ], [ "LUV", "has_genre", "COMEDY" ], [ "LUV", "release_year", "2012" ], [ "MAGIC MIKE", "has_genre", "COMEDY" ], [ "MAGIC MIKE", "release_year", "2012" ], [ "MANHATTAN", "directed_by", "WOODY ALLEN" ], [ "MANHATTAN", "has_genre", "COMEDY" ], [ "MANHATTAN", "has_tags", "COMEDY" ], [ "MANHATTAN", "has_tags", "LOVE" ], [ "MANHATTAN", "has_tags", "WOODY ALLEN" ], [ "MANHATTAN", "starred_actors", "WOODY ALLEN" ], [ "MANHATTAN", "written_by", "WOODY ALLEN" ], [ "ME AND YOU", "in_language", "ITALIAN" ], [ "ME AND YOU", "release_year", "2012" ], [ "MELINDA AND MELINDA", "directed_by", "WOODY ALLEN" ], [ "MELINDA AND MELINDA", "has_genre", "COMEDY" ], [ "MELINDA AND MELINDA", "has_tags", "WOODY ALLEN" ], [ "MELINDA AND MELINDA", "written_by", "WOODY ALLEN" ], [ "MID-AUGUST LUNCH", "has_genre", "COMEDY" ], [ "MID-AUGUST LUNCH", "in_language", "ITALIAN" ], [ "MIDDLE OF NOWHERE", "has_genre", "COMEDY" ], [ "MIDDLE OF NOWHERE", "release_year", "2012" ], [ "ONE MAN UP", "has_genre", "COMEDY" ], [ "ONE MAN UP", "in_language", "ITALIAN" ], [ "PUNCH-DRUNK LOVE", "has_genre", "COMEDY" ], [ "PUNCH-DRUNK LOVE", "has_tags", "COMEDY" ], [ "PUNCH-DRUNK LOVE", "has_tags", "LOVE" ], [ "QUARTET", "has_genre", "COMEDY" ], [ "QUARTET", "has_tags", "COMEDY" ], [ "QUARTET", "release_year", "2012" ], [ "REALITY", "in_language", "ITALIAN" ], [ "REALITY", "release_year", "2012" ], [ "REVENGE FOR JOLLY!", "has_genre", "COMEDY" ], [ "REVENGE FOR JOLLY!", "release_year", "2012" ], [ "RUBY SPARKS", "has_genre", "COMEDY" ], [ "RUBY SPARKS", "release_year", "2012" ], [ "RUSHMORE", "has_genre", "COMEDY" ], [ "RUSHMORE", "has_tags", "COMEDY" ], [ "RUSHMORE", "has_tags", "LOVE" ], [ "SAHARA", "has_genre", "COMEDY" ], [ "SAHARA", "in_language", "ITALIAN" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "has_genre", "COMEDY" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "release_year", "2012" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "has_genre", "COMEDY" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "release_year", "2012" ], [ "SILVER LININGS PLAYBOOK", "has_genre", "COMEDY" ], [ "SILVER LININGS PLAYBOOK", "release_year", "2012" ], [ "SOMEBODY UP THERE LIKES ME", "has_genre", "COMEDY" ], [ "SOMEBODY UP THERE LIKES ME", "release_year", "2012" ], [ "STARDUST MEMORIES", "directed_by", "WOODY ALLEN" ], [ "STARDUST MEMORIES", "has_genre", "COMEDY" ], [ "STARDUST MEMORIES", "has_tags", "WOODY ALLEN" ], [ "STARDUST MEMORIES", "starred_actors", "WOODY ALLEN" ], [ "STARDUST MEMORIES", "written_by", "WOODY ALLEN" ], [ "STRUCK BY LIGHTNING", "has_genre", "COMEDY" ], [ "STRUCK BY LIGHTNING", "release_year", "2012" ], [ "STUCK IN LOVE", "has_genre", "COMEDY" ], [ "STUCK IN LOVE", "has_tags", "LOVE" ], [ "STUCK IN LOVE", "release_year", "2012" ], [ "SWEET AND LOWDOWN", "directed_by", "WOODY ALLEN" ], [ "SWEET AND LOWDOWN", "has_genre", "COMEDY" ], [ "SWEET AND LOWDOWN", "has_tags", "WOODY ALLEN" ], [ "SWEET AND LOWDOWN", "starred_actors", "WOODY ALLEN" ], [ "SWEET AND LOWDOWN", "written_by", "WOODY ALLEN" ], [ "SWEPT AWAY", "has_genre", "COMEDY" ], [ "SWEPT AWAY", "in_language", "ITALIAN" ], [ "THANKS FOR SHARING", "has_genre", "COMEDY" ], [ "THANKS FOR SHARING", "release_year", "2012" ], [ "THE BEST MAN", "has_genre", "COMEDY" ], [ "THE BEST MAN", "in_language", "ITALIAN" ], [ "THE CAIMAN", "has_genre", "COMEDY" ], [ "THE CAIMAN", "in_language", "ITALIAN" ], [ "THE COMEDY", "has_tags", "COMEDY" ], [ "THE COMEDY", "release_year", "2012" ], [ "THE FRONT", "has_genre", "COMEDY" ], [ "THE FRONT", "has_tags", "WOODY ALLEN" ], [ "THE FRONT", "starred_actors", "WOODY ALLEN" ], [ "THE GREEN", "has_genre", "DRAMA" ], [ "THE GREEN", "release_year", "2011" ], [ "THE GREEN", "starred_actors", "JASON BUTLER HARNER" ], [ "THE GUILT TRIP", "has_genre", "COMEDY" ], [ "THE GUILT TRIP", "release_year", "2012" ], [ "THE LAST KISS", "has_genre", "COMEDY" ], [ "THE LAST KISS", "in_language", "ITALIAN" ], [ "THE LIFE AQUATIC WITH STEVE ZISSOU", "has_genre", "COMEDY" ], [ "THE LIFE AQUATIC WITH STEVE ZISSOU", "has_tags", "COMEDY" ], [ "THE LIFE AQUATIC WITH STEVE ZISSOU", "in_language", "ITALIAN" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "has_genre", "COMEDY" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "release_year", "2012" ], [ "THE ROYAL TENENBAUMS", "has_genre", "COMEDY" ], [ "THE ROYAL TENENBAUMS", "has_tags", "COMEDY" ], [ "THE ROYAL TENENBAUMS", "has_tags", "LOVE" ], [ "THE SAPPHIRES", "has_genre", "COMEDY" ], [ "THE SAPPHIRES", "release_year", "2012" ], [ "THE STORY OF LUKE", "has_genre", "COMEDY" ], [ "THE STORY OF LUKE", "release_year", "2012" ], [ "TO ROME WITH LOVE", "directed_by", "WOODY ALLEN" ], [ "TO ROME WITH LOVE", "has_genre", "COMEDY" ], [ "TO ROME WITH LOVE", "has_tags", "COMEDY" ], [ "TO ROME WITH LOVE", "has_tags", "LOVE" ], [ "TO ROME WITH LOVE", "has_tags", "WOODY ALLEN" ], [ "TO ROME WITH LOVE", "in_language", "ITALIAN" ], [ "TO ROME WITH LOVE", "release_year", "2012" ], [ "TO ROME WITH LOVE", "written_by", "WOODY ALLEN" ], [ "WE ALL LOVED EACH OTHER SO MUCH", "has_genre", "COMEDY" ], [ "WE ALL LOVED EACH OTHER SO MUCH", "in_language", "ITALIAN" ], [ "WE HAVE A POPE", "has_genre", "COMEDY" ], [ "WE HAVE A POPE", "has_tags", "COMEDY" ], [ "WE HAVE A POPE", "has_tags", "ITALIAN" ], [ "WE HAVE A POPE", "in_language", "ITALIAN" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "has_genre", "COMEDY" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "release_year", "2012" ], [ "WONDER BOYS", "has_genre", "COMEDY" ], [ "WONDER BOYS", "has_tags", "LOVE" ], [ "YOU WILL MEET A TALL DARK STRANGER", "directed_by", "WOODY ALLEN" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_genre", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_tags", "COMEDY" ], [ "YOU WILL MEET A TALL DARK STRANGER", "has_tags", "WOODY ALLEN" ], [ "YOU WILL MEET A TALL DARK STRANGER", "written_by", "WOODY ALLEN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 6925, 1966 13408, 2001 658, 2012 166, A MAN AND A WOMAN 20786, CLAUDE LELOUCH 24124, GAMBIT 32305, GEORGE W. GEORGE 31828, JAMES DEAN 14601, LES MISÉRABLES 5365, THE BODY 13676, THE JAMES DEAN STORY src, edge_attr, dst 166, directed_by, 20786 166, has_tags, 20786 166, release_year, 6925 24124, release_year, 6925 24124, release_year, 658 31828, release_year, 13408 14601, directed_by, 20786 14601, has_tags, 20786 14601, release_year, 658 14601, written_by, 20786 5365, release_year, 13408 5365, release_year, 658 13676, directed_by, 32305 13676, starred_actors, 31828 Question: For what reason are A MAN AND A WOMAN, GEORGE W. GEORGE, and THE BODY associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "A MAN AND A WOMAN", "GEORGE W. GEORGE", "THE BODY" ], "valid_edges": [ [ "A MAN AND A WOMAN", "directed_by", "CLAUDE LELOUCH" ], [ "A MAN AND A WOMAN", "has_tags", "CLAUDE LELOUCH" ], [ "A MAN AND A WOMAN", "release_year", "1966" ], [ "GAMBIT", "release_year", "1966" ], [ "GAMBIT", "release_year", "2012" ], [ "JAMES DEAN", "release_year", "2001" ], [ "LES MISÉRABLES", "directed_by", "CLAUDE LELOUCH" ], [ "LES MISÉRABLES", "has_tags", "CLAUDE LELOUCH" ], [ "LES MISÉRABLES", "release_year", "2012" ], [ "LES MISÉRABLES", "written_by", "CLAUDE LELOUCH" ], [ "THE BODY", "release_year", "2001" ], [ "THE BODY", "release_year", "2012" ], [ "THE JAMES DEAN STORY", "directed_by", "GEORGE W. GEORGE" ], [ "THE JAMES DEAN STORY", "starred_actors", "JAMES DEAN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 39396, BLACK DYNAMITE 30463, COMEDY 1884, FUN 358, HOWARD THE DUCK 31646, KNIGHT AND DAY 17559, LEGALLY BLONDE 36771, MYRA BRECKINRIDGE 22572, RAFAL ZIELINSKI 6255, THE MAGNET 29641, THE PRINCESS BRIDE 27619, THE WRONG TROUSERS 13738, THERE'S SOMETHING ABOUT MARY 22157, WHEN HARRY MET SALLY... src, edge_attr, dst 39396, has_genre, 30463 39396, has_tags, 1884 1884, directed_by, 22572 358, has_genre, 30463 358, has_tags, 1884 31646, has_genre, 30463 31646, has_tags, 30463 31646, has_tags, 1884 17559, has_genre, 30463 17559, has_tags, 30463 17559, has_tags, 1884 36771, has_genre, 30463 6255, has_genre, 30463 29641, has_genre, 30463 29641, has_tags, 30463 29641, has_tags, 1884 27619, has_genre, 30463 27619, has_tags, 30463 27619, has_tags, 1884 13738, has_genre, 30463 13738, has_tags, 30463 13738, has_tags, 1884 22157, has_genre, 30463 22157, has_tags, 1884 Question: In what context are MYRA BRECKINRIDGE, RAFAL ZIELINSKI, and THE MAGNET connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "MYRA BRECKINRIDGE", "RAFAL ZIELINSKI", "THE MAGNET" ], "valid_edges": [ [ "BLACK DYNAMITE", "has_genre", "COMEDY" ], [ "BLACK DYNAMITE", "has_tags", "FUN" ], [ "FUN", "directed_by", "RAFAL ZIELINSKI" ], [ "HOWARD THE DUCK", "has_genre", "COMEDY" ], [ "HOWARD THE