topic
stringlengths 3
96
| wiki
stringlengths 33
127
| url
stringlengths 101
106
| action
stringclasses 7
values | sent
stringlengths 34
223
| annotation
stringlengths 74
227
| logic
stringlengths 207
5.45k
| logic_str
stringlengths 37
493
| interpret
stringlengths 43
471
| num_func
stringclasses 15
values | nid
stringclasses 13
values | g_ids
stringlengths 70
455
| g_ids_features
stringlengths 98
670
| g_adj
stringlengths 79
515
| table_header
stringlengths 40
458
| table_cont
large_stringlengths 135
4.41k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
wafj | https://en.wikipedia.org/wiki/WAFJ | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12499438-1.html.csv | unique | for wafj , when the class is d , the only time the city is sparta is when the frequency is 98.7 . | {'scope': 'subset', 'row': '5', 'col': '2', 'col_other': '3', 'criterion': 'equal', 'value': '98.7', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'd'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', 'd'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; class ; d }', 'tointer': 'select the rows whose class record fuzzily matches to d .'}, 'frequency mhz', '98.7'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose class record fuzzily matches to d . among these rows , select the rows whose frequency mhz record is equal to 98.7 .', 'tostr': 'filter_eq { filter_eq { all_rows ; class ; d } ; frequency mhz ; 98.7 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; class ; d } ; frequency mhz ; 98.7 } }', 'tointer': 'select the rows whose class record fuzzily matches to d . among these rows , select the rows whose frequency mhz record is equal to 98.7 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', 'd'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; class ; d }', 'tointer': 'select the rows whose class record fuzzily matches to d .'}, 'frequency mhz', '98.7'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose class record fuzzily matches to d . among these rows , select the rows whose frequency mhz record is equal to 98.7 .', 'tostr': 'filter_eq { filter_eq { all_rows ; class ; d } ; frequency mhz ; 98.7 }'}, 'city of license'], 'result': 'sparta , georgia', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; class ; d } ; frequency mhz ; 98.7 } ; city of license }'}, 'sparta , georgia'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; class ; d } ; frequency mhz ; 98.7 } ; city of license } ; sparta , georgia }', 'tointer': 'the city of license record of this unqiue row is sparta , georgia .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; class ; d } ; frequency mhz ; 98.7 } } ; eq { hop { filter_eq { filter_eq { all_rows ; class ; d } ; frequency mhz ; 98.7 } ; city of license } ; sparta , georgia } } = true', 'tointer': 'select the rows whose class record fuzzily matches to d . among these rows , select the rows whose frequency mhz record is equal to 98.7 . there is only one such row in the table . the city of license record of this unqiue row is sparta , georgia .'} | and { only { filter_eq { filter_eq { all_rows ; class ; d } ; frequency mhz ; 98.7 } } ; eq { hop { filter_eq { filter_eq { all_rows ; class ; d } ; frequency mhz ; 98.7 } ; city of license } ; sparta , georgia } } = true | select the rows whose class record fuzzily matches to d . among these rows , select the rows whose frequency mhz record is equal to 98.7 . there is only one such row in the table . the city of license record of this unqiue row is sparta , georgia . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'class_8': 8, 'd_9': 9, 'frequency mhz_10': 10, '98.7_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'city of license_12': 12, 'sparta , georgia_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_eq_1': 'filter_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'class_8': 'class', 'd_9': 'd', 'frequency mhz_10': 'frequency mhz', '98.7_11': '98.7', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'city of license_12': 'city of license', 'sparta , georgia_13': 'sparta , georgia'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'class_8': [0], 'd_9': [0], 'frequency mhz_10': [1], '98.7_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'city of license_12': [3], 'sparta , georgia_13': [4]} | ['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info'] | [['wzae', '93.3', 'wadley , georgia', '4000', 'a', 'fcc'], ['w257bg', '99.3', 'statesboro , georgia', '80', 'd', 'fcc'], ['w252bh', '98.3', 'washington , georgia', '27', 'd', 'fcc'], ['w224be', '92.7', 'sylvania , georgia', '27', 'd', 'fcc'], ['w254bn', '98.7', 'sparta , georgia', '55', 'd', 'fcc'], ['w245an', '96.9', 'milledgeville , georgia', '19', 'd', 'fcc']] |
list of carnivàle episodes | https://en.wikipedia.org/wiki/List_of_Carniv%C3%A0le_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12722302-3.html.csv | unique | the only episode of carnivale that was directed by tucker gates , was the one titled " the road to damascus " . | {'scope': 'all', 'row': '6', 'col': '4', 'col_other': '3', 'criterion': 'equal', 'value': 'tucker gates', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'tucker gates'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose directed by record fuzzily matches to tucker gates .', 'tostr': 'filter_eq { all_rows ; directed by ; tucker gates }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; directed by ; tucker gates } }', 'tointer': 'select the rows whose directed by record fuzzily matches to tucker gates . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'tucker gates'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose directed by record fuzzily matches to tucker gates .', 'tostr': 'filter_eq { all_rows ; directed by ; tucker gates }'}, 'title'], 'result': 'the road to damascus', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; directed by ; tucker gates } ; title }'}, 'the road to damascus'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; directed by ; tucker gates } ; title } ; the road to damascus }', 'tointer': 'the title record of this unqiue row is the road to damascus .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; directed by ; tucker gates } } ; eq { hop { filter_eq { all_rows ; directed by ; tucker gates } ; title } ; the road to damascus } } = true', 'tointer': 'select the rows whose directed by record fuzzily matches to tucker gates . there is only one such row in the table . the title record of this unqiue row is the road to damascus .'} | and { only { filter_eq { all_rows ; directed by ; tucker gates } } ; eq { hop { filter_eq { all_rows ; directed by ; tucker gates } ; title } ; the road to damascus } } = true | select the rows whose directed by record fuzzily matches to tucker gates . there is only one such row in the table . the title record of this unqiue row is the road to damascus . