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media in the fraser valley | https://en.wikipedia.org/wiki/Media_in_the_Fraser_Valley | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18520022-1.html.csv | majority | the majority of fraser valley radio stations are owned by the canadian broadcasting corporation . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'canadian broadcasting corporation', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'owner', 'canadian broadcasting corporation'], 'result': True, 'ind': 0, 'tointer': 'for the owner records of all rows , most of them fuzzily match to canadian broadcasting corporation .', 'tostr': 'most_eq { all_rows ; owner ; canadian broadcasting corporation } = true'} | most_eq { all_rows ; owner ; canadian broadcasting corporation } = true | for the owner records of all rows , most of them fuzzily match to canadian broadcasting corporation . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'owner_3': 3, 'canadian broadcasting corporation_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'owner_3': 'owner', 'canadian broadcasting corporation_4': 'canadian broadcasting corporation'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'owner_3': [0], 'canadian broadcasting corporation_4': [0]} | ['frequency', 'call sign', 'format', 'owner', 'notes'] | [['fm 88.1', 'vf2522', 'tourist information', 'cameron bell consultancy', 'licensed to chilliwack'], ['fm 88.5', 'cbu - 1 - fm', 'news / talk', 'canadian broadcasting corporation', 'licensed to abbotsford'], ['fm 89.5', 'chwk - fm', 'active rock', 'fabmar communications', 'licensed to chilliwack'], ['fm 91.7', 'cbyf - fm', 'news / talk', 'canadian broadcasting corporation', 'licensed to chilliwack'], ['fm 92.5', 'cfun - fm - 1', 'contemporary hit radio', 'rogers media', 'licensed to abbotsford'], ['fm 96.7', 'cbyh - fm', 'news / talk', 'canadian broadcasting corporation', 'licensed to harrison hot springs'], ['fm 98.3', 'cksr - fm', 'adult contemporary', 'rogers media', 'licensed to chilliwack'], ['fm 99.9', 'cbu - fm - 7', 'public music', 'canadian broadcasting corporation', 'licensed to chilliwack'], ['fm 100.5', 'cfsr - fm', 'adult contemporary', 'rogers media', 'licensed to hope'], ['fm 101.7', 'civl - fm', 'campus radio', 'university of the fraser valley', 'licensed to abbotsford'], ['fm 101.7', 'cbue - fm', 'news / talk', 'canadian broadcasting corporation', 'licensed to hope'], ['fm 102.1', 'cbuf - fm - 1', 'news / talk', 'canadian broadcasting corporation', 'licensed to chilliwack'], ['fm 107.1', 'ckqc - fm', 'country', 'rogers media', 'licensed to abbotsford'], ['fm 107.5', 'cfun - fm', 'contemporary hit radio', 'rogers media', 'licensed to chilliwack']] |
ernie irvan | https://en.wikipedia.org/wiki/Ernie_Irvan | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1256150-1.html.csv | majority | when ernie irvan 's manufacturer was chevrolet , the team was always morgan - mcclure . | {'scope': 'subset', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'morgan-mcclure', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'chevrolet'}} | {'func': 'all_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manufacturer', 'chevrolet'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; manufacturer ; chevrolet }', 'tointer': 'select the rows whose manufacturer record fuzzily matches to chevrolet .'}, 'team', 'morgan-mcclure'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose manufacturer record fuzzily matches to chevrolet . for the team records of these rows , all of them fuzzily match to morgan-mcclure .', 'tostr': 'all_eq { filter_eq { all_rows ; manufacturer ; chevrolet } ; team ; morgan-mcclure } = true'} | all_eq { filter_eq { all_rows ; manufacturer ; chevrolet } ; team ; morgan-mcclure } = true | select the rows whose manufacturer record fuzzily matches to chevrolet . for the team records of these rows , all of them fuzzily match to morgan-mcclure . | 2 | 2 | {'all_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'manufacturer_4': 4, 'chevrolet_5': 5, 'team_6': 6, 'morgan-mcclure_7': 7} | {'all_str_eq_1': 'all_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'manufacturer_4': 'manufacturer', 'chevrolet_5': 'chevrolet', 'team_6': 'team', 'morgan-mcclure_7': 'morgan-mcclure'} | {'all_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'manufacturer_4': [0], 'chevrolet_5': [0], 'team_6': [1], 'morgan-mcclure_7': [1]} | ['year', 'manufacturer', 'start', 'finish', 'team'] | [['1988', 'pontiac', 'dnq', 'dnq', 'ulrich'], ['1989', 'pontiac', '33', '41', 'ulrich'], ['1990', 'ford', '18', '13', 'donlavey'], ['1991', 'chevrolet', '2', '1', 'morgan - mcclure'], ['1992', 'chevrolet', '7', '28', 'morgan - mcclure'], ['1993', 'chevrolet', '8', '37', 'morgan - mcclure'], ['1994', 'ford', '3', '2', 'yates'], ['1996', 'ford', '2', '35', 'yates'], ['1997', 'ford', '5', '20', 'yates'], ['1998', 'pontiac', '10', '6', 'mb2'], ['1999', 'pontiac', '31', '14', 'mb2']] |
outcasts ( tv series ) | https://en.wikipedia.org/wiki/Outcasts_%28TV_series%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29574579-1.html.csv | unique | episode 7 was the only episode that had an original air date in the month of march . | {'scope': 'all', 'row': '7', 'col': '7', 'col_other': '1,2', 'criterion': 'fuzzily_match', 'value': 'march', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'original air date', 'march'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose original air date record fuzzily matches to march .', 'tostr': 'filter_eq { all_rows ; original air date ; march }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; original air date ; march } }', 'tointer': 'select the rows whose original air date record fuzzily matches to march . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'original air date', 'march'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose original air date record fuzzily matches to march .', 'tostr': 'filter_eq { all_rows ; original air date ; march }'}, 'episode'], 'result': '7', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; original air date ; march } ; episode }'}, '7'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; original air date ; march } ; episode } ; 7 }', 'tointer': 'the episode record of this unqiue row is 7 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'original air date', 'march'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose original air date record fuzzily matches to march .', 'tostr': 'filter_eq { all_rows ; original air date ; march }'}, 'title'], 'result': 'episode 7', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; original air date ; march } ; title }'}, 'episode 7'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; original air date ; march } ; title } ; episode 7 }', 'tointer': 'the title record of this unqiue row is episode 7 .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; original air date ; march } ; episode } ; 7 } ; eq { hop { filter_eq { all_rows ; original air date ; march } ; title } ; episode 7 } }', 'tointer': 'the episode record of this unqiue row is 7 . the title record of this unqiue row is episode 7 .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; original air date ; march } } ; and { eq { hop { filter_eq { all_rows ; original air date ; march } ; episode } ; 7 } ; eq { hop { filter_eq { all_rows ; original air date ; march } ; title } ; episode 7 } } } = true', 'tointer': 'select the rows whose original air date record fuzzily matches to march . there is only one such row in the table . the episode record of this unqiue row is 7 . the title record of this unqiue row is episode 7 .'} | and { only { filter_eq { all_rows ; original air date ; march } } ; and { eq { hop { filter_eq { all_rows ; original air date ; march } ; episode } ; 7 } ; eq { hop { filter_eq { all_rows ; original air date ; march } ; title } ; episode 7 } } } = true | select the rows whose original air date record fuzzily matches to march . there is only one such row in the table . the episode record of this unqiue row is 7 . the title record of this unqiue row is episode 7 . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'original air date_10': 10, 'march_11': 11, 'and_6': 6, 'eq_3': 3, 'num_hop_2': 2, 'episode_12': 12, '7_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'title_14': 14, 'episode 7_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'original air date_10': 'original air date', 'march_11': 'march', 'and_6': 'and', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'episode_12': 'episode', '7_13': '7', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'title_14': 'title', 'episode 7_15': 'episode 7'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'original air date_10': [0], 'march_11': [0], 'and_6': [7], 'eq_3': [6], 'num_hop_2': [3], 'episode_12': [2], '7_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'title_14': [4], 'episode 7_15': [5]} | ['episode', 'title', 'directed by', 'written by', 'uk viewers ( million )', 'share ( % )', 'original air date'] | [['1', 'episode 1', 'bharat nalluri', 'ben richards', '4.50', '17.9', '7 february 2011'], ['2', 'episode 2', 'bharat nalluri', 'ben richards', '3.30', '13.0', '8 february 2011'], ['3', 'episode 3', 'omar madha', 'ben richards and simon block', '2.95', '11.8', '14 february 2011'], ['4', 'episode 4', 'omar madha', 'jack lothian', '2.63', '10.05', '15 february 2011'], ['5', 'episode 5', 'andy goddard', 'ben richards and jimmy gardner', '2.70', '10.8', '21 february 2011'], ['6', 'episode 6', 'andy goddard', 'david farr', '1.52', '10.5', '27 february 2011'], ['7', 'episode 7', 'jamie payne', 'david farr', '1.33', '9.7', '6 march 2011']] |
1969 world judo championships | https://en.wikipedia.org/wiki/1969_World_Judo_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15807914-2.html.csv | ordinal | japan finished first at the 1969 world judo championships with 12 total medals including 6 gold . | {'scope': 'all', 'row': '1', 'col': '1', 'order': '1', 'col_other': '2,3,6', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'rank', '1'], 'result': '1', 'ind': 0, 'tostr': 'nth_min { all_rows ; rank ; 1 }', 'tointer': 'the 1st minimum rank record of all rows is 1 .'}, '1'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; rank ; 1 } ; 1 }', 'tointer': 'the 1st minimum rank record of all rows is 1 .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; rank ; 1 }'}, 'nation'], 'result': 'japan', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 1 } ; nation }'}, 'japan'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 1 } ; nation } ; japan }', 'tointer': 'the nation record of the row with 1st minimum rank record is japan .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; rank ; 1 }'}, 'gold'], 'result': '6', 'ind': 5, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 1 } ; gold }'}, '6'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 1 } ; gold } ; 6 }', 'tointer': 'the gold record of the row with 1st minimum rank record is 6 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; rank ; 1 }'}, 'total'], 'result': '12', 'ind': 7, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 1 } ; total }'}, '12'], 'result': True, 'ind': 8, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 1 } ; total } ; 12 }', 'tointer': 'the total record of the row with 1st minimum rank record is 12 .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; rank ; 1 } ; gold } ; 6 } ; eq { hop { nth_argmin { all_rows ; rank ; 1 } ; total } ; 12 } }', 'tointer': 'the gold record of the row with 1st minimum rank record is 6 . the total record of the row with 1st minimum rank record is 12 .'}], 'result': True, 'ind': 10, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; rank ; 1 } ; nation } ; japan } ; and { eq { hop { nth_argmin { all_rows ; rank ; 1 } ; gold } ; 6 } ; eq { hop { nth_argmin { all_rows ; rank ; 1 } ; total } ; 12 } } }', 'tointer': 'the nation record of the row with 1st minimum rank record is japan . the gold record of the row with 1st minimum rank record is 6 . the total record of the row with 1st minimum rank record is 12 .'}], 'result': True, 'ind': 11, 'tostr': 'and { eq { nth_min { all_rows ; rank ; 1 } ; 1 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 1 } ; nation } ; japan } ; and { eq { hop { nth_argmin { all_rows ; rank ; 1 } ; gold } ; 6 } ; eq { hop { nth_argmin { all_rows ; rank ; 1 } ; total } ; 12 } } } } = true', 'tointer': 'the 1st minimum rank record of all rows is 1 . the nation record of the row with 1st minimum rank record is japan . the gold record of the row with 1st minimum rank record is 6 . the total record of the row with 1st minimum rank record is 12 .'} | and { eq { nth_min { all_rows ; rank ; 1 } ; 1 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 1 } ; nation } ; japan } ; and { eq { hop { nth_argmin { all_rows ; rank ; 1 } ; gold } ; 6 } ; eq { hop { nth_argmin { all_rows ; rank ; 1 } ; total } ; 12 } } } } = true | the 1st minimum rank record of all rows is 1 . the nation record of the row with 1st minimum rank record is japan . the gold record of the row with 1st minimum rank record is 6 . the total record of the row with 1st minimum rank record is 12 . | 14 | 12 | {'and_11': 11, 'result_12': 12, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_13': 13, 'rank_14': 14, '1_15': 15, '1_16': 16, 'and_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_17': 17, 'rank_18': 18, '1_19': 19, 'nation_20': 20, 'japan_21': 21, 'and_9': 9, 'eq_6': 6, 'num_hop_5': 5, 'gold_22': 22, '6_23': 23, 'eq_8': 8, 'num_hop_7': 7, 'total_24': 24, '12_25': 25} | {'and_11': 'and', 'result_12': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_13': 'all_rows', 'rank_14': 'rank', '1_15': '1', '1_16': '1', 'and_10': 'and', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_17': 'all_rows', 'rank_18': 'rank', '1_19': '1', 'nation_20': 'nation', 'japan_21': 'japan', 'and_9': 'and', 'eq_6': 'eq', 'num_hop_5': 'num_hop', 'gold_22': 'gold', '6_23': '6', 'eq_8': 'eq', 'num_hop_7': 'num_hop', 'total_24': 'total', '12_25': '12'} | {'and_11': [12], 'result_12': [], 'eq_1': [11], 'nth_min_0': [1], 'all_rows_13': [0], 'rank_14': [0], '1_15': [0], '1_16': [1], 'and_10': [11], 'str_eq_4': [10], 'str_hop_3': [4], 'nth_argmin_2': [3, 5, 7], 'all_rows_17': [2], 'rank_18': [2], '1_19': [2], 'nation_20': [3], 'japan_21': [4], 'and_9': [10], 'eq_6': [9], 'num_hop_5': [6], 'gold_22': [5], '6_23': [6], 'eq_8': [9], 'num_hop_7': [8], 'total_24': [7], '12_25': [8]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'japan', '6', '3', '3', '12'], ['2', 'germany', '0', '2', '0', '2'], ['3', 'netherlands', '0', '1', '2', '3'], ['4', 'soviet union', '0', '0', '4', '4'], ['5', 'south korea', '0', '0', '3', '3']] |
mark keil | https://en.wikipedia.org/wiki/Mark_Keil | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10670367-1.html.csv | majority | the majority of tennis tournaments that mark keil played in were on a hard surface . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'hard', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'hard'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to hard .', 'tostr': 'most_eq { all_rows ; surface ; hard } = true'} | most_eq { all_rows ; surface ; hard } = true | for the surface records of all rows , most of them fuzzily match to hard . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'hard_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'hard_4': 'hard'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'hard_4': [0]} | ['date', 'tournament', 'surface', 'partner', 'opponents in the final', 'score'] | [['march 2 , 1992', 'scottsdale , us', 'hard', 'dave randall', 'kent kinnear & sven salumaa', '4 - 6 6 - 1 6 - 2'], ['march 1 , 1993', 'scottsdale , usa', 'hard', 'dave randall', 'luke jensen & sandon stolle', '7 - 5 , 6 - 4'], ['april 4 , 1993', 'osaka , japan', 'hard', 'christo van rensburg', 'glenn michibata & david pate', '7 - 6 6 - 3'], ['march 12 , 1995', 'copenhagen , denmark', 'carpet', 'peter nyborg', 'guillaume raoux & greg rusedski', '6 - 7 6 - 4 7 - 6'], ['march 12 , 1995', 'bucharest , romania', 'clay', 'jeff tarango', 'cyril suk & daniel vacek', '6 - 4 7 - 6']] |
list of ministers for the police force of luxembourg | https://en.wikipedia.org/wiki/List_of_Ministers_for_the_Police_Force_of_Luxembourg | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16620096-1.html.csv | ordinal | of the ministers for the police force of luxembourg , the one with the 4th earliest start date was marc fischbach . | {'row': '4', 'col': '3', 'order': '4', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'start date', '4'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; start date ; 4 }'}, 'minister'], 'result': 'marc fischbach', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; start date ; 4 } ; minister }'}, 'marc fischbach'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; start date ; 4 } ; minister } ; marc fischbach } = true', 'tointer': 'select the row whose start date record of all rows is 4th minimum . the minister record of this row is marc fischbach .'} | eq { hop { nth_argmin { all_rows ; start date ; 4 } ; minister } ; marc fischbach } = true | select the row whose start date record of all rows is 4th minimum . the minister record of this row is marc fischbach . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'start date_5': 5, '4_6': 6, 'minister_7': 7, 'marc fischbach_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'start date_5': 'start date', '4_6': '4', 'minister_7': 'minister', 'marc fischbach_8': 'marc fischbach'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'start date_5': [0], '4_6': [0], 'minister_7': [1], 'marc fischbach_8': [2]} | ['minister', 'party', 'start date', 'end date', 'prime minister'] | [['eugène schaus', 'dp', '6 february 1969', '15 june 1974', 'pierre werner'], ['émile krieps', 'dp', '15 june 1974', '16 july 1979', 'gaston thorn'], ['émile krieps', 'dp', '16 july 1979', '20 july 1984', 'pierre werner'], ['marc fischbach', 'csv', '20 july 1984', '14 july 1989', 'jacques santer'], ['jacques poos', 'lsap', '14 july 1989', '13 july 1994', 'jacques santer'], ['alex bodry', 'lsap', '13 july 1994', '26 january 1995', 'jacques santer'], ['alex bodry', 'lsap', '26 january 1995', '7 august 1999', 'jean - claude juncker']] |
2012 in film | https://en.wikipedia.org/wiki/2012_in_film | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16921964-1.html.csv | unique | the hunger games is the only 2012 that was directed by gary ross . | {'scope': 'all', 'row': '9', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': 'gary ross', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'director ( s )', 'gary ross'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose director ( s ) record fuzzily matches to gary ross .', 'tostr': 'filter_eq { all_rows ; director ( s ) ; gary ross }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; director ( s ) ; gary ross } }', 'tointer': 'select the rows whose director ( s ) record fuzzily matches to gary ross . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'director ( s )', 'gary ross'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose director ( s ) record fuzzily matches to gary ross .', 'tostr': 'filter_eq { all_rows ; director ( s ) ; gary ross }'}, 'title'], 'result': 'the hunger games', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; director ( s ) ; gary ross } ; title }'}, 'the hunger games'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; director ( s ) ; gary ross } ; title } ; the hunger games }', 'tointer': 'the title record of this unqiue row is the hunger games .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; director ( s ) ; gary ross } } ; eq { hop { filter_eq { all_rows ; director ( s ) ; gary ross } ; title } ; the hunger games } } = true', 'tointer': 'select the rows whose director ( s ) record fuzzily matches to gary ross . there is only one such row in the table . the title record of this unqiue row is the hunger games .'} | and { only { filter_eq { all_rows ; director ( s ) ; gary ross } } ; eq { hop { filter_eq { all_rows ; director ( s ) ; gary ross } ; title } ; the hunger games } } = true | select the rows whose director ( s ) record fuzzily matches to gary ross . there is only one such row in the table . the title record of this unqiue row is the hunger games . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'director (s)_7': 7, 'gary ross_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'the hunger games_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'director (s)_7': 'director ( s )', 'gary ross_8': 'gary ross', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'the hunger games_10': 'the hunger games'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'director (s)_7': [0], 'gary ross_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'the hunger games_10': [3]} | ['rank', 'title', 'studio', 'director ( s )', 'worldwide gross'] | [['1', 'the avengers', 'marvel / disney', 'joss whedon', '1511757910'], ['2', 'skyfall', 'mgm / columbia pictures', 'sam mendes', '1108561013'], ['3', 'the dark knight rises', 'warner bros / legendary pictures', 'christopher nolan', '1084439099'], ['4', 'the hobbit : an unexpected journey', 'warner bros / mgm / new line', 'peter jackson', '1017003568'], ['5', 'ice age : continental drift', '20th century fox / blue sky', 'steve martino and mike thurmeier', '877244782'], ['6', 'the twilight saga : breaking dawn - part 2', 'lionsgate / summit', 'bill condon', '829224737'], ['7', 'the amazing spider - man', 'columbia pictures', 'marc webb', '752216557'], ['8', "madagascar 3 : europe 's most wanted", 'paramount / dreamworks', 'eric darnell , tom mcgrath and conrad vernon', '746921274'], ['9', 'the hunger games', 'lionsgate', 'gary ross', '691247768'], ['10', 'men in black 3', 'columbia pictures', 'barry sonnenfeld', '624026776'], ['11', 'life of pi', '20th century fox', 'ang lee', '609016565'], ['12', 'ted', 'universal pictures', 'seth macfarlane', '549368315'], ['13', 'brave', 'walt disney pictures / pixar animation studios', 'mark andrews and brenda chapman', '538983207'], ['14', 'wreck - it ralph', 'walt disney pictures', 'rich moore', '471222889'], ['15', 'les misérables', 'universal pictures', 'tom hooper', '441809770'], ['16', 'the intouchables', 'gaumont film company', 'olivier nakache and éric toledano', '426588510'], ['17', 'django unchained', 'the weinstein company / columbia pictures', 'quentin tarantino', '425368238'], ['18', 'prometheus', '20th century fox', 'ridley scott', '403354469'], ['19', 'snow white and the huntsman', 'universal pictures', 'rupert sanders', '396592829'], ['20', 'taken 2', '20th century fox', 'olivier megaton', '376141306']] |
1980 - 81 segunda división | https://en.wikipedia.org/wiki/1980%E2%80%9381_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12189375-2.html.csv | majority | most of the clubs had less than 15 wins . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '15', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'wins', '15'], 'result': True, 'ind': 0, 'tointer': 'for the wins records of all rows , most of them are less than 15 .', 'tostr': 'most_less { all_rows ; wins ; 15 } = true'} | most_less { all_rows ; wins ; 15 } = true | for the wins records of all rows , most of them are less than 15 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'wins_3': 3, '15_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'wins_3': 'wins', '15_4': '15'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'wins_3': [0], '15_4': [0]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'cd castellón', '38', '45 + 7', '15', '15', '8', '45', '33', '+ 12'], ['2', 'cádiz cf', '38', '45 + 7', '19', '7', '12', '55', '37', '+ 18'], ['3', 'racing de santander', '38', '45 + 7', '18', '9', '11', '48', '40', '+ 8'], ['4', 'elche cf', '38', '45 + 7', '17', '11', '10', '55', '44', '+ 11'], ['5', 'rayo vallecano', '38', '45 + 7', '15', '15', '8', '37', '23', '+ 14'], ['6', 'cd málaga', '38', '42 + 4', '14', '14', '10', '47', '45', '+ 2'], ['7', 'ce sabadell fc', '38', '42 + 4', '16', '10', '12', '45', '44', '+ 1'], ['8', 'deportivo alavés', '38', '39 + 1', '15', '9', '14', '49', '35', '+ 14'], ['9', 'levante ud', '38', '38', '15', '8', '15', '36', '37', '- 1'], ['10', 'real oviedo', '38', '37 - 1', '11', '15', '12', '37', '39', '- 2'], ['11', 'castilla cf', '38', '36 - 2', '14', '8', '16', '50', '44', '+ 6'], ['12', 'linares', '38', '36 - 2', '13', '10', '15', '36', '42', '- 6'], ['13', 'getafe deportivo', '38', '35 - 3', '10', '15', '13', '41', '50', '- 9'], ['14', 'atlético madrileño', '38', '35 - 3', '13', '9', '16', '44', '55', '- 11'], ['15', 'burgos', '38', '35 - 3', '13', '9', '16', '47', '52', '- 5'], ['16', 'recreativo de huelva', '38', '35 - 3', '12', '11', '15', '33', '37', '- 4'], ['17', 'granada cf', '38', '33 - 5', '10', '13', '15', '33', '45', '- 12'], ['18', 'palencia cf', '38', '32 - 6', '12', '8', '18', '30', '37', '- 7'], ['19', 'barakaldo cf', '38', '31 - 7', '11', '9', '18', '34', '46', '- 12'], ['20', 'agd ceuta', '38', '29 - 9', '11', '7', '20', '33', '50', '- 17']] |
emergency shipbuilding program | https://en.wikipedia.org/wiki/Emergency_Shipbuilding_program | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11552751-4.html.csv | count | four of the emergency shipbuilding yards are located in the state of wisconsin . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'wisconsin', 'result': '4', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location ( city , state )', 'wisconsin'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location ( city , state ) record fuzzily matches to wisconsin .', 'tostr': 'filter_eq { all_rows ; location ( city , state ) ; wisconsin }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ( city , state ) ; wisconsin } }', 'tointer': 'select the rows whose location ( city , state ) record fuzzily matches to wisconsin . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ( city , state ) ; wisconsin } } ; 4 } = true', 'tointer': 'select the rows whose location ( city , state ) record fuzzily matches to wisconsin . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; location ( city , state ) ; wisconsin } } ; 4 } = true | select the rows whose location ( city , state ) record fuzzily matches to wisconsin . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location (city , state)_5': 5, 'wisconsin_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location (city , state)_5': 'location ( city , state )', 'wisconsin_6': 'wisconsin', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location (city , state)_5': [0], 'wisconsin_6': [0], '4_7': [2]} | ['yard name', 'location ( city , state )', '1st ship delivery date', 'ship types delivered', 'total number of ways'] | [['cargill inc', 'savage , minnesota', 'november 1941', 't1 type', 'number'], ['leatham d smith shipbuilding co', 'sturgeon bay , wisconsin', 'november 1942', 'c1 - m type , n3 type , s2 ( frigate ) type', 'number'], ['walter butler shipbuilders', 'superior , wisconsin', 'december 1942', 'c1 - m type , n3 type , s2 ( frigate ) type', 'number'], ['froemming brothers', 'milwaukee , wisconsin', 'april 1943', 'c1 - m type , v4 type , s2 ( frigate ) type', 'number'], ['american shipbuilding', 'lorain , ohio', 'may 1943', 'l6 type , s2 ( frigate ) type', 'number'], ['walter butler shipbuilders inc', 'duluth , minnesota', 'may 1943', 'c1 - m type , n3 type , t1 type', 'number'], ['globe shipbuilding co', 'superior , wisconsin', 'may 1943', 'c1 - m type , v4 type , s2 ( frigate ) type', 'number'], ['great lakes engineering co', 'ecorse , michigan', 'may 1943', 'l6 type', 'number'], ['great lakes engineering co', 'ashtabula , ohio', 'may 1943', 'l6 type', 'number'], ['american shipbuilding', 'cleveland , ohio', 'june 1943', 'l6 type , s2 ( frigate ) type', 'number']] |
statistics relating to enlargement of the european union | https://en.wikipedia.org/wiki/Statistics_relating_to_enlargement_of_the_European_Union | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1307842-6.html.csv | majority | the majority of eu countries have a gdp ( billion us ) higher than 100 us billions . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '100', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'gdp ( billion us )', '100'], 'result': True, 'ind': 0, 'tointer': 'for the gdp ( billion us ) records of all rows , most of them are greater than 100 .', 'tostr': 'most_greater { all_rows ; gdp ( billion us ) ; 100 } = true'} | most_greater { all_rows ; gdp ( billion us ) ; 100 } = true | for the gdp ( billion us ) records of all rows , most of them are greater than 100 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'gdp (billion us)_3': 3, '100_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'gdp (billion us)_3': 'gdp ( billion us )', '100_4': '100'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'gdp (billion us)_3': [0], '100_4': [0]} | ['member countries', 'population', 'area ( km square )', 'gdp ( billion us )', 'gdp per capita ( us )'] | [['austria', '8206524', '83871', '145.238', '18048'], ['finland', '5261008', '338145', '80.955', '15859'], ['sweden', '9047752', '449964', '156.640', '17644'], ['accession countries', '22029977', '871980', '382.833', '17378'], ['existing members ( 1995 )', '350909402', '2495174', '5894.232', '16797'], ['eu15 ( 1995 )', '372939379 ( + 6.28 % )', '3367154 ( + 34.95 % )', '6277.065 ( + 6.50 % )', '16831 ( + 0.20 % )']] |
1926 vfl season | https://en.wikipedia.org/wiki/1926_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10746808-13.html.csv | superlative | south melbourne was the highest scoring team in the vfl matches on 7th august 1926 with 107 points . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '4', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1,7', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'home team score'], 'result': '15.17 ( 107 )', 'ind': 0, 'tostr': 'max { all_rows ; home team score }', 'tointer': 'the maximum home team score record of all rows is 15.17 ( 107 ) .'}, '15.17 ( 107 )'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; home team score } ; 15.17 ( 107 ) }', 'tointer': 'the maximum home team score record of all rows is 15.17 ( 107 ) .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'home team score'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; home team score }'}, 'home team'], 'result': 'south melbourne', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; home team score } ; home team }'}, 'south melbourne'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; home team score } ; home team } ; south melbourne }', 'tointer': 'the home team record of the row with superlative home team score record is south melbourne .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'home team score'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; home team score }'}, 'date'], 'result': '7 august 1926', 'ind': 5, 'tostr': 'hop { argmax { all_rows ; home team score } ; date }'}, '7 august 1926'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { argmax { all_rows ; home team score } ; date } ; 7 august 1926 }', 'tointer': 'the date record of the row with superlative home team score record is 7 august 1926 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { argmax { all_rows ; home team score } ; home team } ; south melbourne } ; eq { hop { argmax { all_rows ; home team score } ; date } ; 7 august 1926 } }', 'tointer': 'the home team record of the row with superlative home team score record is south melbourne . the date record of the row with superlative home team score record is 7 august 1926 .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { max { all_rows ; home team score } ; 15.17 ( 107 ) } ; and { eq { hop { argmax { all_rows ; home team score } ; home team } ; south melbourne } ; eq { hop { argmax { all_rows ; home team score } ; date } ; 7 august 1926 } } } = true', 'tointer': 'the maximum home team score record of all rows is 15.17 ( 107 ) . the home team record of the row with superlative home team score record is south melbourne . the date record of the row with superlative home team score record is 7 august 1926 .'} | and { eq { max { all_rows ; home team score } ; 15.17 ( 107 ) } ; and { eq { hop { argmax { all_rows ; home team score } ; home team } ; south melbourne } ; eq { hop { argmax { all_rows ; home team score } ; date } ; 7 august 1926 } } } = true | the maximum home team score record of all rows is 15.17 ( 107 ) . the home team record of the row with superlative home team score record is south melbourne . the date record of the row with superlative home team score record is 7 august 1926 . | 10 | 9 | {'and_8': 8, 'result_9': 9, 'eq_1': 1, 'max_0': 0, 'all_rows_10': 10, 'home team score_11': 11, '15.17 (107)_12': 12, 'and_7': 7, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_13': 13, 'home team score_14': 14, 'home team_15': 15, 'south melbourne_16': 16, 'str_eq_6': 6, 'str_hop_5': 5, 'date_17': 17, '7 august 1926_18': 18} | {'and_8': 'and', 'result_9': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_10': 'all_rows', 'home team score_11': 'home team score', '15.17 (107)_12': '15.17 ( 107 )', 'and_7': 'and', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_13': 'all_rows', 'home team score_14': 'home team score', 'home team_15': 'home team', 'south melbourne_16': 'south melbourne', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'date_17': 'date', '7 august 1926_18': '7 august 1926'} | {'and_8': [9], 'result_9': [], 'eq_1': [8], 'max_0': [1], 'all_rows_10': [0], 'home team score_11': [0], '15.17 (107)_12': [1], 'and_7': [8], 'str_eq_4': [7], 'str_hop_3': [4], 'argmax_2': [3, 5], 'all_rows_13': [2], 'home team score_14': [2], 'home team_15': [3], 'south melbourne_16': [4], 'str_eq_6': [7], 'str_hop_5': [6], 'date_17': [5], '7 august 1926_18': [6]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '10.10 ( 70 )', 'north melbourne', '10.10 ( 70 )', 'glenferrie oval', '4500', '7 august 1926'], ['geelong', '12.18 ( 90 )', 'st kilda', '5.8 ( 38 )', 'corio oval', '10500', '7 august 1926'], ['fitzroy', '10.17 ( 77 )', 'richmond', '12.20 ( 92 )', 'brunswick street oval', '10000', '7 august 1926'], ['south melbourne', '15.17 ( 107 )', 'footscray', '6.12 ( 48 )', 'lake oval', '14000', '7 august 1926'], ['essendon', '6.10 ( 46 )', 'collingwood', '10.9 ( 69 )', 'windy hill', '20000', '7 august 1926'], ['melbourne', '12.18 ( 90 )', 'carlton', '8.10 ( 58 )', 'mcg', '27785', '7 august 1926']] |