DUCK", "has_tags", "FUN" ], [ "KNIGHT AND DAY", "has_genre", "COMEDY" ], [ "KNIGHT AND DAY", "has_tags", "COMEDY" ], [ "KNIGHT AND DAY", "has_tags", "FUN" ], [ "LEGALLY BLONDE", "has_genre", "COMEDY" ], [ "LEGALLY BLONDE", "has_tags", "COMEDY" ], [ "LEGALLY BLONDE", "has_tags", "FUN" ], [ "MYRA BRECKINRIDGE", "has_genre", "COMEDY" ], [ "THE MAGNET", "has_genre", "COMEDY" ], [ "THE PRINCESS BRIDE", "has_genre", "COMEDY" ], [ "THE PRINCESS BRIDE", "has_tags", "COMEDY" ], [ "THE PRINCESS BRIDE", "has_tags", "FUN" ], [ "THE WRONG TROUSERS", "has_genre", "COMEDY" ], [ "THE WRONG TROUSERS", "has_tags", "COMEDY" ], [ "THE WRONG TROUSERS", "has_tags", "FUN" ], [ "THERE'S SOMETHING ABOUT MARY", "has_genre", "COMEDY" ], [ "THERE'S SOMETHING ABOUT MARY", "has_tags", "COMEDY" ], [ "THERE'S SOMETHING ABOUT MARY", "has_tags", "FUN" ], [ "WHEN HARRY MET SALLY...", "has_genre", "COMEDY" ], [ "WHEN HARRY MET SALLY...", "has_tags", "FUN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 18366, 1960 8486, 1999 30284, ALL ABOUT MY MOTHER 2067, BETWEEN YOUR LEGS 27477, BIUTIFUL 12217, DON'T TEMPT ME 35657, GLORIA 10457, GOYA IN BORDEAUX 10242, INHERIT THE WIND 36, JAVIER BARDEM 233, LIVE FLESH 33721, LOVE CAN SERIOUSLY DAMAGE YOUR HEALTH 30409, MANUEL GÓMEZ PEREIRA 31871, MARIANO COHN 37995, MONDAYS IN THE SUN 37006, NO ONE WRITES TO THE COLONEL 23816, NOBODY KNOWS ANYBODY 11432, SECOND SKIN 21147, SOLAS 7556, SPANISH 32597, THE DANCER UPSTAIRS 475, THE MAN NEXT DOOR 29990, THE NAMELESS 37903, THE NINTH GATE 5969, THE SEA INSIDE 15139, THE WARPED ONES 30250, TIE ME UP! TIE ME DOWN! 39026, TWO WOMEN 21343, VICTORIA ABRIL src, edge_attr, dst 30284, has_tags, 7556 30284, in_language, 7556 30284, release_year, 8486 2067, directed_by, 30409 2067, in_language, 7556 2067, release_year, 8486 2067, starred_actors, 36 2067, starred_actors, 21343 2067, written_by, 30409 27477, has_tags, 36 27477, in_language, 7556 27477, starred_actors, 36 12217, in_language, 7556 12217, starred_actors, 21343 35657, in_language, 7556 35657, release_year, 8486 10457, in_language, 7556 10457, release_year, 8486 10242, release_year, 18366 10242, release_year, 8486 233, in_language, 7556 233, starred_actors, 36 33721, directed_by, 30409 33721, in_language, 7556 33721, written_by, 30409 37995, in_language, 7556 37995, starred_actors, 36 37006, in_language, 7556 37006, release_year, 8486 23816, in_language, 7556 23816, release_year, 8486 11432, in_language, 7556 11432, release_year, 8486 11432, starred_actors, 36 21147, in_language, 7556 21147, release_year, 8486 32597, in_language, 7556 32597, starred_actors, 36 475, directed_by, 31871 475, in_language, 7556 29990, in_language, 7556 29990, release_year, 8486 37903, in_language, 7556 37903, release_year, 8486 5969, has_tags, 36 5969, in_language, 7556 5969, starred_actors, 36 15139, release_year, 18366 30250, has_tags, 7556 30250, in_language, 7556 30250, starred_actors, 21343 39026, release_year, 18366 39026, release_year, 8486 Question: In what context are BETWEEN YOUR LEGS, MARIANO COHN, and THE WARPED ONES connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BETWEEN YOUR LEGS", "MARIANO COHN", "THE WARPED ONES" ], "valid_edges": [ [ "ALL ABOUT MY MOTHER", "has_tags", "SPANISH" ], [ "ALL ABOUT MY MOTHER", "in_language", "SPANISH" ], [ "ALL ABOUT MY MOTHER", "release_year", "1999" ], [ "BETWEEN YOUR LEGS", "directed_by", "MANUEL GÓMEZ PEREIRA" ], [ "BETWEEN YOUR LEGS", "in_language", "SPANISH" ], [ "BETWEEN YOUR LEGS", "release_year", "1999" ], [ "BETWEEN YOUR LEGS", "starred_actors", "JAVIER BARDEM" ], [ "BETWEEN YOUR LEGS", "starred_actors", "VICTORIA ABRIL" ], [ "BETWEEN YOUR LEGS", "written_by", "MANUEL GÓMEZ PEREIRA" ], [ "BIUTIFUL", "has_tags", "JAVIER BARDEM" ], [ "BIUTIFUL", "in_language", "SPANISH" ], [ "BIUTIFUL", "starred_actors", "JAVIER BARDEM" ], [ "DON'T TEMPT ME", "in_language", "SPANISH" ], [ "DON'T TEMPT ME", "starred_actors", "VICTORIA ABRIL" ], [ "GLORIA", "in_language", "SPANISH" ], [ "GLORIA", "release_year", "1999" ], [ "GOYA IN BORDEAUX", "in_language", "SPANISH" ], [ "GOYA IN BORDEAUX", "release_year", "1999" ], [ "INHERIT THE WIND", "release_year", "1960" ], [ "INHERIT THE WIND", "release_year", "1999" ], [ "LIVE FLESH", "in_language", "SPANISH" ], [ "LIVE FLESH", "starred_actors", "JAVIER BARDEM" ], [ "LOVE CAN SERIOUSLY DAMAGE YOUR HEALTH", "directed_by", "MANUEL GÓMEZ PEREIRA" ], [ "LOVE CAN SERIOUSLY DAMAGE YOUR HEALTH", "in_language", "SPANISH" ], [ "LOVE CAN SERIOUSLY DAMAGE YOUR HEALTH", "written_by", "MANUEL GÓMEZ PEREIRA" ], [ "MONDAYS IN THE SUN", "in_language", "SPANISH" ], [ "MONDAYS IN THE SUN", "starred_actors", "JAVIER BARDEM" ], [ "NO ONE WRITES TO THE COLONEL", "in_language", "SPANISH" ], [ "NO ONE WRITES TO THE COLONEL", "release_year", "1999" ], [ "NOBODY KNOWS ANYBODY", "in_language", "SPANISH" ], [ "NOBODY KNOWS ANYBODY", "release_year", "1999" ], [ "SECOND SKIN", "in_language", "SPANISH" ], [ "SECOND SKIN", "release_year", "1999" ], [ "SECOND SKIN", "starred_actors", "JAVIER BARDEM" ], [ "SOLAS", "in_language", "SPANISH" ], [ "SOLAS", "release_year", "1999" ], [ "THE DANCER UPSTAIRS", "in_language", "SPANISH" ], [ "THE DANCER UPSTAIRS", "starred_actors", "JAVIER BARDEM" ], [ "THE MAN NEXT DOOR", "directed_by", "MARIANO COHN" ], [ "THE MAN NEXT DOOR", "in_language", "SPANISH" ], [ "THE NAMELESS", "in_language", "SPANISH" ], [ "THE NAMELESS", "release_year", "1999" ], [ "THE NINTH GATE", "in_language", "SPANISH" ], [ "THE NINTH GATE", "release_year", "1999" ], [ "THE SEA INSIDE", "has_tags", "JAVIER BARDEM" ], [ "THE SEA INSIDE", "in_language", "SPANISH" ], [ "THE SEA INSIDE", "starred_actors", "JAVIER BARDEM" ], [ "THE WARPED ONES", "release_year", "1960" ], [ "TIE ME UP! TIE ME DOWN!", "has_tags", "SPANISH" ], [ "TIE ME UP! TIE ME DOWN!", "in_language", "SPANISH" ], [ "TIE ME UP! TIE ME DOWN!", "starred_actors", "VICTORIA ABRIL" ], [ "TWO WOMEN", "release_year", "1960" ], [ "TWO WOMEN", "release_year", "1999" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 22041, A BULLET FOR THE GENERAL 12694, A PISTOL FOR RINGO 14053, ACE HIGH 4763, ADVENTURE 26786, APARTMENT 143 17157, ARABIAN NIGHTS 19219, BACK TO THE FUTURE PART III 38657, BEAT THE DEVIL 14981, BREAKHEART PASS 29632, CHINA 9, LIBERTY 37 13888, CITY OF THE LIVING DEAD 8438, CUT AND RUN 20620, DJANGO 17645, DJANGO THE BASTARD 7704, FOR A FEW DOLLARS MORE 22710, GOD'S GUN 2600, HELL OF THE LIVING DEAD 5870, HORROR 24167, INFERNO 16200, ITALIAN 22136, JESSE JAMES MEETS FRANKENSTEIN'S DAUGHTER 20149, LAST OF THE DOGMEN 18287, MAN OF THE EAST 1909, MANUEL POIRIER 25283, MAVERICK 14237, NEAR DARK 147, ONCE UPON A TIME IN THE WEST 38962, OPERA 16671, RUN, MAN, RUN 31235, SABATA 35586, SAHARA 4109, SEVEN DOLLARS ON THE RED 8436, SPIRITS OF THE DEAD 29427, STARCRASH 26790, SWEPT AWAY 12891, TENTACLES 10334, THE CHURCH 23804, THE COMANCHEROS 36158, THE FIVE MAN ARMY 21435, THE GOOD, THE BAD AND THE UGLY 3711, THE GREAT SILENCE 32270, THE HORSEMAN ON THE ROOF 37677, THE HUNTING PARTY 28599, THE MOUNTAIN MEN 26820, THE MUMMY 7342, THEY CALL ME TRINITY 32079, ULYSSES 919, VAMPIRE IN VENICE 10352, WARLOCK 36026, WESTERN 29631, WHITE FANG src, edge_attr, dst 22041, has_genre, 36026 22041, in_language, 16200 12694, has_genre, 36026 12694, in_language, 16200 14053, has_genre, 36026 14053, in_language, 16200 26786, has_genre, 5870 17157, has_genre, 4763 17157, in_language, 16200 19219, has_genre, 4763 19219, has_tags, 4763 19219, has_tags, 36026 38657, has_genre, 4763 38657, in_language, 16200 14981, has_genre, 36026 14981, has_tags, 4763 14981, has_tags, 36026 29632, has_genre, 36026 29632, in_language, 16200 13888, has_genre, 5870 13888, has_tags, 5870 13888, has_tags, 16200 13888, in_language, 16200 8438, has_genre, 4763 8438, in_language, 16200 20620, has_genre, 36026 20620, in_language, 16200 17645, has_genre, 36026 17645, in_language, 16200 7704, has_genre, 36026 7704, has_tags, 16200 7704, has_tags, 36026 7704, in_language, 16200 22710, has_genre, 36026 22710, in_language, 16200 2600, has_genre, 5870 2600, in_language, 16200 24167, has_genre, 5870 24167, in_language, 16200 22136, has_genre, 5870 22136, has_genre, 36026 20149, has_genre, 4763 20149, has_genre, 36026 18287, has_genre, 36026 18287, in_language, 16200 25283, has_genre, 4763 25283, has_tags, 36026 14237, has_genre, 5870 14237, has_tags, 36026 147, has_genre, 36026 147, has_tags, 36026 147, in_language, 16200 38962, has_genre, 5870 38962, in_language, 16200 16671, has_genre, 36026 16671, in_language, 16200 31235, has_genre, 36026 31235, in_language, 16200 35586, has_genre, 4763 35586, in_language, 16200 4109, has_genre, 36026 4109, in_language, 16200 8436, has_genre, 5870 8436, in_language, 16200 