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'directed by_7': 7, 'tucker gates_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'the road to damascus_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'directed by_7': 'directed by', 'tucker gates_8': 'tucker gates', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'the road to damascus_10': 'the road to damascus'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'directed by_7': [0], 'tucker gates_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'the road to damascus_10': [3]} | ['no', '-', 'title', 'directed by', 'written by', 'bens location', 'original air date', 'us viewers ( million )'] | [['13', '1', 'los moscos', 'jeremy podeswa', 'daniel knauf', 'loving , new mexico', 'january 9 , 2005', '1.81'], ['14', '2', 'alamogordo , nm', 'jack bender', 'william schmidt', 'alamogordo , new mexico', 'january 16 , 2005', 'n / a'], ['15', '3', 'ingram , tx', 'john patterson', 'john j mclaughlin', 'ingram , texas', 'january 23 , 2005', 'n / a'], ['16', '4', 'old cherry blossom road', 'steve shill', 'dawn prestwich & nicole yorkin', 'ingram , texas', 'january 30 , 2005', 'n / a'], ['17', '5', 'creed , ok', 'jeremy podeswa', 'tracy tormé', 'creed , oklahoma', 'february 6 , 2005', 'n / a'], ['18', '6', 'the road to damascus', 'tucker gates', 'dawn prestwich & nicole yorkin', 'n / a', 'february 13 , 2005', '1.5'], ['20', '8', 'outskirts , damascus , ne', 'tim hunter', 'daniel knauf', 'damascus , nebraska', 'february 27 , 2005', 'n / a'], ['21', '9', 'lincoln highway', 'rodrigo garcía', 'william schmidt', 'lincoln highway , wyoming', 'march 6 , 2005', '1.96'], ['22', '10', 'cheyenne , wy', 'todd field', 'tracy tormé', 'cheyenne , wyoming', 'march 13 , 2005', 'n / a']] |
new york state election , 1966 | https://en.wikipedia.org/wiki/New_York_state_election%2C_1966 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15558974-1.html.csv | count | in the 1966 new york state election , the social labor ticket did not run candidates in two races . | {'scope': 'all', 'criterion': 'equal', 'value': '( none )', 'result': '2', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'socialist labor ticket', '( none )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose socialist labor ticket record fuzzily matches to ( none ) .', 'tostr': 'filter_eq { all_rows ; socialist labor ticket ; ( none ) }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; socialist labor ticket ; ( none ) } }', 'tointer': 'select the rows whose socialist labor ticket record fuzzily matches to ( none ) . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; socialist labor ticket ; ( none ) } } ; 2 } = true', 'tointer': 'select the rows whose socialist labor ticket record fuzzily matches to ( none ) . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; socialist labor ticket ; ( none ) } } ; 2 } = true | select the rows whose socialist labor ticket record fuzzily matches to ( none ) . 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, 'socialist labor ticket_5': 5, '(none)_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', 'socialist labor ticket_5': 'socialist labor ticket', '(none)_6': '( none )', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'socialist labor ticket_5': [0], '(none)_6': [0], '2_7': [2]} | ['office', 'republican ticket', 'democratic ticket', 'conservative ticket', 'liberal ticket', 'socialist labor ticket', 'socialist workers ticket'] | [['governor', 'nelson a rockefeller', "frank d o'connor", 'paul l adams', 'franklin d roosevelt , jr', 'milton herder', 'judith white'], ['lieutenant governor', 'malcolm wilson', 'howard j samuels', "kieran o'doherty", 'donald s harrington', 'doris ballantyne', 'richard garza'], ['comptroller', 'charles t lanigan', 'arthur levitt', 'benjamin r crosby', 'arthur levitt', 'john emanuel', 'ralph levitt'], ['attorney general', 'louis j lefkowitz', 'frank a sedita', 'mason l hampton , jr', 'simeon golar', '( none )', 'paul boutelle'], ['chief judge', 'stanley h fuld', 'stanley h fuld', 'stanley h fuld', 'stanley h fuld', '( none )', '( none )']] |
1974 vfl season | https://en.wikipedia.org/wiki/1974_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10869646-9.html.csv | unique | only the richmond vs. collingwood game took place in mcg . | {'scope': 'all', 'row': '5', 'col': '5', 'col_other': '1,3', 'criterion': 'equal', 'value': 'mcg', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', '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 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; venue ; mcg } }', 'tointer': 'select the rows whose venue record fuzzily matches to mcg . 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', 'mcg'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to mcg .', 'tostr': 'filter_eq { all_rows ; venue ; mcg }'}, 'home team'], 'result': 'richmond', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; mcg } ; home team }'}, 'richmond'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; venue ; mcg } ; home team } ; richmond }', 'tointer': 'the home team record of this unqiue row is richmond .'}, {'func': 'str_eq', 'args': [{'func': 'str_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 }'}, 'away team'], 'result': 'collingwood', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; venue ; mcg } ; away team }'}, 'collingwood'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; venue ; mcg } ; away team } ; collingwood }', 'tointer': 'the away team record of this unqiue row is collingwood .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; venue ; mcg } ; home team } ; richmond } ; eq { hop { filter_eq { all_rows ; venue ; mcg } ; away team } ; collingwood } }', 'tointer': 'the home team record of this unqiue row is richmond . the away team record of this unqiue row is collingwood .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; venue ; mcg } } ; and { eq { hop { filter_eq { all_rows ; venue ; mcg } ; home team } ; richmond } ; eq { hop { filter_eq { all_rows ; venue ; mcg } ; away team } ; collingwood } } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to mcg . there is only one such row in the table . the home team record of this unqiue row is richmond . the away team record of this unqiue row is collingwood .'} | and { only { filter_eq { all_rows ; venue ; mcg } } ; and { eq { hop { filter_eq { all_rows ; venue ; mcg } ; home team } ; richmond } ; eq { hop { filter_eq { all_rows ; venue ; mcg } ; away team } ; collingwood } } } = true | select the rows whose venue record fuzzily matches to mcg . there is only one such row in the table . the home team record of this unqiue row is richmond . the away team record of this unqiue row is collingwood . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'venue_10': 10, 'mcg_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'home team_12': 12, 'richmond_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'away team_14': 14, 'collingwood_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', 'mcg_11': 'mcg', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'home team_12': 'home team', 'richmond_13': 'richmond', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'away team_14': 'away team', 'collingwood_15': 'collingwood'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'venue_10': [0], 'mcg_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'home team_12': [2], 'richmond_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'away team_14': [4], 'collingwood_15': [5]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '18.15 ( 123 )', 'st kilda', '10.16 ( 76 )', 'princes park', '12630', '1 june 1974'], ['geelong', '16.12 ( 108 )', 'south melbourne', '17.7 ( 109 )', 'kardinia park', '15664', '1 june 1974'], ['footscray', '13.16 ( 94 )', 'melbourne', '8.8 ( 56 )', 'western oval', '15415', '1 june 1974'], ['north melbourne', '11.15 ( 81 )', 'essendon', '16.15 ( 111 )', 'arden street oval', '20027', '1 june 1974'], ['richmond', '9.20 ( 74 )', 'collingwood', '21.17 ( 143 )', 'mcg', '66829', '1 june 1974'], ['carlton', '16.15 ( 111 )', 'fitzroy', '7.10 ( 52 )', 'vfl park', '19906', '1 june 1974']] |