2010 - 11 indiana pacers season | https://en.wikipedia.org/wiki/2010%E2%80%9311_Indiana_Pacers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27756164-7.html.csv | superlative | the energysolutions arena was the first location used by the indiana pacers in the 2010 - 11 season . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '8', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'date'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; date }'}, 'location attendance'], 'result': 'energysolutions arena 18732', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; date } ; location attendance }'}, 'energysolutions arena 18732'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; date } ; location attendance } ; energysolutions arena 18732 } = true', 'tointer': 'select the row whose date record of all rows is minimum . the location attendance record of this row is energysolutions arena 18732 .'} | eq { hop { argmin { all_rows ; date } ; location attendance } ; energysolutions arena 18732 } = true | select the row whose date record of all rows is minimum . the location attendance record of this row is energysolutions arena 18732 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, 'location attendance_6': 6, 'energysolutions arena 18732_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', 'location attendance_6': 'location attendance', 'energysolutions arena 18732_7': 'energysolutions arena 18732'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], 'location attendance_6': [1], 'energysolutions arena 18732_7': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['17', 'december 1', 'utah', 'l 88 - 110 ( ot )', 'darren collison ( 16 )', 'danny granger ( 7 )', 'darren collison , josh mcroberts ( 5 )', 'energysolutions arena 18732', '9 - 8'], ['18', 'december 3', 'phoenix', 'l 97 - 105 ( ot )', 'brandon rush ( 21 )', 'josh mcroberts ( 9 )', 't j ford ( 9 )', 'us airways center 16991', '9 - 9'], ['19', 'december 6', 'toronto', 'w 124 - 100 ( ot )', 'brandon rush ( 26 )', 'danny granger ( 9 )', 't j ford , roy hibbert , josh mcroberts ( 6 )', 'conseco fieldhouse 11930', '10 - 9'], ['20', 'december 8', 'milwaukee', 'l 95 - 97 ( ot )', 'danny granger ( 26 )', 'josh mcroberts ( 7 )', 'josh mcroberts ( 5 )', 'bradley center 12789', '10 - 10'], ['21', 'december 10', 'charlotte', 'w 100 - 92 ( ot )', 'danny granger ( 18 )', 'roy hibbert ( 14 )', 'darren collison ( 7 )', 'conseco fieldhouse 13128', '11 - 10'], ['22', 'december 11', 'atlanta', 'l 83 - 97 ( ot )', 'mike dunleavy ( 16 )', 'mike dunleavy ( 9 )', 'darren collison ( 5 )', 'philips arena 14131', '11 - 11'], ['23', 'december 13', 'chicago', 'l 73 - 92 ( ot )', 't j ford , brandon rush ( 13 )', 'mike dunleavy ( 8 )', 't j ford ( 4 )', 'united center 21287', '11 - 12'], ['24', 'december 15', 'la lakers', 'l 94 - 109 ( ot )', 'darren collison ( 17 )', 'james posey ( 7 )', 'darren collison , t j ford ( 6 )', 'conseco fieldhouse 18165', '11 - 13'], ['25', 'december 17', 'cleveland', 'w 108 - 99 ( ot )', 'danny granger ( 30 )', 'danny granger ( 12 )', 'darren collison ( 5 )', 'conseco fieldhouse 12021', '12 - 13'], ['26', 'december 19', 'boston', 'l 88 - 99 ( ot )', 'danny granger ( 19 )', 'roy hibbert ( 14 )', 't j ford , james posey ( 3 )', 'td garden 18624', '12 - 14'], ['27', 'december 20', 'new orleans', 'w 94 - 93 ( ot )', 'danny granger ( 27 )', 'jeff foster ( 11 )', 'darren collison , t j ford ( 5 )', 'conseco fieldhouse 12271', '13 - 14'], ['28', 'december 26', 'memphis', 'l 90 - 104 ( ot )', 'danny granger ( 29 )', 'roy hibbert ( 10 )', 'darren collison , t j ford ( 4 )', 'conseco fieldhouse 12630', '13 - 15'], ['29', 'december 28', 'boston', 'l 83 - 95 ( ot )', 'brandon rush ( 17 )', 'roy hibbert ( 8 )', 'danny granger ( 4 )', 'conseco fieldhouse 18165', '13 - 16'], ['30', 'december 29', 'washington', 'l 90 - 104 ( ot )', 'mike dunleavy ( 20 )', 'danny granger ( 9 )', 'darren collison ( 5 )', 'verizon center 16108', '13 - 17']] |
1983 central american and caribbean championships in athletics | https://en.wikipedia.org/wiki/1983_Central_American_and_Caribbean_Championships_in_Athletics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14420686-3.html.csv | superlative | cuba was awarded the highest number of bronze medals at the 1983 central american and caribbean championships in athletics . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'bronze'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; bronze }'}, 'nation'], 'result': 'cuba', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; bronze } ; nation }'}, 'cuba'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; bronze } ; nation } ; cuba } = true', 'tointer': 'select the row whose bronze record of all rows is maximum . the nation record of this row is cuba .'} | eq { hop { argmax { all_rows ; bronze } ; nation } ; cuba } = true | select the row whose bronze record of all rows is maximum . the nation record of this row is cuba . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'bronze_5': 5, 'nation_6': 6, 'cuba_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'bronze_5': 'bronze', 'nation_6': 'nation', 'cuba_7': 'cuba'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'bronze_5': [0], 'nation_6': [1], 'cuba_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'cuba', '26', '15', '16', '57'], ['2', 'mexico', '5', '2', '2', '9'], ['3', 'bahamas', '4', '6', '4', '14'], ['4', 'puerto rico', '2', '4', '5', '11'], ['5', 'dominican republic', '2', '3', '0', '5'], ['6', 'colombia', '1', '4', '2', '7'], ['7', 'venezuela', '0', '2', '9', '11'], ['8', 'barbados', '0', '1', '0', '1'], ['8', 'guyana', '0', '1', '0', '1'], ['8', 'us virgin islands', '0', '1', '0', '1'], ['8', 'bermuda', '0', '1', '0', '1'], ['12', 'panama', '0', '0', '1', '1']] |
list of radio stations in tamaulipas | https://en.wikipedia.org/wiki/List_of_radio_stations_in_Tamaulipas | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17982829-17.html.csv | count | three of the radio stations operate at a frequency below 1000 khz . | {'scope': 'all', 'criterion': 'less_than', 'value': '1000', 'result': '3', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'frequency', '1000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose frequency record is less than 1000 .', 'tostr': 'filter_less { all_rows ; frequency ; 1000 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; frequency ; 1000 } }', 'tointer': 'select the rows whose frequency record is less than 1000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; frequency ; 1000 } } ; 3 } = true', 'tointer': 'select the rows whose frequency record is less than 1000 . the number of such rows is 3 .'} | eq { count { filter_less { all_rows ; frequency ; 1000 } } ; 3 } = true | select the rows whose frequency record is less than 1000 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'frequency_5': 5, '1000_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'frequency_5': 'frequency', '1000_6': '1000', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'frequency_5': [0], '1000_6': [0], '3_7': [2]} | ['frequency', 'callsign', 'brand', 'city of license', 'type'] | [['580', 'xehp', 'la mas prendida', 'ciudad victoria', 'norteño'], ['640', 'xetam', 'la poderosa', 'santa elena', 'norteño'], ['970', 'xebj - am', 'radio 970', 'ciudad victoria', 'contemporary'], ['1340', 'xerpv - am', 'la cotorra', 'ciudad victoria', 'norteño'], ['1380', 'xegw', 'planeta w 1380', 'ciudad victoria', 'christian pop'], ['1480', 'xevic', 'radio tamaulipas', 'ciudad victoria', 'state government']] |
greater antilles | https://en.wikipedia.org/wiki/Greater_Antilles | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-298550-1.html.csv | superlative | puerto rico has the highest population density of all the nations of the greater antilles . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'population density ( per km square )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; population density ( per km square ) }'}, 'country with flag'], 'result': 'puerto rico ( usa )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; population density ( per km square ) } ; country with flag }'}, 'puerto rico ( usa )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; population density ( per km square ) } ; country with flag } ; puerto rico ( usa ) } = true', 'tointer': 'select the row whose population density ( per km square ) record of all rows is maximum . the country with flag record of this row is puerto rico ( usa ) .'} | eq { hop { argmax { all_rows ; population density ( per km square ) } ; country with flag } ; puerto rico ( usa ) } = true | select the row whose population density ( per km square ) record of all rows is maximum . the country with flag record of this row is puerto rico ( usa ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'population density (per km square)_5': 5, 'country with flag_6': 6, 'puerto rico (usa)_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'population density (per km square)_5': 'population density ( per km square )', 'country with flag_6': 'country with flag', 'puerto rico (usa)_7': 'puerto rico ( usa )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'population density (per km square)_5': [0], 'country with flag_6': [1], 'puerto rico (usa)_7': [2]} | ['country with flag', 'area ( km square )', 'population ( 1 july 2005 est )', 'population density ( per km square )', 'capital'] | [['cuba', '110860', '11346670', '102.4', 'havana'], ['cayman islands ( uk )', '264', '54878', '207.9', 'george town'], ['dominican republic', '48730', '8950034', '183.7', 'santo domingo'], ['haiti', '27750', '8121622', '292.7', 'port - au - prince'], ['jamaica', '10991', '2731832', '248.6', 'kingston'], ['puerto rico ( usa )', '9104', '3916632', '430.2', 'san juan']] |
grassroots party | https://en.wikipedia.org/wiki/Grassroots_Party | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1238577-3.html.csv | superlative | among all of the candidates of the grassroots-legalize cannabis party , dennis peron received the greatest number of popular votes . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'popular votes'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; popular votes }'}, 'candidate'], 'result': 'dennis peron', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; popular votes } ; candidate }'}, 'dennis peron'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; popular votes } ; candidate } ; dennis peron } = true', 'tointer': 'select the row whose popular votes record of all rows is maximum . the candidate record of this row is dennis peron .'} | eq { hop { argmax { all_rows ; popular votes } ; candidate } ; dennis peron } = true | select the row whose popular votes record of all rows is maximum . the candidate record of this row is dennis peron . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'popular votes_5': 5, 'candidate_6': 6, 'dennis peron_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'popular votes_5': 'popular votes', 'candidate_6': 'candidate', 'dennis peron_7': 'dennis peron'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'popular votes_5': [0], 'candidate_6': [1], 'dennis peron_7': [2]} | ['year', 'candidate', 'state ( s ) on the ballot', 'popular votes', 'percentage'] | [['1988', 'jack herer', 'mn', '1949', '0.00 %'], ['1992', 'jack herer', 'mn', '3875', '0.00 %'], ['1996', 'dennis peron', 'mn , vt', '5378', '0.01 %'], ['2000', 'denny lane', 'vt', '1044', '0.00 %'], ['2012', 'jim carlson', 'mn', '3149', '0.00 %']] |
denni avdić | https://en.wikipedia.org/wiki/Denni_Avdi%C4%87 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12471124-1.html.csv | comparative | there were more goals scored in the 2009-2010 season than in the 2012-2013 season . | {'row_1': '4', 'row_2': '7', 'col': '6', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2009 - 10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose season record fuzzily matches to 2009 - 10 .', 'tostr': 'filter_eq { all_rows ; season ; 2009 - 10 }'}, 'goals'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; season ; 2009 - 10 } ; goals }', 'tointer': 'select the rows whose season record fuzzily matches to 2009 - 10 . take the goals record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2012 - 13'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose season record fuzzily matches to 2012 - 13 .', 'tostr': 'filter_eq { all_rows ; season ; 2012 - 13 }'}, 'goals'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; season ; 2012 - 13 } ; goals }', 'tointer': 'select the rows whose season record fuzzily matches to 2012 - 13 . take the goals record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; season ; 2009 - 10 } ; goals } ; hop { filter_eq { all_rows ; season ; 2012 - 13 } ; goals } } = true', 'tointer': 'select the rows whose season record fuzzily matches to 2009 - 10 . take the goals record of this row . select the rows whose season record fuzzily matches to 2012 - 13 . take the goals record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; season ; 2009 - 10 } ; goals } ; hop { filter_eq { all_rows ; season ; 2012 - 13 } ; goals } } = true | select the rows whose season record fuzzily matches to 2009 - 10 . take the goals record of this row . select the rows whose season record fuzzily matches to 2012 - 13 . take the goals record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'season_7': 7, '2009 - 10_8': 8, 'goals_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'season_11': 11, '2012 - 13_12': 12, 'goals_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'season_7': 'season', '2009 - 10_8': '2009 - 10', 'goals_9': 'goals', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'season_11': 'season', '2012 - 13_12': '2012 - 13', 'goals_13': 'goals'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'season_7': [0], '2009 - 10_8': [0], 'goals_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'season_11': [1], '2012 - 13_12': [1], 'goals_13': [3]} | ['season', 'club', 'country', 'competition', 'apps', 'goals'] | [['2006 - 07', 'if elfsborg', 'sweden', 'allsvenskan', '19', '0'], ['2007 - 08', 'if elfsborg', 'sweden', 'allsvenskan', '26', '4'], ['2008 - 09', 'if elfsborg', 'sweden', 'allsvenskan', '30', '3'], ['2009 - 10', 'if elfsborg', 'sweden', 'allsvenskan', '29', '19'], ['2010 - 11', 'werder bremen', 'germany', 'bundesliga', '7', '0'], ['2011 - 12', 'werder bremen ii', 'germany', 'regionalliga nord', '12', '0'], ['2012 - 13', 'pec zwolle', 'netherlands', 'eredivisie', '24', '8']] |
2007 - 08 detroit red wings season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Detroit_Red_Wings_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11786815-6.html.csv | aggregation | the average attendance of home games for the detroit red wings in 2007-08 was 18908 . | {'scope': 'subset', 'col': '6', 'type': 'average', 'result': '18908', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'detroit'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home', 'detroit'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; home ; detroit }', 'tointer': 'select the rows whose home record fuzzily matches to detroit .'}, 'attendance'], 'result': '18908', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; home ; detroit } ; attendance }'}, '18908'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; home ; detroit } ; attendance } ; 18908 } = true', 'tointer': 'select the rows whose home record fuzzily matches to detroit . the average of the attendance record of these rows is 18908 .'} | round_eq { avg { filter_eq { all_rows ; home ; detroit } ; attendance } ; 18908 } = true | select the rows whose home record fuzzily matches to detroit . the average of the attendance record of these rows is 18908 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'home_5': 5, 'detroit_6': 6, 'attendance_7': 7, '18908_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'home_5': 'home', 'detroit_6': 'detroit', 'attendance_7': 'attendance', '18908_8': '18908'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'home_5': [0], 'detroit_6': [0], 'attendance_7': [1], '18908_8': [2]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['january 2', 'dallas', '1 - 4', 'detroit', 'osgood', '20066', '30 - 8 - 3'], ['january 5', 'detroit', '3 - 0', 'dallas', 'hasek', '18584', '31 - 8 - 3'], ['january 6', 'detroit', '3 - 1', 'chicago', 'osgood', '21869', '32 - 8 - 3'], ['january 8', 'colorado', '0 - 1', 'detroit', 'hasek', '19160', '33 - 8 - 3'], ['january 10', 'minnesota', '6 - 5', 'detroit', 'osgood', '17848', '33 - 8 - 4'], ['january 12', 'detroit', '2 - 3', 'ottawa', 'hasek', '20208', '33 - 9 - 4'], ['january 15', 'atlanta', '5 - 1', 'detroit', 'osgood', '17408', '33 - 10 - 4'], ['january 17', 'vancouver', '2 - 3', 'detroit', 'hasek', '18878', '34 - 10 - 4'], ['january 19', 'detroit', '6 - 3', 'san jose', 'hasek', '17496', '35 - 10 - 4'], ['january 22', 'detroit', '3 - 0', 'los angeles', 'osgood', '18118', '36 - 10 - 4'], ['january 23', 'detroit', '2 - 1', 'anaheim', 'hasek', '17174', '37 - 10 - 4'], ['january 30', 'phoenix', '2 - 3', 'detroit', 'osgood', '19289', '38 - 10 - 4']] |
2008 supersport world championship season | https://en.wikipedia.org/wiki/2008_Supersport_World_Championship_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15187794-1.html.csv | unique | in the 2008 supersport world championship season , when andrew pitt was the winner , the only time gianluca vizziello had the fastest lap was on april 27 . | {'scope': 'subset', 'row': '4', 'col': '5', 'col_other': '1,6', 'criterion': 'equal', 'value': 'gianluca vizziello', 'subset': {'col': '6', 'criterion': 'equal', 'value': 'andrew pitt'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'race winner', 'andrew pitt'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; race winner ; andrew pitt }', 'tointer': 'select the rows whose race winner record fuzzily matches to andrew pitt .'}, 'fastest lap', 'gianluca vizziello'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose race winner record fuzzily matches to andrew pitt . among these rows , select the rows whose fastest lap record fuzzily matches to gianluca vizziello .', 'tostr': 'filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } }', 'tointer': 'select the rows whose race winner record fuzzily matches to andrew pitt . among these rows , select the rows whose fastest lap record fuzzily matches to gianluca vizziello . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'race winner', 'andrew pitt'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; race winner ; andrew pitt }', 'tointer': 'select the rows whose race winner record fuzzily matches to andrew pitt .'}, 'fastest lap', 'gianluca vizziello'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose race winner record fuzzily matches to andrew pitt . among these rows , select the rows whose fastest lap record fuzzily matches to gianluca vizziello .', 'tostr': 'filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello }'}, 'date'], 'result': '27 april', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } ; date }'}, '27 april'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } ; date } ; 27 april }', 'tointer': 'the date record of this unqiue row is 27 april .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } } ; eq { hop { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } ; date } ; 27 april } } = true', 'tointer': 'select the rows whose race winner record fuzzily matches to andrew pitt . among these rows , select the rows whose fastest lap record fuzzily matches to gianluca vizziello . there is only one such row in the table . the date record of this unqiue row is 27 april .'} | and { only { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } } ; eq { hop { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } ; date } ; 27 april } } = true | select the rows whose race winner record fuzzily matches to andrew pitt . among these rows , select the rows whose fastest lap record fuzzily matches to gianluca vizziello . there is only one such row in the table . the date record of this unqiue row is 27 april . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'race winner_8': 8, 'andrew pitt_9': 9, 'fastest lap_10': 10, 'gianluca vizziello_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'date_12': 12, '27 april_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'race winner_8': 'race winner', 'andrew pitt_9': 'andrew pitt', 'fastest lap_10': 'fastest lap', 'gianluca vizziello_11': 'gianluca vizziello', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'date_12': 'date', '27 april_13': '27 april'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'race winner_8': [0], 'andrew pitt_9': [0], 'fastest lap_10': [1], 'gianluca vizziello_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'date_12': [3], '27 april_13': [4]} | ['date', 'round', 'circuit', 'pole position', 'fastest lap', 'race winner', 'report'] | [['23 february', 'qatar', 'losail', 'fabien foret', 'fabien foret', 'broc parkes', 'report'], ['2 march', 'australia', 'phillip island', 'andrew pitt', 'robbin harms', 'andrew pitt', 'report'], ['6 april', 'spain', 'valencia', 'andrew pitt', 'broc parkes', 'joan lascorz', 'report'], ['27 april', 'netherlands', 'assen', 'broc parkes', 'gianluca vizziello', 'andrew pitt', 'report'], ['11 may', 'italy', 'monza', 'broc parkes', 'fabien foret', 'fabien foret', 'report'], ['15 june', 'germany', 'nürburgring', 'broc parkes', 'broc parkes', 'andrew pitt', 'report'], ['29 june', 'san marino', 'misano', 'broc parkes', 'broc parkes', 'andrew pitt', 'report'], ['20 july', 'czech republic', 'brno', 'broc parkes', 'andrew pitt', 'jonathan rea', 'report'], ['3 august', 'great britain', 'brands hatch', 'matthieu lagrive', 'andrew pitt', 'jonathan rea', 'report'], ['7 september', 'europe', 'donington park', 'matthieu lagrive', 'josh brookes', 'josh brookes', 'report'], ['21 september', 'italy', 'vallelunga', 'broc parkes', 'broc parkes', 'jonathan rea', 'report'], ['5 october', 'france', 'magny - cours', 'broc parkes', 'broc parkes', 'andrew pitt', 'report'], ['2 november', 'portugal', 'portimao', 'kenan sofuoğlu', 'kenan sofuoğlu', 'kenan sofuoğlu', 'report']] |
hubert hahne | https://en.wikipedia.org/wiki/Hubert_Hahne | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1233847-1.html.csv | majority | hubert hahne drove most of his races with a bmw straight - 4 type engine . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'bmw straight - 4', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'engine', 'bmw straight - 4'], 'result': True, 'ind': 0, 'tointer': 'for the engine records of all rows , most of them fuzzily match to bmw straight - 4 .', 'tostr': 'most_eq { all_rows ; engine ; bmw straight - 4 } = true'} | most_eq { all_rows ; engine ; bmw straight - 4 } = true | for the engine records of all rows , most of them fuzzily match to bmw straight - 4 . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'engine_3': 3, 'bmw straight - 4_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'engine_3': 'engine', 'bmw straight - 4_4': 'bmw straight - 4'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'engine_3': [0], 'bmw straight - 4_4': [0]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1966', 'tyrrell racing organisation', 'matra ms5 ( f2 )', 'brm straight - 4', '0'], ['1967', 'bayerische motoren werke', 'lola t100', 'bmw straight - 4', '0'], ['1968', 'bayerische motoren werke', 'lola t100', 'bmw straight - 4', '0'], ['1969', 'bayerische motoren werke', 'bmw t269 ( f2 )', 'bmw straight - 4', '0'], ['1970', 'hubert hahne', 'march 701', 'cosworth v8', '0']] |
2010 - 11 houston rockets season | https://en.wikipedia.org/wiki/2010%E2%80%9311_Houston_Rockets_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27744976-6.html.csv | ordinal | in the 2010 - 11 houston rockets season , the second game against oklahoma city was in toyota center . | {'scope': 'subset', 'row': '12', 'col': '2', 'order': '2', 'col_other': '8', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'oklahoma city'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'oklahoma city'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; team ; oklahoma city }', 'tointer': 'select the rows whose team record fuzzily matches to oklahoma city .'}, 'date', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; team ; oklahoma city } ; date ; 2 }'}, 'location attendance'], 'result': 'toyota center 15316', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; team ; oklahoma city } ; date ; 2 } ; location attendance }'}, 'toyota center 15316'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; team ; oklahoma city } ; date ; 2 } ; location attendance } ; toyota center 15316 } = true', 'tointer': 'select the rows whose team record fuzzily matches to oklahoma city . select the row whose date record of these rows is 2nd minimum . the location attendance record of this row is toyota center 15316 .'} | eq { hop { nth_argmin { filter_eq { all_rows ; team ; oklahoma city } ; date ; 2 } ; location attendance } ; toyota center 15316 } = true | select the rows whose team record fuzzily matches to oklahoma city . select the row whose date record of these rows is 2nd minimum . the location attendance record of this row is toyota center 15316 . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'team_6': 6, 'oklahoma city_7': 7, 'date_8': 8, '2_9': 9, 'location attendance_10': 10, 'toyota center 15316_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'team_6': 'team', 'oklahoma city_7': 'oklahoma city', 'date_8': 'date', '2_9': '2', 'location attendance_10': 'location attendance', 'toyota center 15316_11': 'toyota center 15316'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'team_6': [0], 'oklahoma city_7': [0], 'date_8': [1], '2_9': [1], 'location attendance_10': [2], 'toyota center 15316_11': [3]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['4', 'november 3', 'new orleans', 'l 99 - 107 ( ot )', 'aaron brooks , kevin martin ( 18 )', 'luis scola ( 16 )', 'luis scola ( 5 )', 'toyota center 13484', '0 - 4'], ['5', 'november 6', 'san antonio', 'l 121 - 124 ( ot )', 'kevin martin ( 24 )', 'chuck hayes ( 13 )', 'ishmael smith ( 7 )', 'at & t center 17740', '0 - 5'], ['6', 'november 7', 'minnesota', 'w 120 - 94 ( ot )', 'luis scola ( 24 )', 'luis scola ( 8 )', 'ishmael smith ( 6 )', 'toyota center 15058', '1 - 5'], ['7', 'november 10', 'washington', 'l 91 - 98 ( ot )', 'kevin martin ( 31 )', 'chuck hayes , kevin martin ( 7 )', 'kevin martin ( 6 )', 'verizon center 13665', '1 - 6'], ['8', 'november 12', 'indiana', 'w 102 - 99 ( ot )', 'brad miller ( 23 )', 'luis scola ( 9 )', 'kyle lowry ( 7 )', 'conseco fieldhouse 14414', '2 - 6'], ['9', 'november 14', 'new york', 'w 104 - 96 ( ot )', 'kevin martin ( 28 )', 'chuck hayes ( 9 )', 'kyle lowry ( 6 )', 'madison square garden 19763', '3 - 6'], ['11', 'november 17', 'oklahoma city', 'l 99 - 116 ( ot )', 'luis scola ( 26 )', 'luis scola ( 8 )', 'ishmael smith ( 5 )', 'oklahoma city arena 17509', '3 - 8'], ['12', 'november 19', 'toronto', 'l 96 - 106 ( ot )', 'kevin martin ( 31 )', 'kyle lowry ( 7 )', 'kyle lowry ( 12 )', 'air canada centre 17369', '3 - 9'], ['13', 'november 22', 'phoenix', 'l 116 - 123 ( ot )', 'kevin martin ( 19 )', 'jordan hill ( 10 )', 'kyle lowry ( 8 )', 'toyota center 15080', '3 - 10'], ['14', 'november 24', 'golden state', 'w 111 - 101 ( ot )', 'kevin martin ( 25 )', 'luis scola ( 12 )', 'kyle lowry ( 10 )', 'toyota center 13847', '4 - 10'], ['15', 'november 26', 'charlotte', 'l 89 - 99 ( ot )', 'chase budinger ( 19 )', 'chuck hayes ( 10 )', 'kyle lowry ( 6 )', 'time warner cable arena 16473', '4 - 11'], ['16', 'november 28', 'oklahoma city', 'w 99 - 98 ( ot )', 'kevin martin ( 23 )', 'jordan hill , brad miller ( 7 )', 'kyle lowry ( 8 )', 'toyota center 15316', '5 - 11']] |
law & order ( season 19 ) | https://en.wikipedia.org/wiki/Law_%26_Order_%28season_19%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19995378-1.html.csv | count | three episodes of " law and order " were directed by alex chapple . | {'scope': 'all', 'criterion': 'equal', 'value': 'alex chapple', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'alex chapple'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose directed by record fuzzily matches to alex chapple .', 'tostr': 'filter_eq { all_rows ; directed by ; alex chapple }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; directed by ; alex chapple } }', 'tointer': 'select the rows whose directed by record fuzzily matches to alex chapple . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; directed by ; alex chapple } } ; 3 } = true', 'tointer': 'select the rows whose directed by record fuzzily matches to alex chapple . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; directed by ; alex chapple } } ; 3 } = true | select the rows whose directed by record fuzzily matches to alex chapple . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'directed by_5': 5, 'alex chapple_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'directed by_5': 'directed by', 'alex chapple_6': 'alex chapple', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'directed by_5': [0], 'alex chapple_6': [0], '3_7': [2]} | ['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( millions )'] | [['412', '1', 'rumble', 'constantine makris', 'christopher ambrose & richard sweren', 'november 5 , 2008', '19003', '7.85'], ['413', '2', 'challenged', 'fred berner', 'ed zuckerman & renã balcer', 'november 12 , 2008', '19001', '7.91'], ['414', '3', 'lost boys', 'chris zalla', 'richard sweren & gina gionfriddo', 'november 19 , 2008', '19004', '7.58'], ['415', '4', 'falling', 'michael watkins', 'keith eisner & stephanie sengupta', 'november 26 , 2008', '19005', 'n / a'], ['417', '6', 'sweetie', 'mario van peebles', 'luke schelhaas & ed zuckerman', 'december 10 , 2008', '19007', '7.46'], ['418', '7', 'zero', 'marisol torres', 'luke schelhaas & ed zuckerman', 'december 17 , 2008', '19002', '6.95'], ['419', '8', 'chattel', 'jim mckay', 'matthew mcgough & william n fordes', 'january 7 , 2009', '19009', '10.11'], ['420', '9', 'by perjury', 'darnell martin', 'christopher ambrose & richard sweren', 'january 14 , 2009', '19010', '8.20'], ['421', '10', 'pledge', 'alex chapple', 'gina gionfriddo & richard sweren', 'january 21 , 2009', '19008', '8.49'], ['422', '11', 'lucky stiff', 'marc levin', 'matthew mcgough & ed zuckerman', 'january 28 , 2009', '19012', '8.89'], ['423', '12', 'illegitimate', 'josh marston', 'keith eisner & stephanie sengupta', 'february 4 , 2009', '19011', '8.69'], ['424', '13', 'crimebusters', 'alex chapple', 'richard sweren & gina gionfriddo', 'february 11 , 2009', '19013', '7.52'], ['425', '14', 'rapture', 'fred berner', 'luke schelhaas & ed zuckerman', 'february 18 , 2009', '19014', '7.15'], ['426', '15', 'bailout', 'jean de segonzac', 'christopher ambrose & richard sweren', 'march 11 , 2009', '19015', '7.58'], ['427', '16', 'take - out', 'jim mckay', 'keith eisner & william n fordes', 'march 18 , 2009', '19016', '7.07'], ['428', '17', 'anchors away', 'alex chapple', 'matthew mcgough & ed zuckerman', 'march 25 , 2009', '19017', '7.25'], ['429', '18', 'promote this !', 'michael watkins', 'christopher ambrose & richard sweren', 'april 29 , 2009', '19019', '7.69'], ['430', '19', 'all new', 'roger young', 'keith eisner & william n fordes', 'may 6 , 2009', '19020', '8.14'], ['431', '20', 'exchange', 'ernest dickerson', 'stephanie sengupta', 'may 13 , 2009', '19018', '7.82'], ['432', '21', 'skate or die', 'norberto barba', 'luke schelhaas & ed zuckerman', 'may 20 , 2009', '19021', '6.70']] |
united states house of representatives elections , 2006 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1805191-37.html.csv | majority | the majority of the representatives were re-elected in the united states house of representatives elections , 2006 . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 're - elected', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'results', 're - elected'], 'result': True, 'ind': 0, 'tointer': 'for the results records of all rows , most of them fuzzily match to re - elected .', 'tostr': 'most_eq { all_rows ; results ; re - elected } = true'} | most_eq { all_rows ; results ; re - elected } = true | for the results records of all rows , most of them fuzzily match to re - elected . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'results_3': 3, 're - elected_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'results_3': 'results', 're - elected_4': 're - elected'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'results_3': [0], 're - elected_4': [0]} | ['district', 'incumbent', 'party', 'first elected', 'results'] | [['oklahoma 1', 'john sullivan', 'republican', '2002', 're - elected'], ['oklahoma 2', 'dan boren', 'democratic', '2004', 're - elected'], ['oklahoma 3', 'frank lucas', 'republican', '1994', 're - elected'], ['oklahoma 4', 'tom cole', 'republican', '2002', 're - elected'], ['oklahoma 5', 'ernest istook', 'republican', '1992', 'retired to run for governor republican hold']] |