29427, has_genre, 4763 29427, in_language, 16200 26790, has_genre, 4763 26790, in_language, 16200 12891, has_genre, 5870 12891, has_tags, 5870 12891, has_tags, 16200 10334, has_genre, 5870 10334, in_language, 16200 23804, has_genre, 4763 23804, has_genre, 36026 36158, has_genre, 36026 36158, in_language, 16200 21435, has_genre, 36026 21435, has_tags, 16200 21435, has_tags, 36026 21435, in_language, 16200 3711, has_genre, 36026 3711, in_language, 16200 32270, has_genre, 4763 32270, in_language, 16200 37677, has_genre, 4763 37677, has_genre, 36026 28599, has_genre, 4763 28599, has_genre, 36026 26820, has_genre, 4763 26820, has_genre, 5870 26820, has_tags, 4763 26820, has_tags, 5870 7342, has_genre, 36026 7342, in_language, 16200 32079, has_genre, 4763 32079, in_language, 16200 919, has_genre, 5870 919, in_language, 16200 10352, has_genre, 5870 10352, has_genre, 36026 36026, directed_by, 1909 36026, written_by, 1909 29631, has_genre, 4763 29631, in_language, 16200 Question: In what context are APARTMENT 143, MANUEL POIRIER, and SAHARA connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "APARTMENT 143", "MANUEL POIRIER", "SAHARA" ], "valid_edges": [ [ "A BULLET FOR THE GENERAL", "has_genre", "WESTERN" ], [ "A BULLET FOR THE GENERAL", "in_language", "ITALIAN" ], [ "A PISTOL FOR RINGO", "has_genre", "WESTERN" ], [ "A PISTOL FOR RINGO", "in_language", "ITALIAN" ], [ "ACE HIGH", "has_genre", "WESTERN" ], [ "ACE HIGH", "in_language", "ITALIAN" ], [ "APARTMENT 143", "has_genre", "HORROR" ], [ "ARABIAN NIGHTS", "has_genre", "ADVENTURE" ], [ "ARABIAN NIGHTS", "in_language", "ITALIAN" ], [ "BACK TO THE FUTURE PART III", "has_genre", "ADVENTURE" ], [ "BACK TO THE FUTURE PART III", "has_tags", "ADVENTURE" ], [ "BACK TO THE FUTURE PART III", "has_tags", "WESTERN" ], [ "BEAT THE DEVIL", "has_genre", "ADVENTURE" ], [ "BEAT THE DEVIL", "in_language", "ITALIAN" ], [ "BREAKHEART PASS", "has_genre", "WESTERN" ], [ "BREAKHEART PASS", "has_tags", "ADVENTURE" ], [ "BREAKHEART PASS", "has_tags", "WESTERN" ], [ "CHINA 9, LIBERTY 37", "has_genre", "WESTERN" ], [ "CHINA 9, LIBERTY 37", "in_language", "ITALIAN" ], [ "CITY OF THE LIVING DEAD", "has_genre", "HORROR" ], [ "CITY OF THE LIVING DEAD", "has_tags", "HORROR" ], [ "CITY OF THE LIVING DEAD", "has_tags", "ITALIAN" ], [ "CITY OF THE LIVING DEAD", "in_language", "ITALIAN" ], [ "CUT AND RUN", "has_genre", "ADVENTURE" ], [ "CUT AND RUN", "in_language", "ITALIAN" ], [ "DJANGO", "has_genre", "WESTERN" ], [ "DJANGO", "in_language", "ITALIAN" ], [ "DJANGO THE BASTARD", "has_genre", "WESTERN" ], [ "DJANGO THE BASTARD", "in_language", "ITALIAN" ], [ "FOR A FEW DOLLARS MORE", "has_genre", "WESTERN" ], [ "FOR A FEW DOLLARS MORE", "has_tags", "ITALIAN" ], [ "FOR A FEW DOLLARS MORE", "has_tags", "WESTERN" ], [ "FOR A FEW DOLLARS MORE", "in_language", "ITALIAN" ], [ "GOD'S GUN", "has_genre", "WESTERN" ], [ "GOD'S GUN", "in_language", "ITALIAN" ], [ "HELL OF THE LIVING DEAD", "has_genre", "HORROR" ], [ "HELL OF THE LIVING DEAD", "in_language", "ITALIAN" ], [ "INFERNO", "has_genre", "HORROR" ], [ "INFERNO", "in_language", "ITALIAN" ], [ "JESSE JAMES MEETS FRANKENSTEIN'S DAUGHTER", "has_genre", "HORROR" ], [ "JESSE JAMES MEETS FRANKENSTEIN'S DAUGHTER", "has_genre", "WESTERN" ], [ "LAST OF THE DOGMEN", "has_genre", "ADVENTURE" ], [ "LAST OF THE DOGMEN", "has_genre", "WESTERN" ], [ "MAN OF THE EAST", "has_genre", "WESTERN" ], [ "MAN OF THE EAST", "in_language", "ITALIAN" ], [ "MAVERICK", "has_genre", "ADVENTURE" ], [ "MAVERICK", "has_tags", "WESTERN" ], [ "NEAR DARK", "has_genre", "HORROR" ], [ "NEAR DARK", "has_tags", "WESTERN" ], [ "ONCE UPON A TIME IN THE WEST", "has_genre", "WESTERN" ], [ "ONCE UPON A TIME IN THE WEST", "has_tags", "WESTERN" ], [ "ONCE UPON A TIME IN THE WEST", "in_language", "ITALIAN" ], [ "OPERA", "has_genre", "HORROR" ], [ "OPERA", "in_language", "ITALIAN" ], [ "RUN, MAN, RUN", "has_genre", "WESTERN" ], [ "RUN, MAN, RUN", "in_language", "ITALIAN" ], [ "SABATA", "has_genre", "WESTERN" ], [ "SABATA", "in_language", "ITALIAN" ], [ "SAHARA", "has_genre", "ADVENTURE" ], [ "SAHARA", "in_language", "ITALIAN" ], [ "SEVEN DOLLARS ON THE RED", "has_genre", "WESTERN" ], [ "SEVEN DOLLARS ON THE RED", "in_language", "ITALIAN" ], [ "SPIRITS OF THE DEAD", "has_genre", "HORROR" ], [ "SPIRITS OF THE DEAD", "in_language", "ITALIAN" ], [ "STARCRASH", "has_genre", "ADVENTURE" ], [ "STARCRASH", "in_language", "ITALIAN" ], [ "SWEPT AWAY", "has_genre", "ADVENTURE" ], [ "SWEPT AWAY", "in_language", "ITALIAN" ], [ "TENTACLES", "has_genre", "HORROR" ], [ "TENTACLES", "has_tags", "HORROR" ], [ "TENTACLES", "has_tags", "ITALIAN" ], [ "THE CHURCH", "has_genre", "HORROR" ], [ "THE CHURCH", "in_language", "ITALIAN" ], [ "THE COMANCHEROS", "has_genre", "ADVENTURE" ], [ "THE COMANCHEROS", "has_genre", "WESTERN" ], [ "THE FIVE MAN ARMY", "has_genre", "WESTERN" ], [ "THE FIVE MAN ARMY", "in_language", "ITALIAN" ], [ "THE GOOD, THE BAD AND THE UGLY", "has_genre", "WESTERN" ], [ "THE GOOD, THE BAD AND THE UGLY", "has_tags", "ITALIAN" ], [ "THE GOOD, THE BAD AND THE UGLY", "has_tags", "WESTERN" ], [ "THE GOOD, THE BAD AND THE UGLY", "in_language", "ITALIAN" ], [ "THE GREAT SILENCE", "has_genre", "WESTERN" ], [ "THE GREAT SILENCE", "in_language", "ITALIAN" ], [ "THE HORSEMAN ON THE ROOF", "has_genre", "ADVENTURE" ], [ "THE HORSEMAN ON THE ROOF", "in_language", "ITALIAN" ], [ "THE HUNTING PARTY", "has_genre", "ADVENTURE" ], [ "THE HUNTING PARTY", "has_genre", "WESTERN" ], [ "THE MOUNTAIN MEN", "has_genre", "ADVENTURE" ], [ "THE MOUNTAIN MEN", "has_genre", "WESTERN" ], [ "THE MUMMY", "has_genre", "ADVENTURE" ], [ "THE MUMMY", "has_genre", "HORROR" ], [ "THE MUMMY", "has_tags", "ADVENTURE" ], [ "THE MUMMY", "has_tags", "HORROR" ], [ "THEY CALL ME TRINITY", "has_genre", "WESTERN" ], [ "THEY CALL ME TRINITY", "in_language", "ITALIAN" ], [ "ULYSSES", "has_genre", "ADVENTURE" ], [ "ULYSSES", "in_language", "ITALIAN" ], [ "VAMPIRE IN VENICE", "has_genre", "HORROR" ], [ "VAMPIRE IN VENICE", "in_language", "ITALIAN" ], [ "WARLOCK", "has_genre", "HORROR" ], [ "WARLOCK", "has_genre", "WESTERN" ], [ "WESTERN", "directed_by", "MANUEL POIRIER" ], [ "WESTERN", "written_by", "MANUEL POIRIER" ], [ "WHITE FANG", "has_genre", "ADVENTURE" ], [ "WHITE FANG", "in_language", "ITALIAN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 38097, 1985 31672, A.K. 9989, ALICE AND MARTIN 35549, ANDRÉ TÉCHINÉ 24003, BAROCCO 20489, CHANGING TIMES 6012, FRENCH 31496, MAD MAX BEYOND THUNDERDOME 12358, MY FAVORITE SEASON 29620, POLICE 27191, RENDEZ-VOUS 11723, SHOAH 6323, STRAYED 39270, SUBWAY 32637, THE EARRINGS OF MADAME DE... 36893, THE GIRL ON THE TRAIN 35826, THE PEANUT BUTTER SOLUTION 5537, THE WITNESSES 3617, THIEVES 12294, VAGABOND 23141, WILD REEDS src, edge_attr, dst 31672, in_language, 6012 31672, release_year, 38097 9989, directed_by, 35549 9989, in_language, 6012 9989, written_by, 35549 24003, directed_by, 35549 24003, in_language, 6012 24003, written_by, 35549 20489, directed_by, 35549 20489, in_language, 6012 20489, written_by, 35549 31496, release_year, 38097 12358, directed_by, 35549 12358, has_tags, 35549 12358, in_language, 6012 12358, written_by, 35549 29620, in_language, 6012 29620, release_year, 38097 27191, directed_by, 35549 27191, in_language, 6012 27191, release_year, 38097 27191, written_by, 35549 11723, in_language, 6012 11723, release_year, 38097 6323, directed_by, 35549 6323, has_tags, 35549 6323, in_language, 6012 6323, written_by, 35549 39270, in_language, 6012 39270, release_year, 38097 32637, in_language, 6012 36893, directed_by, 35549 36893, in_language, 6012 36893, written_by, 35549 35826, in_language, 6012 35826, release_year, 38097 5537, directed_by, 35549 5537, has_tags, 35549 5537, in_language, 6012 5537, written_by, 35549 3617, directed_by, 35549 3617, has_tags, 35549 3617, in_language, 6012 3617, written_by, 35549 12294, in_language, 6012 12294, release_year, 38097 23141, directed_by, 35549 23141, has_tags, 35549 23141, in_language, 6012 23141, written_by, 35549 Question: For what reason are MAD MAX BEYOND THUNDERDOME, THE EARRINGS OF MADAME DE..., and WILD REEDS associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "MAD MAX BEYOND THUNDERDOME", "THE EARRINGS OF MADAME DE...", "WILD REEDS" ], "valid_edges": [ [ "A.K.", "in_language", "FRENCH" ], [ "A.K.", "release_year", "1985" ], [ "ALICE AND MARTIN", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "ALICE AND MARTIN", "in_language", "FRENCH" ], [ "ALICE AND MARTIN", "written_by", "ANDRÉ TÉCHINÉ" ], [ "BAROCCO", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "BAROCCO", "in_language", "FRENCH" ], [ "BAROCCO", "written_by", "ANDRÉ TÉCHINÉ" ], [ "CHANGING TIMES", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "CHANGING