list of england national rugby union team results 1960 - 69 | https://en.wikipedia.org/wiki/List_of_England_national_rugby_union_team_results_1960%E2%80%9369 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18179114-2.html.csv | unique | the game of the england national rugby union team against south africa was the only test game in 1961 . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'test match', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'status', 'test match'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose status record fuzzily matches to test match .', 'tostr': 'filter_eq { all_rows ; status ; test match }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; status ; test match } }', 'tointer': 'select the rows whose status record fuzzily matches to test match . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'status', 'test match'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose status record fuzzily matches to test match .', 'tostr': 'filter_eq { all_rows ; status ; test match }'}, 'opposing teams'], 'result': 'south africa', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; status ; test match } ; opposing teams }'}, 'south africa'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; status ; test match } ; opposing teams } ; south africa }', 'tointer': 'the opposing teams record of this unqiue row is south africa .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; status ; test match } } ; eq { hop { filter_eq { all_rows ; status ; test match } ; opposing teams } ; south africa } } = true', 'tointer': 'select the rows whose status record fuzzily matches to test match . there is only one such row in the table . the opposing teams record of this unqiue row is south africa .'} | and { only { filter_eq { all_rows ; status ; test match } } ; eq { hop { filter_eq { all_rows ; status ; test match } ; opposing teams } ; south africa } } = true | select the rows whose status record fuzzily matches to test match . there is only one such row in the table . the opposing teams record of this unqiue row is south africa . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'status_7': 7, 'test match_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'opposing teams_9': 9, 'south africa_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'status_7': 'status', 'test match_8': 'test match', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'opposing teams_9': 'opposing teams', 'south africa_10': 'south africa'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'status_7': [0], 'test match_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'opposing teams_9': [2], 'south africa_10': [3]} | ['opposing teams', 'against', 'date', 'venue', 'status'] | [['south africa', '5', '07 / 01 / 1961', 'twickenham , london', 'test match'], ['wales', '6', '21 / 01 / 1961', 'cardiff arms park , cardiff', 'five nations'], ['ireland', '11', '11 / 02 / 1961', 'lansdowne road , dublin', 'five nations'], ['france', '5', '25 / 02 / 1961', 'twickenham , london', 'five nations'], ['scotland', '0', '18 / 03 / 1961', 'twickenham , london', 'five nations']] |
iran at the asian games | https://en.wikipedia.org/wiki/Iran_at_the_Asian_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10882501-1.html.csv | aggregation | the average rank for iran in asian games they participated in was 5.53 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '5.53', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'rank'], 'result': '5.53', 'ind': 0, 'tostr': 'avg { all_rows ; rank }'}, '5.53'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; rank } ; 5.53 } = true', 'tointer': 'the average of the rank record of all rows is 5.53 .'} | round_eq { avg { all_rows ; rank } ; 5.53 } = true | the average of the rank record of all rows is 5.53 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'rank_4': 4, '5.53_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'rank_4': 'rank', '5.53_5': '5.53'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'rank_4': [0], '5.53_5': [1]} | ['games', 'gold', 'silver', 'bronze', 'total', 'rank'] | [['1951 new delhi', '8', '6', '2', '16', '3'], ['1954 manila', 'did not participate', 'did not participate', 'did not participate', 'did not participate', 'did not participate'], ['1958 tokyo', '7', '14', '11', '32', '4'], ['1962 jakarta', 'did not participate', 'did not participate', 'did not participate', 'did not participate', 'did not participate'], ['1966 bangkok', '6', '8', '17', '31', '6'], ['1970 bangkok', '9', '7', '7', '23', '4'], ['1974 tehran', '36', '28', '17', '81', '2'], ['1978 bangkok', 'did not participate', 'did not participate', 'did not participate', 'did not participate', 'did not participate'], ['1982 new delhi', '4', '4', '4', '12', '7'], ['1986 seoul', '6', '6', '10', '22', '4'], ['1990 beijing', '4', '6', '8', '18', '5'], ['1994 hiroshima', '9', '9', '8', '26', '6'], ['1998 bangkok', '10', '11', '13', '34', '7'], ['2002 busan', '8', '14', '14', '36', '10'], ['2006 doha', '11', '15', '22', '48', '6'], ['2010 guangzhou', '20', '15', '24', '59', '4'], ['total', '138', '143', '157', '438', '4']] |
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 | superlative | among away teams in the 1972 vfl season , footscray had the highest recorded score . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '5', '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', 'away team score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; away team score }'}, 'away team'], 'result': 'footscray', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; away team score } ; away team }'}, 'footscray'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; away team score } ; away team } ; footscray } = true', 'tointer': 'select the row whose away team score record of all rows is maximum . the away team record of this row is footscray .'} | eq { hop { argmax { all_rows ; away team score } ; away team } ; footscray } = true | select the row whose away team score record of all rows is maximum . the away team record of this row is footscray . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'away team score_5': 5, 'away team_6': 6, 'footscray_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'away team score_5': 'away team score', 'away team_6': 'away team', 'footscray_7': 'footscray'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'away team score_5': [0], 'away team_6': [1], 'footscray_7': [2]} | ['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']] |