1990 - 91 segunda división | https://en.wikipedia.org/wiki/1990%E2%80%9391_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12103836-2.html.csv | unique | in the 1990-91 segunda division , the sestao club is the only club that lost exactly nine games . | {'scope': 'all', 'row': '8', 'col': '7', 'col_other': '2', 'criterion': 'equal', 'value': '9', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'losses', '9'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose losses record is equal to 9 .', 'tostr': 'filter_eq { all_rows ; losses ; 9 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; losses ; 9 } }', 'tointer': 'select the rows whose losses record is equal to 9 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'losses', '9'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose losses record is equal to 9 .', 'tostr': 'filter_eq { all_rows ; losses ; 9 }'}, 'club'], 'result': 'sestao', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; losses ; 9 } ; club }'}, 'sestao'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; losses ; 9 } ; club } ; sestao }', 'tointer': 'the club record of this unqiue row is sestao .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; losses ; 9 } } ; eq { hop { filter_eq { all_rows ; losses ; 9 } ; club } ; sestao } } = true', 'tointer': 'select the rows whose losses record is equal to 9 . there is only one such row in the table . the club record of this unqiue row is sestao .'} | and { only { filter_eq { all_rows ; losses ; 9 } } ; eq { hop { filter_eq { all_rows ; losses ; 9 } ; club } ; sestao } } = true | select the rows whose losses record is equal to 9 . there is only one such row in the table . the club record of this unqiue row is sestao . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'losses_7': 7, '9_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'club_9': 9, 'sestao_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'losses_7': 'losses', '9_8': '9', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'club_9': 'club', 'sestao_10': 'sestao'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'losses_7': [0], '9_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'club_9': [2], 'sestao_10': [3]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'albacete bp', '38', '49 + 11', '18', '13', '7', '56', '31', '+ 25'], ['2', 'deportivo de la coruña', '38', '48 + 10', '20', '8', '10', '60', '32', '+ 28'], ['3', 'real murcia', '38', '48 + 10', '18', '12', '8', '56', '36', '+ 20'], ['4', 'cd málaga', '38', '46 + 8', '16', '14', '8', '52', '35', '+ 17'], ['5', 'orihuela deportiva 1', '38', '43 + 5', '12', '19', '7', '46', '39', '+ 7'], ['6', 'ue lleida', '38', '43 + 5', '16', '11', '11', '41', '36', '+ 5'], ['7', 'ue figueres', '38', '39 + 1', '14', '11', '13', '44', '42', '+ 2'], ['8', 'sestao', '38', '38', '9', '20', '9', '29', '27', '+ 2'], ['9', 'real avilés', '38', '38', '10', '18', '10', '35', '37', '- 2'], ['10', 'sd eibar', '38', '37 - 1', '9', '19', '10', '35', '34', '+ 1'], ['11', 'rayo vallecano', '38', '36 - 2', '8', '20', '10', '44', '50', '- 6'], ['12', 'ce sabadell fc', '38', '36 - 2', '11', '14', '13', '32', '45', '- 13'], ['13', 'bilbao athletic', '38', '36 - 2', '11', '14', '13', '35', '43', '- 8'], ['14', 'celta de vigo', '38', '36 - 2', '8', '20', '10', '31', '38', '- 7'], ['15', 'ud las palmas', '38', '36 - 2', '10', '16', '12', '38', '43', '- 5'], ['16', 'palamós cf', '38', '35 - 3', '9', '17', '12', '33', '46', '- 13'], ['17', 'elche cf', '38', '34 - 4', '12', '10', '16', '39', '45', '- 9'], ['18', 'ud salamanca', '38', '31 - 7', '9', '13', '16', '41', '40', '+ 1'], ['19', 'levante ud', '38', '27 - 11', '6', '15', '17', '27', '51', '- 24'], ['20', 'xerez cd', '38', '24 - 14', '6', '12', '20', '37', '61', '- 24']] |
1988 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1988_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17231125-4.html.csv | aggregation | all the players of the 1988 u.s. open ( golf ) tournament had an average score of around 69 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '69', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '69', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '69'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 69 } = true', 'tointer': 'the average of the score record of all rows is 69 .'} | round_eq { avg { all_rows ; score } ; 69 } = true | the average of the score record of all rows is 69 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '69_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '69_5': '69'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '69_5': [1]} | ['place', 'player', 'country', 'score', 'to par'] | [['t1', 'bob gilder', 'united states', '68', '- 3'], ['t1', 'sandy lyle', 'scotland', '68', '- 3'], ['t1', 'mike nicolette', 'united states', '68', '- 3'], ['t4', 'paul azinger', 'united states', '69', '- 2'], ['t4', 'seve ballesteros', 'spain', '69', '- 2'], ['t4', 'dick mast', 'united states', '69', '- 2'], ['t4', 'larry mize', 'united states', '69', '- 2'], ['t4', 'scott simpson', 'united states', '69', '- 2'], ['t9', 'craig stadler', 'united states', '70', '- 1'], ['t9', 'curtis strange', 'united states', '70', '- 1'], ['t9', 'lanny wadkins', 'united states', '70', '- 1']] |
2010 - 13 ncaa conference realignment | https://en.wikipedia.org/wiki/2010%E2%80%9313_NCAA_conference_realignment | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27671835-3.html.csv | count | three of the conferences lost 0 members in the 2010-13 ncaa conference realignment . | {'scope': 'all', 'criterion': 'greater_than', 'value': '0', 'result': '3', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'members lost', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose members lost record is greater than 0 .', 'tostr': 'filter_greater { all_rows ; members lost ; 0 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; members lost ; 0 } }', 'tointer': 'select the rows whose members lost record is greater than 0 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; members lost ; 0 } } ; 3 } = true', 'tointer': 'select the rows whose members lost record is greater than 0 . the number of such rows is 3 .'} | eq { count { filter_greater { all_rows ; members lost ; 0 } } ; 3 } = true | select the rows whose members lost record is greater than 0 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'members lost_5': 5, '0_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'members lost_5': 'members lost', '0_6': '0', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'members lost_5': [0], '0_6': [0], '3_7': [2]} | ['conference', 'old membership total', 'new membership total', 'net change', 'members added', 'members lost'] | [['atlantic hockey ( men only )', '12', '11', '1', '0', '1'], ['big ten ( men only )', '0', '6', '6', '6', '0'], ['ccha ( men only )', '11', '0', '11', '0', '11'], ['cha ( women only )', '4', '6', '2', '3', '1'], ['hockey east ( men )', '10', '12', '2', '2', '0'], ['nchc ( men only )', '0', '8', '8', '8', '0']] |
1995 - 96 toronto raptors season | https://en.wikipedia.org/wiki/1995%E2%80%9396_Toronto_Raptors_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13464416-5.html.csv | aggregation | the toronto raptors averaged 96.5 points per game from december 1 to december 26 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '96.5', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '96.5', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '96.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 96.5 } = true', 'tointer': 'the average of the score record of all rows is 96.5 .'} | round_eq { avg { all_rows ; score } ; 96.5 } = true | the average of the score record of all rows is 96.5 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '96.5_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '96.5_5': '96.5'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '96.5_5': [1]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['16', 'december 1', 'philadelphia', 'w 105 - 102 ( ot )', 'willie anderson ( 23 )', 'ed pinckney ( 16 )', 'damon stoudamire ( 10 )', 'skydome 19789', '6 - 10'], ['17', 'december 3', 'miami', 'l 94 - 112 ( ot )', 'oliver miller ( 29 )', 'ed pinckney ( 12 )', 'damon stoudamire ( 15 )', 'skydome 21238', '6 - 11'], ['18', 'december 5', 'seattle', 'l 89 - 119 ( ot )', 'tracy murray ( 23 )', 'oliver miller , alvin robertson , žan tabak ( 5 )', 'alvin robertson , damon stoudamire ( 5 )', 'keyarena 17072', '6 - 12'], ['19', 'december 7', 'portland', 'l 88 - 96 ( ot )', 'tracy murray ( 28 )', 'ed pinckney ( 15 )', 'damon stoudamire ( 10 )', 'rose garden 20039', '6 - 13'], ['20', 'december 8', 'la lakers', 'l 103 - 120 ( ot )', 'damon stoudamire ( 20 )', 'ed pinckney ( 8 )', 'damon stoudamire ( 10 )', 'great western forum 12982', '6 - 14'], ['21', 'december 10', 'vancouver', 'w 93 - 81 ( ot )', 'damon stoudamire ( 24 )', 'ed pinckney ( 16 )', 'damon stoudamire ( 8 )', 'general motors place 17438', '7 - 14'], ['22', 'december 12', 'boston', 'l 96 - 116 ( ot )', 'damon stoudamire ( 18 )', 'ed pinckney ( 8 )', 'damon stoudamire ( 9 )', 'skydome 21875', '7 - 15'], ['23', 'december 14', 'indiana', 'l 100 - 102 ( ot )', 'oliver miller ( 22 )', 'oliver miller ( 12 )', 'damon stoudamire ( 13 )', 'skydome 19763', '7 - 16'], ['24', 'december 15', 'boston', 'l 103 - 122 ( ot )', 'žan tabak ( 18 )', 'žan tabak ( 8 )', 'alvin robertson , damon stoudamire ( 7 )', 'fleetcenter 17580', '7 - 17'], ['25', 'december 17', 'orlando', 'w 110 - 93 ( ot )', 'damon stoudamire ( 21 )', 'ed pinckney ( 11 )', 'damon stoudamire ( 10 )', 'skydome 25820', '8 - 17'], ['26', 'december 19', 'detroit', 'l 82 - 94 ( ot )', 'damon stoudamire ( 19 )', 'oliver miller ( 11 )', 'damon stoudamire ( 8 )', 'skydome 21128', '8 - 18'], ['27', 'december 22', 'chicago', 'l 104 - 113 ( ot )', 'žan tabak ( 24 )', 'damon stoudamire , žan tabak ( 8 )', 'damon stoudamire ( 13 )', 'united center 22987', '8 - 19'], ['28', 'december 23', 'new york', 'l 91 - 103 ( ot )', 'damon stoudamire ( 25 )', 'ed pinckney ( 10 )', 'damon stoudamire ( 8 )', 'madison square garden 19763', '8 - 20'], ['29', 'december 26', 'milwaukee', 'w 93 - 87 ( ot )', 'damon stoudamire ( 21 )', 'ed pinckney ( 9 )', 'damon stoudamire ( 11 )', 'copps coliseum 17242', '9 - 20']] |
1990 minnesota vikings season | https://en.wikipedia.org/wiki/1990_Minnesota_Vikings_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10362095-2.html.csv | superlative | the minnesota vikings ' game against the new york giants had the most attendance in the 1990 season . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '13', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'opponent'], 'result': 'new york giants', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; opponent }'}, 'new york giants'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; opponent } ; new york giants } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the opponent record of this row is new york giants .'} | eq { hop { argmax { all_rows ; attendance } ; opponent } ; new york giants } = true | select the row whose attendance record of all rows is maximum . the opponent record of this row is new york giants . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'opponent_6': 6, 'new york giants_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'opponent_6': 'opponent', 'new york giants_7': 'new york giants'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'opponent_6': [1], 'new york giants_7': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 9 , 1990', 'kansas city chiefs', 'l 24 - 21', '68363'], ['2', 'september 16 , 1990', 'new orleans saints', 'w 32 - 3', '56272'], ['3', 'september 23 , 1990', 'chicago bears', 'l 19 - 16', '65420'], ['4', 'september 30 , 1990', 'tampa bay buccaneers', 'l 23 - 20 ( ot )', '54462'], ['5', 'october 7 , 1990', 'detroit lions', 'l 34 - 27', '57586'], ['6', 'october 15 , 1990', 'philadelphia eagles', 'l 32 - 24', '66296'], ['8', 'october 28 , 1990', 'green bay packers ( milw )', 'l 24 - 10', '55125'], ['9', 'november 4 , 1990', 'denver broncos', 'w 27 - 22', '57331'], ['10', 'november 11 , 1990', 'detroit lions', 'w 17 - 7', '68264'], ['11', 'november 18 , 1990', 'seattle seahawks', 'w 24 - 21', '59735'], ['12', 'november 25 , 1990', 'chicago bears', 'w 41 - 13', '58866'], ['13', 'december 2 , 1990', 'green bay packers', 'w 23 - 7', '62058'], ['14', 'december 9 , 1990', 'new york giants', 'l 23 - 15', '76121'], ['15', 'december 16 , 1990', 'tampa bay buccaneers', 'l 26 - 13', '47272'], ['16', 'december 22 , 1990', 'los angeles raiders', 'l 28 - 24', '53899'], ['17', 'december 30 , 1990', 'san francisco 49ers', 'l 20 - 17', '51590']] |
2004 - 05 greek cup | https://en.wikipedia.org/wiki/2004%E2%80%9305_Greek_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19130829-4.html.csv | unique | illsiakos was the only team with the aggragate score of 0-2 . | {'scope': 'all', 'row': '5', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': '0 - 2', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'agg score', '0 - 2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose agg score record fuzzily matches to 0 - 2 .', 'tostr': 'filter_eq { all_rows ; agg score ; 0 - 2 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; agg score ; 0 - 2 } }', 'tointer': 'select the rows whose agg score record fuzzily matches to 0 - 2 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'agg score', '0 - 2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose agg score record fuzzily matches to 0 - 2 .', 'tostr': 'filter_eq { all_rows ; agg score ; 0 - 2 }'}, 'team 1'], 'result': 'ilisiakos', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; agg score ; 0 - 2 } ; team 1 }'}, 'ilisiakos'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; agg score ; 0 - 2 } ; team 1 } ; ilisiakos }', 'tointer': 'the team 1 record of this unqiue row is ilisiakos .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; agg score ; 0 - 2 } } ; eq { hop { filter_eq { all_rows ; agg score ; 0 - 2 } ; team 1 } ; ilisiakos } } = true', 'tointer': 'select the rows whose agg score record fuzzily matches to 0 - 2 . there is only one such row in the table . the team 1 record of this unqiue row is ilisiakos .'} | and { only { filter_eq { all_rows ; agg score ; 0 - 2 } } ; eq { hop { filter_eq { all_rows ; agg score ; 0 - 2 } ; team 1 } ; ilisiakos } } = true | select the rows whose agg score record fuzzily matches to 0 - 2 . there is only one such row in the table . the team 1 record of this unqiue row is ilisiakos . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'agg score_7': 7, '0 - 2_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team 1_9': 9, 'ilisiakos_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'agg score_7': 'agg score', '0 - 2_8': '0 - 2', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team 1_9': 'team 1', 'ilisiakos_10': 'ilisiakos'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'agg score_7': [0], '0 - 2_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team 1_9': [2], 'ilisiakos_10': [3]} | ['team 1', 'agg score', 'team 2', '1st leg', '2nd leg'] | [['iraklis', '1 - 2', 'olympiacos', '1 - 0', '0 - 2'], ['kastoria', '4 - 2', 'ptolemaida - lignitorikhi', '2 - 0', '2 - 3'], ['aris', '4 - 2', 'ethnikos', '2 - 1', '2 - 1'], ['skoda xanthi', '1 - 0', 'egaleo', '1 - 0', '0 - 0'], ['ilisiakos', '0 - 2', 'panionios', '0 - 1', '0 - 1'], ['larissa', '3 - 2', 'chalkidon near east', '3 - 1', '0 - 1'], ['ofi', '1 - 1', 'apollon kalamaria', '1 - 1', '0 - 0']] |
television in thailand | https://en.wikipedia.org/wiki/Television_in_Thailand | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18987481-3.html.csv | count | a total of two tv stations in thailand have recorded a market share of greater than 20 . | {'scope': 'all', 'criterion': 'greater_than', 'value': '20', 'result': '2', 'col': '8', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', '2011 1h', '20'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2011 1h record is greater than 20 .', 'tostr': 'filter_greater { all_rows ; 2011 1h ; 20 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; 2011 1h ; 20 } }', 'tointer': 'select the rows whose 2011 1h record is greater than 20 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; 2011 1h ; 20 } } ; 2 } = true', 'tointer': 'select the rows whose 2011 1h record is greater than 20 . the number of such rows is 2 .'} | eq { count { filter_greater { all_rows ; 2011 1h ; 20 } } ; 2 } = true | select the rows whose 2011 1h record is greater than 20 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, '2011 1h_5': 5, '20_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', '2011 1h_5': '2011 1h', '20_6': '20', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], '2011 1h_5': [0], '20_6': [0], '2_7': [2]} | ['tv station ( operator )', '2005', '2006', '2007', '2008', '2009', '2010', '2011 1h'] | [['bbtv ch7', '42.4', '41.3', '42.0', '44.7', '45.4', '43.8', '47.5'], ['tv3', '24.5', '25.6', '29.5', '26.8', '27.7', '29.5', '29.0'], ['tv5', '8.1', '7.3', '6.7', '7.6', '8.6', '8.0', '6.9'], ['modernine tv', '10.3', '10.2', '9.2', '9.6', '9.9', '9.7', '9.2'], ['nbt', '2.9', '3.0', '2.4', '4.9', '3.4', '3.4', '2.4'], ['thai pbs', '11.8', '12.6', '10.2', '6.1', '4.9', '5.6', '5.0']] |
2008 - 09 los angeles clippers season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Los_Angeles_Clippers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17323529-6.html.csv | superlative | in the 2008 - 09 los angeles clippers season , the highest scorer in a game after december 16 was zach randolph with 34 on december 19 . | {'scope': 'subset', 'col_superlative': '5', 'row_superlative': '10', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '2', 'subset': {'col': '2', 'criterion': 'greater_than', 'value': 'december 16'}} | {'func': 'eq', 'args': [{'func': 'max', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'date', 'december 16'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; date ; december 16 }', 'tointer': 'select the rows whose date record is greater than december 16 .'}, 'high points'], 'result': 'zach randolph ( 34 )', 'ind': 1, 'tostr': 'max { filter_greater { all_rows ; date ; december 16 } ; high points }', 'tointer': 'select the rows whose date record is greater than december 16 . the maximum high points record of these rows is zach randolph ( 34 ) .'}, 'zach randolph ( 34 )'], 'result': True, 'ind': 2, 'tostr': 'eq { max { filter_greater { all_rows ; date ; december 16 } ; high points } ; zach randolph ( 34 ) } = true', 'tointer': 'select the rows whose date record is greater than december 16 . the maximum high points record of these rows is zach randolph ( 34 ) .'} | eq { max { filter_greater { all_rows ; date ; december 16 } ; high points } ; zach randolph ( 34 ) } = true | select the rows whose date record is greater than december 16 . the maximum high points record of these rows is zach randolph ( 34 ) . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'max_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'date_5': 5, 'december 16_6': 6, 'high points_7': 7, 'zach randolph (34)_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'max_1': 'max', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'date_5': 'date', 'december 16_6': 'december 16', 'high points_7': 'high points', 'zach randolph (34)_8': 'zach randolph ( 34 )'} | {'eq_2': [3], 'result_3': [], 'max_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'date_5': [0], 'december 16_6': [0], 'high points_7': [1], 'zach randolph (34)_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['17', 'december 2', 'dallas', 'l 98 - 100 ( ot )', 'zach randolph ( 27 )', 'marcus camby ( 15 )', 'baron davis ( 6 )', 'american airlines center 19670', '3 - 14'], ['18', 'december 3', 'houston', 'l 96 - 103 ( ot )', 'al thornton ( 24 )', 'zach randolph , marcus camby ( 11 )', 'baron davis ( 9 )', 'toyota center 15358', '3 - 15'], ['19', 'december 5', 'memphis', 'l 81 - 93 ( ot )', 'baron davis ( 23 )', 'marcus camby ( 10 )', 'baron davis ( 8 )', 'fedexforum 10484', '3 - 16'], ['20', 'december 6', 'minnesota', 'w 107 - 84 ( ot )', 'baron davis ( 27 )', 'marcus camby ( 19 )', 'baron davis ( 9 )', 'target center 10863', '4 - 16'], ['21', 'december 8', 'orlando', 'l 88 - 95 ( ot )', 'baron davis ( 27 )', 'marcus camby ( 17 )', 'baron davis ( 7 )', 'staples center 15222', '4 - 17'], ['22', 'december 12', 'portland', 'w 120 - 112 ( 2ot )', 'zach randolph ( 38 )', 'marcus camby ( 13 )', 'baron davis ( 6 )', 'rose garden 20558', '5 - 17'], ['23', 'december 13', 'houston', 'w 95 - 82 ( ot )', 'zach randolph ( 30 )', 'zach randolph , marcus camby ( 13 )', 'baron davis ( 9 )', 'staples center 16203', '6 - 17'], ['24', 'december 16', 'oklahoma city', 'w 98 - 88 ( ot )', 'eric gordon , zach randolph ( 22 )', 'marcus camby ( 15 )', 'baron davis ( 7 )', 'ford center 18275', '7 - 17'], ['25', 'december 17', 'chicago', 'l 109 - 115 ( ot )', 'zach randolph ( 30 )', 'marcus camby ( 27 )', 'baron davis ( 12 )', 'united center 20102', '7 - 18'], ['26', 'december 19', 'indiana', 'w 117 - 109 ( 2ot )', 'zach randolph ( 34 )', 'zach randolph ( 16 )', 'baron davis ( 11 )', 'conseco fieldhouse 12653', '8 - 18'], ['27', 'december 20', 'milwaukee', 'l 85 - 119 ( ot )', 'al thornton ( 20 )', 'marcus camby ( 11 )', 'jason hart ( 7 )', 'bradley center 15014', '8 - 19'], ['28', 'december 22', 'toronto', 'l 75 - 97 ( ot )', 'eric gordon , zach randolph ( 19 )', 'al thornton ( 9 )', 'baron davis ( 9 )', 'staples center 16094', '8 - 20'], ['29', 'december 28', 'dallas', 'l 76 - 98 ( ot )', 'al thornton , marcus camby ( 16 )', 'marcus camby ( 12 )', 'baron davis ( 9 )', 'staples center 16685', '8 - 21'], ['30', 'december 30', 'sacramento', 'l 90 - 92 ( ot )', 'eric gordon ( 24 )', 'marcus camby ( 24 )', 'marcus camby , baron davis ( 4 )', 'arco arena 11420', '8 - 22'], ['31', 'december 31', 'philadelphia', 'l 92 - 100 ( ot )', 'al thornton ( 24 )', 'marcus camby ( 17 )', 'baron davis ( 8 )', 'staples center 14021', '8 - 23']] |
boxing at the 2002 south american games | https://en.wikipedia.org/wiki/Boxing_at_the_2002_South_American_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18781567-2.html.csv | aggregation | at the 2002 south american games , 12 gold medals were handed out for boxing . | {'scope': 'all', 'col': '3', 'type': 'sum', 'result': '12', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'gold'], 'result': '12', 'ind': 0, 'tostr': 'sum { all_rows ; gold }'}, '12'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; gold } ; 12 } = true', 'tointer': 'the sum of the gold record of all rows is 12 .'} | round_eq { sum { all_rows ; gold } ; 12 } = true | the sum of the gold record of all rows is 12 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'gold_4': 4, '12_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'gold_4': 'gold', '12_5': '12'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'gold_4': [0], '12_5': [1]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'brazil', '6', '2', '3', '11'], ['2', 'venezuela', '3', '3', '1', '7'], ['3', 'ecuador', '3', '1', '1', '5'], ['4', 'argentina', '0', '3', '5', '8'], ['5', 'peru', '0', '1', '1', '2'], ['6', 'aruba', '0', '1', '0', '1'], ['7', 'guyana', '0', '0', '5', '5'], ['8', 'chile', '0', '0', '2', '2'], ['9', 'paraguay', '0', '0', '0', '0']] |
1994 miami dolphins season | https://en.wikipedia.org/wiki/1994_Miami_Dolphins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16023821-1.html.csv | ordinal | the miami dolphins ' game on september 4 was their earliest in the 1994 season . | {'row': '1', 'col': '1', 'order': '1', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'week', '1'], 'result': '1', 'ind': 0, 'tostr': 'nth_min { all_rows ; week ; 1 }', 'tointer': 'the 1st minimum week record of all rows is 1 .'}, '1'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; week ; 1 } ; 1 }', 'tointer': 'the 1st minimum week record of all rows is 1 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'week', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; week ; 1 }'}, 'date'], 'result': 'september 4 , 1994', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; week ; 1 } ; date }'}, 'september 4 , 1994'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; week ; 1 } ; date } ; september 4 , 1994 }', 'tointer': 'the date record of the row with 1st minimum week record is september 4 , 1994 .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_min { all_rows ; week ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; week ; 1 } ; date } ; september 4 , 1994 } } = true', 'tointer': 'the 1st minimum week record of all rows is 1 . the date record of the row with 1st minimum week record is september 4 , 1994 .'} | and { eq { nth_min { all_rows ; week ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; week ; 1 } ; date } ; september 4 , 1994 } } = true | the 1st minimum week record of all rows is 1 . the date record of the row with 1st minimum week record is september 4 , 1994 . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_7': 7, 'week_8': 8, '1_9': 9, '1_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_11': 11, 'week_12': 12, '1_13': 13, 'date_14': 14, 'september 4 , 1994_15': 15} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_7': 'all_rows', 'week_8': 'week', '1_9': '1', '1_10': '1', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_11': 'all_rows', 'week_12': 'week', '1_13': '1', 'date_14': 'date', 'september 4 , 1994_15': 'september 4 , 1994'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_min_0': [1], 'all_rows_7': [0], 'week_8': [0], '1_9': [0], '1_10': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nth_argmin_2': [3], 'all_rows_11': [2], 'week_12': [2], '1_13': [2], 'date_14': [3], 'september 4 , 1994_15': [4]} | ['week', 'date', 'opponent', 'result', 'tv time', 'attendance'] | [['1', 'september 4 , 1994', 'new england patriots', 'w 39 - 35', 'nbc 4:15 pm', '71023'], ['2', 'september 11 , 1994', 'green bay packers', 'w 24 - 14', 'nbc 1:00 pm', '55011'], ['3', 'september 18 , 1994', 'new york jets', 'w 28 - 14', 'nbc 1:00 pm', '68977'], ['4', 'september 25 , 1994', 'minnesota vikings', 'l 38 - 35', 'nbc 1:00 pm', '64035'], ['5', 'october 2 , 1994', 'cincinnati bengals', 'w 23 - 7', 'tnt 8:15 pm', '55056'], ['6', 'october 9 , 1994', 'buffalo bills', 'l 21 - 11', 'nbc 1:00 pm', '79491'], ['7', 'october 16 , 1994', 'los angeles raiders', 'w 20 - 17', 'nbc 1:00 pm', '70112'], ['9', 'october 30 , 1994', 'new england patriots', 'w 23 - 3', 'nbc 4:15 pm', '59167'], ['10', 'november 6 , 1994', 'indianapolis colts', 'w 22 - 21', 'nbc 1:00 pm', '71158'], ['11', 'november 13 , 1994', 'chicago bears', 'l 17 - 14', 'fox 1:00 pm', '64871'], ['12', 'november 20 , 1994', 'pittsburgh steelers', 'l 16 - 13', 'nbc 1:00 pm', '59148'], ['13', 'november 27 , 1994', 'new york jets', 'w 28 - 24', 'nbc 4:15 pm', '75606'], ['14', 'december 4 , 1994', 'buffalo bills', 'l 42 - 31', 'espn 8:15 pm', '69538'], ['15', 'december 12 , 1994', 'kansas city chiefs', 'w 45 - 28', 'abc 9:00 pm', '71578'], ['16', 'december 18 , 1994', 'indianapolis colts', 'l 10 - 6', 'nbc 1:00 pm', '58867'], ['17', 'december 25 , 1994', 'detroit lions', 'w 27 - 20', 'espn 8:15 pm', '70980']] |
united states presidential election in nevada , 2000 | https://en.wikipedia.org/wiki/United_States_presidential_election_in_Nevada%2C_2000 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23014476-1.html.csv | aggregation | bush got around 65-70 percent of the votes in all nevada counties , on average . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '65-70', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'bush %'], 'result': '65-70', 'ind': 0, 'tostr': 'avg { all_rows ; bush % }'}, '65-70'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; bush % } ; 65-70 } = true', 'tointer': 'the average of the bush % record of all rows is 65-70 .'} | round_eq { avg { all_rows ; bush % } ; 65-70 } = true | the average of the bush % record of all rows is 65-70 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'bush %_4': 4, '65-70_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'bush %_4': 'bush %', '65-70_5': '65-70'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'bush %_4': [0], '65-70_5': [1]} | ['county', 'gore %', 'gore', 'bush %', 'bush', 'others %', 'others'] | [['churchill', '24.8 %', '2191', '70.7 %', '6237', '4.5 %', '395'], ['clark', '51.3 %', '196100', '44.7 %', '170932', '4.0 %', '15166'], ['douglas', '32.5 %', '5837', '62.3 %', '11193', '5.2 %', '944'], ['elko', '17.9 %', '2542', '77.8 %', '11025', '4.3 %', '613'], ['esmeralda', '23.6 %', '116', '67.8 %', '333', '8.6 %', '42'], ['eureka', '17.9 %', '150', '75.5 %', '632', '3.1 %', '6.6 %'], ['humboldt', '22.4 %', '1128', '72.3 %', '3638', '5.3 %', '264'], ['lander', '18.6 %', '395', '76.4 %', '1619', '5.0 %', '105'], ['lincoln', '23.6 %', '461', '70.2 %', '1372', '6.2 %', '123'], ['lyon', '33.0 %', '3955', '60.6 %', '7270', '6.4 %', '767'], ['mineral', '40.0 %', '916', '53.5 %', '1227', '6.5 %', '150'], ['nye', '37.2 %', '4525', '56.7 %', '6904', '6.1 %', '752'], ['pershing', '26.4 %', '476', '67.8 %', '1221', '5.8', '105'], ['storey', '37.0 %', '666', '56.4 %', '1014', '6.6 %', '118'], ['washoe', '42.6 %', '52097', '52.0 %', '63640', '5.4', '6564']] |
swimming at the 1956 summer olympics | https://en.wikipedia.org/wiki/Swimming_at_the_1956_Summer_Olympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1846470-1.html.csv | ordinal | japan was 3rd in the swimming event at the 1956 summer olympics . | {'row': '3', 'col': '6', 'order': '3', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'total', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; total ; 3 }'}, 'nation'], 'result': 'japan ( jpn )', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; total ; 3 } ; nation }'}, 'japan ( jpn )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; total ; 3 } ; nation } ; japan ( jpn ) } = true', 'tointer': 'select the row whose total record of all rows is 3rd maximum . the nation record of this row is japan ( jpn ) .'} | eq { hop { nth_argmax { all_rows ; total ; 3 } ; nation } ; japan ( jpn ) } = true | select the row whose total record of all rows is 3rd maximum . the nation record of this row is japan ( jpn ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'total_5': 5, '3_6': 6, 'nation_7': 7, 'japan (jpn)_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'total_5': 'total', '3_6': '3', 'nation_7': 'nation', 'japan (jpn)_8': 'japan ( jpn )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'total_5': [0], '3_6': [0], 'nation_7': [1], 'japan (jpn)_8': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'australia ( aus )', '8', '4', '2', '14'], ['2', 'united states ( usa )', '2', '4', '5', '11'], ['3', 'japan ( jpn )', '1', '4', '0', '5'], ['4', 'great britain ( gbr )', '1', '0', '1', '2'], ['4', 'germany ( eua )', '1', '0', '1', '2'], ['6', 'hungary ( hun )', '0', '1', '1', '2'], ['7', 'soviet union ( urs )', '0', '0', '2', '2'], ['8', 'south africa ( rsa )', '0', '0', '1', '1']] |
1973 nhl amateur draft | https://en.wikipedia.org/wiki/1973_NHL_Amateur_Draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1965650-4.html.csv | count | four left wings were chosen in picks 49-64 of the 1973 nhl amateur draft . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'left wing', 'result': '4', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'left wing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to left wing .', 'tostr': 'filter_eq { all_rows ; position ; left wing }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; position ; left wing } }', 'tointer': 'select the rows whose position record fuzzily matches to left wing . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; position ; left wing } } ; 4 } = true', 'tointer': 'select the rows whose position record fuzzily matches to left wing . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; position ; left wing } } ; 4 } = true | select the rows whose position record fuzzily matches to left wing . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'position_5': 5, 'left wing_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'position_5': 'position', 'left wing_6': 'left wing', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'left wing_6': [0], '4_7': [2]} | ['pick', 'player', 'position', 'nationality', 'nhl team', 'college / junior / club team'] | [['49', 'andre st laurent', 'centre', 'canada', 'new york islanders', 'montreal junior canadiens ( qmjhl )'], ['50', 'ron serafini', 'defence', 'united states', 'california golden seals', 'st catharines black hawks ( oha )'], ['51', 'keith mackie', 'defence', 'united kingdom canada', 'vancouver canucks', 'edmonton oil kings ( wchl )'], ['52', 'frank rochon', 'left wing', 'canada', 'toronto maple leafs', 'sherbrooke castors ( qmjhl )'], ['53', 'dean talafous', 'centre', 'united states', 'atlanta flames', 'university of wisconsin ( ncaa )'], ['54', 'jim mccrimmon', 'defence', 'canada', 'los angeles kings', 'medicine hat tigers ( wchl )'], ['55', 'dennis owchar', 'defence', 'canada', 'pittsburgh penguins', 'toronto marlboros ( oha )'], ['56', 'alan hangsleben', 'defence', 'united states', 'montreal canadiens', 'university of north dakota ( ncaa )'], ['57', 'tom colley', 'centre', 'canada', 'minnesota north stars', 'sudbury wolves ( oha )'], ['58', 'dale cook', 'left wing', 'canada', 'philadelphia flyers', 'victoria cougars ( wchl )'], ['59', 'mike korney', 'defence', 'canada', 'detroit red wings', 'winnipeg jets ( wchl )'], ['60', 'yvon dupuis', 'right wing', 'canada', 'buffalo sabres', 'quebec remparts ( qmjhl )'], ['61', 'dave elliott', 'left wing', 'canada', 'chicago black hawks', 'winnipeg jets ( wchl )'], ['62', 'brian molvik', 'defence', 'canada', 'new york rangers', 'calgary centennials ( wchl )'], ['63', 'steve langdon', 'left wing', 'canada', 'boston bruins', 'london knights ( oha )'], ['64', 'richard latulippe', 'centre', 'canada', 'montreal canadiens', 'quebec remparts ( qmjhl )']] |