TIMES", "in_language", "FRENCH" ], [ "CHANGING TIMES", "written_by", "ANDRÉ TÉCHINÉ" ], [ "MAD MAX BEYOND THUNDERDOME", "release_year", "1985" ], [ "MY FAVORITE SEASON", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "MY FAVORITE SEASON", "has_tags", "ANDRÉ TÉCHINÉ" ], [ "MY FAVORITE SEASON", "in_language", "FRENCH" ], [ "MY FAVORITE SEASON", "written_by", "ANDRÉ TÉCHINÉ" ], [ "POLICE", "in_language", "FRENCH" ], [ "POLICE", "release_year", "1985" ], [ "RENDEZ-VOUS", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "RENDEZ-VOUS", "in_language", "FRENCH" ], [ "RENDEZ-VOUS", "release_year", "1985" ], [ "RENDEZ-VOUS", "written_by", "ANDRÉ TÉCHINÉ" ], [ "SHOAH", "in_language", "FRENCH" ], [ "SHOAH", "release_year", "1985" ], [ "STRAYED", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "STRAYED", "has_tags", "ANDRÉ TÉCHINÉ" ], [ "STRAYED", "in_language", "FRENCH" ], [ "STRAYED", "written_by", "ANDRÉ TÉCHINÉ" ], [ "SUBWAY", "in_language", "FRENCH" ], [ "SUBWAY", "release_year", "1985" ], [ "THE EARRINGS OF MADAME DE...", "in_language", "FRENCH" ], [ "THE GIRL ON THE TRAIN", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "THE GIRL ON THE TRAIN", "in_language", "FRENCH" ], [ "THE GIRL ON THE TRAIN", "written_by", "ANDRÉ TÉCHINÉ" ], [ "THE PEANUT BUTTER SOLUTION", "in_language", "FRENCH" ], [ "THE PEANUT BUTTER SOLUTION", "release_year", "1985" ], [ "THE WITNESSES", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "THE WITNESSES", "has_tags", "ANDRÉ TÉCHINÉ" ], [ "THE WITNESSES", "in_language", "FRENCH" ], [ "THE WITNESSES", "written_by", "ANDRÉ TÉCHINÉ" ], [ "THIEVES", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "THIEVES", "has_tags", "ANDRÉ TÉCHINÉ" ], [ "THIEVES", "in_language", "FRENCH" ], [ "THIEVES", "written_by", "ANDRÉ TÉCHINÉ" ], [ "VAGABOND", "in_language", "FRENCH" ], [ "VAGABOND", "release_year", "1985" ], [ "WILD REEDS", "directed_by", "ANDRÉ TÉCHINÉ" ], [ "WILD REEDS", "has_tags", "ANDRÉ TÉCHINÉ" ], [ "WILD REEDS", "in_language", "FRENCH" ], [ "WILD REEDS", "written_by", "ANDRÉ TÉCHINÉ" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15177, ANDY BELLIN 30463, COMEDY 36212, DRAMA 24002, LOVELACE 26738, OSAMA BIN LADEN 584, THREE SMART GIRLS GROW UP 11987, TRUST 16849, ZERO DARK THIRTY src, edge_attr, dst 24002, has_genre, 36212 24002, written_by, 15177 584, has_genre, 30463 11987, has_genre, 30463 11987, has_genre, 36212 11987, written_by, 15177 16849, has_genre, 36212 16849, has_tags, 26738 Question: In what context are ANDY BELLIN, OSAMA BIN LADEN, and THREE SMART GIRLS GROW UP connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ANDY BELLIN", "OSAMA BIN LADEN", "THREE SMART GIRLS GROW UP" ], "valid_edges": [ [ "LOVELACE", "has_genre", "DRAMA" ], [ "LOVELACE", "written_by", "ANDY BELLIN" ], [ "THREE SMART GIRLS GROW UP", "has_genre", "COMEDY" ], [ "TRUST", "has_genre", "COMEDY" ], [ "TRUST", "has_genre", "DRAMA" ], [ "TRUST", "written_by", "ANDY BELLIN" ], [ "ZERO DARK THIRTY", "has_genre", "DRAMA" ], [ "ZERO DARK THIRTY", "has_tags", "OSAMA BIN LADEN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 7841, 1987 13634, ARTHUR PENN 2325, BILLY CRUDUP 19131, BONNIE AND CLYDE 13176, DEAD OF WINTER 5429, FAYE DUNAWAY 34231, GENE HACKMAN 11565, GOOD 28397, KEVIN COSTNER 33575, LITTLE BIG MAN 8253, MICKEY ONE 6446, NIGHT MOVES 32910, NO WAY OUT 6742, TARGET 7, THE LEFT HANDED GUN 18782, THE QUICK AND THE DEAD 37585, WARREN BEATTY 20465, WATCHMEN 19845, WITHOUT LIMITS 29933, WYATT EARP src, edge_attr, dst 19131, directed_by, 13634 19131, has_imdb_rating, 11565 19131, has_tags, 13634 19131, has_tags, 5429 19131, has_tags, 34231 19131, has_tags, 37585 19131, starred_actors, 5429 19131, starred_actors, 34231 19131, starred_actors, 37585 13176, directed_by, 13634 13176, release_year, 7841 33575, directed_by, 13634 33575, has_tags, 13634 33575, starred_actors, 5429 8253, directed_by, 13634 8253, starred_actors, 37585 6446, directed_by, 13634 6446, has_tags, 13634 6446, has_tags, 34231 6446, starred_actors, 34231 32910, has_tags, 34231 32910, has_tags, 28397 32910, release_year, 7841 32910, starred_actors, 34231 32910, starred_actors, 28397 6742, directed_by, 13634 6742, starred_actors, 34231 7, directed_by, 13634 7, has_imdb_rating, 11565 18782, has_tags, 34231 18782, release_year, 7841 18782, starred_actors, 34231 20465, has_imdb_rating, 11565 20465, has_tags, 2325 20465, starred_actors, 2325 19845, has_imdb_rating, 11565 19845, has_tags, 2325 19845, starred_actors, 2325 29933, has_tags, 34231 29933, has_tags, 28397 29933, starred_actors, 34231 29933, starred_actors, 28397 Question: How are ARTHUR PENN, BILLY CRUDUP, and NO WAY OUT related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ARTHUR PENN", "BILLY CRUDUP", "NO WAY OUT" ], "valid_edges": [ [ "BONNIE AND CLYDE", "directed_by", "ARTHUR PENN" ], [ "BONNIE AND CLYDE", "has_imdb_rating", "GOOD" ], [ "BONNIE AND CLYDE", "has_tags", "ARTHUR PENN" ], [ "BONNIE AND CLYDE", "has_tags", "FAYE DUNAWAY" ], [ "BONNIE AND CLYDE", "has_tags", "GENE HACKMAN" ], [ "BONNIE AND CLYDE", "has_tags", "WARREN BEATTY" ], [ "BONNIE AND CLYDE", "starred_actors", "FAYE DUNAWAY" ], [ "BONNIE AND CLYDE", "starred_actors", "GENE HACKMAN" ], [ "BONNIE AND CLYDE", "starred_actors", "WARREN BEATTY" ], [ "DEAD OF WINTER", "directed_by", "ARTHUR PENN" ], [ "DEAD OF WINTER", "release_year", "1987" ], [ "LITTLE BIG MAN", "directed_by", "ARTHUR PENN" ], [ "LITTLE BIG MAN", "has_tags", "ARTHUR PENN" ], [ "LITTLE BIG MAN", "starred_actors", "FAYE DUNAWAY" ], [ "MICKEY ONE", "directed_by", "ARTHUR PENN" ], [ "MICKEY ONE", "starred_actors", "WARREN BEATTY" ], [ "NIGHT MOVES", "directed_by", "ARTHUR PENN" ], [ "NIGHT MOVES", "has_tags", "ARTHUR PENN" ], [ "NIGHT MOVES", "has_tags", "GENE HACKMAN" ], [ "NIGHT MOVES", "starred_actors", "GENE HACKMAN" ], [ "NO WAY OUT", "has_tags", "GENE HACKMAN" ], [ "NO WAY OUT", "has_tags", "KEVIN COSTNER" ], [ "NO WAY OUT", "release_year", "1987" ], [ "NO WAY OUT", "starred_actors", "GENE HACKMAN" ], [ "NO WAY OUT", "starred_actors", "KEVIN COSTNER" ], [ "TARGET", "directed_by", "ARTHUR PENN" ], [ "TARGET", "starred_actors", "GENE HACKMAN" ], [ "THE LEFT HANDED GUN", "directed_by", "ARTHUR PENN" ], [ "THE LEFT HANDED GUN", "has_imdb_rating", "GOOD" ], [ "THE QUICK AND THE DEAD", "has_tags", "GENE HACKMAN" ], [ "THE QUICK AND THE DEAD", "release_year", "1987" ], [ "THE QUICK AND THE DEAD", "starred_actors", "GENE HACKMAN" ], [ "WATCHMEN", "has_imdb_rating", "GOOD" ], [ "WATCHMEN", "has_tags", "BILLY CRUDUP" ], [ "WATCHMEN", "starred_actors", "BILLY CRUDUP" ], [ "WITHOUT LIMITS", "has_imdb_rating", "GOOD" ], [ "WITHOUT LIMITS", "has_tags", "BILLY CRUDUP" ], [ "WITHOUT LIMITS", "starred_actors", "BILLY CRUDUP" ], [ "WYATT EARP", "has_tags", "GENE HACKMAN" ], [ "WYATT EARP", "has_tags", "KEVIN COSTNER" ], [ "WYATT EARP", "starred_actors", "GENE HACKMAN" ], [ "WYATT EARP", "starred_actors", "KEVIN COSTNER" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 19407, 8½ 17698, ALBERTO SORDI 30463, COMEDY 10941, DRIVING MISS DAISY 29506, ENNIO FLAIANO 3353, FEDERICO FELLINI 11227, GIULIETTA MASINA 17344, I VITELLONI 16200, ITALIAN 8923, JOHNNY ENGLISH REBORN 20913, LA STRADA 24926, SWEET CHARITY 34529, THE WHITE SHEIK 25136, TULLIO PINELLI src, edge_attr, dst 19407, directed_by, 3353 19407, has_tags, 3353 19407, in_language, 16200 19407, written_by, 29506 19407, written_by, 3353 19407, written_by, 25136 10941, has_genre, 30463 17344, directed_by, 3353 17344, has_genre, 30463 17344, has_tags, 3353 17344, has_tags, 16200 17344, in_language, 16200 17344, starred_actors, 17698 17344, written_by, 29506 17344, written_by, 3353 17344, written_by, 25136 8923, has_genre, 30463 8923, has_tags, 30463 20913, directed_by, 3353 20913, has_tags, 3353 20913, has_tags, 11227 20913, has_tags, 16200 20913, in_language, 16200 20913, starred_actors, 11227 20913, written_by, 29506 20913, written_by, 3353 20913, written_by, 25136 24926, written_by, 29506 24926, written_by, 3353 24926, written_by, 25136 34529, directed_by, 3353 34529, has_genre, 30463 34529, has_tags, 3353 34529, in_language, 16200 34529, starred_actors, 17698 34529, starred_actors, 11227 34529, written_by, 29506 34529, written_by, 3353 34529, written_by, 25136 Question: For what reason are DRIVING MISS DAISY, JOHNNY ENGLISH REBORN, and TULLIO PINELLI associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "DRIVING MISS DAISY", "JOHNNY ENGLISH REBORN", "TULLIO PINELLI" ], "valid_edges": [ [ "8½", "directed_by", "FEDERICO FELLINI" ], [ "8½", "has_tags", "FEDERICO FELLINI" ], [ "8½", "in_language", "ITALIAN" ], [ "8½", "written_by", "ENNIO FLAIANO" ], [ "8½", "written_by", "FEDERICO FELLINI" ], [ "8½", "written_by", "TULLIO PINELLI" ], [ "DRIVING MISS DAISY", "has_genre", "COMEDY" ], [ "I VITELLONI", "directed_by", "FEDERICO FELLINI" ], [ "I VITELLONI", "has_genre", "COMEDY" ], [ "I VITELLONI", "has_tags", "FEDERICO FELLINI" ], [ "I VITELLONI", "has_tags", "ITALIAN" ], [ "I VITELLONI", "in_language", "ITALIAN" ], [ "I VITELLONI", "starred_actors", "ALBERTO SORDI" ], [ "I VITELLONI", "written_by", "ENNIO FLAIANO" ], [ "I VITELLONI", "written_by", "FEDERICO FELLINI" ], [ "I VITELLONI", "written_by", "TULLIO PINELLI" ], [ "JOHNNY ENGLISH REBORN", "has_genre", "COMEDY" ], [ "JOHNNY ENGLISH REBORN", "has_tags", "COMEDY" ], [ "LA STRADA", "directed_by", "FEDERICO FELLINI" ], [ "LA STRADA", "has_tags", "FEDERICO FELLINI" ], [ "LA STRADA", "has_tags", "GIULIETTA MASINA" ], [ "LA STRADA", "has_tags", "ITALIAN" ], [ "LA STRADA", "in_language", "ITALIAN" ], [ "LA STRADA", "starred_actors", "GIULIETTA MASINA" ], [ "LA STRADA", "written_by", "ENNIO FLAIANO" ], [ "LA STRADA", "written_by", "FEDERICO FELLINI" ], [ "LA STRADA", "written_by", "TULLIO PINELLI" ], [ "SWEET CHARITY", "written_by", "ENNIO FLAIANO" ], [ "SWEET CHARITY", "written_by", "FEDERICO FELLINI" ], [ "SWEET CHARITY", "written_by", "TULLIO PINELLI" ], [ "THE WHITE SHEIK", "directed_by", "FEDERICO FELLINI" ], [ "THE WHITE SHEIK", "has_genre", "COMEDY" ], [ "THE WHITE SHEIK", "has_tags", "FEDERICO FELLINI" ], [ "THE WHITE SHEIK", "in_language", "ITALIAN" ], [ "THE WHITE SHEIK", "starred_actors", "ALBERTO SORDI" ], [ "THE WHITE SHEIK", "starred_actors", "GIULIETTA MASINA" ], [ "THE WHITE SHEIK", "written_by", "ENNIO FLAIANO" ], [ "THE WHITE SHEIK", "written_by", "FEDERICO FELLINI" ], [ "THE WHITE SHEIK", "written_by", "TULLIO PINELLI" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 13241, 2 39289, ACTION 31367, ED O'ROSS 5870, HORROR 11016, MARTITA HUNT 38677, MIAMI VICE 24020, NADJA 13081, R 9555, RED HEAT 29005, SIN CITY 2709, THE BRIDES OF DRACULA src, edge_attr, dst 38677, has_genre, 39289 38677, has_tags, 13241 38677, has_tags, 13081 24020, has_genre, 5870 24020, has_tags, 13241 9555, has_genre, 39289 9555, has_tags, 39289 9555, starred_actors, 31367 29005, has_tags, 13241 29005, has_tags, 39289 29005, has_tags, 13081 2709, has_genre, 5870 2709, starred_actors, 11016 Question: How are 2, ED O'ROSS, and MARTITA HUNT related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "2", "ED O'ROSS", "MARTITA HUNT" ], "valid_edges": [ [ "MIAMI VICE", "has_genre", "ACTION" ], [ "MIAMI VICE", "has_tags", "2" ], [ "MIAMI VICE", "has_tags", "R" ], [ "NADJA", "has_genre", "HORROR" ], [ "NADJA", "has_tags", "2" ], [ "RED HEAT", "has_genre", "ACTION" ], [ "RED HEAT", "has_tags", "ACTION" ], [ "RED HEAT", "starred_actors", "ED O'ROSS" ], [ "SIN CITY", "has_tags", "2" ], [ "SIN CITY", "has_tags", "ACTION" ], [ "SIN CITY", "has_tags", "R" ], [ "THE BRIDES OF DRACULA", "has_genre", "HORROR" ], [ "THE BRIDES OF DRACULA", "starred_actors", "MARTITA HUNT" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 13408, 2001 30463, COMEDY 4107, DIRECTOR'S CUT 22255, DONNIE DARKO 36212, DRAMA 21955, JACINDA BARRETT 21401, KIM DARBY 32018, TEEN WOLF TOO 28107, THE LAST KISS src, edge_attr, dst 22255, has_genre, 36212 22255, has_tags, 4107 22255, release_year, 13408 32018, has_genre, 30463 32018, starred_actors, 21401 28107, has_genre, 30463 28107, has_genre, 36212 28107, release_year, 13408 28107, starred_actors, 21955 Question: In what context are DIRECTOR'S CUT, JACINDA BARRETT, and KIM DARBY connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "DIRECTOR'S CUT", "JACINDA BARRETT", "KIM DARBY" ], "valid_edges": [ [ "DONNIE DARKO", "has_genre", "DRAMA" ], [ "DONNIE DARKO", "has_tags", "DIRECTOR'S CUT" ], [ "DONNIE DARKO", "release_year", "2001" ], [ "TEEN WOLF TOO", "has_genre", "COMEDY" ], [ "TEEN WOLF TOO", "starred_actors", "KIM DARBY" ], [ "THE LAST KISS", "has_genre", "COMEDY" ], [ "THE LAST KISS", "has_genre", "DRAMA" ], [ "THE LAST KISS", "release_year", "2001" ], [ "THE LAST KISS", "starred_actors", "JACINDA BARRETT" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 8486, 1999 27261, 2009 30146, A CHRISTMAS CAROL 10045, BD-R 20946, BIG PUN 722, CHARACTERS 22958, FAMOUS 3179, NANETTE NEWMAN 21512, SHERLOCK HOLMES 16219, STORY 23847, THE STEPFORD WIVES 14499, TOY STORY 2 7105, URBAN MENACE src, edge_attr, dst 30146, has_imdb_votes, 22958 30146, has_tags, 10045 30146, has_tags, 722 30146, release_year, 8486 30146, release_year, 27261 21512, has_imdb_votes, 22958 21512, has_tags, 722 21512, has_tags, 21512 21512, has_tags, 16219 21512, release_year, 27261 23847, has_tags, 10045 23847, starred_actors, 3179 14499, has_tags, 722 14499, has_tags, 16219 14499, release_year, 8486 7105, release_year, 8486 7105, starred_actors, 20946 Question: In what context are BIG PUN, CHARACTERS, and NANETTE NEWMAN connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BIG PUN", "CHARACTERS", "NANETTE NEWMAN" ], "valid_edges": [ [ "A CHRISTMAS CAROL", "has_imdb_votes", "FAMOUS" ], [ "A CHRISTMAS CAROL", "has_tags", "BD-R" ], [ "A CHRISTMAS CAROL", "has_tags", "CHARACTERS" ], [ "A CHRISTMAS CAROL", "release_year", "1999" ], [ "A CHRISTMAS CAROL", "release_year", "2009" ], [ "SHERLOCK HOLMES", "has_imdb_votes", "FAMOUS" ], [ "SHERLOCK HOLMES", "has_tags", "CHARACTERS" ], [ "SHERLOCK HOLMES", "has_tags", "SHERLOCK HOLMES" ], [ "SHERLOCK HOLMES", "has_tags", "STORY" ], [ "SHERLOCK HOLMES", "release_year", "2009" ], [ "THE STEPFORD WIVES", "has_tags", "BD-R" ], [ "THE STEPFORD WIVES", "starred_actors", "NANETTE NEWMAN" ], [ "TOY STORY 2", "has_tags", "CHARACTERS" ], [ "TOY STORY 2", "has_tags", "STORY" ], [ "TOY STORY 2", "release_year", "1999" ], [ "URBAN MENACE", "release_year", "1999" ], [ "URBAN MENACE", "starred_actors", "BIG PUN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 12998, 1 29424, 2011 2105, ANAND TUCKER 30976, DOUGLAS BUCK 7139, HILARY AND JACKIE 19247, ITALIAN FOR BEGINNERS 17950, LONE SCHERFIG 29488, ONE DAY 25301, THE THEATRE BIZARRE src, edge_attr, dst 7139, directed_by, 2105 7139, has_tags, 12998 19247, directed_by, 17950 19247, has_tags, 12998 19247, has_tags, 17950 19247, written_by, 17950 29488, directed_by, 17950 29488, release_year, 29424 25301, directed_by, 30976 25301, release_year, 29424 25301, written_by, 30976 Question: How are ANAND TUCKER, DOUGLAS BUCK, and LONE SCHERFIG related? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "ANAND TUCKER", "DOUGLAS BUCK", "LONE SCHERFIG" ], "valid_edges": [ [ "HILARY AND JACKIE", "directed_by", "ANAND TUCKER" ], [ "HILARY AND JACKIE", "has_tags", "1" ], [ "ITALIAN FOR BEGINNERS", "directed_by", "LONE SCHERFIG" ], [ "ITALIAN FOR BEGINNERS", "has_tags", "1" ], [ "ITALIAN FOR BEGINNERS", "has_tags", "LONE SCHERFIG" ], [ "ITALIAN FOR BEGINNERS", "written_by", "LONE SCHERFIG" ], [ "ONE DAY", "directed_by", "LONE SCHERFIG" ], [ "ONE DAY", "release_year", "2011" ], [ "THE THEATRE BIZARRE", "directed_by", "DOUGLAS BUCK" ], [ "THE THEATRE BIZARRE", "release_year", "2011" ], [ "THE THEATRE BIZARRE", "written_by", "DOUGLAS BUCK" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 35063, 1976 29424, 2011 1421, 2013 11835, 21 HOURS AT MUNICH 39501, 30 MINUTES OR LESS 24454, CARRIE 21922, JANE EYRE 14847, JESSE EISENBERG 15254, JORDANA BEATTY 208, JUDY MOODY AND THE NOT BUMMER SUMMER 4136, MIA WASIKOWSKA 35664, RESTLESS 33994, TAKE ME HOME TONIGHT 3680, THE DOUBLE 18520, TOPHER GRACE src, edge_attr, dst 11835, release_year, 35063 39501, release_year, 29424 39501, starred_actors, 14847 24454, release_year, 35063 24454, release_year, 1421 21922, has_tags, 4136 21922, release_year, 29424 21922, starred_actors, 4136 208, release_year, 29424 208, starred_actors, 15254 35664, release_year, 29424 35664, starred_actors, 4136 33994, has_tags, 18520 33994, release_year, 29424 33994, starred_actors, 18520 33994, written_by, 18520 3680, has_tags, 4136 3680, release_year, 29424 3680, release_year, 1421 3680, starred_actors, 14847 3680, starred_actors, 4136 3680, starred_actors, 18520 Question: In what context are 21 HOURS AT MUNICH, JORDANA BEATTY, and THE DOUBLE connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "21 HOURS AT MUNICH", "JORDANA BEATTY", "THE DOUBLE" ], "valid_edges": [ [ "21 HOURS AT MUNICH", "release_year", "1976" ], [ "30 MINUTES OR LESS", "release_year", "2011" ], [ "30 MINUTES OR LESS", "starred_actors", "JESSE EISENBERG" ], [ "CARRIE", "release_year", "1976" ], [ "CARRIE", "release_year", "2013" ], [ "JANE EYRE", "has_tags", "MIA WASIKOWSKA" ], [ "JANE EYRE", "release_year", "2011" ], [ "JANE EYRE", "starred_actors", "MIA WASIKOWSKA" ], [ "JUDY MOODY AND THE NOT BUMMER SUMMER", "release_year", "2011" ], [ "JUDY MOODY AND THE NOT BUMMER SUMMER", "starred_actors", "JORDANA