1989 all - ireland senior hurling championship | https://en.wikipedia.org/wiki/1989_All-Ireland_Senior_Hurling_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12296897-3.html.csv | superlative | in the 1989 all - ireland senior hurling championship , finbarr delaney had the lowest total among all players with 8.00 average . | {'scope': 'subset', 'col_superlative': '5', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2,7', 'subset': {'col': '7', 'criterion': 'equal', 'value': '8.0'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'average', '8.0'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; average ; 8.0 }', 'tointer': 'select the rows whose average record is equal to 8.0 .'}, 'total'], 'result': None, 'ind': 1, 'tostr': 'argmin { filter_eq { all_rows ; average ; 8.0 } ; total }'}, 'player'], 'result': 'finbarr delaney', 'ind': 2, 'tostr': 'hop { argmin { filter_eq { all_rows ; average ; 8.0 } ; total } ; player }'}, 'finbarr delaney'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmin { filter_eq { all_rows ; average ; 8.0 } ; total } ; player } ; finbarr delaney } = true', 'tointer': 'select the rows whose average record is equal to 8.0 . select the row whose total record of these rows is minimum . the player record of this row is finbarr delaney .'} | eq { hop { argmin { filter_eq { all_rows ; average ; 8.0 } ; total } ; player } ; finbarr delaney } = true | select the rows whose average record is equal to 8.0 . select the row whose total record of these rows is minimum . the player record of this row is finbarr delaney . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmin_1': 1, 'filter_eq_0': 0, 'all_rows_5': 5, 'average_6': 6, '8.0_7': 7, 'total_8': 8, 'player_9': 9, 'finbarr delaney_10': 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmin_1': 'argmin', 'filter_eq_0': 'filter_eq', 'all_rows_5': 'all_rows', 'average_6': 'average', '8.0_7': '8.0', 'total_8': 'total', 'player_9': 'player', 'finbarr delaney_10': 'finbarr delaney'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmin_1': [2], 'filter_eq_0': [1], 'all_rows_5': [0], 'average_6': [0], '8.0_7': [0], 'total_8': [1], 'player_9': [2], 'finbarr delaney_10': [3]} | ['rank', 'player', 'county', 'tally', 'total', 'matches', 'average'] | [['1', 'nicky english', 'tipperary', '4 - 38', '50', '4', '12.50'], ['2', 'adrian ronan', 'kilkenny', '1 - 21', '24', '3', '8.00'], ['2', 'mark corrigan', 'offaly', '4 - 12', '24', '3', '8.00'], ['4', 'finbarr delaney', 'cork', '1 - 19', '23', '2', '8.00'], ['5', 'pat fox', 'tipperary', '3 - 11', '20', '4', '5.00']] |
2008 - 09 kansas jayhawks men 's basketball team | https://en.wikipedia.org/wiki/2008%E2%80%9309_Kansas_Jayhawks_men%27s_basketball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17728794-2.html.csv | superlative | the heaviest player on the 09 kansas jayhawks men 's basketball team is matt kleinmen weighing 247 pounds . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '7', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'weight'], 'result': '247', 'ind': 0, 'tostr': 'max { all_rows ; weight }', 'tointer': 'the maximum weight record of all rows is 247 .'}, '247'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; weight } ; 247 }', 'tointer': 'the maximum weight record of all rows is 247 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'weight'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; weight }'}, 'name'], 'result': 'matt kleinmann', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; weight } ; name }'}, 'matt kleinmann'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; weight } ; name } ; matt kleinmann }', 'tointer': 'the name record of the row with superlative weight record is matt kleinmann .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; weight } ; 247 } ; eq { hop { argmax { all_rows ; weight } ; name } ; matt kleinmann } } = true', 'tointer': 'the maximum weight record of all rows is 247 . the name record of the row with superlative weight record is matt kleinmann .'} | and { eq { max { all_rows ; weight } ; 247 } ; eq { hop { argmax { all_rows ; weight } ; name } ; matt kleinmann } } = true | the maximum weight record of all rows is 247 . the name record of the row with superlative weight record is matt kleinmann . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'weight_8': 8, '247_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'weight_11': 11, 'name_12': 12, 'matt kleinmann_13': 13} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'weight_8': 'weight', '247_9': '247', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'weight_11': 'weight', 'name_12': 'name', 'matt kleinmann_13': 'matt kleinmann'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'weight_8': [0], '247_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'weight_11': [2], 'name_12': [3], 'matt kleinmann_13': [4]} | ['name', 'position', 'height', 'weight', 'year', 'home town'] | [['cole aldrich', 'center', '6 - 11', '245', 'sophomore', 'bloomington , mn'], ['tyrone appleton', 'guard', '6 - 3', '190', 'junior', 'midland , texas'], ['brennan bechard', 'guard', '6 - 0', '183', 'senior', 'lawrence , ks'], ['chase buford', 'guard', '6 - 3', '200', 'sophomore', 'san antonio , texas'], ['sherron collins', 'guard', '5 - 11', '200', 'junior', 'chicago , il'], ['jordan juenemann', 'guard', '6 - 4', '195', 'freshman', 'hays , ks'], ['matt kleinmann', 'center', '6 - 10', '247', 'senior', 'overland park , ks'], ['mario little', 'forward', '6 - 5', '210', 'junior', 'marianna , fl'], ['brady morningstar', 'guard', '6 - 3', '185', 'sophomore', 'lawrence , ks'], ['marcus morris', 'forward', '6 - 8', '230', 'freshman', 'pennsauken , nj'], ['markieff morris', 'forward', '6 - 9', '220', 'freshman', 'pennsauken , nj'], ['tyrel reed', 'guard', '6 - 3', '180', 'sophomore', 'burlington , ks'], ['travis releford', 'guard', '6 - 5', '190', 'freshman', 'shawnee mission , ks'], ['tyshawn taylor', 'guard', '6 - 3', '160', 'freshman', 'jersey city , nj'], ['conner teahan', 'guard', '6 - 5', '200', 'sophomore', 'leawood , ks'], ['quintrell thomas', 'forward', '6 - 8', '225', 'freshman', 'elizabeth , nj']] |
list of ottawa senators draft picks | https://en.wikipedia.org/wiki/List_of_Ottawa_Senators_draft_picks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11803648-17.html.csv | aggregation | ottawa senators had a total number of 503 picks overall . | {'scope': 'all', 'col': '2', 'type': 'sum', 'result': '503', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'overall'], 'result': '503', 'ind': 0, 'tostr': 'sum { all_rows ; overall }'}, '503'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; overall } ; 503 } = true', 'tointer': 'the sum of the overall record of all rows is 503 .'} | round_eq { sum { all_rows ; overall } ; 503 } = true | the sum of the overall record of all rows is 503 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'overall_4': 4, '503_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'overall_4': 'overall', '503_5': '503'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'overall_4': [0], '503_5': [1]} | ['round', 'overall', 'player', 'position', 'nationality', 'club team'] | [['1', '15', 'erik karlsson', 'defence', 'sweden', 'frãlunda hc ( gothenburg ) ( sweden )'], ['2', '42', 'patrick wiercioch', 'defence', 'canada', 'omaha ( ushl )'], ['3', '79', 'zack smith', 'center', 'canada', 'swift current broncos ( whl )'], ['4', '109', 'andre petersson', 'forward', 'sweden', 'hv71 ( sweden )'], ['4', '119', 'derek grant', 'center', 'canada', 'langley chiefs ( bchl )'], ['5', '139', 'mark borowiecki', 'defence', 'canada', 'smiths falls bears ( cjhl )']] |