leaf ( israeli company ) | https://en.wikipedia.org/wiki/Leaf_%28Israeli_company%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16395908-2.html.csv | comparative | the aptus ii 10 range has a faster seconds/frame rate than the aptus ii 12 range . | {'row_1': '4', 'row_2': '2', 'col': '8', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'model', 'aptus - ii 10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose model record fuzzily matches to aptus - ii 10 .', 'tostr': 'filter_eq { all_rows ; model ; aptus - ii 10 }'}, 'seconds / frame'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; model ; aptus - ii 10 } ; seconds / frame }', 'tointer': 'select the rows whose model record fuzzily matches to aptus - ii 10 . take the seconds / frame record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'model', 'aptus - ii 12'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose model record fuzzily matches to aptus - ii 12 .', 'tostr': 'filter_eq { all_rows ; model ; aptus - ii 12 }'}, 'seconds / frame'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; model ; aptus - ii 12 } ; seconds / frame }', 'tointer': 'select the rows whose model record fuzzily matches to aptus - ii 12 . take the seconds / frame record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; model ; aptus - ii 10 } ; seconds / frame } ; hop { filter_eq { all_rows ; model ; aptus - ii 12 } ; seconds / frame } } = true', 'tointer': 'select the rows whose model record fuzzily matches to aptus - ii 10 . take the seconds / frame record of this row . select the rows whose model record fuzzily matches to aptus - ii 12 . take the seconds / frame record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; model ; aptus - ii 10 } ; seconds / frame } ; hop { filter_eq { all_rows ; model ; aptus - ii 12 } ; seconds / frame } } = true | select the rows whose model record fuzzily matches to aptus - ii 10 . take the seconds / frame record of this row . select the rows whose model record fuzzily matches to aptus - ii 12 . take the seconds / frame record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'model_7': 7, 'aptus - ii 10_8': 8, 'seconds / frame_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'model_11': 11, 'aptus - ii 12_12': 12, 'seconds / frame_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'model_7': 'model', 'aptus - ii 10_8': 'aptus - ii 10', 'seconds / frame_9': 'seconds / frame', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'model_11': 'model', 'aptus - ii 12_12': 'aptus - ii 12', 'seconds / frame_13': 'seconds / frame'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'model_7': [0], 'aptus - ii 10_8': [0], 'seconds / frame_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'model_11': [1], 'aptus - ii 12_12': [1], 'seconds / frame_13': [3]} | ['model', 'released', 'sensor size', 'resolution', 'active pixels', 'iso range', 'dynamic range ( f - stops )', 'seconds / frame', 'lens conversion factor', 'display', 'storage'] | [['aptus - ii 12r', '2010', '53.7 x40 .3 mm', '80 mp , 16 - bit', '10320 x 7752', '80 - 800', '12', '1.5', '1.0', '3.5 - inch touchscreen', 'firewire , cf'], ['aptus - ii 12', '2010', '53.7 x40 .3 mm', '80 mp , 16 - bit', '10320 x 7752', '80 - 800', '12', '1.5', '1.0', '3.5 - inch touchscreen', 'firewire , cf'], ['aptus - ii 10r', '2010', '56x36 mm', '56 mp , 16 - bit', '9288 x 6000', '80 - 800', '12', '1', '1.0', '3.5 - inch touchscreen', 'firewire , cf'], ['aptus - ii 10', '2010', '56x36 mm', '56 mp , 16 - bit', '9288 x 6000', '80 - 800', '12', '1', '1.0', '3.5 - inch touchscreen', 'firewire , cf'], ['aptus - ii 8', '2010', '44x33 mm', '40 mp , 16 - bit', '7360 x 5562', '80 - 800', '12', '8', '1.3', '3.5 - inch touchscreen', 'firewire , cf'], ['aptus - ii 7', '2009', '48x36 mm', '33 mp , 16 - bit', '6726 x 5040', '50 - 800', '12', '1.1', '1.1', '3.5 - inch touchscreen', 'firewire , cf'], ['aptus - ii 6', '2009', '44x33 mm', '28 mp , 16 - bit', '6144 x 4622', '50 - 800', '12', '1', '1.3', '3.5 - inch touchscreen', 'firewire , cf'], ['aptus - ii 5', '2009', '48x36 mm', '22 mp , 16 - bit', '5356 x 4056', '25 - 400', '12', '9', '1.1', '3.5 - inch touchscreen', 'firewire , cf']] |
generation adidas | https://en.wikipedia.org/wiki/Generation_Adidas | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1076503-13.html.csv | comparative | the player stefan frei graduated from college in 2010 while the player peri maroå ¡ evic graduated from college in 2011 . | {'row_1': '3', 'row_2': '7', 'col': '5', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'stefan frei'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to stefan frei .', 'tostr': 'filter_eq { all_rows ; player ; stefan frei }'}, 'graduated'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; stefan frei } ; graduated }', 'tointer': 'select the rows whose player record fuzzily matches to stefan frei . take the graduated record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'peri maroå ¡ evic'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to peri maroå ¡ evic .', 'tostr': 'filter_eq { all_rows ; player ; peri maroå ¡ evic }'}, 'graduated'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; peri maroå ¡ evic } ; graduated }', 'tointer': 'select the rows whose player record fuzzily matches to peri maroå ¡ evic . take the graduated record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; stefan frei } ; graduated } ; hop { filter_eq { all_rows ; player ; peri maroå ¡ evic } ; graduated } }', 'tointer': 'select the rows whose player record fuzzily matches to stefan frei . take the graduated record of this row . select the rows whose player record fuzzily matches to peri maroå ¡ evic . take the graduated record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'stefan frei'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to stefan frei .', 'tostr': 'filter_eq { all_rows ; player ; stefan frei }'}, 'graduated'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; stefan frei } ; graduated }', 'tointer': 'select the rows whose player record fuzzily matches to stefan frei . take the graduated record of this row .'}, '2010'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; player ; stefan frei } ; graduated } ; 2010 }', 'tointer': 'the graduated record of the first row is 2010 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'peri maroå ¡ evic'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to peri maroå ¡ evic .', 'tostr': 'filter_eq { all_rows ; player ; peri maroå ¡ evic }'}, 'graduated'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; peri maroå ¡ evic } ; graduated }', 'tointer': 'select the rows whose player record fuzzily matches to peri maroå ¡ evic . take the graduated record of this row .'}, '2011'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; player ; peri maroå ¡ evic } ; graduated } ; 2011 }', 'tointer': 'the graduated record of the second row is 2011 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; player ; stefan frei } ; graduated } ; 2010 } ; eq { hop { filter_eq { all_rows ; player ; peri maroå ¡ evic } ; graduated } ; 2011 } }', 'tointer': 'the graduated record of the first row is 2010 . the graduated record of the second row is 2011 .'}], 'result': True, 'ind': 8, 'tostr': 'and { less { hop { filter_eq { all_rows ; player ; stefan frei } ; graduated } ; hop { filter_eq { all_rows ; player ; peri maroå ¡ evic } ; graduated } } ; and { eq { hop { filter_eq { all_rows ; player ; stefan frei } ; graduated } ; 2010 } ; eq { hop { filter_eq { all_rows ; player ; peri maroå ¡ evic } ; graduated } ; 2011 } } } = true', 'tointer': 'select the rows whose player record fuzzily matches to stefan frei . take the graduated record of this row . select the rows whose player record fuzzily matches to peri maroå ¡ evic . take the graduated record of this row . the first record is less than the second record . the graduated record of the first row is 2010 . the graduated record of the second row is 2011 .'} | and { less { hop { filter_eq { all_rows ; player ; stefan frei } ; graduated } ; hop { filter_eq { all_rows ; player ; peri maroå ¡ evic } ; graduated } } ; and { eq { hop { filter_eq { all_rows ; player ; stefan frei } ; graduated } ; 2010 } ; eq { hop { filter_eq { all_rows ; player ; peri maroå ¡ evic } ; graduated } ; 2011 } } } = true | select the rows whose player record fuzzily matches to stefan frei . take the graduated record of this row . select the rows whose player record fuzzily matches to peri maroå ¡ evic . take the graduated record of this row . the first record is less than the second record . the graduated record of the first row is 2010 . the graduated record of the second row is 2011 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'less_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'player_11': 11, 'stefan frei_12': 12, 'graduated_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'player_15': 15, 'peri maroå¡evic_16': 16, 'graduated_17': 17, 'and_7': 7, 'eq_5': 5, '2010_18': 18, 'eq_6': 6, '2011_19': 19} | {'and_8': 'and', 'result_9': 'true', 'less_4': 'less', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'stefan frei_12': 'stefan frei', 'graduated_13': 'graduated', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'player_15': 'player', 'peri maroå¡evic_16': 'peri maroå ¡ evic', 'graduated_17': 'graduated', 'and_7': 'and', 'eq_5': 'eq', '2010_18': '2010', 'eq_6': 'eq', '2011_19': '2011'} | {'and_8': [9], 'result_9': [], 'less_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'player_11': [0], 'stefan frei_12': [0], 'graduated_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'player_15': [1], 'peri maroå¡evic_16': [1], 'graduated_17': [3], 'and_7': [8], 'eq_5': [7], '2010_18': [5], 'eq_6': [7], '2011_19': [6]} | ['player', 'home town', 'college / prior', 'drafting team', 'graduated'] | [['kevin alston', 'silver spring , md', 'indiana', 'new england revolution', '2009'], ['danny cruz', 'glendale , az', 'unlv', 'houston dynamo', '2011'], ['stefan frei', 'widnau , switzerland', 'california', 'toronto fc', '2010'], ['omar gonzalez', 'dallas , tx', 'maryland', 'los angeles galaxy', '2009'], ['jeremy hall', 'tampa , fl', 'maryland', 'new york red bulls', '2009'], ['baggio husidic', 'libertyville , il', 'uic', 'chicago fire', '2010'], ['peri maroå ¡ evic', 'rockford , il', 'michigan', 'fc dallas', '2011'], ['rodney wallace', 'rockville , md', 'maryland', 'dc united', '2009'], ['steve zakuani', 'london , england', 'akron', 'seattle sounders fc', '2009']] |
lancia flavia | https://en.wikipedia.org/wiki/Lancia_Flavia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1654827-1.html.csv | count | six of the lancia flavia models have a single carburetor fuel system . | {'scope': 'all', 'criterion': 'equal', 'value': 'single carburetor', 'result': '6', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'fuel system', 'single carburetor'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose fuel system record fuzzily matches to single carburetor .', 'tostr': 'filter_eq { all_rows ; fuel system ; single carburetor }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; fuel system ; single carburetor } }', 'tointer': 'select the rows whose fuel system record fuzzily matches to single carburetor . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; fuel system ; single carburetor } } ; 6 } = true', 'tointer': 'select the rows whose fuel system record fuzzily matches to single carburetor . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; fuel system ; single carburetor } } ; 6 } = true | select the rows whose fuel system record fuzzily matches to single carburetor . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'fuel system_5': 5, 'single carburetor_6': 6, '6_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'fuel system_5': 'fuel system', 'single carburetor_6': 'single carburetor', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'fuel system_5': [0], 'single carburetor_6': [0], '6_7': [2]} | ['model', 'years', 'engine', 'displacement', 'power', 'fuel system'] | [['berlina', '1960 - 62', 'lancia h4 ohv', '1500cc', 'n / a', 'single carburetor'], ['coupã , cab , sport', '1962', 'lancia h4 ohv', '1500cc', 'n / a', 'double carburetor'], ['1500', '1963 - 68', 'lancia h4 ohv', '1488cc', 'n / a', 'single carburetor'], ['1800', '1963 - 68', 'lancia h4 ohv', '1800cc', 'n / a', 'single carburetor'], ['1800 sport', '1963 - 67', 'lancia h4 ohv', '1800cc', 'n / a', 'double carburetor'], ['1800 iniezione', '1965 - 68', 'lancia h4 ohv', '1800cc', 'n / a', 'fuel injection'], ['1500', '1969 - 70', 'lancia h4 ohv', '1490cc', 'n / a', 'single carburetor'], ['1800', '1969 - 70', 'lancia h4 ohv', '1816cc', 'n / a', 'single carburetor'], ['2000', '1969 - 74', 'lancia h4 ohv', '1991cc', 'n / a', 'single carburetor'], ['2000 iniezione', '1969 - 74', 'lancia h4 ohv', '1991cc', 'n / a', 'fuel injection']] |
suburban league | https://en.wikipedia.org/wiki/Suburban_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28051859-3.html.csv | ordinal | coventry 's tenure began the fifth earliest of all the schools in the suburban league . | {'row': '2', 'col': '5', 'order': '5', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'tenure', '5'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; tenure ; 5 }'}, 'school'], 'result': 'coventry', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; tenure ; 5 } ; school }'}, 'coventry'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; tenure ; 5 } ; school } ; coventry } = true', 'tointer': 'select the row whose tenure record of all rows is 5th minimum . the school record of this row is coventry .'} | eq { hop { nth_argmin { all_rows ; tenure ; 5 } ; school } ; coventry } = true | select the row whose tenure record of all rows is 5th minimum . the school record of this row is coventry . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'tenure_5': 5, '5_6': 6, 'school_7': 7, 'coventry_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'tenure_5': 'tenure', '5_6': '5', 'school_7': 'school', 'coventry_8': 'coventry'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'tenure_5': [0], '5_6': [0], 'school_7': [1], 'coventry_8': [2]} | ['school', 'nickname', 'location', 'colors', 'tenure'] | [['barberton', 'magics', 'barberton , summit county', 'purple , white', '2005 - 2011'], ['coventry', 'comets', 'coventry twp , summit county', 'blue , gold', '1969 - 1983'], ['field', 'falcons', 'brimfield , portage county', 'red , white , black', '1978 - 1990'], ['hudson', 'explorers', 'hudson , summit county', 'navy blue , white', '1949 - 1997'], ['manchester', 'panthers', 'new franklin , summit county', 'red , black', '1949 - 1976'], ['mogadore', 'wildcats', 'mogadore , portage county', 'green , white', '1958 - 1968'], ['norton', 'panthers', 'norton , summit county', 'red , black , white', '1972 - 2005'], ['twinsburg', 'tigers', 'twinsburg , summit county', 'blue , white', '1958 - 1964']] |
1972 vfl season | https://en.wikipedia.org/wiki/1972_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10826385-8.html.csv | aggregation | the average crowd attendance for games in the 1972 vfl season was 21486 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '21486', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '21486', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '21486'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 21486 } = true', 'tointer': 'the average of the crowd record of all rows is 21486 .'} | round_eq { avg { all_rows ; crowd } ; 21486 } = true | the average of the crowd record of all rows is 21486 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '21486_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '21486_5': '21486'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '21486_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['collingwood', '7.14 ( 56 )', 'footscray', '11.14 ( 80 )', 'victoria park', '25986', '20 may 1972'], ['melbourne', '20.14 ( 134 )', 'geelong', '14.17 ( 101 )', 'mcg', '19023', '20 may 1972'], ['south melbourne', '9.7 ( 61 )', 'fitzroy', '18.11 ( 119 )', 'lake oval', '12421', '20 may 1972'], ['north melbourne', '8.13 ( 61 )', 'essendon', '14.12 ( 96 )', 'arden street oval', '14091', '20 may 1972'], ['st kilda', '10.12 ( 72 )', 'carlton', '14.15 ( 99 )', 'moorabbin oval', '31547', '20 may 1972'], ['richmond', '11.25 ( 91 )', 'hawthorn', '13.6 ( 84 )', 'vfl park', '25845', '20 may 1972']] |
electric car | https://en.wikipedia.org/wiki/Electric_car | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16105186-2.html.csv | aggregation | the average epa rated combined fuel economy for the electric car is around 100 mpg . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '100 mpg', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'epa rated combined fuel economy'], 'result': '100 mpg', 'ind': 0, 'tostr': 'avg { all_rows ; epa rated combined fuel economy }'}, '100 mpg'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; epa rated combined fuel economy } ; 100 mpg } = true', 'tointer': 'the average of the epa rated combined fuel economy record of all rows is 100 mpg .'} | round_eq { avg { all_rows ; epa rated combined fuel economy } ; 100 mpg } = true | the average of the epa rated combined fuel economy record of all rows is 100 mpg . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'epa rated combined fuel economy_4': 4, '100 mpg_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'epa rated combined fuel economy_4': 'epa rated combined fuel economy', '100 mpg_5': '100 mpg'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'epa rated combined fuel economy_4': [0], '100 mpg_5': [1]} | ['vehicle', 'epa rated all - electric range', 'epa rated combined fuel economy', 'alaska ( juneau )', 'california ( san francisco )', 'mid - atlantic south ( washington , dc )', 'us national average electric mix', 'southeast ( atlanta )', 'midwest ( des moines )', 'rocky mountains ( denver )'] | [['mitsubishi i - miev', '-', '112 mpg - e ( 30 kw - hrs / 100 miles )', '80 g / mi ( 50 g / km )', '100 g / mi ( 62 g / km )', '160 g / mi ( 99 g / km )', '200 g / mi ( 124 g / km )', '230 g / mi ( 143 g / km )', '270 g / mi ( 168 g / km )', '290 g / mi ( 180 g / km )'], ['ford focus electric', '-', '105 mpg - e ( 32 kw - hrs / 100 miles )', '80 g / mi ( 50 g / km )', '110 g / mi ( 68 g / km )', '170 g / mi ( 106 g / km )', '210 g / mi ( 131 g / km )', '250 g / mi ( 155 g / km )', '280 g / mi ( 174 g / km )', '310 g / mi ( 193 g / km )'], ['bmw activee', '-', '102 mpg - e ( 33 kw - hrs / 100 miles )', '90 g / mi ( 56 g / km )', '110 g / mi ( 68 g / km )', '180 g / mi ( 112 g / km )', '220 g / mi ( 137 g / km )', '250 g / mi ( 155 g / km )', '290 g / mi ( 180 g / km )', '320 g / mi ( 199 g / km )'], ['nissan leaf', '-', '99 mpg - e ( 34 kw - hrs / 100 miles )', '90 g / mi ( 56 g / km )', '120 g / mi ( 75 g / km )', '190 g / mi ( 118 g / km )', '230 g / mi ( 143 g / km )', '260 g / mi ( 162 g / km )', '300 g / mi ( 186 g / km )', '330 g / mi ( 205 g / km )'], ['chevrolet volt', '-', '94 mpg - e ( 36 kw - hrs / 100 miles )', '170 g / mi ( 106 g / km ) ( 1 )', '190 g / mi ( 118 g / km ) ( 1 )', '230 g / mi ( 143 g / km ) ( 1 )', '260 g / mi ( 162 g / km ) ( 1 )', '290 g / mi ( 180 g / km ) ( 1 )', '310 g / mi ( 193 g / km ) ( 1 )', '330 g / mi ( 205 g / km ) 1 )'], ['smart ed', '-', '87 mpg - e ( 39 kw - hrs / 100 miles )', '100 g / mi ( 62 g / km )', '130 g / mi ( 81 g / km )', '210 g / mi ( 131 g / km )', '260 g / mi ( 162 g / km )', '300 g / mi ( 186 g / km )', '350 g / mi ( 218 g / km )', '380 g / mi ( 236 g / km )']] |
bc lietuvos rytas | https://en.wikipedia.org/wiki/BC_Lietuvos_rytas | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1771141-1.html.csv | count | jonas kazlauskas was the head coach of bc lietuvos rytas for a total of two seasons . | {'scope': 'all', 'criterion': 'equal', 'value': 'jonas kazlauskas', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'head coach', 'jonas kazlauskas'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose head coach record fuzzily matches to jonas kazlauskas .', 'tostr': 'filter_eq { all_rows ; head coach ; jonas kazlauskas }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; head coach ; jonas kazlauskas } }', 'tointer': 'select the rows whose head coach record fuzzily matches to jonas kazlauskas . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; head coach ; jonas kazlauskas } } ; 2 } = true', 'tointer': 'select the rows whose head coach record fuzzily matches to jonas kazlauskas . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; head coach ; jonas kazlauskas } } ; 2 } = true | select the rows whose head coach record fuzzily matches to jonas kazlauskas . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'head coach_5': 5, 'jonas kazlauskas_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'head coach_5': 'head coach', 'jonas kazlauskas_6': 'jonas kazlauskas', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'head coach_5': [0], 'jonas kazlauskas_6': [0], '2_7': [2]} | ['season', 'lkf cup', 'regional competitions', 'europe', 'head coach'] | [['1997 - 98', 'champion', '-', 'korać cup group stage', 'modestas paulauskas , alfredas vainauskas'], ['1998 - 99', '-', 'nebl 3rd place', 'saporta cup group stage', 'vainauskas , sakalauskas'], ['1999 - 00', '-', 'nebl finalist', 'saporta cup semifinalist', 'vainauskas , sakalauskas'], ['2000 - 01', '-', 'nebl 3rd place', 'suproleague top 16', 'šarūnas sakalauskas , alfredas vainauskas'], ['2001 - 02', '-', 'nebl champion', 'saporta cup quarterfinalist', 'jonas kazlauskas'], ['2002 - 03', '-', 'nebl finalist', 'champions cup group stage', 'jonas kazlauskas'], ['2003 - 04', '-', '-', 'uleb cup quarterfinalist', 'kazlauskas , kemzūra'], ['2004 - 05', '-', 'bbl elite division finalist', 'uleb cup champion', 'vlade djurović , tomo mahorić'], ['2005 - 06', '-', 'bbl elite division champion', 'euroleague top 16', 'neven spahija'], ['2006 - 07', 'finalist', 'bbl elite division champion', 'uleb cup finalist', 'drucker , sagadin , trifunović'], ['2007 - 08', 'finalist', 'bbl elite division finalist', 'euroleague top 16', 'aleksandar trifunović'], ['2008 - 09', 'champion', 'bbl elite division champion', 'eurocup champion', 'antanas sireika , rimas kurtinaitis'], ['2009 - 10', 'champion', 'bbl elite division finalist', 'euroleague group stage', 'rimas kurtinaitis'], ['2010 - 11', 'finalist', 'bbl elite division 3rd place', 'euroleague top 16', 'anzulović , trifunović , maskoliūnas'], ['2010 - 11', 'finalist', 'vtb group stage', 'euroleague top 16', 'anzulović , trifunović , maskoliūnas'], ['2011 - 12', '-', 'bbl elite division finalist', 'eurocup 3rd place', 'aleksandar džikić'], ['2011 - 12', '-', 'vtb 3rd place', 'eurocup 3rd place', 'aleksandar džikić'], ['2012 - 13', '-', 'vtb group stage', 'euroleague group stage', 'džikić , maskoliūnas , bauermann']] |
juan garriga | https://en.wikipedia.org/wiki/Juan_Garriga | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14820149-3.html.csv | superlative | juan garriga achieved the most wins of his career in the year 1988 . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '5', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'wins'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; wins }'}, 'year'], 'result': '1988', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; wins } ; year }'}, '1988'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; wins } ; year } ; 1988 } = true', 'tointer': 'select the row whose wins record of all rows is maximum . the year record of this row is 1988 .'} | eq { hop { argmax { all_rows ; wins } ; year } ; 1988 } = true | select the row whose wins record of all rows is maximum . the year record of this row is 1988 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'wins_5': 5, 'year_6': 6, '1988_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'wins_5': 'wins', 'year_6': 'year', '1988_7': '1988'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'wins_5': [0], 'year_6': [1], '1988_7': [2]} | ['year', 'class', 'team', 'points', 'wins'] | [['1984', '250cc', 'yamaha', '0', '0'], ['1985', '250cc', 'jj cobas', '8', '0'], ['1986', '500cc', 'cagiva', '4', '0'], ['1987', '250cc', 'ducados - yamaha', '46', '0'], ['1988', '250cc', 'ducados - yamaha', '221', '3'], ['1989', '250cc', 'ducados - yamaha', '98', '0'], ['1990', '500cc', 'ducados - yamaha', '121', '0'], ['1991', '500cc', 'ducados - yamaha', '121', '0'], ['1992', '500cc', 'ducados - yamaha', '61', '0'], ['1993', '500cc', 'cagiva', '7', '0']] |
just a closer walk with thee ( album ) | https://en.wikipedia.org/wiki/Just_a_Closer_Walk_with_Thee_%28album%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13536392-2.html.csv | majority | all of the songs on the album just a closer walk with thee were adapted by malcolm dodds . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': '( adapted by malcolm dodds )', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'songwriter ( s )', '( adapted by malcolm dodds )'], 'result': True, 'ind': 0, 'tointer': 'for the songwriter ( s ) records of all rows , all of them fuzzily match to ( adapted by malcolm dodds ) .', 'tostr': 'all_eq { all_rows ; songwriter ( s ) ; ( adapted by malcolm dodds ) } = true'} | all_eq { all_rows ; songwriter ( s ) ; ( adapted by malcolm dodds ) } = true | for the songwriter ( s ) records of all rows , all of them fuzzily match to ( adapted by malcolm dodds ) . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'songwriter (s)_3': 3, '(adapted by malcolm dodds)_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'songwriter (s)_3': 'songwriter ( s )', '(adapted by malcolm dodds)_4': '( adapted by malcolm dodds )'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'songwriter (s)_3': [0], '(adapted by malcolm dodds)_4': [0]} | ['track number', 'title', 'songwriter ( s )', 'recording date', 'time'] | [['1', 'swing low , sweet chariot', 'wallis willis ( adapted by malcolm dodds )', 'november 13 , 1959', '3:15'], ['2', 'steal away', '( adapted by malcolm dodds )', 'november 13 , 1959', '3:15'], ['3', 'little david', '( adapted by malcolm dodds )', 'january 28 , 1960', '2:20'], ['4', 'nobody knows', '( adapted by malcolm dodds )', 'november 13 , 1959', '3:10'], ['5', "i could n't hear nobody pray", '( adapted by malcolm dodds )', 'november 16 , 1959', '2:55'], ['6', 'motherless child', 'traditional ( adapted by malcolm dodds )', 'november 13 , 1959', '2:48'], ['7', 'just a closer walk with thee', 'stuart hine ( adapted by malcolm dodds )', 'november 16 , 1959', '3:30'], ['8', "my lord what a mornin '", 'h t burleigh ( adapted by malcolm dodds )', 'january 28 , 1960', '2:30'], ['9', "great getting up mornin '", '( adapted by malcolm dodds )', 'january 28 , 1960', '3:25'], ['10', 'were you there', '( adapted by malcolm dodds )', 'november 16 , 1959', '3:23'], ['11', 'break bread', '( adapted by malcolm dodds )', 'november 16 , 1959', '3:25'], ['12', 'me ! oh lord', '( adapted by malcolm dodds )', 'november 13 , 1959', '2:10']] |
united states army air forces | https://en.wikipedia.org/wiki/United_States_Army_Air_Forces | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23508196-5.html.csv | majority | in the united states army air forces , most groups had more than 40 aircraft . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '40', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'number of aircraft', '40'], 'result': True, 'ind': 0, 'tointer': 'for the number of aircraft records of all rows , most of them are greater than 40 .', 'tostr': 'most_greater { all_rows ; number of aircraft ; 40 } = true'} | most_greater { all_rows ; number of aircraft ; 40 } = true | for the number of aircraft records of all rows , most of them are greater than 40 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'number of aircraft_3': 3, '40_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'number of aircraft_3': 'number of aircraft', '40_4': '40'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'number of aircraft_3': [0], '40_4': [0]} | ['type of unit', 'type of aircraft', 'number of aircraft', 'number of crews', 'men per crew', 'total personnel', 'officers', 'enlisted'] | [['very heavy bombardment group', 'b - 29', '45', '60', '11', '2078', '462', '1816'], ['heavy bombardment group', 'b - 17 , b - 24', '72', '96', '9 to 11', '2261', '465', '1796'], ['medium bombardment group', 'b - 25 , b - 26', '96', '96', '5 or 6', '1759', '393', '1386'], ['light bombardment group', 'a - 20 , a - 26', '96', '96', '3 or 4', '1304', '211', '1093'], ['single - engine fighter group', 'p - 40 , p - 47 p - 51', '111 to 126', '108 to 126', '1', '994', '183', '811'], ['twin - engine fighter group', 'p - 38', '111 to 126', '108 to 126', '1', '1081', '183', '838'], ['troop carrier group', 'c - 47', '80 - 110', '128', '4 or 5', '1837', '514', '1323'], ['combat cargo group', 'c - 46 , c - 47', '125', '150', '4', '883', '350', '533'], ['night fighter squadron', 'p - 61 , p - 70', '18', '16', '2 or 3', '288', '50', '238'], ['tactical reconnaissance squadron', 'f - 6 , p - 40 l - 4 , l - 5', '27', '23', '1', '233', '39', '194'], ['photo reconnaissance squadron', 'f - 5', '24', '21', '1', '347', '50', '297']] |
greg sacks | https://en.wikipedia.org/wiki/Greg_Sacks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2308381-1.html.csv | aggregation | during greg sacks ' career , his average number of starts was 15 per year . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '15', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'starts'], 'result': '15', 'ind': 0, 'tostr': 'avg { all_rows ; starts }'}, '15'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; starts } ; 15 } = true', 'tointer': 'the average of the starts record of all rows is 15 .'} | round_eq { avg { all_rows ; starts } ; 15 } = true | the average of the starts record of all rows is 15 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'starts_4': 4, '15_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'starts_4': 'starts', '15_5': '15'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'starts_4': [0], '15_5': [1]} | ['year', 'starts', 'wins', 'top 5', 'top 10', 'poles', 'avg start', 'avg finish', 'winnings', 'position', 'team ( s )'] | [['1983', '5', '0', '0', '0', '0', '25.6', '30.4', '8060', '47th', '5 sacks & sons'], ['1984', '29', '0', '0', '1', '0', '24.3', '25.1', '75183', '19th', '51 sacks & sons'], ['1986', '8', '0', '0', '1', '0', '22.4', '30.4', '64810', '41st', '10 digard motorsports'], ['1987', '16', '0', '0', '0', '0', '23.6', '29.8', '54815', '33rd', '50 dingman brothers racing'], ['1990', '16', '0', '2', '4', '1', '18.6', '20.8', '216148', '32nd', '17 / 18 / 46 hendrick motorsports'], ['1991', '11', '0', '0', '0', '0', '27.5', '30.4', '84215', '39th', '18 daytona speed inc 47 close racing'], ['1992', '20', '0', '0', '0', '0', '23.5', '25.1', '178120', '30th', '41 larry hedrick motorsports'], ['1993', '19', '0', '0', '1', '0', '24.3', '24.2', '168055', '35th', '9 melling racing 68 tristar motorsports'], ['1994', '31', '0', '0', '3', '1', '19.7', '27.0', '411728', '31st', '77 us motorsports inc'], ['1998', '7', '0', '0', '0', '0', '23.6', '35.3', '296880', '53rd', '98 yarborough - burdette motorsports'], ['2004', '3', '0', '0', '0', '0', '36.3', '41.7', '154100', '71st', '13 daytona speed inc']] |
kristína kučová | https://en.wikipedia.org/wiki/Krist%C3%ADna_Ku%C4%8Dov%C3%A1 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14359057-4.html.csv | count | kristína kučová was the runner up twice in the doubles between 17 march 2007 and 14 june 2009 . | {'scope': 'all', 'criterion': 'equal', 'value': 'runner - up', 'result': '2', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outcome', 'runner - up'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose outcome record fuzzily matches to runner - up .', 'tostr': 'filter_eq { all_rows ; outcome ; runner - up }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; outcome ; runner - up } }', 'tointer': 'select the rows whose outcome record fuzzily matches to runner - up . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; outcome ; runner - up } } ; 2 } = true', 'tointer': 'select the rows whose outcome record fuzzily matches to runner - up . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; outcome ; runner - up } } ; 2 } = true | select the rows whose outcome record fuzzily matches to runner - up . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'outcome_5': 5, 'runner - up_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'outcome_5': 'outcome', 'runner - up_6': 'runner - up', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'outcome_5': [0], 'runner - up_6': [0], '2_7': [2]} | ['outcome', 'date', 'tournament', 'surface', 'partner', 'opponents in the final', 'score'] | [['winner', '17 march 2007', 'cairo', 'clay', 'zuzana kučová', 'melissa berry michelle gerards', '6 - 7 ( 3 ) 6 - 4 6 - 3'], ['winner', '20 may 2007', 'michalovce', 'clay', 'klaudia boczová', 'olga brózda justyna jegiołka', '7 - 5 4 - 6 6 - 3'], ['runner - up', '11 may 2008', 'jounieh', 'clay', 'stefanie vögele', 'nina bratchikova veronika kapshay', '5 - 7 , 6 - 3 ,'], ['winner', '25 may 2008', 'galați', 'clay', 'valentina sulpizio', 'alexandra cadanțu antonia xenia tout', '6 - 0 6 - 2'], ['runner - up', '3 may 2009', 'johannesburg', 'hard', 'anastasija sevastova', 'naomi cavaday lesia tsurenko', '2 - 6 6 - 2'], ['winner', '14 june 2009', 'zlín', 'clay', 'zuzana kučová', 'nikola fraňková carmen klaschka', '6 - 3 6 - 4']] |
1957 vfl season | https://en.wikipedia.org/wiki/1957_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10774891-4.html.csv | unique | only the game between carlton and geelong was held at princes park . | {'scope': 'all', 'row': '4', 'col': '5', 'col_other': '1,3', 'criterion': 'equal', 'value': 'princes park', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'princes park'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to princes park .', 'tostr': 'filter_eq { all_rows ; venue ; princes park }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; venue ; princes park } }', 'tointer': 'select the rows whose venue record fuzzily matches to princes park . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'princes park'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to princes park .', 'tostr': 'filter_eq { all_rows ; venue ; princes park }'}, 'home team'], 'result': 'carlton', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; princes park } ; home team }'}, 'carlton'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; venue ; princes park } ; home team } ; carlton }', 'tointer': 'the home team record of this unqiue row is carlton .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'princes park'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to princes park .', 'tostr': 'filter_eq { all_rows ; venue ; princes park }'}, 'away team'], 'result': 'geelong', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; venue ; princes park } ; away team }'}, 'geelong'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; venue ; princes park } ; away team } ; geelong }', 'tointer': 'the away team record of this unqiue row is geelong .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; venue ; princes park } ; home team } ; carlton } ; eq { hop { filter_eq { all_rows ; venue ; princes park } ; away team } ; geelong } }', 'tointer': 'the home team record of this unqiue row is carlton . the away team record of this unqiue row is geelong .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; venue ; princes park } } ; and { eq { hop { filter_eq { all_rows ; venue ; princes park } ; home team } ; carlton } ; eq { hop { filter_eq { all_rows ; venue ; princes park } ; away team } ; geelong } } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to princes park . there is only one such row in the table . the home team record of this unqiue row is carlton . the away team record of this unqiue row is geelong .'} | and { only { filter_eq { all_rows ; venue ; princes park } } ; and { eq { hop { filter_eq { all_rows ; venue ; princes park } ; home team } ; carlton } ; eq { hop { filter_eq { all_rows ; venue ; princes park } ; away team } ; geelong } } } = true | select the rows whose venue record fuzzily matches to princes park . there is only one such row in the table . the home team record of this unqiue row is carlton . the away team record of this unqiue row is geelong . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'venue_10': 10, 'princes park_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'home team_12': 12, 'carlton_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'away team_14': 14, 'geelong_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'venue_10': 'venue', 'princes park_11': 'princes park', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'home team_12': 'home team', 'carlton_13': 'carlton', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'away team_14': 'away team', 'geelong_15': 'geelong'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'venue_10': [0], 'princes park_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'home team_12': [2], 'carlton_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'away team_14': [4], 'geelong_15': [5]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '6.15 ( 51 )', 'melbourne', '4.9 ( 33 )', 'glenferrie oval', '20000', '11 may 1957'], ['essendon', '11.14 ( 80 )', 'footscray', '4.14 ( 38 )', 'windy hill', '30000', '11 may 1957'], ['collingwood', '14.9 ( 93 )', 'south melbourne', '11.17 ( 83 )', 'victoria park', '32500', '11 may 1957'], ['carlton', '15.12 ( 102 )', 'geelong', '13.11 ( 89 )', 'princes park', '28888', '11 may 1957'], ['st kilda', '16.5 ( 101 )', 'north melbourne', '12.12 ( 84 )', 'junction oval', '20000', '11 may 1957'], ['richmond', '16.10 ( 106 )', 'fitzroy', '10.31 ( 91 )', 'punt road oval', '16500', '11 may 1957']] |