BEATTY" ], [ "RESTLESS", "release_year", "2011" ], [ "RESTLESS", "starred_actors", "MIA WASIKOWSKA" ], [ "TAKE ME HOME TONIGHT", "has_tags", "TOPHER GRACE" ], [ "TAKE ME HOME TONIGHT", "release_year", "2011" ], [ "TAKE ME HOME TONIGHT", "starred_actors", "TOPHER GRACE" ], [ "TAKE ME HOME TONIGHT", "written_by", "TOPHER GRACE" ], [ "THE DOUBLE", "has_tags", "MIA WASIKOWSKA" ], [ "THE DOUBLE", "release_year", "2011" ], [ "THE DOUBLE", "release_year", "2013" ], [ "THE DOUBLE", "starred_actors", "JESSE EISENBERG" ], [ "THE DOUBLE", "starred_actors", "MIA WASIKOWSKA" ], [ "THE DOUBLE", "starred_actors", "TOPHER GRACE" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 20247, 44 INCH CHEST 36212, DRAMA 31783, ENGLISH 9931, JANE GOLDMAN 5650, LOUIS MELLIS 16986, PAVEL LUNGIN 11556, TAXI BLUES 25509, THE DEBT 3437, THE WOMAN IN BLACK 34970, TOM WILKINSON src, edge_attr, dst 20247, has_genre, 36212 20247, starred_actors, 34970 20247, written_by, 5650 11556, directed_by, 16986 11556, has_genre, 36212 11556, written_by, 16986 25509, has_genre, 36212 25509, has_tags, 34970 25509, in_language, 31783 25509, starred_actors, 34970 25509, written_by, 9931 3437, has_genre, 36212 3437, in_language, 31783 3437, written_by, 9931 Question: For what reason are JANE GOLDMAN, LOUIS MELLIS, and PAVEL LUNGIN associated? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "JANE GOLDMAN", "LOUIS MELLIS", "PAVEL LUNGIN" ], "valid_edges": [ [ "44 INCH CHEST", "has_genre", "DRAMA" ], [ "44 INCH CHEST", "starred_actors", "TOM WILKINSON" ], [ "44 INCH CHEST", "written_by", "LOUIS MELLIS" ], [ "TAXI BLUES", "directed_by", "PAVEL LUNGIN" ], [ "TAXI BLUES", "has_genre", "DRAMA" ], [ "TAXI BLUES", "written_by", "PAVEL LUNGIN" ], [ "THE DEBT", "has_genre", "DRAMA" ], [ "THE DEBT", "has_tags", "TOM WILKINSON" ], [ "THE DEBT", "in_language", "ENGLISH" ], [ "THE DEBT", "starred_actors", "TOM WILKINSON" ], [ "THE DEBT", "written_by", "JANE GOLDMAN" ], [ "THE WOMAN IN BLACK", "has_genre", "DRAMA" ], [ "THE WOMAN IN BLACK", "in_language", "ENGLISH" ], [ "THE WOMAN IN BLACK", "written_by", "JANE GOLDMAN" ] ] }

metaqa

You are given a directed graph as two CSV-like sections in this order: 1) Node table (header included): node_id, node_attr 2) Edge table (header included): src, edge_attr, dst Task - Use ONLY edges from the Edge table to answer the question by outputting a path. - When printing each edge, replace IDs with the exact node_attr from the Node table. - Output MUST be text triples, not numeric IDs. Output format (STRICT — no extra text): PATH: ("subject"|predicate|"object") ... END Rules - Use only listed edges; do NOT invent edges. - Map IDs → node_attr; preserve node_attr exactly. - Output NOTHING outside the PATH block. - If no path exists, output exactly: PATH: END Graph: node_id, node_attr 15421, 100 BLOODY ACRES 28153, 2 DAYS IN NEW YORK 658, 2012 37304, 21 JUMP STREET 31304, 30 BEATS 30586, A FANTASTIC FEAR OF EVERYTHING 24585, A GLIMPSE INSIDE THE MIND OF CHARLES SWAN III 20562, A THOUSAND WORDS 29800, ACE ATTORNEY 35724, ALTER EGOS 36099, AMERICAN REUNION 35584, ANY QUESTIONS FOR BEN? 26059, BACHELORETTE 23994, BARFI! 26923, BATTLESHIP 2284, BENDING THE RULES 32770, BEST MAN DOWN 10002, BIO-DOME 5946, BLOODY BLOODY BIBLE CAMP 39848, BLUE LIKE JAZZ 14480, BUDDIES 32450, CHEERFUL WEATHER FOR THE WEDDING 274, COCKNEYS VS ZOMBIES 30463, COMEDY 29161, DABANGG 2 30529, DARK SHADOWS 17466, DETENTION OF THE DEAD 32892, ENGLISH VINGLISH 10151, FAMILY WAY 35384, FAT KID RULES THE WORLD 21972, FOODFIGHT! 20213, FOR A GOOD TIME, CALL... 6076, FRANCES HA 34131, FREE SAMPLES 3550, FUN SIZE 24124, GAMBIT 22623, GIMME THE LOOT 39859, GIRL MOST LIKELY 13971, GOATS 13477, GUNS, GIRLS AND GAMBLING 34345, HAPPINESS NEVER COMES ALONE 32513, HERE COMES THE BOOM 6518, HIGH SOCIETY 34440, HIT AND RUN 35319, HOPE SPRINGS 34869, HOTEL TRANSYLVANIA 6886, HOUSEFULL 2 37844, HYDE PARK ON HUDSON 38589, I DO 15403, IT'S A DISASTER 6685, JASON BLOOM 33283, JESUS HENRY CHRIST 36133, JOHN DIES AT THE END 35632, JOKER 16510, JOYFUL NOISE 21883, LAY THE FAVORITE 14931, LIBERAL ARTS 17372, LOL 22226, LOLA VERSUS 38894, LOVE AND OTHER TROUBLES 4564, LOVE IS ALL YOU NEED 34517, LUV 6450, MADEA'S WITNESS PROTECTION 35871, MAGIC MIKE 902, MEN IN BLACK 3 35361, MENTAL 31735, MIDDLE OF NOWHERE 424, MIRROR MIRROR 15981, MOONRISE KINGDOM 31377, MUCH ADO ABOUT NOTHING 8368, MUSHROOMING 34821, MY AWKWARD SEXUAL ADVENTURE 33972, NOT SUITABLE FOR CHILDREN 29217, ONE FOR THE MONEY 1300, OVERNIGHT DELIVERY 19562, PAPERMAN 35289, PARANORMAN 29610, PARENTAL GUIDANCE 11056, PAULETTE 17916, PIRANHA 3DD 6864, PITCH PERFECT 2388, PLAYING FOR KEEPS 10493, POPULAIRE 29303, PRICE CHECK 12939, PROJECT X 25023, QUARTET 11839, REVENGE FOR JOLLY! 14305, ROCK OF AGES 12597, RUBY SPARKS 17321, SAFETY NOT GUARANTEED 17168, SEEKING A FRIEND FOR THE END OF THE WORLD 30190, SEVEN PSYCHOPATHS 25060, SEXUAL CHRONICLES OF A FRENCH FAMILY 10227, SHANGHAI CALLING 37459, SIGHTSEERS 34584, SILVER LININGS PLAYBOOK 31434, SLEEPWALK WITH ME 28361, SMALL APARTMENTS 28026, SOMEBODY UP THERE LIKES ME 36369, STAND UP GUYS 9247, STITCHES 25002, STRUCK BY LIGHTNING 3038, STUCK IN LOVE 10856, STUDENT OF THE YEAR 273, TED 19501, THANKS FOR SHARING 16163, THAT'S MY BOY 31162, THE ABCS OF DEATH 22040, THE BABYMAKERS 10138, THE BAYTOWN OUTLAWS 33050, THE CAMPAIGN 6644, THE COMEDY 12300, THE DICTATOR 16732, THE END 31019, THE FIRST TIME 13130, THE FIVE-YEAR ENGAGEMENT 15980, THE GUILT TRIP 34429, THE LORAX 27052, THE NEWEST PLEDGE 15798, THE ODD LIFE OF TIMOTHY GREEN 24519, THE PLAYERS 18162, THE RAVEN 2739, THE SAPPHIRES 37915, THE STORY OF LUKE 1249, THE THREE STOOGES 31679, THE WATCH 24380, THINK LIKE A MAN 939, THIS IS 40 20169, THIS MEANS WAR 6357, TIM AND ERIC'S BILLION DOLLAR MOVIE 6598, TO ROME WITH LOVE 21694, TOOTH FAIRY 2 24570, VAMPS 21268, VICKY DONOR 26675, WANDERLUST 34118, WHAT TO EXPECT WHEN YOU'RE EXPECTING 38905, WHAT'S IN A NAME? 21236, WRECK-IT RALPH 10235, WRONG src, edge_attr, dst 15421, has_genre, 30463 15421, release_year, 658 28153, has_genre, 30463 28153, release_year, 658 37304, has_genre, 30463 37304, has_tags, 30463 37304, release_year, 658 31304, has_genre, 30463 31304, release_year, 658 30586, has_genre, 30463 30586, has_tags, 30463 30586, release_year, 658 24585, has_genre, 30463 24585, release_year, 658 20562, has_genre, 30463 20562, release_year, 658 29800, has_genre, 30463 29800, release_year, 658 35724, has_genre, 30463 35724, release_year, 658 36099, has_genre, 30463 36099, release_year, 658 35584, has_genre, 30463 35584, release_year, 658 26059, has_genre, 30463 26059, release_year, 658 23994, has_genre, 30463 23994, release_year, 658 26923, release_year, 658 2284, has_genre, 30463 2284, release_year, 658 32770, has_genre, 30463 32770, release_year, 658 10002, directed_by, 6685 10002, has_genre, 30463 5946, has_genre, 30463 5946, release_year, 658 39848, has_genre, 30463 39848, release_year, 658 14480, has_genre, 30463 14480, release_year, 658 32450, has_genre, 30463 32450, release_year, 658 274, has_genre, 30463 274, release_year, 658 29161, has_genre, 30463 29161, release_year, 658 30529, has_genre, 30463 30529, release_year, 658 17466, has_genre, 30463 17466, release_year, 658 32892, has_genre, 30463 32892, release_year, 658 10151, has_genre, 30463 10151, release_year, 658 35384, has_genre, 30463 35384, release_year, 658 21972, has_genre, 30463 21972, release_year, 658 20213, has_genre, 30463 20213, release_year, 658 6076, has_genre, 30463 6076, release_year, 658 34131, has_genre, 30463 34131, release_year, 658 3550, has_genre, 30463 3550, release_year, 658 24124, has_genre, 30463 24124, release_year, 658 22623, has_genre, 30463 22623, release_year, 658 39859, has_genre, 30463 39859, release_year, 658 13971, has_genre, 30463 13971, release_year, 658 13477, has_genre, 30463 13477, release_year, 658 34345, has_genre, 30463 34345, release_year, 658 32513, has_genre, 30463 32513, release_year, 658 6518, has_genre, 30463 34440, has_genre, 30463 34440, release_year, 658 35319, has_genre, 30463 35319, release_year, 658 34869, has_genre, 30463 34869, release_year, 658 6886, has_genre, 30463 6886, release_year, 658 37844, has_genre, 30463 37844, release_year, 658 38589, has_genre, 30463 38589, release_year, 658 15403, has_genre, 30463 15403, has_tags, 30463 15403, release_year, 658 33283, has_genre, 30463 33283, release_year, 658 36133, has_genre, 30463 36133, has_tags, 30463 36133, release_year, 658 35632, has_genre, 30463 35632, release_year, 658 16510, has_genre, 30463 16510, release_year, 658 21883, has_genre, 30463 21883, release_year, 658 14931, has_genre, 30463 14931, release_year, 658 17372, has_genre, 30463 17372, release_year, 658 