telmex grand prix of monterrey | https://en.wikipedia.org/wiki/2004_Tecate/Telmex_Grand_Prix_of_Monterrey | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16326318-1.html.csv | ordinal | sébastien bourdais had the second fastest time in the first qualifying race of the telmex grand prix of monterrey . | {'row': '1', 'col': '3', 'order': '2', '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', 'qual 1', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; qual 1 ; 2 }'}, 'name'], 'result': 'sébastien bourdais', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; qual 1 ; 2 } ; name }'}, 'sébastien bourdais'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; qual 1 ; 2 } ; name } ; sébastien bourdais } = true', 'tointer': 'select the row whose qual 1 record of all rows is 2nd minimum . the name record of this row is sébastien bourdais .'} | eq { hop { nth_argmin { all_rows ; qual 1 ; 2 } ; name } ; sébastien bourdais } = true | select the row whose qual 1 record of all rows is 2nd minimum . the name record of this row is sébastien bourdais . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'qual 1_5': 5, '2_6': 6, 'name_7': 7, 'sébastien bourdais_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', 'qual 1_5': 'qual 1', '2_6': '2', 'name_7': 'name', 'sébastien bourdais_8': 'sébastien bourdais'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'qual 1_5': [0], '2_6': [0], 'name_7': [1], 'sébastien bourdais_8': [2]} | ['name', 'team', 'qual 1', 'qual 2', 'best'] | [['sébastien bourdais', 'newman / haas racing', '1:15.978', '1:13.915', '1:13.915'], ['mario domínguez', 'herdez competition', '1:16.422', '1:14.343', '1:14.343'], ['justin wilson', 'mi - jack conquest racing', '1:16.087', '1:14.354', '1:14.354'], ['bruno junqueira', 'newman / haas racing', '1:15.834', '1:14.405', '1:14.405'], ['patrick carpentier', 'forsythe racing', '1:16.617', '1:14.625', '1:14.625'], ['paul tracy', 'forsythe racing', '1:16.417', '1:14.723', '1:14.723'], ['jimmy vasser', 'pkv racing', '1:16.620', '1:15.183', '1:15.183'], ['ryan hunter - reay', 'herdez competition', '1:17.637', '1:15.265', '1:15.265'], ['oriol servià', 'dale coyne racing', '1:17.890', '1:15.395', '1:15.395'], ['tarso marques', 'dale coyne racing', '1:18.100', '1:15.582', '1:15.582'], ['a j allmendinger', 'rusport', '1:17.644', '1:15.673', '1:15.673'], ['roberto gonzález', 'pkv racing', '1:18.154', '1:15.791', '1:15.791'], ['michel jourdain , jr', 'rusport', '1:17.873', '1:15.805', '1:15.805'], ['rodolfo lavín', 'forsythe racing', '1:18.553', '1:16.096', '1:16.096'], ['alex tagliani', 'rocketsports racing', '1:16.712', '1:16.103', '1:16.103'], ['mario haberfeld', 'walker racing', '1:16.491', '1:16.691', '1:16.491'], ['nelson philippe', 'rocketsports racing', '1:18.373', '1:17.191', '1:17.191'], ['alex sperafico', 'mi - jack conquest racing', '1:20.139', '1:17.736', '1:17.736']] |
2007 - 08 chelsea f.c. season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Chelsea_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11927320-3.html.csv | count | chelsea was the runner-up in four of the competitions . | {'scope': 'all', 'criterion': 'equal', 'value': 'runner - up', 'result': '4', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'final position / round', 'runner - up'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose final position / round record fuzzily matches to runner - up .', 'tostr': 'filter_eq { all_rows ; final position / round ; runner - up }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; final position / round ; runner - up } }', 'tointer': 'select the rows whose final position / round record fuzzily matches to runner - up . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; final position / round ; runner - up } } ; 4 } = true', 'tointer': 'select the rows whose final position / round record fuzzily matches to runner - up . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; final position / round ; runner - up } } ; 4 } = true | select the rows whose final position / round record fuzzily matches to runner - up . 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, 'final position / round_5': 5, 'runner - up_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', 'final position / round_5': 'final position / round', 'runner - up_6': 'runner - up', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'final position / round_5': [0], 'runner - up_6': [0], '4_7': [2]} | ['competition', 'current position / round', 'final position / round', 'first match', 'last match'] | [['fa community shield', '-', 'runner - up', '5 aug 2007', '5 aug 2007'], ['premier league', '-', 'runner - up', '12 aug 2007', '11 may 2008'], ['uefa champions league', '-', 'runner - up', '18 sep 2007', '21 may 2008'], ['football league cup', '-', 'runner - up', '24 sep 2007', '24 feb 2008'], ['fa cup', '-', 'quarter - finals', '5 jan 2008', '3 mar 2008']] |
list of fc barcelona records and statistics | https://en.wikipedia.org/wiki/List_of_FC_Barcelona_records_and_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14707564-7.html.csv | majority | the majority of players in the list of fc barcelona records and statistics have spain nationality . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'spain', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'nationality', 'spain'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , most of them fuzzily match to spain .', 'tostr': 'most_eq { all_rows ; nationality ; spain } = true'} | most_eq { all_rows ; nationality ; spain } = true | for the nationality records of all rows , most of them fuzzily match to spain . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'spain_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'spain_4': 'spain'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'spain_4': [0]} | ['ranking', 'nationality', 'name', 'goals', 'years'] | [['1', 'philippines', 'paulino alcántara', '369', '1912 - 1916 , 1918 - 1927'], ['2', 'argentina', 'lionel messi', '352', '2004 -'], ['3', 'spain', 'josep samitier', '333', '1919 - 1932'], ['4', 'spain', 'césar rodríguez', '301', '1942 - 1955'], ['5', 'hungary', 'ladislao kubala', '280', '1950 - 1961'], ['6', 'spain', 'josep escolà', '223', '1934 - 1949'], ['7', 'spain', 'ángel arocha', '215', '1926 - 1933'], ['8', 'spain', 'vicenç martínez', '200', '1912 - 1923'], ['9', 'spain', 'carles rexach', '195', '1965 - 1981'], ['10', 'spain', 'mariano martín', '188', '1939 - 1946']] |