2007 - 08 detroit pistons season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Detroit_Pistons_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11960944-10.html.csv | count | in the 2007 -- 08 detroit pistons season , mcdyess achieved the highest rebounds figure twice . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'mcdyess', 'result': '2', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high rebounds', 'mcdyess'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high rebounds record fuzzily matches to mcdyess .', 'tostr': 'filter_eq { all_rows ; high rebounds ; mcdyess }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; high rebounds ; mcdyess } }', 'tointer': 'select the rows whose high rebounds record fuzzily matches to mcdyess . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; high rebounds ; mcdyess } } ; 2 } = true', 'tointer': 'select the rows whose high rebounds record fuzzily matches to mcdyess . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; high rebounds ; mcdyess } } ; 2 } = true | select the rows whose high rebounds record fuzzily matches to mcdyess . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'high rebounds_5': 5, 'mcdyess_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'high rebounds_5': 'high rebounds', 'mcdyess_6': 'mcdyess', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high rebounds_5': [0], 'mcdyess_6': [0], '2_7': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series'] | [['1', 'may 3', 'orlando', 'w 91 - 72', 'billups ( 19 )', 'maxiell ( 9 )', 'billups ( 7 )', 'the palace of auburn hills 22076', '1 - 0'], ['2', 'may 5', 'orlando', 'w 100 - 93', 'billups ( 28 )', 'prince ( 10 )', 'prince ( 5 )', 'the palace of auburn hills 22076', '2 - 0'], ['3', 'may 7', 'orlando', 'l 111 - 86', 'hamilton ( 24 )', 'prince ( 7 )', 'hamilton , prince ( 3 )', 'amway arena 17519', '2 - 1'], ['4', 'may 10', 'orlando', 'w 90 - 89', 'hamilton ( 32 )', 'mcdyess ( 14 )', 'prince ( 5 )', 'amway arena 17519', '3 - 1'], ['5', 'may 13', 'orlando', 'w 91 - 86', 'hamilton ( 31 )', 'mcdyess ( 11 )', 'stuckey ( 6 )', 'the palace of auburn hills 22076', '4 - 1']] |
carla suárez navarro | https://en.wikipedia.org/wiki/Carla_Su%C3%A1rez_Navarro | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15551996-3.html.csv | comparative | estoril open , estoril , portugal occured before portugal open , oeiras , portugal . | {'row_1': '3', 'row_2': '5', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'estoril open , estoril , portugal'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to estoril open , estoril , portugal .', 'tostr': 'filter_eq { all_rows ; tournament ; estoril open , estoril , portugal }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tournament ; estoril open , estoril , portugal } ; date }', 'tointer': 'select the rows whose tournament record fuzzily matches to estoril open , estoril , portugal . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'portugal open , oeiras , portugal'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tournament record fuzzily matches to portugal open , oeiras , portugal .', 'tostr': 'filter_eq { all_rows ; tournament ; portugal open , oeiras , portugal }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; tournament ; portugal open , oeiras , portugal } ; date }', 'tointer': 'select the rows whose tournament record fuzzily matches to portugal open , oeiras , portugal . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; tournament ; estoril open , estoril , portugal } ; date } ; hop { filter_eq { all_rows ; tournament ; portugal open , oeiras , portugal } ; date } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to estoril open , estoril , portugal . take the date record of this row . select the rows whose tournament record fuzzily matches to portugal open , oeiras , portugal . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; tournament ; estoril open , estoril , portugal } ; date } ; hop { filter_eq { all_rows ; tournament ; portugal open , oeiras , portugal } ; date } } = true | select the rows whose tournament record fuzzily matches to estoril open , estoril , portugal . take the date record of this row . select the rows whose tournament record fuzzily matches to portugal open , oeiras , portugal . take the date record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'tournament_7': 7, 'estoril open , estoril , portugal_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'tournament_11': 11, 'portugal open , oeiras , portugal_12': 12, 'date_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'tournament_7': 'tournament', 'estoril open , estoril , portugal_8': 'estoril open , estoril , portugal', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'tournament_11': 'tournament', 'portugal open , oeiras , portugal_12': 'portugal open , oeiras , portugal', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'tournament_7': [0], 'estoril open , estoril , portugal_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'tournament_11': [1], 'portugal open , oeiras , portugal_12': [1], 'date_13': [3]} | ['outcome', 'date', 'tournament', 'surface', 'opponent', 'score'] | [['runner - up', 'april 12 , 2009', 'andalucia tennis experience , marbella , spain', 'clay', 'jelena janković', '3 - 6 , 6 - 3 , 3 - 6'], ['runner - up', 'april 11 , 2010', 'andalucia tennis experience , marbella , spain', 'clay', 'flavia pennetta', '2 - 6 , 6 - 4 , 3 - 6'], ['runner - up', 'may 5 , 2012', 'estoril open , estoril , portugal', 'clay', 'kaia kanepi', '6 - 3 , 6 - 7 ( 6 - 8 ) , 4 - 6'], ['runner - up', 'march 2 , 2013', 'abierto mexicano telcel , acapulco , mexico', 'clay', 'sara errani', '0 - 6 , 4 - 6'], ['runner - up', 'may 4 , 2013', 'portugal open , oeiras , portugal', 'clay', 'anastasia pavlyuchenkova', '5 - 7 , 2 - 6']] |
american seafoods | https://en.wikipedia.org/wiki/American_Seafoods | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15230458-1.html.csv | ordinal | of the american seafoods ' ships , the one with the 2nd highest tonnage was ocean rover . | {'row': '6', 'col': '3', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'tonnage', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; tonnage ; 2 }'}, 'name'], 'result': 'ocean rover', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; tonnage ; 2 } ; name }'}, 'ocean rover'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; tonnage ; 2 } ; name } ; ocean rover } = true', 'tointer': 'select the row whose tonnage record of all rows is 2nd maximum . the name record of this row is ocean rover .'} | eq { hop { nth_argmax { all_rows ; tonnage ; 2 } ; name } ; ocean rover } = true | select the row whose tonnage record of all rows is 2nd maximum . the name record of this row is ocean rover . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'tonnage_5': 5, '2_6': 6, 'name_7': 7, 'ocean rover_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'tonnage_5': 'tonnage', '2_6': '2', 'name_7': 'name', 'ocean rover_8': 'ocean rover'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'tonnage_5': [0], '2_6': [0], 'name_7': [1], 'ocean rover_8': [2]} | ['name', 'length', 'tonnage', 'built by', 'year', 'engines', 'horsepowers', 'former names'] | [['american dynasty', '272.0 feet', '3471', 'mangone shipyard , houston , tx', '1974', '2 , bergen diesel , brm - 8', '8000', 'artabaze , bure , sea bure'], ['american triumph', '285.0 feet', '4294', 'ls baier & co , portland , or', '1961', '2 , w채rtsil채 , 8r32d', '7939', 'acona'], ['northern jaeger', '337 feet', '3732', 'levingston shipbuilding , orange , tx', '1969', '2 , mak m453c', '6322', 'jaeger , inagua ranger ii , wisco ranger'], ['northern eagle', '344.1 feet', '4437', 'ulstein hatlo norway', '1966', '2 , bergen diesel , brm - 8', '6590', 'mauna kea , hawaiian princess'], ['northern hawk', '310.1 feet', '3732', 'brount marine corp , warren , ri', '1981', '2 , bergen diesel , brm - 8', '8790', 'state trust'], ['ocean rover', '223.0 feet', '4345', 'mcdermott shipyards , amelia , la', '1973', '3 , w채rtsil채', '7080', 'enterprise']] |
eurovision song contest 1965 | https://en.wikipedia.org/wiki/Eurovision_Song_Contest_1965 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-184806-1.html.csv | comparative | in the 1965 eurovision song contest the song sung portuguese was more successful than that sign in spanish . | {'row_1': '12', 'row_2': '3', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'language', 'portuguese'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose language record fuzzily matches to portuguese .', 'tostr': 'filter_eq { all_rows ; language ; portuguese }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; language ; portuguese } ; points }', 'tointer': 'select the rows whose language record fuzzily matches to portuguese . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'language', 'spanish'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose language record fuzzily matches to spanish .', 'tostr': 'filter_eq { all_rows ; language ; spanish }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; language ; spanish } ; points }', 'tointer': 'select the rows whose language record fuzzily matches to spanish . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; language ; portuguese } ; points } ; hop { filter_eq { all_rows ; language ; spanish } ; points } } = true', 'tointer': 'select the rows whose language record fuzzily matches to portuguese . take the points record of this row . select the rows whose language record fuzzily matches to spanish . take the points record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; language ; portuguese } ; points } ; hop { filter_eq { all_rows ; language ; spanish } ; points } } = true | select the rows whose language record fuzzily matches to portuguese . take the points record of this row . select the rows whose language record fuzzily matches to spanish . take the points record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'language_7': 7, 'portuguese_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'language_11': 11, 'spanish_12': 12, 'points_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'language_7': 'language', 'portuguese_8': 'portuguese', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'language_11': 'language', 'spanish_12': 'spanish', 'points_13': 'points'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'language_7': [0], 'portuguese_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'language_11': [1], 'spanish_12': [1], 'points_13': [3]} | ['draw', 'language', 'artist', 'place', 'points'] | [['01', 'dutch', 'conny van den bos', '11', '5'], ['02', 'english', 'kathy kirby', '2', '26'], ['03', 'spanish', 'conchita bautista', '15', '0'], ['04', 'english', 'butch moore', '6', '11'], ['05', 'german', 'ulla wiesner', '15', '0'], ['06', 'german', 'udo jürgens', '4', '16'], ['07', 'norwegian', 'kirsti sparboe', '13', '1'], ['08', 'dutch', 'lize marke', '15', '0'], ['09', 'french', 'marjorie noël', '9', '7'], ['10', 'english', 'ingvar wixell', '10', '6'], ['11', 'french', 'guy mardel', '3', '22'], ['12', 'portuguese', 'simone de oliveira', '13', '1'], ['13', 'italian', 'bobby solo', '5', '15'], ['14', 'danish', 'birgit brüel', '7', '10'], ['15', 'french', 'france gall', '1', '32'], ['16', 'finnish', 'viktor klimenko', '15', '0'], ['17', 'croatian', 'vice vukov', '12', '2'], ['18', 'french', 'yovanna', '8', '8']] |
advanced television systems committee standards | https://en.wikipedia.org/wiki/Advanced_Television_Systems_Committee_standards | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-272313-1.html.csv | aggregation | the average vertical frequency of the advanced television systems committee standards listed is 720 . | {'scope': 'all', 'col': '1', 'type': 'average', 'result': '720', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'vertical'], 'result': '720', 'ind': 0, 'tostr': 'avg { all_rows ; vertical }'}, '720'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; vertical } ; 720 } = true', 'tointer': 'the average of the vertical record of all rows is 720 .'} | round_eq { avg { all_rows ; vertical } ; 720 } = true | the average of the vertical record of all rows is 720 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'vertical_4': 4, '720_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'vertical_4': 'vertical', '720_5': '720'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'vertical_4': [0], '720_5': [1]} | ['vertical', 'horizontal', 'aspect ratio', 'pixel aspect ratio', 'scanning', 'frame rate ( hz )'] | [['1080', '1920', '16:9', '1:1', 'progressive', '23.976 24 29.97 30'], ['1080', '1920', '16:9', '1:1', 'interlaced', '29.97 ( 59.94 fields / s ) 30 ( 60 fields / s )'], ['720', '1280', '16:9', '1:1', 'progressive', '23.976 24 29.97 30 59.94 60'], ['480', '704', '4:3 or 16:9', 'smpte 259 m', 'progressive', '23.976 24 29.97 30 59.94 60'], ['480', '704', '4:3 or 16:9', 'smpte 259 m', 'interlaced', '29.97 ( 59.94 fields / s ) 30 ( 60 fields / s )'], ['480', '640', '4:3', '1:1', 'progressive', '23.976 24 29.97 30 59.94 60']] |
2003 - 04 european challenge cup | https://en.wikipedia.org/wiki/2003%E2%80%9304_European_Challenge_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27987767-2.html.csv | majority | most of the matches with match points of 4-0 had a points margin over 30 . | {'scope': 'subset', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '30', 'subset': {'col': '2', 'criterion': 'equal', 'value': '4 - 0'}} | {'func': 'most_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'match points', '4 - 0'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; match points ; 4 - 0 }', 'tointer': 'select the rows whose match points record fuzzily matches to 4 - 0 .'}, 'points margin', '30'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose match points record fuzzily matches to 4 - 0 . for the points margin records of these rows , most of them are greater than 30 .', 'tostr': 'most_greater { filter_eq { all_rows ; match points ; 4 - 0 } ; points margin ; 30 } = true'} | most_greater { filter_eq { all_rows ; match points ; 4 - 0 } ; points margin ; 30 } = true | select the rows whose match points record fuzzily matches to 4 - 0 . for the points margin records of these rows , most of them are greater than 30 . | 2 | 2 | {'most_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'match points_4': 4, '4 - 0_5': 5, 'points margin_6': 6, '30_7': 7} | {'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'match points_4': 'match points', '4 - 0_5': '4 - 0', 'points margin_6': 'points margin', '30_7': '30'} | {'most_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'match points_4': [0], '4 - 0_5': [0], 'points margin_6': [1], '30_7': [1]} | ['winners', 'match points', 'aggregate score', 'points margin', 'losers'] | [['bath', '4 - 0', '125 - 11', '114', "l'aquila"], ['montferrand', '4 - 0', '113 - 3', '110', 'leonessa'], ['saracens', '4 - 0', '127 - 18', '109', 'rugby roma'], ['castres olympique', '4 - 0', '128 - 24', '104', 'rovigo'], ['newcastle falcons', '4 - 0', '137 - 37', '100', 'valladolid rac'], ['nec harlequins', '4 - 0', '94 - 21', '73', 'el salvador'], ['grenoble', '4 - 0', '76 - 16', '60', 'gran parma'], ['glasgow', '4 - 0', '68 - 24', '44', 'montpellier'], ['colomiers', '4 - 0', '75 - 32', '43', 'petrarca padova'], ['narbonne', '4 - 0', '52 - 23', '29', 'rotherham'], ['pau', '4 - 0', '58 - 34', '24', 'overmach parma'], ['brive', '2 - 2', '61 - 41', '20', 'viadana'], ['london irish', '2 - 2', '62 - 54', '8', 'montauban']] |
2004 new york giants season | https://en.wikipedia.org/wiki/2004_New_York_Giants_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16783007-2.html.csv | majority | in the 2004 season , the new york giants lost most of their games . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 'l'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to l .', 'tostr': 'most_eq { all_rows ; result ; l } = true'} | most_eq { all_rows ; result ; l } = true | for the result records of all rows , most of them fuzzily match to l . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'l_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'l_4': 'l'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'l_4': [0]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 12 , 2004', 'philadelphia eagles', 'l 31 - 17', '67532'], ['2', 'september 19 , 2004', 'washington redskins', 'w 20 - 14', '78767'], ['3', 'september 26 , 2004', 'cleveland browns', 'w 27 - 10', '78521'], ['4', 'october 3 , 2004', 'green bay packers', 'w 14 - 7', '70623'], ['5', 'october 10 , 2004', 'dallas cowboys', 'w 26 - 10', '64018'], ['7', 'october 24 , 2004', 'detroit lions', 'l 28 - 13', '78841'], ['8', 'october 31 , 2004', 'minnesota vikings', 'w 34 - 13', '64012'], ['9', 'november 7 , 2004', 'chicago bears', 'l 28 - 21', '78786'], ['10', 'november 14 , 2004', 'arizona cardinals', 'l 17 - 14', '42297'], ['11', 'november 21 , 2004', 'atlanta falcons', 'l 14 - 10', '78793'], ['12', 'november 28 , 2004', 'philadelphia eagles', 'l 27 - 6', '78830'], ['13', 'december 5 , 2004', 'washington redskins', 'l 31 - 7', '87872'], ['14', 'december 12 , 2004', 'baltimore ravens', 'l 37 - 14', '69856'], ['15', 'december 18 , 2004', 'pittsburgh steelers', 'l 33 - 30', '78836'], ['16', 'december 26 , 2004', 'cincinnati bengals', 'l 23 - 22', '64606'], ['17', 'january 2 , 2005', 'dallas cowboys', 'w 28 - 24', '78500']] |
1974 minnesota vikings season | https://en.wikipedia.org/wiki/1974_Minnesota_Vikings_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10361453-2.html.csv | comparative | the game against green bay packers on september 15 was better attended than the game against chicago bears on november 3 . | {'row_1': '1', 'row_2': '8', 'col': '8', 'col_other': '2,3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'sept 15'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to sept 15 .', 'tostr': 'filter_eq { all_rows ; date ; sept 15 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; sept 15 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to sept 15 . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'nov 3'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to nov 3 .', 'tostr': 'filter_eq { all_rows ; date ; nov 3 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; nov 3 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to nov 3 . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; sept 15 } ; attendance } ; hop { filter_eq { all_rows ; date ; nov 3 } ; attendance } }', 'tointer': 'select the rows whose date record fuzzily matches to sept 15 . take the attendance record of this row . select the rows whose date record fuzzily matches to nov 3 . take the attendance record of this row . the first record is greater than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'sept 15'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to sept 15 .', 'tostr': 'filter_eq { all_rows ; date ; sept 15 }'}, 'opponent'], 'result': 'green bay packers', 'ind': 5, 'tostr': 'hop { filter_eq { all_rows ; date ; sept 15 } ; opponent }'}, 'green bay packers'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; sept 15 } ; opponent } ; green bay packers }', 'tointer': 'the opponent record of the first row is green bay packers .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'nov 3'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to nov 3 .', 'tostr': 'filter_eq { all_rows ; date ; nov 3 }'}, 'opponent'], 'result': 'chicago bears', 'ind': 7, 'tostr': 'hop { filter_eq { all_rows ; date ; nov 3 } ; opponent }'}, 'chicago bears'], 'result': True, 'ind': 8, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; nov 3 } ; opponent } ; chicago bears }', 'tointer': 'the opponent record of the second row is chicago bears .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { hop { filter_eq { all_rows ; date ; sept 15 } ; opponent } ; green bay packers } ; eq { hop { filter_eq { all_rows ; date ; nov 3 } ; opponent } ; chicago bears } }', 'tointer': 'the opponent record of the first row is green bay packers . the opponent record of the second row is chicago bears .'}], 'result': True, 'ind': 10, 'tostr': 'and { greater { hop { filter_eq { all_rows ; date ; sept 15 } ; attendance } ; hop { filter_eq { all_rows ; date ; nov 3 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; sept 15 } ; opponent } ; green bay packers } ; eq { hop { filter_eq { all_rows ; date ; nov 3 } ; opponent } ; chicago bears } } } = true', 'tointer': 'select the rows whose date record fuzzily matches to sept 15 . take the attendance record of this row . select the rows whose date record fuzzily matches to nov 3 . take the attendance record of this row . the first record is greater than the second record . the opponent record of the first row is green bay packers . the opponent record of the second row is chicago bears .'} | and { greater { hop { filter_eq { all_rows ; date ; sept 15 } ; attendance } ; hop { filter_eq { all_rows ; date ; nov 3 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; sept 15 } ; opponent } ; green bay packers } ; eq { hop { filter_eq { all_rows ; date ; nov 3 } ; opponent } ; chicago bears } } } = true | select the rows whose date record fuzzily matches to sept 15 . take the attendance record of this row . select the rows whose date record fuzzily matches to nov 3 . take the attendance record of this row . the first record is greater than the second record . the opponent record of the first row is green bay packers . the opponent record of the second row is chicago bears . | 13 | 11 | {'and_10': 10, 'result_11': 11, 'greater_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_12': 12, 'date_13': 13, 'sept 15_14': 14, 'attendance_15': 15, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_16': 16, 'date_17': 17, 'nov 3_18': 18, 'attendance_19': 19, 'and_9': 9, 'str_eq_6': 6, 'str_hop_5': 5, 'opponent_20': 20, 'green bay packers_21': 21, 'str_eq_8': 8, 'str_hop_7': 7, 'opponent_22': 22, 'chicago bears_23': 23} | {'and_10': 'and', 'result_11': 'true', 'greater_4': 'greater', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_12': 'all_rows', 'date_13': 'date', 'sept 15_14': 'sept 15', 'attendance_15': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_16': 'all_rows', 'date_17': 'date', 'nov 3_18': 'nov 3', 'attendance_19': 'attendance', 'and_9': 'and', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'opponent_20': 'opponent', 'green bay packers_21': 'green bay packers', 'str_eq_8': 'str_eq', 'str_hop_7': 'str_hop', 'opponent_22': 'opponent', 'chicago bears_23': 'chicago bears'} | {'and_10': [11], 'result_11': [], 'greater_4': [10], 'num_hop_2': [4], 'filter_str_eq_0': [2, 5], 'all_rows_12': [0], 'date_13': [0], 'sept 15_14': [0], 'attendance_15': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3, 7], 'all_rows_16': [1], 'date_17': [1], 'nov 3_18': [1], 'attendance_19': [3], 'and_9': [10], 'str_eq_6': [9], 'str_hop_5': [6], 'opponent_20': [5], 'green bay packers_21': [6], 'str_eq_8': [9], 'str_hop_7': [8], 'opponent_22': [7], 'chicago bears_23': [8]} | ['game', 'date', 'opponent', 'result', 'vikings points', 'opponents', 'record', 'attendance'] | [['1', 'sept 15', 'green bay packers', 'win', '32', '17', '1 - 0', '56267'], ['2', 'sept 22', 'detroit lions', 'win', '7', '6', '2 - 0', '49703'], ['3', 'sept 29', 'chicago bears', 'win', '11', '7', '3 - 0', '46217'], ['4', 'oct 6', 'dallas cowboys', 'win', '23', '21', '4 - 0', '57847'], ['5', 'oct 13', 'houston oilers', 'win', '51', '10', '5 - 0', '48006'], ['6', 'oct 20', 'detroit lions', 'loss', '16', '20', '5 - 1', '47807'], ['7', 'oct 27', 'new england patriots', 'loss', '14', '17', '5 - 2', '48177'], ['8', 'nov 3', 'chicago bears', 'win', '17', '0', '6 - 2', '33343'], ['9', 'nov 11', 'st louis cardinals', 'win', '28', '24', '7 - 2', '50183'], ['10', 'nov 17', 'green bay packers', 'loss', '7', '19', '7 - 3', '47924'], ['11', 'nov 24', 'los angeles rams', 'loss', '17', '20', '7 - 4', '90266'], ['12', 'dec 1', 'new orleans saints', 'win', '29', '9', '8 - 4', '44202'], ['13', 'dec 7', 'atlanta falcons', 'win', '23', '10', '9 - 4', '47105']] |
list of career achievements by dwight howard | https://en.wikipedia.org/wiki/List_of_career_achievements_by_Dwight_Howard | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25774493-3.html.csv | aggregation | of the career achievements by dwight howard , the average number of blocks per game is 2.12 . | {'scope': 'all', 'col': '8', 'type': 'average', 'result': '2.12', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'blocks per game'], 'result': '2.12', 'ind': 0, 'tostr': 'avg { all_rows ; blocks per game }'}, '2.12'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; blocks per game } ; 2.12 } = true', 'tointer': 'the average of the blocks per game record of all rows is 2.12 .'} | round_eq { avg { all_rows ; blocks per game } ; 2.12 } = true | the average of the blocks per game record of all rows is 2.12 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'blocks per game_4': 4, '2.12_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'blocks per game_4': 'blocks per game', '2.12_5': '2.12'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'blocks per game_4': [0], '2.12_5': [1]} | ['selection', 'month', 'season', 'team record', 'points per game', 'field goal percentage', 'rebounds per game', 'blocks per game'] | [['1', 'april 2006', '2005 - 06 ( 1 / 1 )', '7-2', '18.1', '531', '14.0', '0.7'], ['2', 'october / november 2006', '2006 - 07 ( 1 / 1 )', '12-4', '17.1', '576', '13.6', '1.9'], ['3', 'october / november 2007', '2007 - 08 ( 1 / 2 )', '14-4', '23.8', '618', '15.0', '2.7'], ['4', 'december 2007', '2007 - 08 ( 2 / 2 )', '8-7', '21.7', '598', '16.1', '2.9'], ['5', 'october / november 2010', '2010 - 11 ( 1 / 2 )', '13-4', '21.8 ( 5th )', '594 ( 2nd )', '12.1 ( 4th in league )', '2.4']] |
united states intelligence budget | https://en.wikipedia.org/wiki/United_States_intelligence_budget | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17198719-1.html.csv | superlative | the us intelligence agency that spends the most on management and support is the consolidated cryptologic program . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'management and support'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; management and support }'}, 'administrating agencies by nip funds only'], 'result': '0 consolidated cryptologic program', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; management and support } ; administrating agencies by nip funds only }'}, '0 consolidated cryptologic program'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; management and support } ; administrating agencies by nip funds only } ; 0 consolidated cryptologic program } = true', 'tointer': 'select the row whose management and support record of all rows is maximum . the administrating agencies by nip funds only record of this row is 0 consolidated cryptologic program .'} | eq { hop { argmax { all_rows ; management and support } ; administrating agencies by nip funds only } ; 0 consolidated cryptologic program } = true | select the row whose management and support record of all rows is maximum . the administrating agencies by nip funds only record of this row is 0 consolidated cryptologic program . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'management and support_5': 5, 'administrating agencies by nip funds only_6': 6, '0 consolidated cryptologic program_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'management and support_5': 'management and support', 'administrating agencies by nip funds only_6': 'administrating agencies by nip funds only', '0 consolidated cryptologic program_7': '0 consolidated cryptologic program'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'management and support_5': [0], 'administrating agencies by nip funds only_6': [1], '0 consolidated cryptologic program_7': [2]} | ['administrating agencies by nip funds only', 'management and support', 'data collection', 'data processing and exploitation', 'total'] | [['0 central intelligence agency program', '1 , 8', '11 , 5', '00 0387', '14787'], ['0 consolidated cryptologic program', '5 , 2', '0 2 , 5', '1 , 6', '10 , 8'], ['0 national reconnaissance program', '1 , 8', '0 6 , 0', '2 , 5', '10 , 3'], ['0 national geospatial - intelligence program', '2 , 0', '000 0537', '1 , 4', '4 , 91'], ['0 defense intelligence program', '1 , 7', '0 1 , 3', '00 0228', '4428'], ['total', '12 , 5', '21837', '6115', '45225']] |
list of supernanny episodes | https://en.wikipedia.org/wiki/List_of_Supernanny_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19897294-5.html.csv | ordinal | the williams family is the latest episode in the supernanny series . | {'row': '5', 'col': '2', 'order': '1', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'no in series', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; no in series ; 1 }'}, 'family / families'], 'result': 'the williams family', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; no in series ; 1 } ; family / families }'}, 'the williams family'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; no in series ; 1 } ; family / families } ; the williams family } = true', 'tointer': 'select the row whose no in series record of all rows is 1st maximum . the family / families record of this row is the williams family .'} | eq { hop { nth_argmax { all_rows ; no in series ; 1 } ; family / families } ; the williams family } = true | select the row whose no in series record of all rows is 1st maximum . the family / families record of this row is the williams family . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'no in series_5': 5, '1_6': 6, 'family / families_7': 7, 'the williams family_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'no in series_5': 'no in series', '1_6': '1', 'family / families_7': 'family / families', 'the williams family_8': 'the williams family'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'no in series_5': [0], '1_6': [0], 'family / families_7': [1], 'the williams family_8': [2]} | ['no overall', 'no in series', 'family / families', 'location ( s )', 'original air date'] | [['uk16', '1', 'the hillhouse - docherty family', 'ayr ( scotland )', '29 august 2006'], ['uk17', '2', 'the howat family', 'shenley', '5 september 2006'], ['uk18', '3', 'the brown - smith family', 'warrington', '12 september 2006'], ['uk19', '4', 'the bates family', 'evesham', '19 september 2006'], ['uk20', '5', 'the williams family', 'birmingham', '26 september 2006']] |
1977 kentucky wildcats football team | https://en.wikipedia.org/wiki/1977_Kentucky_Wildcats_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21063459-1.html.csv | count | the kentucky wildcats had 2 games in 1977 where they scored 33 points . | {'scope': 'all', 'criterion': 'equal', 'value': '33', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'wildcats points', '33'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wildcats points record is equal to 33 .', 'tostr': 'filter_eq { all_rows ; wildcats points ; 33 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; wildcats points ; 33 } }', 'tointer': 'select the rows whose wildcats points record is equal to 33 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; wildcats points ; 33 } } ; 2 } = true', 'tointer': 'select the rows whose wildcats points record is equal to 33 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; wildcats points ; 33 } } ; 2 } = true | select the rows whose wildcats points record is equal to 33 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'wildcats points_5': 5, '33_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'wildcats points_5': 'wildcats points', '33_6': '33', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'wildcats points_5': [0], '33_6': [0], '2_7': [2]} | ['game', 'date', 'opponent', 'result', 'wildcats points', 'opponents', 'record'] | [['1', 'sept 10', 'north carolina', 'win', '10', '7', '1 - 0'], ['2', 'sept 17', 'baylor', 'loss', '6', '21', '1 - 1'], ['3', 'sept 24', '17 west virginia', 'win', '28', '13', '2 - 1'], ['4', 'oct 1', '4 penn state', 'win', '24', '20', '3 - 1 , 16'], ['5', 'oct 8', 'mississippi state', 'win', '23', '7', '4 - 1 , 12'], ['6', 'oct 15', '16 louisiana state', 'win', '33', '13', '5 - 1 , 8'], ['7', 'oct 22', 'georgia', 'win', '33', '0', '6 - 1 , 7'], ['8', 'oct 29', 'virginia tech', 'win', '32', '0', '7 - 1 , 7'], ['9', 'nov 5', 'vanderbilt', 'win', '28', '6', '8 - 1 , 7'], ['10', 'nov 12', 'florida', 'win', '14', '7', '9 - 1 , 7']] |