22226, has_genre, 30463 22226, release_year, 658 38894, has_genre, 30463 38894, release_year, 658 4564, has_genre, 30463 4564, release_year, 658 34517, has_genre, 30463 34517, release_year, 658 6450, has_genre, 30463 6450, release_year, 658 35871, has_genre, 30463 35871, release_year, 658 902, has_genre, 30463 902, release_year, 658 35361, has_genre, 30463 35361, release_year, 658 31735, has_genre, 30463 31735, release_year, 658 424, has_genre, 30463 424, release_year, 658 15981, has_genre, 30463 15981, has_tags, 30463 15981, release_year, 658 31377, has_genre, 30463 31377, has_tags, 30463 31377, release_year, 658 8368, has_genre, 30463 8368, release_year, 658 34821, has_genre, 30463 34821, release_year, 658 33972, has_genre, 30463 33972, has_tags, 30463 33972, release_year, 658 29217, has_genre, 30463 29217, release_year, 658 1300, directed_by, 6685 1300, has_genre, 30463 19562, has_genre, 30463 19562, release_year, 658 35289, has_genre, 30463 35289, release_year, 658 29610, has_genre, 30463 29610, release_year, 658 11056, has_genre, 30463 11056, release_year, 658 17916, has_genre, 30463 17916, release_year, 658 6864, has_genre, 30463 6864, release_year, 658 2388, has_genre, 30463 2388, release_year, 658 10493, has_genre, 30463 10493, release_year, 658 29303, has_genre, 30463 29303, release_year, 658 12939, has_genre, 30463 12939, release_year, 658 25023, has_genre, 30463 25023, has_tags, 30463 25023, release_year, 658 11839, has_genre, 30463 11839, release_year, 658 14305, has_genre, 30463 14305, release_year, 658 12597, has_genre, 30463 12597, release_year, 658 17321, has_genre, 30463 17321, release_year, 658 17168, has_genre, 30463 17168, release_year, 658 30190, has_genre, 30463 30190, release_year, 658 25060, has_genre, 30463 25060, release_year, 658 10227, has_genre, 30463 10227, release_year, 658 37459, has_genre, 30463 37459, release_year, 658 34584, has_genre, 30463 34584, release_year, 658 31434, has_genre, 30463 31434, release_year, 658 28361, has_genre, 30463 28361, release_year, 658 28026, has_genre, 30463 28026, release_year, 658 36369, has_genre, 30463 36369, release_year, 658 9247, has_genre, 30463 9247, release_year, 658 25002, has_genre, 30463 25002, release_year, 658 3038, has_genre, 30463 3038, release_year, 658 10856, has_genre, 30463 10856, release_year, 658 273, has_genre, 30463 273, has_tags, 30463 273, release_year, 658 19501, has_genre, 30463 19501, release_year, 658 16163, has_genre, 30463 16163, release_year, 658 31162, has_genre, 30463 31162, release_year, 658 22040, has_genre, 30463 22040, release_year, 658 10138, has_genre, 30463 10138, release_year, 658 33050, has_genre, 30463 33050, release_year, 658 6644, has_tags, 30463 6644, release_year, 658 12300, has_genre, 30463 12300, release_year, 658 16732, has_genre, 30463 16732, release_year, 658 31019, has_genre, 30463 31019, release_year, 658 13130, has_genre, 30463 13130, release_year, 658 15980, has_genre, 30463 15980, release_year, 658 34429, has_genre, 30463 34429, release_year, 658 27052, has_genre, 30463 27052, release_year, 658 15798, has_genre, 30463 15798, release_year, 658 24519, has_genre, 30463 24519, release_year, 658 18162, has_genre, 30463 18162, release_year, 658 2739, has_genre, 30463 2739, release_year, 658 37915, has_genre, 30463 37915, release_year, 658 1249, has_genre, 30463 1249, release_year, 658 31679, has_genre, 30463 31679, release_year, 658 24380, has_genre, 30463 24380, release_year, 658 939, has_genre, 30463 939, release_year, 658 20169, has_genre, 30463 20169, release_year, 658 6357, has_genre, 30463 6357, release_year, 658 6598, has_genre, 30463 6598, has_tags, 30463 6598, release_year, 658 21694, has_genre, 30463 21694, release_year, 658 24570, has_genre, 30463 24570, release_year, 658 21268, has_genre, 30463 21268, release_year, 658 26675, has_genre, 30463 26675, release_year, 658 34118, has_genre, 30463 34118, release_year, 658 38905, has_genre, 30463 38905, release_year, 658 21236, has_genre, 30463 21236, release_year, 658 10235, has_genre, 30463 10235, release_year, 658 Question: In what context are BATTLESHIP, HIGH SOCIETY, and JASON BLOOM connected? Your output must be ONLY the PATH block.

graph_path

{ "style": "rule" }

{ "entities": [ "BATTLESHIP", "HIGH SOCIETY", "JASON BLOOM" ], "valid_edges": [ [ "100 BLOODY ACRES", "has_genre", "COMEDY" ], [ "100 BLOODY ACRES", "release_year", "2012" ], [ "2 DAYS IN NEW YORK", "has_genre", "COMEDY" ], [ "2 DAYS IN NEW YORK", "release_year", "2012" ], [ "21 JUMP STREET", "has_genre", "COMEDY" ], [ "21 JUMP STREET", "has_tags", "COMEDY" ], [ "21 JUMP STREET", "release_year", "2012" ], [ "30 BEATS", "has_genre", "COMEDY" ], [ "30 BEATS", "release_year", "2012" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_genre", "COMEDY" ], [ "A FANTASTIC FEAR OF EVERYTHING", "has_tags", "COMEDY" ], [ "A FANTASTIC FEAR OF EVERYTHING", "release_year", "2012" ], [ "A GLIMPSE INSIDE THE MIND OF CHARLES SWAN III", "has_genre", "COMEDY" ], [ "A GLIMPSE INSIDE THE MIND OF CHARLES SWAN III", "release_year", "2012" ], [ "A THOUSAND WORDS", "has_genre", "COMEDY" ], [ "A THOUSAND WORDS", "release_year", "2012" ], [ "ACE ATTORNEY", "has_genre", "COMEDY" ], [ "ACE ATTORNEY", "release_year", "2012" ], [ "ALTER EGOS", "has_genre", "COMEDY" ], [ "ALTER EGOS", "release_year", "2012" ], [ "AMERICAN REUNION", "has_genre", "COMEDY" ], [ "AMERICAN REUNION", "release_year", "2012" ], [ "ANY QUESTIONS FOR BEN?", "has_genre", "COMEDY" ], [ "ANY QUESTIONS FOR BEN?", "release_year", "2012" ], [ "BACHELORETTE", "has_genre", "COMEDY" ], [ "BACHELORETTE", "release_year", "2012" ], [ "BARFI!", "has_genre", "COMEDY" ], [ "BARFI!", "release_year", "2012" ], [ "BATTLESHIP", "release_year", "2012" ], [ "BENDING THE RULES", "has_genre", "COMEDY" ], [ "BENDING THE RULES", "release_year", "2012" ], [ "BEST MAN DOWN", "has_genre", "COMEDY" ], [ "BEST MAN DOWN", "release_year", "2012" ], [ "BIO-DOME", "directed_by", "JASON BLOOM" ], [ "BIO-DOME", "has_genre", "COMEDY" ], [ "BLOODY BLOODY BIBLE CAMP", "has_genre", "COMEDY" ], [ "BLOODY BLOODY BIBLE CAMP", "release_year", "2012" ], [ "BLUE LIKE JAZZ", "has_genre", "COMEDY" ], [ "BLUE LIKE JAZZ", "release_year", "2012" ], [ "BUDDIES", "has_genre", "COMEDY" ], [ "BUDDIES", "release_year", "2012" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "has_genre", "COMEDY" ], [ "CHEERFUL WEATHER FOR THE WEDDING", "release_year", "2012" ], [ "COCKNEYS VS ZOMBIES", "has_genre", "COMEDY" ], [ "COCKNEYS VS ZOMBIES", "release_year", "2012" ], [ "DABANGG 2", "has_genre", "COMEDY" ], [ "DABANGG 2", "release_year", "2012" ], [ "DARK SHADOWS", "has_genre", "COMEDY" ], [ "DARK SHADOWS", "release_year", "2012" ], [ "DETENTION OF THE DEAD", "has_genre", "COMEDY" ], [ "DETENTION OF THE DEAD", "release_year", "2012" ], [ "ENGLISH VINGLISH", "has_genre", "COMEDY" ], [ "ENGLISH VINGLISH", "release_year", "2012" ], [ "FAMILY WAY", "has_genre", "COMEDY" ], [ "FAMILY WAY", "release_year", "2012" ], [ "FAT KID RULES THE WORLD", "has_genre", "COMEDY" ], [ "FAT KID RULES THE WORLD", "release_year", "2012" ], [ "FOODFIGHT!", "has_genre", "COMEDY" ], [ "FOODFIGHT!", "release_year", "2012" ], [ "FOR A GOOD TIME, CALL...", "has_genre", "COMEDY" ], [ "FOR A GOOD TIME, CALL...", "release_year", "2012" ], [ "FRANCES HA", "has_genre", "COMEDY" ], [ "FRANCES HA", "release_year", "2012" ], [ "FREE SAMPLES", "has_genre", "COMEDY" ], [ "FREE SAMPLES", "release_year", "2012" ], [ "FUN SIZE", "has_genre", "COMEDY" ], [ "FUN SIZE", "release_year", "2012" ], [ "GAMBIT", "has_genre", "COMEDY" ], [ "GAMBIT", "release_year", "2012" ], [ "GIMME THE LOOT", "has_genre", "COMEDY" ], [ "GIMME THE LOOT", "release_year", "2012" ], [ "GIRL MOST LIKELY", "has_genre", "COMEDY" ], [ "GIRL MOST LIKELY", "release_year", "2012" ], [ "GOATS", "has_genre", "COMEDY" ], [ "GOATS", "release_year", "2012" ], [ "GUNS, GIRLS AND GAMBLING", "has_genre", "COMEDY" ], [ "GUNS, GIRLS AND GAMBLING", "release_year", "2012" ], [ "HAPPINESS NEVER COMES ALONE", "has_genre", "COMEDY" ], [ "HAPPINESS NEVER COMES ALONE", "release_year", "2012" ], [ "HERE COMES THE BOOM", "has_genre", "COMEDY" ], [ "HERE COMES THE BOOM", "release_year", "2012" ], [ "HIGH SOCIETY", "has_genre", "COMEDY" ], [ "HIT AND RUN", "has_genre", "COMEDY" ], [ "HIT AND RUN", "release_year", "2012" ], [ "HOPE SPRINGS", "has_genre", "COMEDY" ], [ "HOPE SPRINGS", "release_year", "2012" ], [ "HOTEL TRANSYLVANIA", "has_genre", "COMEDY" ], [ "HOTEL TRANSYLVANIA", "release_year", "2012" ], [ "HOUSEFULL 2", "has_genre", "COMEDY" ], [ "HOUSEFULL 2", "release_year", "2012" ], [ "HYDE PARK ON HUDSON", "has_genre", "COMEDY" ], [ "HYDE PARK ON HUDSON", "release_year", "2012" ], [ "I DO", "has_genre", "COMEDY" ], [ "I