1934 vfl season | https://en.wikipedia.org/wiki/1934_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10790510-12.html.csv | count | one of the away team scores in the 1934 vfl season was less than 10 . | {'scope': 'all', 'criterion': 'less_than', 'value': '10', 'result': '1', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'away team score', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose away team score record is less than 10 .', 'tostr': 'filter_less { all_rows ; away team score ; 10 }'}], 'result': '1', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; away team score ; 10 } }', 'tointer': 'select the rows whose away team score record is less than 10 . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; away team score ; 10 } } ; 1 } = true', 'tointer': 'select the rows whose away team score record is less than 10 . the number of such rows is 1 .'} | eq { count { filter_less { all_rows ; away team score ; 10 } } ; 1 } = true | select the rows whose away team score record is less than 10 . the number of such rows is 1 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'away team score_5': 5, '10_6': 6, '1_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'away team score_5': 'away team score', '10_6': '10', '1_7': '1'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'away team score_5': [0], '10_6': [0], '1_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '13.23 ( 101 )', 'richmond', '14.11 ( 95 )', 'mcg', '13805', '28 july 1934'], ['collingwood', '13.19 ( 97 )', 'south melbourne', '21.19 ( 145 )', 'victoria park', '28000', '28 july 1934'], ['carlton', '22.13 ( 145 )', 'hawthorn', '10.6 ( 66 )', 'princes park', '12000', '28 july 1934'], ['st kilda', '13.6 ( 84 )', 'geelong', '16.18 ( 114 )', 'junction oval', '17000', '28 july 1934'], ['north melbourne', '15.8 ( 98 )', 'fitzroy', '21.16 ( 142 )', 'arden street oval', '10000', '28 july 1934'], ['footscray', '15.9 ( 99 )', 'essendon', '8.9 ( 57 )', 'western oval', '13000', '28 july 1934']] |
orlando magic all - time roster | https://en.wikipedia.org/wiki/Orlando_Magic_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15621965-2.html.csv | unique | michael bradley is the only player who played at villanova . | {'scope': 'all', 'row': '11', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': 'villanova', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school / club team', 'villanova'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school / club team record fuzzily matches to villanova .', 'tostr': 'filter_eq { all_rows ; school / club team ; villanova }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; school / club team ; villanova } }', 'tointer': 'select the rows whose school / club team record fuzzily matches to villanova . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school / club team', 'villanova'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school / club team record fuzzily matches to villanova .', 'tostr': 'filter_eq { all_rows ; school / club team ; villanova }'}, 'player'], 'result': 'michael bradley', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; school / club team ; villanova } ; player }'}, 'michael bradley'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; school / club team ; villanova } ; player } ; michael bradley }', 'tointer': 'the player record of this unqiue row is michael bradley .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; school / club team ; villanova } } ; eq { hop { filter_eq { all_rows ; school / club team ; villanova } ; player } ; michael bradley } } = true', 'tointer': 'select the rows whose school / club team record fuzzily matches to villanova . there is only one such row in the table . the player record of this unqiue row is michael bradley .'} | and { only { filter_eq { all_rows ; school / club team ; villanova } } ; eq { hop { filter_eq { all_rows ; school / club team ; villanova } ; player } ; michael bradley } } = true | select the rows whose school / club team record fuzzily matches to villanova . there is only one such row in the table . the player record of this unqiue row is michael bradley . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'school / club team_7': 7, 'villanova_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'michael bradley_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'school / club team_7': 'school / club team', 'villanova_8': 'villanova', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'michael bradley_10': 'michael bradley'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'school / club team_7': [0], 'villanova_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'michael bradley_10': [3]} | ['player', 'no', 'nationality', 'position', 'years in orlando', 'school / club team'] | [['matt barnes', '22', 'united states', 'guard - forward', '2009 - 2010', 'ucla'], ['andre barrett', '11', 'united states', 'guard', '2005', 'seton hall'], ['brandon bass', '30', 'united states', 'forward', '2009 - 2011', 'louisiana state'], ['tony battie', '4', 'united states', 'forward - center', '2004 - 2009', 'texas tech'], ['david benoit', '2', 'united states', 'forward', '1998', 'alabama'], ['keith bogans', '3', 'united states', 'guard', '2003 - 2004', 'kentucky'], ['keith bogans', '10', 'united states', 'guard', '2006 - 2009', 'kentucky'], ['anthony bonner', '24', 'united states', 'forward', '1995 - 1996', 'st louis'], ['anthony bowie', '14', 'united states', 'guard', '1991 - 1996', 'oklahoma'], ['earl boykins', '11', 'united states', 'guard', '1999', 'eastern michigan'], ['michael bradley', '7', 'united states', 'forward', '2004 - 2005', 'villanova'], ['dee brown', '7', 'united states', 'guard', '2000 - 2002', 'jacksonville'], ['jud buechler', '30', 'united states', 'guard - forward', '2001 - 2002', 'arizona']] |
arsen avakov | https://en.wikipedia.org/wiki/Arsen_Avakov | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15532127-1.html.csv | count | arsen avakov won by a score of 5-0 three times . | {'scope': 'all', 'criterion': 'equal', 'value': '5 - 0', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', '5 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to 5 - 0 .', 'tostr': 'filter_eq { all_rows ; result ; 5 - 0 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; 5 - 0 } }', 'tointer': 'select the rows whose result record fuzzily matches to 5 - 0 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; 5 - 0 } } ; 3 } = true', 'tointer': 'select the rows whose result record fuzzily matches to 5 - 0 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; result ; 5 - 0 } } ; 3 } = true | select the rows whose result record fuzzily matches to 5 - 0 . 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, '5 - 0_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', '5 - 0_6': '5 - 0', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], '5 - 0_6': [0], '3_7': [2]} | ['date', 'venue', 'score', 'result', 'competition'] | [['8 may 1996', 'dushanbe , tajikistan', '4 - 0', '4 - 0', '1996 afc asian cup qualification'], ['1 june 1997', 'ho chi minh city , vietnam', '0 - 3', '0 - 4', '1998 fifa world cup qualification'], ['22 june 1997', 'dushanbe , tajikistan', '1 - 0', '5 - 0', '1998 fifa world cup qualification'], ['22 june 1997', 'dushanbe , tajikistan', '3 - 0', '5 - 0', '1998 fifa world cup qualification'], ['22 june 1997', 'dushanbe , tajikistan', '4 - 0', '5 - 0', '1998 fifa world cup qualification']] |