1979 vfl season | https://en.wikipedia.org/wiki/1979_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10823719-13.html.csv | superlative | the game played at victoria park had the highest crowd of all games in the 1979 vfl season . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '5', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'venue'], 'result': 'victoria park', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; crowd } ; venue }'}, 'victoria park'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; venue } ; victoria park } = true', 'tointer': 'select the row whose crowd record of all rows is maximum . the venue record of this row is victoria park .'} | eq { hop { argmax { all_rows ; crowd } ; venue } ; victoria park } = true | select the row whose crowd record of all rows is maximum . the venue record of this row is victoria park . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, 'venue_6': 6, 'victoria park_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', 'venue_6': 'venue', 'victoria park_7': 'victoria park'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], 'venue_6': [1], 'victoria park_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['collingwood', '25.19 ( 169 )', 'hawthorn', '8.16 ( 64 )', 'victoria park', '22903', '30 june 1979'], ['carlton', '21.12 ( 138 )', 'richmond', '12.10 ( 82 )', 'princes park', '21792', '30 june 1979'], ['north melbourne', '8.21 ( 69 )', 'footscray', '12.9 ( 81 )', 'arden street oval', '13925', '30 june 1979'], ['south melbourne', '15.20 ( 110 )', 'fitzroy', '20.16 ( 136 )', 'lake oval', '13850', '30 june 1979'], ['st kilda', '9.12 ( 66 )', 'essendon', '15.15 ( 105 )', 'moorabbin oval', '18802', '30 june 1979'], ['geelong', '16.12 ( 108 )', 'melbourne', '9.3 ( 57 )', 'vfl park', '13272', '30 june 1979']] |
lonhro | https://en.wikipedia.org/wiki/Lonhro | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1360997-2.html.csv | aggregation | lonhro 's distance between august 18 , 2001 and september 15 , 2001 was 1300 m. | {'scope': 'subset', 'col': '6', 'type': 'average', 'result': '1300', 'subset': {'col': '2', 'criterion': 'less_than_eq', 'value': 'september 15 2001'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_less_eq', 'args': ['all_rows', 'date', 'september 15 2001'], 'result': None, 'ind': 0, 'tostr': 'filter_less_eq { all_rows ; date ; september 15 2001 }', 'tointer': 'select the rows whose date record is less than or equal to september 15 2001 .'}, 'distance'], 'result': '1300', 'ind': 1, 'tostr': 'avg { filter_less_eq { all_rows ; date ; september 15 2001 } ; distance }'}, '1300'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_less_eq { all_rows ; date ; september 15 2001 } ; distance } ; 1300 } = true', 'tointer': 'select the rows whose date record is less than or equal to september 15 2001 . the average of the distance record of these rows is 1300 .'} | round_eq { avg { filter_less_eq { all_rows ; date ; september 15 2001 } ; distance } ; 1300 } = true | select the rows whose date record is less than or equal to september 15 2001 . the average of the distance record of these rows is 1300 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_less_eq_0': 0, 'all_rows_4': 4, 'date_5': 5, 'september 15 2001_6': 6, 'distance_7': 7, '1300_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_less_eq_0': 'filter_less_eq', 'all_rows_4': 'all_rows', 'date_5': 'date', 'september 15 2001_6': 'september 15 2001', 'distance_7': 'distance', '1300_8': '1300'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_less_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], 'september 15 2001_6': [0], 'distance_7': [1], '1300_8': [2]} | ['result', 'date', 'race', 'venue', 'group', 'distance', 'weight ( kg )', 'time', 'jockey', 'winner / 2nd'] | [['won', '18 aug 2001', 'warwick stakes', 'warwick farm', 'g2', '1300 m', '49.5', '1:17.30', 'd mclellan', '2nd - diamond dane'], ['won', '01 sep 2001', 'ming dynasty quality', 'randwick', 'g3', '1400 m', '57.5', '1:23.87', 'r quinn', '2nd - prince of play'], ['won', '15 sep 2001', 'heritage stakes', 'rosehill', 'lr', '1200 m', '55.5', '1:10.28', 'r quinn', '2nd - perfect crime'], ['won', '29 sep 2001', 'stan fox stakes', 'randwick', 'g2', '1400 m', '55', '1:24.00', 'r quinn', '2nd - magic albert'], ['won', '13 oct 2001', 'caulfield guineas', 'caulfield', 'g1', '1600 m', '55.5', '1:36.70', 'd gauci', '2nd - ustinov'], ['won', '02 feb 2002', 'royal sovereign stakes', 'warwick farm', 'g2', '1200 m', '57.5', '1:11.06', 'r quinn', '2nd - viking ruler'], ['won', '16 feb 2002', 'hobartville stakes', 'randwick', 'g2', '1400 m', '55.5', '1:23.58', 'r quinn', '2nd - athens']] |
fittipaldi automotive | https://en.wikipedia.org/wiki/Fittipaldi_Automotive | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1262596-2.html.csv | count | a total of three drivers had a retirement result for fittipaldi automotive team . | {'scope': 'all', 'criterion': 'equal', 'value': 'ret', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'ret'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to ret .', 'tostr': 'filter_eq { all_rows ; result ; ret }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; ret } }', 'tointer': 'select the rows whose result record fuzzily matches to ret . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; ret } } ; 3 } = true', 'tointer': 'select the rows whose result record fuzzily matches to ret . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; result ; ret } } ; 3 } = true | select the rows whose result record fuzzily matches to ret . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 'ret_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 'ret_6': 'ret', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'ret_6': [0], '3_7': [2]} | ['year', 'event', 'venue', 'driver', 'result'] | [['1975', 'brdc international trophy', 'silverstone', 'wilson fittipaldi', 'ret'], ['1978', 'brdc international trophy', 'silverstone', 'emerson fittipaldi', '2'], ['1979', 'gran premio dino ferrari', 'imola', 'alex ribeiro', 'ret'], ['1980', 'spanish grand prix', 'jarama', 'emerson fittipaldi', '5'], ['1980', 'spanish grand prix', 'jarama', 'keke rosberg', 'ret'], ['1981', 'south african grand prix', 'kyalami', 'keke rosberg', '4'], ['1981', 'south african grand prix', 'kyalami', 'chico serra', '9']] |
1954 vfl season | https://en.wikipedia.org/wiki/1954_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10773616-14.html.csv | count | there were 3 venues with more than 20000 in the crowd . | {'scope': 'all', 'criterion': 'greater_than', 'value': '20000', 'result': '3', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'crowd', '20000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose crowd record is greater than 20000 .', 'tostr': 'filter_greater { all_rows ; crowd ; 20000 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; crowd ; 20000 } }', 'tointer': 'select the rows whose crowd record is greater than 20000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; crowd ; 20000 } } ; 3 } = true', 'tointer': 'select the rows whose crowd record is greater than 20000 . the number of such rows is 3 .'} | eq { count { filter_greater { all_rows ; crowd ; 20000 } } ; 3 } = true | select the rows whose crowd record is greater than 20000 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '20000_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '20000_6': '20000', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '20000_6': [0], '3_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['collingwood', '12.13 ( 85 )', 'st kilda', '7.15 ( 57 )', 'victoria park', '16500', '31 july 1954'], ['carlton', '9.11 ( 65 )', 'richmond', '6.10 ( 46 )', 'princes park', '25863', '31 july 1954'], ['melbourne', '16.14 ( 110 )', 'hawthorn', '5.3 ( 33 )', 'mcg', '26708', '31 july 1954'], ['south melbourne', '6.5 ( 41 )', 'footscray', '11.12 ( 78 )', 'lake oval', '19500', '31 july 1954'], ['north melbourne', '8.19 ( 67 )', 'fitzroy', '8.6 ( 54 )', 'arden street oval', '11000', '31 july 1954'], ['geelong', '16.14 ( 110 )', 'essendon', '11.10 ( 76 )', 'kardinia park', '28158', '31 july 1954']] |
2011 the dominion tankard | https://en.wikipedia.org/wiki/2011_The_Dominion_Tankard | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29565601-2.html.csv | aggregation | in the dominion tankard in 2011 , the average number of blank ends was 6.9 . | {'scope': 'all', 'col': '8', 'type': 'average', 'result': '6.9', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'blank ends'], 'result': '6.9', 'ind': 0, 'tostr': 'avg { all_rows ; blank ends }'}, '6.9'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; blank ends } ; 6.9 } = true', 'tointer': 'the average of the blank ends record of all rows is 6.9 .'} | round_eq { avg { all_rows ; blank ends } ; 6.9 } = true | the average of the blank ends record of all rows is 6.9 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'blank ends_4': 4, '6.9_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'blank ends_4': 'blank ends', '6.9_5': '6.9'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'blank ends_4': [0], '6.9_5': [1]} | ['skip ( club )', 'w', 'l', 'pf', 'pa', 'ends won', 'ends lost', 'blank ends', 'stolen ends'] | [['peter corner ( brampton )', '8', '2', '69', '54', '41', '36', '8', '11'], ['glenn howard ( coldwater )', '8', '2', '79', '35', '40', '22', '8', '11'], ['greg balsdon ( loonie )', '7', '3', '80', '57', '46', '37', '5', '12'], ['john epping ( donalda )', '7', '3', '76', '64', '43', '41', '5', '10'], ['mark bice ( sarnia )', '6', '4', '70', '76', '45', '44', '8', '7'], ['chris gardner ( renfrew )', '5', '5', '73', '72', '47', '41', '7', '16'], ['dale matchett ( bradford )', '4', '6', '57', '75', '35', '42', '7', '7'], ['mark kean ( annandale )', '3', '7', '53', '67', '43', '35', '12', '8'], ['howard rajala ( rideau )', '3', '7', '67', '71', '43', '48', '5', '9'], ['nick rizzo ( brantford )', '3', '7', '56', '74', '35', '42', '4', '5']] |
gymnastics at the 2008 summer olympics - women 's uneven bars | https://en.wikipedia.org/wiki/Gymnastics_at_the_2008_Summer_Olympics_%E2%80%93_Women%27s_uneven_bars | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18662048-4.html.csv | superlative | yang yilin had the most total in gymnastics at the 2008 summer olympics - women 's uneven bars . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'total'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; total }'}, 'gymnast'], 'result': 'yang yilin ( chn )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; total } ; gymnast }'}, 'yang yilin ( chn )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; total } ; gymnast } ; yang yilin ( chn ) } = true', 'tointer': 'select the row whose total record of all rows is maximum . the gymnast record of this row is yang yilin ( chn ) .'} | eq { hop { argmax { all_rows ; total } ; gymnast } ; yang yilin ( chn ) } = true | select the row whose total record of all rows is maximum . the gymnast record of this row is yang yilin ( chn ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'total_5': 5, 'gymnast_6': 6, 'yang yilin ( chn )_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'total_5': 'total', 'gymnast_6': 'gymnast', 'yang yilin ( chn )_7': 'yang yilin ( chn )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'total_5': [0], 'gymnast_6': [1], 'yang yilin ( chn )_7': [2]} | ['position', 'gymnast', 'a score', 'b score', 'total'] | [['1', 'yang yilin ( chn )', '7.700', '8.950', '16.650'], ['2', 'ksenia semenova ( rus )', '7.400', '9.075', '16.475'], ['3', 'anastasia koval ( ukr )', '7.300', '9.025', '16.325'], ['4', 'steliana nistor ( rou )', '7.300', '8.675', '15.975'], ['5', 'nastia liukin ( usa )', '7.700', '8.250', '15.950'], ['6', 'he kexin ( chn )', '7.500', '8.225', '15.725'], ['7', 'dariya zgoba ( ukr )', '6.900', '8.875', '15.675'], ['8', 'beth tweddle ( gbr )', '7.600', '8.050', '15.650']] |
michigan wolverines men 's ice hockey | https://en.wikipedia.org/wiki/Michigan_Wolverines_men%27s_ice_hockey | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22165661-3.html.csv | aggregation | the michigan wolverine 's men 's ice hockey team scored an average of 3.625 per championship game from 1994-2008 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '3.625', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '3.625', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '3.625'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 3.625 } = true', 'tointer': 'the average of the score record of all rows is 3.625 .'} | round_eq { avg { all_rows ; score } ; 3.625 } = true | the average of the score record of all rows is 3.625 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '3.625_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '3.625_5': '3.625'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '3.625_5': [1]} | ['tournament', 'conference', 'championship game opponent', 'score', 'location', 'head coach'] | [['1994', 'ccha', 'lake superior state', '3 - 0', 'joe louis arena detroit , mi', 'red berenson'], ['1996', 'ccha', 'lake superior state', '4 - 3', 'joe louis arena detroit , mi', 'red berenson'], ['1997', 'ccha', 'michigan state', '3 - 1', 'joe louis arena detroit , mi', 'red berenson'], ['1999', 'ccha', 'northern michigan', '5 - 1', 'joe louis arena detroit , mi', 'red berenson'], ['2002', 'ccha', 'michigan state', '3 - 2', 'joe louis arena detroit , mi', 'red berenson'], ['2003', 'ccha', 'ferris state', '5 - 3', 'joe louis arena detroit , mi', 'red berenson'], ['2005', 'ccha', 'ohio state', '4 - 2', 'joe louis arena detroit , mi', 'red berenson'], ['2008', 'ccha', 'miami university', '2 - 1', 'joe louis arena detroit , mi', 'red berenson']] |
jim clark | https://en.wikipedia.org/wiki/Jim_Clark | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-181892-4.html.csv | comparative | in 1967 , jim clark completed 12 fewer laps than he did previously in 1964 . | {'row_1': '5', 'row_2': '2', 'col': '7', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '12', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1967'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1967 .', 'tostr': 'filter_eq { all_rows ; year ; 1967 }'}, 'laps completed'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1967 } ; laps completed }', 'tointer': 'select the rows whose year record fuzzily matches to 1967 . take the laps completed record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1964'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1964 .', 'tostr': 'filter_eq { all_rows ; year ; 1964 }'}, 'laps completed'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1964 } ; laps completed }', 'tointer': 'select the rows whose year record fuzzily matches to 1964 . take the laps completed record of this row .'}], 'result': '-12', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; year ; 1967 } ; laps completed } ; hop { filter_eq { all_rows ; year ; 1964 } ; laps completed } }'}, '-12'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; year ; 1967 } ; laps completed } ; hop { filter_eq { all_rows ; year ; 1964 } ; laps completed } } ; -12 } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1967 . take the laps completed record of this row . select the rows whose year record fuzzily matches to 1964 . take the laps completed record of this row . the second record is 12 larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; year ; 1967 } ; laps completed } ; hop { filter_eq { all_rows ; year ; 1964 } ; laps completed } } ; -12 } = true | select the rows whose year record fuzzily matches to 1967 . take the laps completed record of this row . select the rows whose year record fuzzily matches to 1964 . take the laps completed record of this row . the second record is 12 larger than the first record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'year_8': 8, '1967_9': 9, 'laps completed_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'year_12': 12, '1964_13': 13, 'laps completed_14': 14, '-12_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'year_8': 'year', '1967_9': '1967', 'laps completed_10': 'laps completed', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'year_12': 'year', '1964_13': '1964', 'laps completed_14': 'laps completed', '-12_15': '-12'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'year_8': [0], '1967_9': [0], 'laps completed_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'year_12': [1], '1964_13': [1], 'laps completed_14': [3], '-12_15': [5]} | ['year', 'car number', 'start', 'qual speed', 'speed rank', 'finish', 'laps completed', 'laps led', 'race status', 'chassis'] | [['1963', '92', '5', '149.750', '7', '2', '200', '28', 'running', 'lotus - ford 29 / 3'], ['1964', '6', '1', '158.828', '1', '24', '47', '14', 'suspension', 'lotus - ford 34 / 3'], ['1965', '82', '2', '160.729', '2', '1', '200', '190', 'running', 'lotus - ford 38 / 1'], ['1966', '19', '2', '164.114', '2', '2', '200', '66', 'running', 'lotus - ford 38 / 4'], ['1967', '31', '16', '163.213', '23', '31', '35', '0', 'piston', 'lotus - ford 38 / 7']] |
united states house of representatives elections , 2006 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1805191-43.html.csv | count | five of tennessee 's representatives to congress are from the democratic party . | {'scope': 'all', 'criterion': 'equal', 'value': 'democratic', 'result': '5', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'democratic'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose party record fuzzily matches to democratic .', 'tostr': 'filter_eq { all_rows ; party ; democratic }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; party ; democratic } }', 'tointer': 'select the rows whose party record fuzzily matches to democratic . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; party ; democratic } } ; 5 } = true', 'tointer': 'select the rows whose party record fuzzily matches to democratic . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; party ; democratic } } ; 5 } = true | select the rows whose party record fuzzily matches to democratic . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'party_5': 5, 'democratic_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'party_5': 'party', 'democratic_6': 'democratic', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'party_5': [0], 'democratic_6': [0], '5_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'results'] | [['tennessee 1', 'william l jenkins', 'republican', '1996', 'retired republican hold'], ['tennessee 2', 'jimmy duncan jr', 'republican', '1998', 're - elected'], ['tennessee 3', 'zach wamp', 'republican', '1994', 're - elected'], ['tennessee 4', 'lincoln davis', 'democratic', '2002', 're - elected'], ['tennessee 5', 'jim cooper', 'democratic', '2002', 're - elected'], ['tennessee 6', 'bart gordon', 'democratic', '1984', 're - elected'], ['tennessee 7', 'marsha blackburn', 'republican', '2002', 're - elected'], ['tennessee 8', 'john tanner', 'democratic', '1988', 're - elected'], ['tennessee 9', 'harold ford jr', 'democratic', '1996', 'retired to run for us senate democratic hold']] |
bombay jayashri | https://en.wikipedia.org/wiki/Bombay_Jayashri | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11203591-2.html.csv | ordinal | hrudayam ekkadunnadi is the fourth song made by bombay jayashri . | {'row': '4', 'col': '1', 'order': '4', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'year', '4'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year ; 4 }'}, 'song title'], 'result': 'hrudayam ekkadunnadi', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year ; 4 } ; song title }'}, 'hrudayam ekkadunnadi'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 4 } ; song title } ; hrudayam ekkadunnadi } = true', 'tointer': 'select the row whose year record of all rows is 4th minimum . the song title record of this row is hrudayam ekkadunnadi .'} | eq { hop { nth_argmin { all_rows ; year ; 4 } ; song title } ; hrudayam ekkadunnadi } = true | select the row whose year record of all rows is 4th minimum . the song title record of this row is hrudayam ekkadunnadi . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, '4_6': 6, 'song title_7': 7, 'hrudayam ekkadunnadi_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'year_5': 'year', '4_6': '4', 'song title_7': 'song title', 'hrudayam ekkadunnadi_8': 'hrudayam ekkadunnadi'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], '4_6': [0], 'song title_7': [1], 'hrudayam ekkadunnadi_8': [2]} | ['year', 'song title', 'movie', 'music director', 'co - singers'] | [['1997', 'sasivadane', 'iddaru', 'a r rahman', 'unni krishnan'], ['2001', 'manohara', 'cheli', 'harris jayaraj', 'solo'], ['2002', 'tiya tiyani kalalanu', 'sreeram', 'r p patnaik', 'solo'], ['2005', 'hrudayam ekkadunnadi', 'ghajini', 'harris jayaraj', 'harish raghavendra'], ['2005', 'aamani koyilanai', 'premikulu', 'sajan madhav', 'solo'], ['2006', 'vere maina anani', 'amma cheppindi', 'm m keeravani', 'solo'], ['2006', 'yentho dooram', 'amma cheppindi', 'm m keeravani', 'solo'], ['2006', 'ulike o chilake', 'jalakanta', 'harris jayaraj', 'karthik'], ['2007', 'banam', 'raghavan', 'harris jayaraj', 'harish raghavendra'], ['2008', 'anti pettukundhuna', '16 days', 'dharan', 'haricharan'], ['2008', 'enduko madi', 'nenu meeku telusa', 'achu', 'hemachandra'], ['2008', 'muddula muddula', 'salute', 'harris jayaraj', 'balram , sunitha sarathy'], ['2009', 'eenaadu ee samaram', 'eeenadu', 'shruthi hassan', 'kamal haasan'], ['2011', 'ee manchullo', 'rangam', 'harris jayaraj', 'sriram parthasarathy'], ['2012', 'vennelave', 'thuppakki', 'harris jayaraj', 'hariharan'], ['2013', 'kamalaasana', 'intinta annamaya', 'm m keeravani', 'solo']] |
2006 kansas city brigade season | https://en.wikipedia.org/wiki/2006_Kansas_City_Brigade_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11974088-4.html.csv | count | 14 players are listed as members of the kansas city brigade team during the 2006 season games . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '14', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'player'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record is arbitrary .', 'tostr': 'filter_all { all_rows ; player }'}], 'result': '14', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; player } }', 'tointer': 'select the rows whose player record is arbitrary . the number of such rows is 14 .'}, '14'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; player } } ; 14 } = true', 'tointer': 'select the rows whose player record is arbitrary . the number of such rows is 14 .'} | eq { count { filter_all { all_rows ; player } } ; 14 } = true | select the rows whose player record is arbitrary . the number of such rows is 14 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'player_5': 5, '14_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'player_5': 'player', '14_6': '14'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'player_5': [0], '14_6': [2]} | ['player', 'rec', 'yards', 'avg', "td 's", 'long'] | [['jerel meyers', '122', '1245', '10.2', '16', '46'], ['james jordan', '69', '797', '11.6', '14', '45'], ['aaron boone', '66', '748', '11.3', '19', '40'], ['sam simmons', '24', '221', '9.2', '2', '34'], ['calvin spears', '15', '152', '10.1', '2', '17'], ['rob johnson', '15', '137', '9.1', '2', '32'], ['john booth', '11', '104', '9.5', '2', '39'], ['brian poli - dixon', '6', '40', '6.7', '0', '11'], ['bryan henderson', '3', '34', '11.3', '2', '23'], ['bj cohen', '3', '33', '11', '0', '11'], ['nathan black', '2', '15', '7.5', '0', '9'], ['tremaine neal', '3', '14', '4.7', '0', '6'], ['jeremy beutler', '1', '12', '12', '0', '12'], ['cyron brown', '1', '9', '9', '0', '9']] |
2007 - 08 denver nuggets season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Denver_Nuggets_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11963735-3.html.csv | comparative | more people attended the nuggets game on november 29 than the game on november 30 . | {'row_1': '15', 'row_2': '16', 'col': '6', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '29 november 2007'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 29 november 2007 .', 'tostr': 'filter_eq { all_rows ; date ; 29 november 2007 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; 29 november 2007 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to 29 november 2007 . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '30 november 2007'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 30 november 2007 .', 'tostr': 'filter_eq { all_rows ; date ; 30 november 2007 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; 30 november 2007 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to 30 november 2007 . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; 29 november 2007 } ; attendance } ; hop { filter_eq { all_rows ; date ; 30 november 2007 } ; attendance } } = true', 'tointer': 'select the rows whose date record fuzzily matches to 29 november 2007 . take the attendance record of this row . select the rows whose date record fuzzily matches to 30 november 2007 . take the attendance record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; date ; 29 november 2007 } ; attendance } ; hop { filter_eq { all_rows ; date ; 30 november 2007 } ; attendance } } = true | select the rows whose date record fuzzily matches to 29 november 2007 . take the attendance record of this row . select the rows whose date record fuzzily matches to 30 november 2007 . take the attendance record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, '29 november 2007_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, '30 november 2007_12': 12, 'attendance_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', '29 november 2007_8': '29 november 2007', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', '30 november 2007_12': '30 november 2007', 'attendance_13': 'attendance'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], '29 november 2007_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], '30 november 2007_12': [1], 'attendance_13': [3]} | ['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record'] | [['2 november 2007', 'nuggets', '99 - 91', 'timberwolves', 'carmelo anthony ( 33 )', '19443', '2 - 0'], ['4 november 2007', 'hornets', '93 - 88', 'nuggets', 'allen iverson ( 23 )', '13156', '2 - 1'], ['6 november 2007', 'nuggets', '112 - 119', 'knicks', 'allen iverson ( 32 )', '19763', '2 - 2'], ['7 november 2007', 'nuggets', '93 - 119', 'celtics', 'allen iverson ( 22 )', '18624', '2 - 3'], ['9 november 2007', 'nuggets', '118 - 92', 'wizards', 'carmelo anthony ( 32 )', '20173', '3 - 3'], ['10 november 2007', 'nuggets', '113 - 106', 'pacers', 'carmelo anthony ( 32 )', '12748', '4 - 3'], ['12 november 2007', 'cavaliers', '100 - 122', 'nuggets', 'allen iverson ( 37 )', '19155', '5 - 3'], ['14 november 2007', 'trail blazers', '93 - 110', 'nuggets', 'carmelo anthony ( 25 )', '13289', '6 - 3'], ['17 november 2007', 'knicks', '83 - 115', 'nuggets', 'carmelo anthony ( 24 )', '19679', '7 - 3'], ['20 november 2007', 'bulls', '91 - 112', 'nuggets', 'carmelo anthony ( 26 )', '17106', '8 - 3'], ['21 november 2007', 'nuggets', '90 - 101', 'clippers', 'allen iverson ( 29 )', '17221', '8 - 4'], ['23 november 2007', 'timberwolves', '93 - 99', 'nuggets', 'carmelo anthony ( 31 )', '17097', '9 - 4'], ['24 november 2007', 'nuggets', '81 - 109', 'rockets', 'allen iverson ( 18 )', '18228', '9 - 5'], ['27 november 2007', 'pacers', '112 - 110', 'nuggets', 'allen iverson ( 26 )', '13274', '9 - 6'], ['29 november 2007', 'nuggets', '99 - 127', 'lakers', 'carmelo anthony ( 23 )', '18997', '9 - 7'], ['30 november 2007', 'clippers', '107 - 123', 'nuggets', 'allen iverson ( 26 )', '14230', '10 - 7']] |
carlos pace | https://en.wikipedia.org/wiki/Carlos_Pace | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1219709-1.html.csv | count | carlos pace was a formula one entrant with martini racing a total of four times . | {'scope': 'all', 'criterion': 'equal', 'value': 'martini racing', 'result': '4', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'entrant', 'martini racing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose entrant record fuzzily matches to martini racing .', 'tostr': 'filter_eq { all_rows ; entrant ; martini racing }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; entrant ; martini racing } }', 'tointer': 'select the rows whose entrant record fuzzily matches to martini racing . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; entrant ; martini racing } } ; 4 } = true', 'tointer': 'select the rows whose entrant record fuzzily matches to martini racing . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; entrant ; martini racing } } ; 4 } = true | select the rows whose entrant record fuzzily matches to martini racing . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'entrant_5': 5, 'martini racing_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'entrant_5': 'entrant', 'martini racing_6': 'martini racing', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'entrant_5': [0], 'martini racing_6': [0], '4_7': [2]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1972', 'team williams - motul', 'march 711', 'cosworth v8', '3'], ['1973', 'brooke bond oxo team surtees', 'surtees ts14a', 'cosworth v8', '7'], ['1974', 'team surtees', 'surtees ts16', 'cosworth v8', '11'], ['1974', 'bang & olufsen team surtees', 'surtees ts16', 'cosworth v8', '11'], ['1974', 'goldie hexagon racing', 'brabham bt42', 'cosworth v8', '11'], ['1974', 'motor racing developments', 'brabham bt44', 'cosworth v8', '11'], ['1975', 'martini racing', 'brabham bt44b', 'cosworth v8', '24'], ['1976', 'martini racing', 'brabham bt45', 'alfa romeo flat - 12', '7'], ['1977', 'martini racing', 'brabham bt45', 'alfa romeo flat - 12', '6'], ['1977', 'martini racing', 'brabham bt45b', 'alfa romeo flat - 12', '6']] |
1996 - 97 philadelphia flyers season | https://en.wikipedia.org/wiki/1996%E2%80%9397_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14208850-4.html.csv | unique | game 36 was the only time that the philadelphia flyers faced the chicago blackhawks . | {'scope': 'all', 'row': '10', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'chicago blackhawks', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'chicago blackhawks'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to chicago blackhawks .', 'tostr': 'filter_eq { all_rows ; opponent ; chicago blackhawks }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; opponent ; chicago blackhawks } }', 'tointer': 'select the rows whose opponent record fuzzily matches to chicago blackhawks . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'chicago blackhawks'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to chicago blackhawks .', 'tostr': 'filter_eq { all_rows ; opponent ; chicago blackhawks }'}, 'game'], 'result': '36', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; chicago blackhawks } ; game }'}, '36'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; opponent ; chicago blackhawks } ; game } ; 36 }', 'tointer': 'the game record of this unqiue row is 36 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; opponent ; chicago blackhawks } } ; eq { hop { filter_eq { all_rows ; opponent ; chicago blackhawks } ; game } ; 36 } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to chicago blackhawks . there is only one such row in the table . the game record of this unqiue row is 36 .'} | and { only { filter_eq { all_rows ; opponent ; chicago blackhawks } } ; eq { hop { filter_eq { all_rows ; opponent ; chicago blackhawks } ; game } ; 36 } } = true | select the rows whose opponent record fuzzily matches to chicago blackhawks . there is only one such row in the table . the game record of this unqiue row is 36 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'chicago blackhawks_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'game_9': 9, '36_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'chicago blackhawks_8': 'chicago blackhawks', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'game_9': 'game', '36_10': '36'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'opponent_7': [0], 'chicago blackhawks_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'game_9': [2], '36_10': [3]} | ['game', 'december', 'opponent', 'score', 'record', 'points'] | [['27', '1', 'vancouver canucks', '4 - 3', '14 - 12 - 1', '29'], ['28', '4', 'new york rangers', '1 - 1 ot', '14 - 12 - 2', '30'], ['29', '6', 'dallas stars', '6 - 3', '15 - 12 - 2', '32'], ['30', '10', 'florida panthers', '5 - 4', '16 - 12 - 2', '34'], ['31', '12', 'hartford whalers', '3 - 2', '17 - 12 - 2', '36'], ['32', '14', 'hartford whalers', '4 - 0', '18 - 12 - 2', '38'], ['33', '15', 'boston bruins', '6 - 0', '19 - 12 - 2', '40'], ['34', '19', 'new york islanders', '5 - 0', '20 - 12 - 2', '42'], ['35', '21', 'st louis blues', '4 - 0', '21 - 12 - 2', '44'], ['36', '22', 'chicago blackhawks', '2 - 2 ot', '21 - 12 - 3', '45'], ['37', '27', 'edmonton oilers', '6 - 4', '22 - 12 - 3', '47'], ['38', '29', 'calgary flames', '4 - 2', '23 - 12 - 3', '49'], ['39', '31', 'vancouver canucks', '5 - 3', '24 - 12 - 3', '51']] |