DO", "release_year", "2012" ], [ "IT'S A DISASTER", "has_genre", "COMEDY" ], [ "IT'S A DISASTER", "has_tags", "COMEDY" ], [ "IT'S A DISASTER", "release_year", "2012" ], [ "JESUS HENRY CHRIST", "has_genre", "COMEDY" ], [ "JESUS HENRY CHRIST", "release_year", "2012" ], [ "JOHN DIES AT THE END", "has_genre", "COMEDY" ], [ "JOHN DIES AT THE END", "has_tags", "COMEDY" ], [ "JOHN DIES AT THE END", "release_year", "2012" ], [ "JOKER", "has_genre", "COMEDY" ], [ "JOKER", "release_year", "2012" ], [ "JOYFUL NOISE", "has_genre", "COMEDY" ], [ "JOYFUL NOISE", "release_year", "2012" ], [ "LAY THE FAVORITE", "has_genre", "COMEDY" ], [ "LAY THE FAVORITE", "release_year", "2012" ], [ "LIBERAL ARTS", "has_genre", "COMEDY" ], [ "LIBERAL ARTS", "release_year", "2012" ], [ "LOL", "has_genre", "COMEDY" ], [ "LOL", "release_year", "2012" ], [ "LOLA VERSUS", "has_genre", "COMEDY" ], [ "LOLA VERSUS", "release_year", "2012" ], [ "LOVE AND OTHER TROUBLES", "has_genre", "COMEDY" ], [ "LOVE AND OTHER TROUBLES", "release_year", "2012" ], [ "LOVE IS ALL YOU NEED", "has_genre", "COMEDY" ], [ "LOVE IS ALL YOU NEED", "release_year", "2012" ], [ "LUV", "has_genre", "COMEDY" ], [ "LUV", "release_year", "2012" ], [ "MADEA'S WITNESS PROTECTION", "has_genre", "COMEDY" ], [ "MADEA'S WITNESS PROTECTION", "release_year", "2012" ], [ "MAGIC MIKE", "has_genre", "COMEDY" ], [ "MAGIC MIKE", "release_year", "2012" ], [ "MEN IN BLACK 3", "has_genre", "COMEDY" ], [ "MEN IN BLACK 3", "release_year", "2012" ], [ "MENTAL", "has_genre", "COMEDY" ], [ "MENTAL", "release_year", "2012" ], [ "MIDDLE OF NOWHERE", "has_genre", "COMEDY" ], [ "MIDDLE OF NOWHERE", "release_year", "2012" ], [ "MIRROR MIRROR", "has_genre", "COMEDY" ], [ "MIRROR MIRROR", "release_year", "2012" ], [ "MOONRISE KINGDOM", "has_genre", "COMEDY" ], [ "MOONRISE KINGDOM", "has_tags", "COMEDY" ], [ "MOONRISE KINGDOM", "release_year", "2012" ], [ "MUCH ADO ABOUT NOTHING", "has_genre", "COMEDY" ], [ "MUCH ADO ABOUT NOTHING", "has_tags", "COMEDY" ], [ "MUCH ADO ABOUT NOTHING", "release_year", "2012" ], [ "MUSHROOMING", "has_genre", "COMEDY" ], [ "MUSHROOMING", "release_year", "2012" ], [ "MY AWKWARD SEXUAL ADVENTURE", "has_genre", "COMEDY" ], [ "MY AWKWARD SEXUAL ADVENTURE", "release_year", "2012" ], [ "NOT SUITABLE FOR CHILDREN", "has_genre", "COMEDY" ], [ "NOT SUITABLE FOR CHILDREN", "has_tags", "COMEDY" ], [ "NOT SUITABLE FOR CHILDREN", "release_year", "2012" ], [ "ONE FOR THE MONEY", "has_genre", "COMEDY" ], [ "ONE FOR THE MONEY", "release_year", "2012" ], [ "OVERNIGHT DELIVERY", "directed_by", "JASON BLOOM" ], [ "OVERNIGHT DELIVERY", "has_genre", "COMEDY" ], [ "PAPERMAN", "has_genre", "COMEDY" ], [ "PAPERMAN", "release_year", "2012" ], [ "PARANORMAN", "has_genre", "COMEDY" ], [ "PARANORMAN", "release_year", "2012" ], [ "PARENTAL GUIDANCE", "has_genre", "COMEDY" ], [ "PARENTAL GUIDANCE", "release_year", "2012" ], [ "PAULETTE", "has_genre", "COMEDY" ], [ "PAULETTE", "release_year", "2012" ], [ "PIRANHA 3DD", "has_genre", "COMEDY" ], [ "PIRANHA 3DD", "release_year", "2012" ], [ "PITCH PERFECT", "has_genre", "COMEDY" ], [ "PITCH PERFECT", "release_year", "2012" ], [ "PLAYING FOR KEEPS", "has_genre", "COMEDY" ], [ "PLAYING FOR KEEPS", "release_year", "2012" ], [ "POPULAIRE", "has_genre", "COMEDY" ], [ "POPULAIRE", "release_year", "2012" ], [ "PRICE CHECK", "has_genre", "COMEDY" ], [ "PRICE CHECK", "release_year", "2012" ], [ "PROJECT X", "has_genre", "COMEDY" ], [ "PROJECT X", "release_year", "2012" ], [ "QUARTET", "has_genre", "COMEDY" ], [ "QUARTET", "has_tags", "COMEDY" ], [ "QUARTET", "release_year", "2012" ], [ "REVENGE FOR JOLLY!", "has_genre", "COMEDY" ], [ "REVENGE FOR JOLLY!", "release_year", "2012" ], [ "ROCK OF AGES", "has_genre", "COMEDY" ], [ "ROCK OF AGES", "release_year", "2012" ], [ "RUBY SPARKS", "has_genre", "COMEDY" ], [ "RUBY SPARKS", "release_year", "2012" ], [ "SAFETY NOT GUARANTEED", "has_genre", "COMEDY" ], [ "SAFETY NOT GUARANTEED", "release_year", "2012" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "has_genre", "COMEDY" ], [ "SEEKING A FRIEND FOR THE END OF THE WORLD", "release_year", "2012" ], [ "SEVEN PSYCHOPATHS", "has_genre", "COMEDY" ], [ "SEVEN PSYCHOPATHS", "release_year", "2012" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "has_genre", "COMEDY" ], [ "SEXUAL CHRONICLES OF A FRENCH FAMILY", "release_year", "2012" ], [ "SHANGHAI CALLING", "has_genre", "COMEDY" ], [ "SHANGHAI CALLING", "release_year", "2012" ], [ "SIGHTSEERS", "has_genre", "COMEDY" ], [ "SIGHTSEERS", "release_year", "2012" ], [ "SILVER LININGS PLAYBOOK", "has_genre", "COMEDY" ], [ "SILVER LININGS PLAYBOOK", "release_year", "2012" ], [ "SLEEPWALK WITH ME", "has_genre", "COMEDY" ], [ "SLEEPWALK WITH ME", "release_year", "2012" ], [ "SMALL APARTMENTS", "has_genre", "COMEDY" ], [ "SMALL APARTMENTS", "release_year", "2012" ], [ "SOMEBODY UP THERE LIKES ME", "has_genre", "COMEDY" ], [ "SOMEBODY UP THERE LIKES ME", "release_year", "2012" ], [ "STAND UP GUYS", "has_genre", "COMEDY" ], [ "STAND UP GUYS", "release_year", "2012" ], [ "STITCHES", "has_genre", "COMEDY" ], [ "STITCHES", "release_year", "2012" ], [ "STRUCK BY LIGHTNING", "has_genre", "COMEDY" ], [ "STRUCK BY LIGHTNING", "release_year", "2012" ], [ "STUCK IN LOVE", "has_genre", "COMEDY" ], [ "STUCK IN LOVE", "release_year", "2012" ], [ "STUDENT OF THE YEAR", "has_genre", "COMEDY" ], [ "STUDENT OF THE YEAR", "release_year", "2012" ], [ "TED", "has_genre", "COMEDY" ], [ "TED", "has_tags", "COMEDY" ], [ "TED", "release_year", "2012" ], [ "THANKS FOR SHARING", "has_genre", "COMEDY" ], [ "THANKS FOR SHARING", "release_year", "2012" ], [ "THAT'S MY BOY", "has_genre", "COMEDY" ], [ "THAT'S MY BOY", "release_year", "2012" ], [ "THE ABCS OF DEATH", "has_genre", "COMEDY" ], [ "THE ABCS OF DEATH", "release_year", "2012" ], [ "THE BABYMAKERS", "has_genre", "COMEDY" ], [ "THE BABYMAKERS", "release_year", "2012" ], [ "THE BAYTOWN OUTLAWS", "has_genre", "COMEDY" ], [ "THE BAYTOWN OUTLAWS", "release_year", "2012" ], [ "THE CAMPAIGN", "has_genre", "COMEDY" ], [ "THE CAMPAIGN", "release_year", "2012" ], [ "THE COMEDY", "has_tags", "COMEDY" ], [ "THE COMEDY", "release_year", "2012" ], [ "THE DICTATOR", "has_genre", "COMEDY" ], [ "THE DICTATOR", "release_year", "2012" ], [ "THE END", "has_genre", "COMEDY" ], [ "THE END", "release_year", "2012" ], [ "THE FIRST TIME", "has_genre", "COMEDY" ], [ "THE FIRST TIME", "release_year", "2012" ], [ "THE FIVE-YEAR ENGAGEMENT", "has_genre", "COMEDY" ], [ "THE FIVE-YEAR ENGAGEMENT", "release_year", "2012" ], [ "THE GUILT TRIP", "has_genre", "COMEDY" ], [ "THE GUILT TRIP", "release_year", "2012" ], [ "THE LORAX", "has_genre", "COMEDY" ], [ "THE LORAX", "release_year", "2012" ], [ "THE NEWEST PLEDGE", "has_genre", "COMEDY" ], [ "THE NEWEST PLEDGE", "release_year", "2012" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "has_genre", "COMEDY" ], [ "THE ODD LIFE OF TIMOTHY GREEN", "release_year", "2012" ], [ "THE PLAYERS", "has_genre", "COMEDY" ], [ "THE PLAYERS", "release_year", "2012" ], [ "THE RAVEN", "has_genre", "COMEDY" ], [ "THE RAVEN", "release_year", "2012" ], [ "THE SAPPHIRES", "has_genre", "COMEDY" ], [ "THE SAPPHIRES", "release_year", "2012" ], [ "THE STORY OF LUKE", "has_genre", "COMEDY" ], [ "THE STORY OF LUKE", "release_year", "2012" ], [ "THE THREE STOOGES", "has_genre", "COMEDY" ], [ "THE THREE STOOGES", "release_year", "2012" ], [ "THE WATCH", "has_genre", "COMEDY" ], [ "THE WATCH", "release_year", "2012" ], [ "THINK LIKE A MAN", "has_genre", "COMEDY" ], [ "THINK LIKE A MAN", "release_year", "2012" ], [ "THIS IS 40", "has_genre", "COMEDY" ], [ "THIS IS 40", "release_year", "2012" ], [ "THIS MEANS WAR", "has_genre", "COMEDY" ], [ "THIS MEANS WAR", "release_year", "2012" ], [ "TIM AND ERIC'S BILLION DOLLAR MOVIE", "has_genre", "COMEDY" ], [ "TIM AND ERIC'S BILLION DOLLAR MOVIE", "release_year", "2012" ], [ "TO ROME WITH LOVE", "has_genre", "COMEDY" ], [ "TO ROME WITH LOVE", "has_tags", "COMEDY" ], [ "TO ROME WITH LOVE", "release_year", "2012" ], [ "TOOTH FAIRY 2", "has_genre", "COMEDY" ], [ "TOOTH FAIRY 2", "release_year", "2012" ], [ "VAMPS", "has_genre", "COMEDY" ], [ "VAMPS", "release_year", "2012" ], [ "VICKY DONOR", "has_genre", "COMEDY" ], [ "VICKY DONOR", "release_year", "2012" ], [ "WANDERLUST", "has_genre", "COMEDY" ], [ "WANDERLUST", "release_year", "2012" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "has_genre", "COMEDY" ], [ "WHAT TO EXPECT WHEN YOU'RE EXPECTING", "release_year", "2012" ], [ "WHAT'S IN A NAME?", "has_genre", "COMEDY" ], [ "WHAT'S IN A NAME?", "release_year", "2012" ], [ "WRECK-IT RALPH", "has_genre", "COMEDY" ], [ "WRECK-IT RALPH", "release_year", "2012" ], [ "WRONG", "has_genre", "COMEDY" ], [ "WRONG", "release_year", "2012" ] ] }