doctor who ( series 1 ) | https://en.wikipedia.org/wiki/Doctor_Who_%28series_1%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18012738-1.html.csv | aggregation | doctor who ( series 1 ) had a total of 96.41 million views in the uk . | {'scope': 'all', 'col': '6', 'type': 'sum', 'result': '96.41', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'uk viewers ( million )'], 'result': '96.41', 'ind': 0, 'tostr': 'sum { all_rows ; uk viewers ( million ) }'}, '96.41'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; uk viewers ( million ) } ; 96.41 } = true', 'tointer': 'the sum of the uk viewers ( million ) record of all rows is 96.41 .'} | round_eq { sum { all_rows ; uk viewers ( million ) } ; 96.41 } = true | the sum of the uk viewers ( million ) record of all rows is 96.41 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'uk viewers (million)_4': 4, '96.41_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'uk viewers (million)_4': 'uk viewers ( million )', '96.41_5': '96.41'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'uk viewers (million)_4': [0], '96.41_5': [1]} | ['story no', 'episode', 'title', 'directed by', 'written by', 'uk viewers ( million )', 'ai ( % )', 'original air date', 'production code'] | [['157', '1', 'rose', 'keith boak', 'russell t davies', '10.81', '81', '26 march 2005', '1.1'], ['158', '2', 'the end of the world', 'euros lyn', 'russell t davies', '7.97', '79', '2 april 2005', '1.2'], ['159', '3', 'the unquiet dead', 'euros lyn', 'mark gatiss', '8.86', '80', '9 april 2005', '1.3'], ['160a', '4', 'aliens of london', 'keith boak', 'russell t davies', '7.63', '81', '16 april 2005', '1.4'], ['160b', '5', 'world war three', 'keith boak', 'russell t davies', '7.98', '82', '23 april 2005', '1.5'], ['161', '6', 'dalek', 'joe ahearne', 'robert shearman', '8.63', '84', '30 april 2005', '1.6'], ['162', '7', 'the long game', 'brian grant', 'russell t davies', '8.01', '81', '7 may 2005', '1.7'], ['163', '8', "father 's day", 'joe ahearne', 'paul cornell', '8.06', '83', '14 may 2005', '1.8'], ['164a', '9', 'the empty child', 'james hawes', 'steven moffat', '7.11', '84', '21 may 2005', '1.9'], ['164b', '10', 'the doctor dances', 'james hawes', 'steven moffat', '6.86', '85', '28 may 2005', '1.10'], ['165', '11', 'boom town', 'joe ahearne', 'russell t davies', '7.68', '82', '4 june 2005', '1.11'], ['166a', '12', 'bad wolf', 'joe ahearne', 'russell t davies', '6.81', '85', '11 june 2005', '1.12']] |
jj lehto | https://en.wikipedia.org/wiki/JJ_Lehto | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226476-5.html.csv | majority | a majority-3 - of jj lehto 's engines from 2003-2005 he used to race , were audi 3.6 l turbo v8 engines . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'audi 3.6 l turbo v8', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'engine', 'audi 3.6 l turbo v8'], 'result': True, 'ind': 0, 'tointer': 'for the engine records of all rows , most of them fuzzily match to audi 3.6 l turbo v8 .', 'tostr': 'most_eq { all_rows ; engine ; audi 3.6 l turbo v8 } = true'} | most_eq { all_rows ; engine ; audi 3.6 l turbo v8 } = true | for the engine records of all rows , most of them fuzzily match to audi 3.6 l turbo v8 . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'engine_3': 3, 'audi 3.6l turbo v8_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'engine_3': 'engine', 'audi 3.6l turbo v8_4': 'audi 3.6 l turbo v8'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'engine_3': [0], 'audi 3.6l turbo v8_4': [0]} | ['year', 'entrant', 'class', 'chassis', 'engine', 'tyres', 'rank', 'points'] | [['1999', 'bmw motorsport', 'lmp', 'bmw v12 lmr', 'bmw s70 6.0 l v12', 'm', '4th', '123'], ['2000', 'bmw motorsport', 'lmp', 'bmw v12 lmr', 'bmw s70 6.0 l v12', 'm', '6th', '220'], ['2001', 'bmw motorsport', 'gt', 'bmw m3', 'bmw 3.2 l i6', 'm', '2nd', '180'], ['2001', 'bmw motorsport', 'gt', 'bmw m3 gtr', 'bmw 4.0 l v8', 'm', '2nd', '180'], ['2002', 'team cadillac', 'lmp900', 'cadillac northstar lmp02', 'cadillac northstar 4.0 l turbo v8', 'm', '13th', '101'], ['2003', 'adt champion racing', 'lmp900', 'audi r8', 'audi 3.6 l turbo v8', 'm', '3rd', '163'], ['2004', 'adt champion racing', 'lmp1', 'audi r8', 'audi 3.6 l turbo v8', 'm', '1st', '164'], ['2005', 'adt champion racing', 'lmp1', 'audi r8', 'audi 3.6 l turbo v8', 'm', '3rd', '148']] |
1987 masters tournament | https://en.wikipedia.org/wiki/1987_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16490473-1.html.csv | superlative | in the 1987 masters tournament , seve ballesteros ranked the highest . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'total'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; total }'}, 'player'], 'result': 'seve ballesteros', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; total } ; player }'}, 'seve ballesteros'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; total } ; player } ; seve ballesteros } = true', 'tointer': 'select the row whose total record of all rows is minimum . the player record of this row is seve ballesteros .'} | eq { hop { argmin { all_rows ; total } ; player } ; seve ballesteros } = true | select the row whose total record of all rows is minimum . the player record of this row is seve ballesteros . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'total_5': 5, 'player_6': 6, 'seve ballesteros_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'total_5': 'total', 'player_6': 'player', 'seve ballesteros_7': 'seve ballesteros'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'total_5': [0], 'player_6': [1], 'seve ballesteros_7': [2]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['seve ballesteros', 'spain', '1980 , 1983', '285', '3', 't2'], ['ben crenshaw', 'united states', '1984', '286', '2', 't4'], ['bernhard langer', 'west germany', '1985', '289', '+ 1', 't7'], ['jack nicklaus', 'united states', '1963 , 1965 , 1966 , 1972 , 1975 , 1986', '289', '+ 1', 't7'], ['tom watson', 'united states', '1977 , 1981', '289', '+ 1', 't7'], ['craig stadler', 'united states', '1982', '291', '+ 3', 't17'], ['fuzzy zoeller', 'united states', '1979', '295', '+ 7', 't27'], ['gary player', 'south africa', '1961 , 1974 , 1978', '297', '+ 9', 't35'], ['tommy aaron', 'united states', '1973', '305', '+ 17', 't50'], ['billy casper', 'united states', '1970', '305', '+ 17', 't50']] |