united states house of representatives elections , 1990 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1990 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341568-44.html.csv | comparative | dick armey won a higher percentage of the vote in his race than jack brooks won in his . | {'row_1': '19', 'row_2': '7', 'col': '6', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'dick armey'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to dick armey .', 'tostr': 'filter_eq { all_rows ; incumbent ; dick armey }'}, 'opponent'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; dick armey } ; opponent }', 'tointer': 'select the rows whose incumbent record fuzzily matches to dick armey . take the opponent record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'jack brooks'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to jack brooks .', 'tostr': 'filter_eq { all_rows ; incumbent ; jack brooks }'}, 'opponent'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; jack brooks } ; opponent }', 'tointer': 'select the rows whose incumbent record fuzzily matches to jack brooks . take the opponent record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; incumbent ; dick armey } ; opponent } ; hop { filter_eq { all_rows ; incumbent ; jack brooks } ; opponent } } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to dick armey . take the opponent record of this row . select the rows whose incumbent record fuzzily matches to jack brooks . take the opponent record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; incumbent ; dick armey } ; opponent } ; hop { filter_eq { all_rows ; incumbent ; jack brooks } ; opponent } } = true | select the rows whose incumbent record fuzzily matches to dick armey . take the opponent record of this row . select the rows whose incumbent record fuzzily matches to jack brooks . take the opponent record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'incumbent_7': 7, 'dick armey_8': 8, 'opponent_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'incumbent_11': 11, 'jack brooks_12': 12, 'opponent_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'incumbent_7': 'incumbent', 'dick armey_8': 'dick armey', 'opponent_9': 'opponent', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'incumbent_11': 'incumbent', 'jack brooks_12': 'jack brooks', 'opponent_13': 'opponent'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'incumbent_7': [0], 'dick armey_8': [0], 'opponent_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'incumbent_11': [1], 'jack brooks_12': [1], 'opponent_13': [3]} | ['district', 'incumbent', 'party', 'elected', 'status', 'opponent'] | [['texas1', 'jim chapman', 'democratic', '1985', 're - elected', 'jim chapman ( d ) 61.0 % hamp hodges ( r ) 39.0 %'], ['texas2', 'charlie wilson', 'democratic', '1972', 're - elected', 'charlie wilson ( d ) 55.6 % donna peterson ( r ) 44.4 %'], ['texas3', 'steve bartlett', 'republican', '1982', 're - elected', 'steve bartlett ( r ) 99.6 % noel kopala ( i - wi ) 0.4 %'], ['texas4', 'ralph hall', 'democratic', '1980', 're - elected', 'ralph hall ( d ) 99.6 % tim j mccord ( i - wi ) 0.4 %'], ['texas7', 'bill archer', 'republican', '1970', 're - elected', 'bill archer ( r ) unopposed'], ['texas8', 'jack fields', 'republican', '1980', 're - elected', 'jack fields ( r ) unopposed'], ['texas9', 'jack brooks', 'democratic', '1952', 're - elected', 'jack brooks ( d ) 57.7 % maury meyers ( r ) 42.3 %'], ['texas11', 'marvin leath', 'democratic', '1978', 'retired democratic hold', 'chet edwards ( d ) 53.5 % hugh shine ( r ) 46.5 %'], ['texas12', 'pete geren', 'democratic', '1989', 're - elected', 'pete geren ( d ) 71.3 % mike mcginn ( r ) 28.7 %'], ['texas14', 'greg laughlin', 'democratic', '1988', 're - elected', 'greg laughlin ( d ) 54.3 % joe dial ( r ) 45.7 %'], ['texas15', 'kika de la garza', 'democratic', '1964', 're - elected', 'kika de la garza ( d ) unopposed'], ['texas17', 'charles stenholm', 'democratic', '1978', 're - elected', 'charles stenholm ( d ) unopposed'], ['texas18', 'craig anthony washington', 'democratic', '1989', 're - elected', 'craig anthony washington ( d ) 99.6 %'], ['texas19', 'larry combest', 'republican', '1984', 're - elected', 'larry combest ( r ) unopposed'], ['texas20', 'henry b gonzalez', 'democratic', '1960', 're - elected', 'henry b gonzalez ( d ) unopposed'], ['texas22', 'tom delay', 'republican', '1984', 're - elected', 'tom delay ( r ) 71.2 % bruce director ( d ) 28.8 %'], ['texas24', 'martin frost', 'democratic', '1978', 're - elected', 'martin frost ( d ) unopposed'], ['texas25', 'michael a andrews', 'democratic', '1982', 're - elected', 'michael a andrews ( d ) unopposed'], ['texas26', 'dick armey', 'republican', '1984', 're - elected', 'dick armey ( r ) 70.4 % john wayne caton ( d ) 29.6 %']] |
guillermo franco | https://en.wikipedia.org/wiki/Guillermo_Franco | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1257226-3.html.csv | comparative | on june 10 , 2009 and september 5 , 2009 , guillermo franco played at the 2010 fifa world cup qualification . | {'row_1': '3', 'row_2': '6', 'col': '6', 'col_other': '2', 'relation': 'equal', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '10 june 2009'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 10 june 2009 .', 'tostr': 'filter_eq { all_rows ; date ; 10 june 2009 }'}, 'competition'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition }', 'tointer': 'select the rows whose date record fuzzily matches to 10 june 2009 . take the competition record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '5 september 2009'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 5 september 2009 .', 'tostr': 'filter_eq { all_rows ; date ; 5 september 2009 }'}, 'competition'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition }', 'tointer': 'select the rows whose date record fuzzily matches to 5 september 2009 . take the competition record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } }', 'tointer': 'select the rows whose date record fuzzily matches to 10 june 2009 . take the competition record of this row . select the rows whose date record fuzzily matches to 5 september 2009 . take the competition record of this row . the first record fuzzily matches to the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '10 june 2009'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 10 june 2009 .', 'tostr': 'filter_eq { all_rows ; date ; 10 june 2009 }'}, 'competition'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition }', 'tointer': 'select the rows whose date record fuzzily matches to 10 june 2009 . take the competition record of this row .'}, '2010 fifa world cup qualification'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; 2010 fifa world cup qualification }', 'tointer': 'the competition record of the first row is 2010 fifa world cup qualification .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '5 september 2009'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 5 september 2009 .', 'tostr': 'filter_eq { all_rows ; date ; 5 september 2009 }'}, 'competition'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition }', 'tointer': 'select the rows whose date record fuzzily matches to 5 september 2009 . take the competition record of this row .'}, '2010 fifa world cup qualification'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } ; 2010 fifa world cup qualification }', 'tointer': 'the competition record of the second row is 2010 fifa world cup qualification .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; 2010 fifa world cup qualification } ; eq { hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } ; 2010 fifa world cup qualification } }', 'tointer': 'the competition record of the first row is 2010 fifa world cup qualification . the competition record of the second row is 2010 fifa world cup qualification .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; 2010 fifa world cup qualification } ; eq { hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } ; 2010 fifa world cup qualification } } } = true', 'tointer': 'select the rows whose date record fuzzily matches to 10 june 2009 . take the competition record of this row . select the rows whose date record fuzzily matches to 5 september 2009 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is 2010 fifa world cup qualification . the competition record of the second row is 2010 fifa world cup qualification .'} | and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; 2010 fifa world cup qualification } ; eq { hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } ; 2010 fifa world cup qualification } } } = true | select the rows whose date record fuzzily matches to 10 june 2009 . take the competition record of this row . select the rows whose date record fuzzily matches to 5 september 2009 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is 2010 fifa world cup qualification . the competition record of the second row is 2010 fifa world cup qualification . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'str_eq_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'date_11': 11, '10 june 2009_12': 12, 'competition_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'date_15': 15, '5 september 2009_16': 16, 'competition_17': 17, 'and_7': 7, 'str_eq_5': 5, '2010 fifa world cup qualification_18': 18, 'str_eq_6': 6, '2010 fifa world cup qualification_19': 19} | {'and_8': 'and', 'result_9': 'true', 'str_eq_4': 'str_eq', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', '10 june 2009_12': '10 june 2009', 'competition_13': 'competition', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'date_15': 'date', '5 september 2009_16': '5 september 2009', 'competition_17': 'competition', 'and_7': 'and', 'str_eq_5': 'str_eq', '2010 fifa world cup qualification_18': '2010 fifa world cup qualification', 'str_eq_6': 'str_eq', '2010 fifa world cup qualification_19': '2010 fifa world cup qualification'} | {'and_8': [9], 'result_9': [], 'str_eq_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'date_11': [0], '10 june 2009_12': [0], 'competition_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'date_15': [1], '5 september 2009_16': [1], 'competition_17': [3], 'and_7': [8], 'str_eq_5': [7], '2010 fifa world cup qualification_18': [5], 'str_eq_6': [7], '2010 fifa world cup qualification_19': [6]} | ['goal', 'date', 'venue', 'score', 'result', 'competition'] | [['1', '8 october 2005', 'estadio alfonso lastras , san luis potosí , mexico', '1 - 1', '5 - 2', '2006 fifa world cup qualification'], ['2', '1 march 2006', 'pizza hut park , frisco , united states', '1 - 0', '1 - 0', 'friendly'], ['3', '10 june 2009', 'estadio azteca , mexico city , mexico', '1 - 0', '2 - 1', '2010 fifa world cup qualification'], ['4', '23 july 2009', 'soldier field , chicago , united states', '1 - 0', '5 - 3 ( pso )', '2009 concacaf gold cup'], ['5', '26 july 2009', 'giants stadium , east rutherford , united states', '0 - 5', '0 - 5', '2009 concacaf gold cup'], ['6', '5 september 2009', 'estadio ricardo saprissa , san josé , costa rica', '0 - 2', '3 - 0', '2010 fifa world cup qualification'], ['7', '24 may 2010', 'wembley stadium , london , england', '2 - 1', '3 - 1', 'friendly']] |
united states house of representatives elections in georgia , 2000 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections_in_Georgia%2C_2000 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26336739-1.html.csv | count | 10 incumbents were re - elected during the 2000 united states house of representatives elections . | {'scope': 'all', 'criterion': 'equal', 'value': 're - elected', 'result': '10', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'status', 're - elected'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose status record fuzzily matches to re - elected .', 'tostr': 'filter_eq { all_rows ; status ; re - elected }'}], 'result': '10', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; status ; re - elected } }', 'tointer': 'select the rows whose status record fuzzily matches to re - elected . the number of such rows is 10 .'}, '10'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; status ; re - elected } } ; 10 } = true', 'tointer': 'select the rows whose status record fuzzily matches to re - elected . the number of such rows is 10 .'} | eq { count { filter_eq { all_rows ; status ; re - elected } } ; 10 } = true | select the rows whose status record fuzzily matches to re - elected . the number of such rows is 10 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'status_5': 5, 're - elected_6': 6, '10_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'status_5': 'status', 're - elected_6': 're - elected', '10_7': '10'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'status_5': [0], 're - elected_6': [0], '10_7': [2]} | ['district', 'incumbent', 'party', 'elected', 'status', 'result'] | [["georgia 's 1st", 'jack kingston', 'republican', '1992', 're - elected', 'jack kingston ( r ) 69 % joyce marie griggs ( d ) 31 %'], ["georgia 's 2nd", 'sanford bishop', 'democratic', '1992', 're - elected', 'sanford bishop ( d ) 53 % dylan glenn ( r ) 47 %'], ["georgia 's 3rd", 'mac collins', 'republican', '1992', 're - elected', 'mac collins ( r ) 63 % gail notti ( d ) 37 %'], ["georgia 's 4th", 'cynthia mckinney', 'democratic', '1992', 're - elected', 'cynthia mckinney ( d ) 60 % sunny warren ( r ) 40 %'], ["georgia 's 5th", 'john lewis', 'democratic', '1986', 're - elected', 'john lewis ( d ) 77 % hank schwab ( r ) 23 %'], ["georgia 's 6th", 'johnny isakson', 'republican', '1999', 're - elected', 'johnny isakson ( r ) 75 % brett dehart ( d ) 25 %'], ["georgia 's 7th", 'bob barr', 'republican', '1994', 're - elected', 'bob barr ( r ) 54 % roger kahn ( d ) 46 %'], ["georgia 's 8th", 'saxby chambliss', 'republican', '1994', 're - elected', 'saxby chambliss ( r ) 59 % jim marshall ( d ) 41 %'], ["georgia 's 9th", 'nathan deal', 'republican', '1992', 're - elected', 'nathan deal ( r ) 75 % james harrington ( d ) 25 %'], ["georgia 's 10th", 'charlie norwood', 'republican', '1994', 're - elected', 'charlie norwood ( r ) 63 % marion freeman ( d ) 37 %']] |
list of ngc objects ( 5001 - 6000 ) | https://en.wikipedia.org/wiki/List_of_NGC_objects_%285001%E2%80%936000%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11051845-8.html.csv | aggregation | the average apparent magnitude for ngc objects ( 5001 - 6000 ) is 12.24 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '12.24', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'apparent magnitude'], 'result': '12.24', 'ind': 0, 'tostr': 'avg { all_rows ; apparent magnitude }'}, '12.24'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; apparent magnitude } ; 12.24 } = true', 'tointer': 'the average of the apparent magnitude record of all rows is 12.24 .'} | round_eq { avg { all_rows ; apparent magnitude } ; 12.24 } = true | the average of the apparent magnitude record of all rows is 12.24 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'apparent magnitude_4': 4, '12.24_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'apparent magnitude_4': 'apparent magnitude', '12.24_5': '12.24'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'apparent magnitude_4': [0], '12.24_5': [1]} | ['ngc number', 'object type', 'constellation', 'right ascension ( j2000 )', 'declination ( j2000 )', 'apparent magnitude'] | [['5705', 'spiral galaxy', 'virgo', '14h39 m49 .6 s', 'degree43 ′ 08 ″', '14.5'], ['5713', 'spiral galaxy', 'virgo', '14h40 m11 .5 s', 'degree17 ′ 25 ″', '11.7'], ['5749', 'open cluster', 'lupus', '14h49 m', 'degree30 ′', '8.8'], ['5750', 'spiral galaxy', 'virgo', '14h46 m11 .3 s', 'degree13 ′ 23 ″', '13.1'], ['5777', 'spiral galaxy', 'draco', '14h51 m18s', 'degree58 ′ 39 ″', '13.1']] |
2008 indianapolis colts season | https://en.wikipedia.org/wiki/2008_Indianapolis_Colts_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14999879-2.html.csv | aggregation | for the 2008 indianapolis colts the average height for the tight end position was 6 ' 4 . | {'scope': 'subset', 'col': '5', 'type': 'average', 'result': "6 ' 4", 'subset': {'col': '4', 'criterion': 'equal', 'value': 'tight end'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'tight end'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; position ; tight end }', 'tointer': 'select the rows whose position record fuzzily matches to tight end .'}, 'height'], 'result': "6 ' 4", 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; position ; tight end } ; height }'}, "6 ' 4"], 'result': True, 'ind': 2, 'tostr': "round_eq { avg { filter_eq { all_rows ; position ; tight end } ; height } ; 6 ' 4 } = true", 'tointer': "select the rows whose position record fuzzily matches to tight end . the average of the height record of these rows is 6 ' 4 ."} | round_eq { avg { filter_eq { all_rows ; position ; tight end } ; height } ; 6 ' 4 } = true | select the rows whose position record fuzzily matches to tight end . the average of the height record of these rows is 6 ' 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'position_5': 5, 'tight end_6': 6, 'height_7': 7, "6'4_8": 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'position_5': 'position', 'tight end_6': 'tight end', 'height_7': 'height', "6'4_8": "6 ' 4"} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'tight end_6': [0], 'height_7': [1], "6'4_8": [2]} | ['round', 'choice', 'player', 'position', 'height', 'weight', 'college'] | [['2', '59', 'mike pollak', 'center', "6 ' 4", '-', 'arizona state'], ['3', '93', 'philip wheeler', 'linebacker', "6 ' 2", '-', 'georgia tech'], ['4', '127', 'jacob tamme', 'tight end', "6 ' 5", '-', 'kentucky'], ['5', '161', 'marcus howard', 'defensive end', "6 ' 0", '-', 'georgia'], ['6', '196', 'tom santi', 'tight end', "6 ' 3", '-', 'virginia'], ['6', '201', 'steve justice', 'center', "6 ' 3", '-', 'wake forest'], ['6', '202', 'mike hart', 'running back', "5 ' 8", '-', 'michigan'], ['6', '205', 'pierre garcon', 'wide receiver', "5 ' 11", '-', 'mount union']] |
2008 issf world cup final ( rifle and pistol ) | https://en.wikipedia.org/wiki/2008_ISSF_World_Cup_Final_%28rifle_and_pistol%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18191407-4.html.csv | aggregation | for the 2008 issf world cup final rifle and pistol the wc beijing events had a total combined rank points of 23 . | {'scope': 'subset', 'col': '3', 'type': 'sum', 'result': '23', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'wc beijing'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'wc beijing'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; event ; wc beijing }', 'tointer': 'select the rows whose event record fuzzily matches to wc beijing .'}, 'rank points'], 'result': '23', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; event ; wc beijing } ; rank points }'}, '23'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; event ; wc beijing } ; rank points } ; 23 } = true', 'tointer': 'select the rows whose event record fuzzily matches to wc beijing . the sum of the rank points record of these rows is 23 .'} | round_eq { sum { filter_eq { all_rows ; event ; wc beijing } ; rank points } ; 23 } = true | select the rows whose event record fuzzily matches to wc beijing . the sum of the rank points record of these rows is 23 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'event_5': 5, 'wc beijing_6': 6, 'rank points_7': 7, '23_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'event_5': 'event', 'wc beijing_6': 'wc beijing', 'rank points_7': 'rank points', '23_8': '23'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'event_5': [0], 'wc beijing_6': [0], 'rank points_7': [1], '23_8': [2]} | ['shooter', 'event', 'rank points', 'score points', 'total'] | [['sergei martynov ( blr )', 'wcf 2007', 'defending champion', 'defending champion', 'defending champion'], ['artur ayvazyan ( ukr )', 'og beijing', 'olympic gold medalist', 'olympic gold medalist', 'olympic gold medalist'], ['matthew emmons ( usa )', 'og beijing', 'olympic silver medalist', 'olympic silver medalist', 'olympic silver medalist'], ['warren potent ( aus )', 'og beijing', 'olympic bronze medalist', 'olympic bronze medalist', 'olympic bronze medalist'], ['stevan pletikosić ( srb )', 'wc rio de janeiro', '15', '13', '28'], ['torben grimmel ( den )', 'wc milan', '15', '12', '27'], ['thomas tamas ( usa )', 'wc beijing', '10', '14', '24'], ['neil stirton ( gbr )', 'wc munich', '10', '14', '24'], ['michael mcphail ( usa )', 'wc beijing', '8', '13', '21'], ['toshikazu yamashita ( jpn )', 'wc milan', '10', '11', '21'], ['jury sukhorukov ( ukr )', 'wc beijing', '5', '13', '18'], ['tomáš jeřábek ( cze )', 'wc munich', '5', '12', '17']] |
roberto moreno | https://en.wikipedia.org/wiki/Roberto_Moreno | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226554-3.html.csv | comparative | roberto moreno scored more points in his 1987 race than he did in his 1995 race . | {'row_1': '2', 'row_2': '13', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1987'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1987 .', 'tostr': 'filter_eq { all_rows ; year ; 1987 }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1987 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1987 . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1995'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1995 .', 'tostr': 'filter_eq { all_rows ; year ; 1995 }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1995 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1995 . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; year ; 1987 } ; points } ; hop { filter_eq { all_rows ; year ; 1995 } ; points } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1987 . take the points record of this row . select the rows whose year record fuzzily matches to 1995 . take the points record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; year ; 1987 } ; points } ; hop { filter_eq { all_rows ; year ; 1995 } ; points } } = true | select the rows whose year record fuzzily matches to 1987 . take the points record of this row . select the rows whose year record fuzzily matches to 1995 . take the points record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '1987_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '1995_12': 12, 'points_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '1987_8': '1987', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1995_12': '1995', 'points_13': 'points'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '1987_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '1995_12': [1], 'points_13': [3]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1982', 'john player lotus', 'lotus 91', 'cosworth v8', '0'], ['1987', 'team ags', 'ags jh22', 'cosworth v8', '1'], ['1989', 'coloni spa', 'coloni fc188b', 'cosworth v8', '0'], ['1989', 'coloni spa', 'coloni c3', 'cosworth v8', '0'], ['1990', 'eurobrun racing', 'eurobrun er189', 'judd v8', '6'], ['1990', 'eurobrun racing', 'eurobrun er189b', 'judd v8', '6'], ['1990', 'benetton formula', 'benetton b190', 'ford v8', '6'], ['1991', 'camel benetton ford', 'benetton b190b', 'ford v8', '8'], ['1991', 'camel benetton ford', 'benetton b191', 'ford v8', '8'], ['1991', 'team 7up jordan', 'jordan 191', 'ford v8', '8'], ['1991', 'minardi team', 'minardi m191', 'ferrari v12', '8'], ['1992', 'andrea moda formula', 'andrea moda s921', 'judd v10', '0'], ['1995', 'parmalat forti ford', 'forti fg01', 'ford v8', '0']] |
1982 vfl season | https://en.wikipedia.org/wiki/1982_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10824095-16.html.csv | comparative | richmond had a higher scoring game than swans . | {'row_1': '4', 'row_2': '5', 'col': '4', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'away team', 'richmond'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose away team record fuzzily matches to richmond .', 'tostr': 'filter_eq { all_rows ; away team ; richmond }'}, 'away team score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; away team ; richmond } ; away team score }', 'tointer': 'select the rows whose away team record fuzzily matches to richmond . take the away team score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'away team', 'swans'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose away team record fuzzily matches to swans .', 'tostr': 'filter_eq { all_rows ; away team ; swans }'}, 'away team score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; away team ; swans } ; away team score }', 'tointer': 'select the rows whose away team record fuzzily matches to swans . take the away team score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; hop { filter_eq { all_rows ; away team ; swans } ; away team score } } = true', 'tointer': 'select the rows whose away team record fuzzily matches to richmond . take the away team score record of this row . select the rows whose away team record fuzzily matches to swans . take the away team score record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; hop { filter_eq { all_rows ; away team ; swans } ; away team score } } = true | select the rows whose away team record fuzzily matches to richmond . take the away team score record of this row . select the rows whose away team record fuzzily matches to swans . take the away team score record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'away team_7': 7, 'richmond_8': 8, 'away team score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'away team_11': 11, 'swans_12': 12, 'away team score_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'away team_7': 'away team', 'richmond_8': 'richmond', 'away team score_9': 'away team score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'away team_11': 'away team', 'swans_12': 'swans', 'away team score_13': 'away team score'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'away team_7': [0], 'richmond_8': [0], 'away team score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'away team_11': [1], 'swans_12': [1], 'away team score_13': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '32.14 ( 206 )', 'north melbourne', '15.22 ( 112 )', 'princes park', '18760', '10 july 1982'], ['footscray', '25.7 ( 157 )', 'geelong', '17.13 ( 115 )', 'western oval', '14004', '10 july 1982'], ['carlton', '21.13 ( 139 )', 'st kilda', '9.9 ( 63 )', 'vfl park', '27829', '10 july 1982'], ['melbourne', '9.18 ( 72 )', 'richmond', '23.9 ( 147 )', 'mcg', '36161', '17 july 1982'], ['essendon', '12.10 ( 82 )', 'swans', '17.13 ( 115 )', 'windy hill', '22278', '17 july 1982'], ['fitzroy', '10.24 ( 84 )', 'collingwood', '9.10 ( 64 )', 'vfl park', '26105', '17 july 1982']] |
chennai super kings | https://en.wikipedia.org/wiki/Chennai_Super_Kings | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15829930-5.html.csv | majority | for the chennai super kings , when they were runners-up , they had over 10 wins most of the time . | {'scope': 'subset', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10', 'subset': {'col': '9', 'criterion': 'equal', 'value': 'runners - up'}} | {'func': 'most_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'summary', 'runners - up'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; summary ; runners - up }', 'tointer': 'select the rows whose summary record fuzzily matches to runners - up .'}, 'wins', '10'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose summary record fuzzily matches to runners - up . for the wins records of these rows , most of them are greater than 10 .', 'tostr': 'most_greater { filter_eq { all_rows ; summary ; runners - up } ; wins ; 10 } = true'} | most_greater { filter_eq { all_rows ; summary ; runners - up } ; wins ; 10 } = true | select the rows whose summary record fuzzily matches to runners - up . for the wins records of these rows , most of them are greater than 10 . | 2 | 2 | {'most_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'summary_4': 4, 'runners - up_5': 5, 'wins_6': 6, '10_7': 7} | {'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'summary_4': 'summary', 'runners - up_5': 'runners - up', 'wins_6': 'wins', '10_7': '10'} | {'most_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'summary_4': [0], 'runners - up_5': [0], 'wins_6': [1], '10_7': [1]} | ['year', 'matches', 'wins', 'losses', 'no result', 'tied', 'success rate', 'position', 'summary'] | [['2008', '16', '9', '7', '0', '0', '56.25 %', '2nd', 'runners - up'], ['2009', '15', '8', '6', '1', '0', '53.33 %', '4th', 'semi - finalists'], ['2010', '16', '9', '7', '0', '0', '56.25 %', '1st', 'champions'], ['2011', '16', '11', '5', '0', '0', '68.75 %', '1st', 'champions'], ['2012', '19', '19', '11', '8', '0', '52.63 %', '2nd', 'runners - up'], ['2013', '18', '12', '6', '0', '0', '66.67 %', '2nd', 'runners - up']] |
1972 vfl season | https://en.wikipedia.org/wiki/1972_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10826385-12.html.csv | comparative | in the 1972 vfl season , the mcg venue had a larger crowd than the vfl park venue . | {'row_1': '3', 'row_2': '6', 'col': '6', 'col_other': '5', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'mcg'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to mcg .', 'tostr': 'filter_eq { all_rows ; venue ; mcg }'}, 'crowd'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; mcg } ; crowd }', 'tointer': 'select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'vfl park'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose venue record fuzzily matches to vfl park .', 'tostr': 'filter_eq { all_rows ; venue ; vfl park }'}, 'crowd'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; venue ; vfl park } ; crowd }', 'tointer': 'select the rows whose venue record fuzzily matches to vfl park . take the crowd record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; venue ; mcg } ; crowd } ; hop { filter_eq { all_rows ; venue ; vfl park } ; crowd } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row . select the rows whose venue record fuzzily matches to vfl park . take the crowd record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; venue ; mcg } ; crowd } ; hop { filter_eq { all_rows ; venue ; vfl park } ; crowd } } = true | select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row . select the rows whose venue record fuzzily matches to vfl park . take the crowd record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'venue_7': 7, 'mcg_8': 8, 'crowd_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'venue_11': 11, 'vfl park_12': 12, 'crowd_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'venue_7': 'venue', 'mcg_8': 'mcg', 'crowd_9': 'crowd', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'venue_11': 'venue', 'vfl park_12': 'vfl park', 'crowd_13': 'crowd'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'venue_7': [0], 'mcg_8': [0], 'crowd_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'venue_11': [1], 'vfl park_12': [1], 'crowd_13': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '7.8 ( 50 )', 'st kilda', '12.19 ( 91 )', 'arden street oval', '10681', '17 june 1972'], ['collingwood', '11.24 ( 90 )', 'richmond', '12.13 ( 85 )', 'victoria park', '28188', '17 june 1972'], ['melbourne', '11.10 ( 76 )', 'hawthorn', '11.9 ( 75 )', 'mcg', '31314', '17 june 1972'], ['geelong', '15.14 ( 104 )', 'south melbourne', '10.13 ( 73 )', 'kardinia park', '14426', '24 june 1972'], ['essendon', '14.15 ( 99 )', 'footscray', '19.19 ( 133 )', 'windy hill', '23903', '24 june 1972'], ['carlton', '13.13 ( 91 )', 'fitzroy', '8.7 ( 55 )', 'vfl park', '29380', '24 june 1972']] |
list of game of the year awards | https://en.wikipedia.org/wiki/List_of_Game_of_the_Year_awards | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1851722-15.html.csv | unique | resident evil 4 is the only game of the year for the gamecube platform . | {'scope': 'all', 'row': '2', 'col': '4', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'gamecube', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'platform ( s )', 'gamecube'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose platform ( s ) record fuzzily matches to gamecube .', 'tostr': 'filter_eq { all_rows ; platform ( s ) ; gamecube }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; platform ( s ) ; gamecube } }', 'tointer': 'select the rows whose platform ( s ) record fuzzily matches to gamecube . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'platform ( s )', 'gamecube'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose platform ( s ) record fuzzily matches to gamecube .', 'tostr': 'filter_eq { all_rows ; platform ( s ) ; gamecube }'}, 'game'], 'result': 'resident evil 4', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; platform ( s ) ; gamecube } ; game }'}, 'resident evil 4'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; platform ( s ) ; gamecube } ; game } ; resident evil 4 }', 'tointer': 'the game record of this unqiue row is resident evil 4 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; gamecube } ; game } ; resident evil 4 } } = true', 'tointer': 'select the rows whose platform ( s ) record fuzzily matches to gamecube . there is only one such row in the table . the game record of this unqiue row is resident evil 4 .'} | and { only { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; gamecube } ; game } ; resident evil 4 } } = true | select the rows whose platform ( s ) record fuzzily matches to gamecube . there is only one such row in the table . the game record of this unqiue row is resident evil 4 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'platform (s)_7': 7, 'gamecube_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'game_9': 9, 'resident evil 4_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'platform (s)_7': 'platform ( s )', 'gamecube_8': 'gamecube', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'game_9': 'game', 'resident evil 4_10': 'resident evil 4'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'platform (s)_7': [0], 'gamecube_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'game_9': [2], 'resident evil 4_10': [3]} | ['year', 'game', 'genre', 'platform ( s )', 'developer ( s )'] | [['2004', 'grand theft auto : san andreas', 'action - adventure , third - person shooter , sandbox', 'playstation 2 , windows , xbox', 'rockstar games'], ['2005', 'resident evil 4', 'survival horror , shooter', 'gamecube , playstation 2', 'capcom'], ['2006', 'gears of war', 'third - person shooter', 'xbox 360', 'epic games'], ['2007', 'halo 3', 'first - person shooter', 'xbox 360', 'bungie'], ['2008', 'metal gear solid 4 : guns of the patriots', 'stealth action', 'playstation 3', 'kojima productions'], ['2009', 'uncharted 2 : among thieves', 'action - adventure : ( third - person ) shooter', 'playstation 3', 'naughty dog'], ['2010', 'call of duty : black ops', '( first - person ) shooter', 'microsoft windows , playstation 3 , xbox 360', 'treyarch']] |
wheelchair basketball at the 2000 summer paralympics | https://en.wikipedia.org/wiki/Wheelchair_basketball_at_the_2000_Summer_Paralympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18781865-4.html.csv | comparative | australia won more silver medals than japan in wheelchair basketball at the 2000 summer paralympics . | {'row_1': '2', 'row_2': '5', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'australia ( aus )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to australia ( aus ) .', 'tostr': 'filter_eq { all_rows ; nation ; australia ( aus ) }'}, 'silver'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; australia ( aus ) } ; silver }', 'tointer': 'select the rows whose nation record fuzzily matches to australia ( aus ) . take the silver record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'japan ( jpn )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to japan ( jpn ) .', 'tostr': 'filter_eq { all_rows ; nation ; japan ( jpn ) }'}, 'silver'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; japan ( jpn ) } ; silver }', 'tointer': 'select the rows whose nation record fuzzily matches to japan ( jpn ) . take the silver record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; nation ; australia ( aus ) } ; silver } ; hop { filter_eq { all_rows ; nation ; japan ( jpn ) } ; silver } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to australia ( aus ) . take the silver record of this row . select the rows whose nation record fuzzily matches to japan ( jpn ) . take the silver record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; nation ; australia ( aus ) } ; silver } ; hop { filter_eq { all_rows ; nation ; japan ( jpn ) } ; silver } } = true | select the rows whose nation record fuzzily matches to australia ( aus ) . take the silver record of this row . select the rows whose nation record fuzzily matches to japan ( jpn ) . take the silver record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nation_7': 7, 'australia (aus)_8': 8, 'silver_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'nation_11': 11, 'japan (jpn)_12': 12, 'silver_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nation_7': 'nation', 'australia (aus)_8': 'australia ( aus )', 'silver_9': 'silver', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'japan (jpn)_12': 'japan ( jpn )', 'silver_13': 'silver'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'nation_7': [0], 'australia (aus)_8': [0], 'silver_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'nation_11': [1], 'japan (jpn)_12': [1], 'silver_13': [3]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'canada ( can )', '2', '0', '0', '2'], ['2', 'australia ( aus )', '0', '1', '0', '1'], ['2', 'netherlands ( ned )', '0', '1', '0', '1'], ['4', 'united states ( usa )', '0', '0', '1', '1'], ['4', 'japan ( jpn )', '0', '0', '1', '1']] |
1987 - 88 fa cup | https://en.wikipedia.org/wiki/1987%E2%80%9388_FA_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17751827-5.html.csv | comparative | manchester city scored more goals than arsenal in the 1987 - 88 fa cup . | {'row_1': '3', 'row_2': '9', 'col': '3', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'manchester city'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home team record fuzzily matches to manchester city .', 'tostr': 'filter_eq { all_rows ; home team ; manchester city }'}, 'score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; home team ; manchester city } ; score }', 'tointer': 'select the rows whose home team record fuzzily matches to manchester city . take the score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'arsenal'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to arsenal .', 'tostr': 'filter_eq { all_rows ; home team ; arsenal }'}, 'score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; home team ; arsenal } ; score }', 'tointer': 'select the rows whose home team record fuzzily matches to arsenal . take the score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; home team ; manchester city } ; score } ; hop { filter_eq { all_rows ; home team ; arsenal } ; score } } = true', 'tointer': 'select the rows whose home team record fuzzily matches to manchester city . take the score record of this row . select the rows whose home team record fuzzily matches to arsenal . take the score record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; home team ; manchester city } ; score } ; hop { filter_eq { all_rows ; home team ; arsenal } ; score } } = true | select the rows whose home team record fuzzily matches to manchester city . take the score record of this row . select the rows whose home team record fuzzily matches to arsenal . take the score record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'home team_7': 7, 'manchester city_8': 8, 'score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'home team_11': 11, 'arsenal_12': 12, 'score_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'home team_7': 'home team', 'manchester city_8': 'manchester city', 'score_9': 'score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'home team_11': 'home team', 'arsenal_12': 'arsenal', 'score_13': 'score'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'home team_7': [0], 'manchester city_8': [0], 'score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'home team_11': [1], 'arsenal_12': [1], 'score_13': [3]} | ['tie no', 'home team', 'score', 'away team', 'date'] | [['1', 'everton', '0 - 1', 'liverpool', '21 february 1988'], ['2', 'newcastle united', '1 - 3', 'wimbledon', '20 february 1988'], ['3', 'manchester city', '3 - 1', 'plymouth argyle', '20 february 1988'], ['4', 'queens park rangers', '1 - 1', 'luton town', '20 february 1988'], ['replay', 'luton town', '1 - 0', 'queens park rangers', '24 february 1988'], ['5', 'portsmouth', '3 - 0', 'bradford city', '20 february 1988'], ['6', 'port vale', '0 - 0', 'watford', '20 february 1988'], ['replay', 'watford', '2 - 0', 'port vale', '23 february 1988'], ['7', 'arsenal', '2 - 1', 'manchester united', '20 february 1988'], ['8', 'birmingham city', '0 - 1', 'nottingham forest', '20 february 1988']] |
1959 vfl season | https://en.wikipedia.org/wiki/1959_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10775038-17.html.csv | unique | hawthorn was the ony away team to play at princes park . | {'scope': 'all', 'row': '3', 'col': '5', 'col_other': '3', 'criterion': 'equal', 'value': 'princes park', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'princes park'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to princes park .', 'tostr': 'filter_eq { all_rows ; venue ; princes park }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; venue ; princes park } }', 'tointer': 'select the rows whose venue record fuzzily matches to princes park . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'princes park'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to princes park .', 'tostr': 'filter_eq { all_rows ; venue ; princes park }'}, 'away team'], 'result': 'hawthorn', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; princes park } ; away team }'}, 'hawthorn'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; venue ; princes park } ; away team } ; hawthorn }', 'tointer': 'the away team record of this unqiue row is hawthorn .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; venue ; princes park } } ; eq { hop { filter_eq { all_rows ; venue ; princes park } ; away team } ; hawthorn } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to princes park . there is only one such row in the table . the away team record of this unqiue row is hawthorn .'} | and { only { filter_eq { all_rows ; venue ; princes park } } ; eq { hop { filter_eq { all_rows ; venue ; princes park } ; away team } ; hawthorn } } = true | select the rows whose venue record fuzzily matches to princes park . there is only one such row in the table . the away team record of this unqiue row is hawthorn . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'venue_7': 7, 'princes park_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'away team_9': 9, 'hawthorn_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'venue_7': 'venue', 'princes park_8': 'princes park', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'away team_9': 'away team', 'hawthorn_10': 'hawthorn'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'venue_7': [0], 'princes park_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'away team_9': [2], 'hawthorn_10': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '6.6 ( 42 )', 'south melbourne', '19.20 ( 134 )', 'arden street oval', '12500', '22 august 1959'], ['essendon', '11.8 ( 74 )', 'fitzroy', '7.12 ( 54 )', 'windy hill', '30000', '22 august 1959'], ['carlton', '13.20 ( 98 )', 'hawthorn', '14.12 ( 96 )', 'princes park', '18720', '22 august 1959'], ['st kilda', '18.15 ( 123 )', 'richmond', '13.19 ( 97 )', 'junction oval', '14500', '22 august 1959'], ['melbourne', '17.18 ( 120 )', 'geelong', '9.12 ( 66 )', 'mcg', '21646', '22 august 1959'], ['footscray', '5.5 ( 35 )', 'collingwood', '8.17 ( 65 )', 'western oval', '33960', '22 august 1959']] |
1952 washington redskins season | https://en.wikipedia.org/wiki/1952_Washington_Redskins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15124415-1.html.csv | count | the washington redskins lost eight games in the 1952 season . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'l', 'result': '8', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'l'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to l .', 'tostr': 'filter_eq { all_rows ; result ; l }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; l } }', 'tointer': 'select the rows whose result record fuzzily matches to l . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; l } } ; 8 } = true', 'tointer': 'select the rows whose result record fuzzily matches to l . the number of such rows is 8 .'} | eq { count { filter_eq { all_rows ; result ; l } } ; 8 } = true | select the rows whose result record fuzzily matches to l . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 'l_6': 6, '8_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 'l_6': 'l', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'l_6': [0], '8_7': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 29 , 1952', 'chicago cardinals', 'w 23 - 7', '17837'], ['2', 'october 5 , 1952', 'green bay packers', 'l 35 - 20', '9657'], ['3', 'october 12 , 1952', 'chicago cardinals', 'l 17 - 6', '24600'], ['4', 'october 19 , 1952', 'pittsburgh steelers', 'w 28 - 24', '22604'], ['5', 'october 26 , 1952', 'cleveland browns', 'l 19 - 15', '32496'], ['6', 'november 2 , 1952', 'pittsburgh steelers', 'l 24 - 23', '25866'], ['7', 'november 9 , 1952', 'philadelphia eagles', 'l 38 - 20', '16932'], ['8', 'november 16 , 1952', 'san francisco 49ers', 'l 23 - 17', '28997'], ['9', 'november 23 , 1952', 'new york giants', 'l 14 - 10', '21125'], ['10', 'november 30 , 1952', 'cleveland browns', 'l 48 - 21', '22679'], ['11', 'december 7 , 1952', 'new york giants', 'w 27 - 17', '21237'], ['12', 'december 14 , 1952', 'philadelphia eagles', 'w 27 - 21', '22468']] |
list of state leaders in 800s bc | https://en.wikipedia.org/wiki/List_of_state_leaders_in_800s_BC | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17338226-13.html.csv | majority | a majority of leaders in the 800s bc were sovereign leaders . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'sovereign', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'type', 'sovereign'], 'result': True, 'ind': 0, 'tointer': 'for the type records of all rows , most of them fuzzily match to sovereign .', 'tostr': 'most_eq { all_rows ; type ; sovereign } = true'} | most_eq { all_rows ; type ; sovereign } = true | for the type records of all rows , most of them fuzzily match to sovereign . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'type_3': 3, 'sovereign_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'type_3': 'type', 'sovereign_4': 'sovereign'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'type_3': [0], 'sovereign_4': [0]} | ['state', 'type', 'name', 'title', 'royal house', 'from'] | [['cai', 'sovereign', 'yi', 'marquis', 'ji', '837 bc'], ['cai', 'sovereign', 'xi', 'marquis', 'ji', '809 bc'], ['cao', 'sovereign', 'dai', 'count', '-', '826 bc'], ['chen', 'sovereign', 'li', 'duke', '-', '831 bc'], ['chu', 'sovereign', 'xiong xun', 'viscount', 'mi', '821 bc'], ['jin', 'sovereign', 'mu', 'marquis', 'ji', '811 bc'], ['lu', 'sovereign', 'yi', 'duke', 'ji', '815 bc'], ['lu', 'sovereign', 'boyu', 'ruler', 'ji', '806 bc'], ['qi', 'sovereign', 'wen', 'duke', 'jiang', '815 bc'], ['qi', 'sovereign', 'cheng', 'duke', 'jiang', '803 bc'], ['qin', 'sovereign', 'zhuang', 'duke', 'ying', '822 bc'], ['song', 'sovereign', 'hui', 'duke', '-', '830 bc'], ['song', 'sovereign', 'ai', 'duke', '-', '800 bc'], ['wey', 'sovereign', 'wu', 'duke', '-', '813 bc'], ['yan', 'sovereign', 'li', 'marquis', '-', '826 bc'], ['zheng', 'sovereign', 'huan', 'duke', '-', '806 bc']] |
jacksonville jaguars draft history | https://en.wikipedia.org/wiki/Jacksonville_Jaguars_draft_history | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15100419-15.html.csv | aggregation | for the jacksonville jaguars draft history the overall average pick was 125.1 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '125.1', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'overall'], 'result': '125.1', 'ind': 0, 'tostr': 'avg { all_rows ; overall }'}, '125.1'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; overall } ; 125.1 } = true', 'tointer': 'the average of the overall record of all rows is 125.1 .'} | round_eq { avg { all_rows ; overall } ; 125.1 } = true | the average of the overall record of all rows is 125.1 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'overall_4': 4, '125.1_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'overall_4': 'overall', '125.1_5': '125.1'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'overall_4': [0], '125.1_5': [1]} | ['round', 'pick', 'overall', 'name', 'position', 'college'] | [['1', '8', '8', 'eugene monroe', 'offensive tackle', 'virginia'], ['2', '7', '39', 'eben britton', 'offensive tackle', 'arizona'], ['3', '8', '72', 'terrance knighton', 'defensive tackle', 'temple'], ['3', '9', '73', 'derek cox', 'cornerback', 'william & mary'], ['4', '7', '107', 'mike thomas', 'wide receiver', 'arizona'], ['5', '8', '144', 'jarett dillard', 'wide receiver', 'rice'], ['6', '7', '180', 'zach miller', 'tight end', 'nebraska - omaha'], ['7', '41', '250', 'rashad jennings', 'running back', 'liberty'], ['7', '44', '253', 'tiquan underwood', 'wide receiver', 'rutgers']] |
cities of the underworld | https://en.wikipedia.org/wiki/Cities_of_the_Underworld | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10926568-1.html.csv | comparative | in june 2007 , eric geller hosted an episode of " cities of the underworld " before don wildman did . | {'row_1': '7', 'row_2': '9', 'col': '3', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'episode title', 'new york'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose episode title record fuzzily matches to new york .', 'tostr': 'filter_eq { all_rows ; episode title ; new york }'}, 'original airdate'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; episode title ; new york } ; original airdate }', 'tointer': 'select the rows whose episode title record fuzzily matches to new york . take the original airdate record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'episode title', 'beneath vesuvius'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose episode title record fuzzily matches to beneath vesuvius .', 'tostr': 'filter_eq { all_rows ; episode title ; beneath vesuvius }'}, 'original airdate'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; episode title ; beneath vesuvius } ; original airdate }', 'tointer': 'select the rows whose episode title record fuzzily matches to beneath vesuvius . take the original airdate record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; episode title ; new york } ; original airdate } ; hop { filter_eq { all_rows ; episode title ; beneath vesuvius } ; original airdate } } = true', 'tointer': 'select the rows whose episode title record fuzzily matches to new york . take the original airdate record of this row . select the rows whose episode title record fuzzily matches to beneath vesuvius . take the original airdate record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; episode title ; new york } ; original airdate } ; hop { filter_eq { all_rows ; episode title ; beneath vesuvius } ; original airdate } } = true | select the rows whose episode title record fuzzily matches to new york . take the original airdate record of this row . select the rows whose episode title record fuzzily matches to beneath vesuvius . take the original airdate record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'episode title_7': 7, 'new york_8': 8, 'original airdate_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'episode title_11': 11, 'beneath vesuvius_12': 12, 'original airdate_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'episode title_7': 'episode title', 'new york_8': 'new york', 'original airdate_9': 'original airdate', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'episode title_11': 'episode title', 'beneath vesuvius_12': 'beneath vesuvius', 'original airdate_13': 'original airdate'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'episode title_7': [0], 'new york_8': [0], 'original airdate_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'episode title_11': [1], 'beneath vesuvius_12': [1], 'original airdate_13': [3]} | ['production no', 'episode no', 'original airdate', 'episode title', 'host'] | [['pilot / 1', '101', 'march 2 , 2007', 'istanbul', 'eric geller'], ['2', '102', 'april 23 , 2007', "scotland 's sin city", 'eric geller'], ['3', '103', 'april 30 , 2007', "hitler 's underground lair", 'eric geller'], ['4', '104', 'may 7 , 2007', "rome 's hidden empire", 'eric geller'], ['5', '105', 'may 14 , 2007', 'catacombs of death', 'eric geller'], ['6', '106', 'may 21 , 2007', 'city of caves', 'eric geller'], ['7', '107', 'june 4 , 2007', 'new york', 'eric geller'], ['8', '108', 'june 11 , 2007', "london 's lost cities", 'eric geller'], ['9', '109', 'june 18 , 2007', 'beneath vesuvius', 'don wildman'], ['10', '110', 'june 25 , 2007', 'freemason underground', 'don wildman'], ['11', '111', 'july 9 , 2007', "dracula 's underground", 'don wildman'], ['12', '112', 'july 16 , 2007', 'secret pagan underground', 'don wildman'], ['13', '113', 'july 23 , 2007', 'underground bootleggers', 'don wildman'], ['14', '114', 'july 30 , 2007', 'rome : the rise', 'eric geller']] |
2008 - 09 nbl season | https://en.wikipedia.org/wiki/2008%E2%80%9309_NBL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16653153-28.html.csv | superlative | in the 2008-09 nbl season , the game that had the highest attendance was when the south dragons were the home team . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'home team'], 'result': 'south dragons', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; crowd } ; home team }'}, 'south dragons'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; home team } ; south dragons } = true', 'tointer': 'select the row whose crowd record of all rows is maximum . the home team record of this row is south dragons .'} | eq { hop { argmax { all_rows ; crowd } ; home team } ; south dragons } = true | select the row whose crowd record of all rows is maximum . the home team record of this row is south dragons . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, 'home team_6': 6, 'south dragons_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', 'home team_6': 'home team', 'south dragons_7': 'south dragons'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], 'home team_6': [1], 'south dragons_7': [2]} | ['date', 'home team', 'score', 'away team', 'venue', 'crowd', 'box score', 'report'] | [['4 february', 'south dragons', '97 - 77', 'adelaide 36ers', 'hisense arena', '4621', 'box score', '-'], ['4 february', 'sydney spirit', '81 - 89', 'cairns taipans', 'state sports centre', '920', 'box score', '-'], ['6 february', 'townsville crocodiles', '101 - 98', 'south dragons', 'townsville entertainment centre', '4485', 'box score', '-'], ['6 february', 'wollongong hawks', '103 - 98', 'new zealand breakers', 'win entertainment centre', '2740', 'box score', '-'], ['7 february', 'adelaide 36ers', '102 - 91', 'new zealand breakers', 'distinctive homes dome', '8000', 'box score', '-'], ['7 february', 'cairns taipans', '88 - 93', 'gold coast blaze', 'cairns convention centre', '4022', 'box score', '-'], ['7 february', 'perth wildcats', '106 - 88', 'sydney spirit', 'challenge stadium', '4000', 'box score', '-'], ['8 february', 'south dragons', '93 - 83', 'melbourne tigers', 'hisense arena', '8093', 'box score', '-']] |
list of state leaders in 990s bc | https://en.wikipedia.org/wiki/List_of_state_leaders_in_990s_BC | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17310478-8.html.csv | unique | zhong was the only leader who was from cao . | {'scope': 'all', 'row': '1', 'col': '1', 'col_other': '3', 'criterion': 'equal', 'value': 'cao', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'state', 'cao'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose state record fuzzily matches to cao .', 'tostr': 'filter_eq { all_rows ; state ; cao }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; state ; cao } }', 'tointer': 'select the rows whose state record fuzzily matches to cao . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'state', 'cao'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose state record fuzzily matches to cao .', 'tostr': 'filter_eq { all_rows ; state ; cao }'}, 'name'], 'result': 'zhong', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; state ; cao } ; name }'}, 'zhong'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; state ; cao } ; name } ; zhong }', 'tointer': 'the name record of this unqiue row is zhong .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; state ; cao } } ; eq { hop { filter_eq { all_rows ; state ; cao } ; name } ; zhong } } = true', 'tointer': 'select the rows whose state record fuzzily matches to cao . there is only one such row in the table . the name record of this unqiue row is zhong .'} | and { only { filter_eq { all_rows ; state ; cao } } ; eq { hop { filter_eq { all_rows ; state ; cao } ; name } ; zhong } } = true | select the rows whose state record fuzzily matches to cao . there is only one such row in the table . the name record of this unqiue row is zhong . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'state_7': 7, 'cao_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'zhong_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'state_7': 'state', 'cao_8': 'cao', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'zhong_10': 'zhong'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'state_7': [0], 'cao_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'zhong_10': [3]} | ['state', 'type', 'name', 'title', 'royal house', 'from'] | [['cao', 'sovereign', 'zhong', 'lord', '-', '1002 bc'], ['lu', 'sovereign', 'bo qin', 'ruler', 'ji', '1043 bc'], ['lu', 'sovereign', 'kao', 'duke', 'ji', '997 bc'], ['lu', 'sovereign', 'yang', 'duke', 'ji', '993 bc'], ['qi', 'sovereign', 'ding', 'duke', '-', '999 bc']] |
united states house of representatives elections , 1918 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1918 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1346118-5.html.csv | ordinal | john e raker was the 2nd longest tenured representative in the 1918 elections . | {'row': '2', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'first elected', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; first elected ; 2 }'}, 'incumbent'], 'result': 'john e raker', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent }'}, 'john e raker'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; john e raker } = true', 'tointer': 'select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is john e raker .'} | eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; john e raker } = true | select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is john e raker . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'first elected_5': 5, '2_6': 6, 'incumbent_7': 7, 'john e raker_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'first elected_5': 'first elected', '2_6': '2', 'incumbent_7': 'incumbent', 'john e raker_8': 'john e raker'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '2_6': [0], 'incumbent_7': [1], 'john e raker_8': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['california 1', 'clarence f lea', 'democratic', '1916', 're - elected', 'clarence f lea ( d ) ( unopposed )'], ['california 2', 'john e raker', 'democratic', '1910', 're - elected', 'john e raker ( d ) ( unopposed )'], ['california 4', 'julius kahn', 'republican', '1898', 're - elected', 'julius kahn ( r ) 86.6 % william short ( s ) 13.4 %'], ['california 5', 'john i nolan', 'republican', '1912', 're - elected', 'john i nolan ( r ) 87 % thomas f feeley ( s ) 13 %'], ['california 6', 'john a elston', 'progressive', '1912', 're - elected as republican republican gain', 'john a elston ( r ) 88.4 % luella twining ( s ) 11.6 %'], ['california 7', 'denver s church', 'democratic', '1912', 'retired republican gain', 'henry e barbour ( r ) 52.1 % henry hawson ( d ) 47.9 %']] |
mannar district | https://en.wikipedia.org/wiki/Mannar_District | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24574438-1.html.csv | comparative | the popluation density of adampan is higher than the population density of chilawathurai . | {'row_1': '3', 'row_2': '4', 'col': '11', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'main town', 'adampan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose main town record fuzzily matches to adampan .', 'tostr': 'filter_eq { all_rows ; main town ; adampan }'}, 'population density ( / km 2 )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; main town ; adampan } ; population density ( / km 2 ) }', 'tointer': 'select the rows whose main town record fuzzily matches to adampan . take the population density ( / km 2 ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'main town', 'chilawathurai'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose main town record fuzzily matches to chilawathurai .', 'tostr': 'filter_eq { all_rows ; main town ; chilawathurai }'}, 'population density ( / km 2 )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; main town ; chilawathurai } ; population density ( / km 2 ) }', 'tointer': 'select the rows whose main town record fuzzily matches to chilawathurai . take the population density ( / km 2 ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; main town ; adampan } ; population density ( / km 2 ) } ; hop { filter_eq { all_rows ; main town ; chilawathurai } ; population density ( / km 2 ) } } = true', 'tointer': 'select the rows whose main town record fuzzily matches to adampan . take the population density ( / km 2 ) record of this row . select the rows whose main town record fuzzily matches to chilawathurai . take the population density ( / km 2 ) record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; main town ; adampan } ; population density ( / km 2 ) } ; hop { filter_eq { all_rows ; main town ; chilawathurai } ; population density ( / km 2 ) } } = true | select the rows whose main town record fuzzily matches to adampan . take the population density ( / km 2 ) record of this row . select the rows whose main town record fuzzily matches to chilawathurai . take the population density ( / km 2 ) record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'main town_7': 7, 'adampan_8': 8, 'population density ( / km 2 )_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'main town_11': 11, 'chilawathurai_12': 12, 'population density ( / km 2 )_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'main town_7': 'main town', 'adampan_8': 'adampan', 'population density ( / km 2 )_9': 'population density ( / km 2 )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'main town_11': 'main town', 'chilawathurai_12': 'chilawathurai', 'population density ( / km 2 )_13': 'population density ( / km 2 )'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'main town_7': [0], 'adampan_8': [0], 'population density ( / km 2 )_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'main town_11': [1], 'chilawathurai_12': [1], 'population density ( / km 2 )_13': [3]} | ['ds division', 'main town', 'gn divisions', 'area ( km 2 )', 'sri lankan tamil', 'sri lankan moors', 'sinhalese', 'indian tamil', 'other', 'total', 'population density ( / km 2 )'] | [['madhu', 'madhu', '17', '553', '6793', '559', '273', '5', '1', '7631', '14'], ['mannar', 'mannar', '49', '212', '40865', '8982', '953', '131', '6', '50937', '240'], ['manthai west', 'adampan', '36', '608', '12993', '1123', '337', '177', '0', '14630', '24'], ['musali', 'chilawathurai', '20', '475', '3042', '4818', '147', '2', '0', '8009', '17'], ['nanaddan', 'nanaddan', '31', '148', '16875', '605', '251', '79', '34', '17844', '121']] |
melville , saskatchewan | https://en.wikipedia.org/wiki/Melville%2C_Saskatchewan | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1034685-1.html.csv | comparative | the cjjc - fm call sign channel operates on a higher frequency than the cfgw - fm call sign channel . | {'row_1': '5', 'row_2': '4', 'col': '1', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'cjjc - fm'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose call sign record fuzzily matches to cjjc - fm .', 'tostr': 'filter_eq { all_rows ; call sign ; cjjc - fm }'}, 'frequency'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; call sign ; cjjc - fm } ; frequency }', 'tointer': 'select the rows whose call sign record fuzzily matches to cjjc - fm . take the frequency record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'cfgw - fm'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose call sign record fuzzily matches to cfgw - fm .', 'tostr': 'filter_eq { all_rows ; call sign ; cfgw - fm }'}, 'frequency'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; call sign ; cfgw - fm } ; frequency }', 'tointer': 'select the rows whose call sign record fuzzily matches to cfgw - fm . take the frequency record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; call sign ; cjjc - fm } ; frequency } ; hop { filter_eq { all_rows ; call sign ; cfgw - fm } ; frequency } } = true', 'tointer': 'select the rows whose call sign record fuzzily matches to cjjc - fm . take the frequency record of this row . select the rows whose call sign record fuzzily matches to cfgw - fm . take the frequency record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; call sign ; cjjc - fm } ; frequency } ; hop { filter_eq { all_rows ; call sign ; cfgw - fm } ; frequency } } = true | select the rows whose call sign record fuzzily matches to cjjc - fm . take the frequency record of this row . select the rows whose call sign record fuzzily matches to cfgw - fm . take the frequency record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'call sign_7': 7, 'cjjc - fm_8': 8, 'frequency_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'call sign_11': 11, 'cfgw - fm_12': 12, 'frequency_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'call sign_7': 'call sign', 'cjjc - fm_8': 'cjjc - fm', 'frequency_9': 'frequency', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'call sign_11': 'call sign', 'cfgw - fm_12': 'cfgw - fm', 'frequency_13': 'frequency'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'call sign_7': [0], 'cjjc - fm_8': [0], 'frequency_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'call sign_11': [1], 'cfgw - fm_12': [1], 'frequency_13': [3]} | ['frequency', 'call sign', 'branding', 'format', 'owner'] | [['am 940', 'cjgx', 'gx94', 'country music', 'harvard broadcasting'], ['fm 91.7', 'cbk - fm3', 'cbc radio 2', 'public broadcasting', 'canadian broadcasting corporation'], ['fm 92.9', 'cjlr - fm - 5', 'mbc radio', 'first nationscommunity radio', 'missinipi broadcasting corporation'], ['fm 94.1', 'cfgw - fm', 'fox fm', 'hot adult contemporary', 'harvard broadcasting'], ['fm 98.5', 'cjjc - fm', '98.5 the rock', 'christian music', 'dennis m dyck']] |
1949 vfl season | https://en.wikipedia.org/wiki/1949_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809351-8.html.csv | count | two of the venues in the 1949 vfl season drew a crowd size of 12500 . | {'scope': 'all', 'criterion': 'equal', 'value': '12500', 'result': '2', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'crowd', '12500'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose crowd record is equal to 12500 .', 'tostr': 'filter_eq { all_rows ; crowd ; 12500 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; crowd ; 12500 } }', 'tointer': 'select the rows whose crowd record is equal to 12500 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; crowd ; 12500 } } ; 2 } = true', 'tointer': 'select the rows whose crowd record is equal to 12500 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; crowd ; 12500 } } ; 2 } = true | select the rows whose crowd record is equal to 12500 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '12500_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '12500_6': '12500', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '12500_6': [0], '2_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '10.15 ( 75 )', 'richmond', '11.13 ( 79 )', 'kardinia park', '22500', '4 june 1949'], ['collingwood', '21.22 ( 148 )', 'st kilda', '4.12 ( 36 )', 'victoria park', '12000', '4 june 1949'], ['carlton', '14.13 ( 97 )', 'north melbourne', '10.7 ( 67 )', 'princes park', '29500', '4 june 1949'], ['melbourne', '10.17 ( 77 )', 'hawthorn', '10.6 ( 66 )', 'mcg', '11000', '4 june 1949'], ['south melbourne', '12.7 ( 79 )', 'fitzroy', '14.14 ( 98 )', 'lake oval', '12500', '4 june 1949'], ['footscray', '7.17 ( 59 )', 'essendon', '10.12 ( 72 )', 'western oval', '12500', '4 june 1949']] |
taniec z gwiazdami | https://en.wikipedia.org/wiki/Taniec_z_gwiazdami | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15988037-19.html.csv | superlative | janja lesar is the professional partner who recorded the highest average scoring dances in the taniec z gwiazdami competition . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'average'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; average }'}, 'professional partner'], 'result': 'janja lesar', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; average } ; professional partner }'}, 'janja lesar'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; average } ; professional partner } ; janja lesar } = true', 'tointer': 'select the row whose average record of all rows is maximum . the professional partner record of this row is janja lesar .'} | eq { hop { argmax { all_rows ; average } ; professional partner } ; janja lesar } = true | select the row whose average record of all rows is maximum . the professional partner record of this row is janja lesar . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'average_5': 5, 'professional partner_6': 6, 'janja lesar_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'average_5': 'average', 'professional partner_6': 'professional partner', 'janja lesar_7': 'janja lesar'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'average_5': [0], 'professional partner_6': [1], 'janja lesar_7': [2]} | ['rank', 'celebrity', 'professional partner', 'season', 'average'] | [['1', 'maciej jachowski', 'janja lesar', '12', '32.0'], ['2', 'stachursky', 'dominika kublik - marzec', '6', '29.0'], ['3', 'przemysław miarczyński', 'magdalena soszyńska - michno', '11', '28.5'], ['4', 'piotr adamski', 'blanka winiarska', '2', '28.0'], ['5', 'paweł stasiak', 'janja lesar', '8', '27.0'], ['5', 'marek kościkiewicz', 'agnieszka pomorska', '10', '27.0'], ['6', 'wojciech majchrzak', 'magdalena soszyńska - michno', '5', '25.5'], ['7', 'michał milowicz', 'izabela mika', '4', '25.0'], ['7', 'michał lesień', 'katarzyna krupa', '7', '25.0'], ['8', 'robert kudelski', 'agnieszka pomorska', '1', '24.0'], ['9', 'paolo cozza', 'kamila drezno', '3', '18.5'], ['10', 'zbigniew urbański', 'izabela janachowska', '13', '13.0']] |
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