1939 - 40 new york rangers season | https://en.wikipedia.org/wiki/1939%E2%80%9340_New_York_Rangers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14473419-5.html.csv | count | the new york rangers played the detroit red wings three times in february . | {'scope': 'all', 'criterion': 'equal', 'value': 'detroit red wings', 'result': '3', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'detroit red wings'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to detroit red wings .', 'tostr': 'filter_eq { all_rows ; opponent ; detroit red wings }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; opponent ; detroit red wings } }', 'tointer': 'select the rows whose opponent record fuzzily matches to detroit red wings . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; opponent ; detroit red wings } } ; 3 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to detroit red wings . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; opponent ; detroit red wings } } ; 3 } = true | select the rows whose opponent record fuzzily matches to detroit red wings . 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, 'opponent_5': 5, 'detroit red wings_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', 'opponent_5': 'opponent', 'detroit red wings_6': 'detroit red wings', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'detroit red wings_6': [0], '3_7': [2]} | ['game', 'february', 'opponent', 'score', 'record'] | [['32', '1', 'detroit red wings', '2 - 0', '20 - 5 - 7'], ['33', '4', 'montreal canadiens', '9 - 0', '21 - 5 - 7'], ['34', '6', 'boston bruins', '6 - 2', '21 - 6 - 7'], ['35', '8', 'toronto maple leafs', '2 - 1', '22 - 6 - 7'], ['36', '10', 'toronto maple leafs', '4 - 4 ot', '22 - 6 - 8'], ['37', '11', 'chicago black hawks', '3 - 0', '22 - 7 - 8'], ['38', '15', 'detroit red wings', '3 - 1', '23 - 7 - 8'], ['39', '18', 'detroit red wings', '2 - 0', '23 - 8 - 8'], ['40', '22', 'new york americans', '1 - 0 ot', '23 - 9 - 8'], ['41', '24', 'montreal canadiens', '2 - 0', '24 - 9 - 8'], ['42', '25', 'montreal canadiens', '6 - 2', '25 - 9 - 8'], ['43', '29', 'chicago black hawks', '2 - 1', '25 - 10 - 8']] |
1959 vfl season | https://en.wikipedia.org/wiki/1959_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10775038-4.html.csv | count | two of the venues have the word oval in their name . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'oval', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'oval'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to oval .', 'tostr': 'filter_eq { all_rows ; venue ; oval }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; venue ; oval } }', 'tointer': 'select the rows whose venue record fuzzily matches to oval . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; venue ; oval } } ; 2 } = true', 'tointer': 'select the rows whose venue record fuzzily matches to oval . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; venue ; oval } } ; 2 } = true | select the rows whose venue record fuzzily matches to oval . 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, 'venue_5': 5, 'oval_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', 'venue_5': 'venue', 'oval_6': 'oval', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'venue_5': [0], 'oval_6': [0], '2_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '19.14 ( 128 )', 'south melbourne', '13.19 ( 97 )', 'glenferrie oval', '25000', '16 may 1959'], ['essendon', '8.14 ( 62 )', 'north melbourne', '11.11 ( 77 )', 'windy hill', '22500', '16 may 1959'], ['carlton', '14.17 ( 101 )', 'richmond', '9.7 ( 61 )', 'princes park', '24500', '16 may 1959'], ['melbourne', '19.17 ( 131 )', 'footscray', '9.12 ( 66 )', 'mcg', '24538', '16 may 1959'], ['geelong', '13.12 ( 90 )', 'fitzroy', '12.8 ( 80 )', 'kardinia park', '16112', '16 may 1959'], ['st kilda', '12.19 ( 91 )', 'collingwood', '8.16 ( 64 )', 'junction oval', '35260', '16 may 1959']] |
2006 - 07 manchester united f.c. season | https://en.wikipedia.org/wiki/2006%E2%80%9307_Manchester_United_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11115098-4.html.csv | aggregation | the average attendance for the 2006-07 manchester united fc was 57797 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '57797', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '57797', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '57797'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 57797 } = true', 'tointer': 'the average of the attendance record of all rows is 57797 .'} | round_eq { avg { all_rows ; attendance } ; 57797 } = true | the average of the attendance record of all rows is 57797 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '57797_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '57797_5': '57797'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '57797_5': [1]} | ['date', 'round', 'opponents', 'h / a', 'result f - a', 'attendance'] | [['7 january 2007', 'round 3', 'aston villa', 'h', '2 - 1', '74924'], ['27 january 2007', 'round 4', 'portsmouth', 'h', '2 - 1', '71137'], ['17 february 2007', 'round 5', 'reading', 'h', '1 - 1', '70608'], ['27 february 2007', 'round 5 replay', 'reading', 'a', '3 - 2', '23821'], ['10 march 2007', 'round 6', 'middlesbrough', 'a', '2 - 2', '33308'], ['19 march 2007', 'round 6 replay', 'middlesbrough', 'h', '1 - 0', '61325'], ['14 april 2007', 'semi - final', 'watford', 'n', '4 - 1', '37425'], ['19 may 2007', 'final', 'chelsea', 'n', '0 - 1 ( aet )', '89826']] |
1993 - 94 philadelphia flyers season | https://en.wikipedia.org/wiki/1993%E2%80%9394_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14344407-2.html.csv | aggregation | in october of 1993 the philadelphia flyers scored an average of 4.67 goals over 12 games . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '4.67', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '4.67', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '4.67'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 4.67 } = true', 'tointer': 'the average of the score record of all rows is 4.67 .'} | round_eq { avg { all_rows ; score } ; 4.67 } = true | the average of the score record of all rows is 4.67 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '4.67_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '4.67_5': '4.67'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '4.67_5': [1]} | ['game', 'october', 'opponent', 'score', 'record', 'points'] | [['1', '5', 'pittsburgh penguins', '4 - 3', '1 - 0 - 0', '2'], ['2', '9', 'hartford whalers', '5 - 2', '2 - 0 - 0', '4'], ['3', '10', 'toronto maple leafs', '4 - 5', '2 - 1 - 0', '4'], ['4', '12', 'buffalo sabres', '5 - 3', '3 - 1 - 0', '6'], ['5', '15', 'washington capitals', '3 - 0', '4 - 1 - 0', '8'], ['6', '16', 'new york rangers', '4 - 3', '5 - 1 - 0', '10'], ['7', '22', 'new york islanders', '4 - 3', '6 - 1 - 0', '12'], ['8', '23', 'winnipeg jets', '6 - 9', '6 - 2 - 0', '12'], ['9', '26', 'quebec nordiques', '4 - 2', '7 - 2 - 0', '14'], ['10', '27', 'ottawa senators', '5 - 2', '8 - 2 - 0', '16'], ['11', '30', 'new jersey devils', '3 - 5', '8 - 3 - 0', '16'], ['12', '31', 'chicago blackhawks', '9 - 6', '9 - 3 - 0', '18']] |
united states house of representatives elections , 1964 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1964 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341865-11.html.csv | majority | most of the elected representatives were unopposed during the election . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'unopposed', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'candidates', 'unopposed'], 'result': True, 'ind': 0, 'tointer': 'for the candidates records of all rows , most of them fuzzily match to unopposed .', 'tostr': 'most_eq { all_rows ; candidates ; unopposed } = true'} | most_eq { all_rows ; candidates ; unopposed } = true | for the candidates records of all rows , most of them fuzzily match to unopposed . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'candidates_3': 3, 'unopposed_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'candidates_3': 'candidates', 'unopposed_4': 'unopposed'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'candidates_3': [0], 'unopposed_4': [0]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['florida 1', 'robert l f sikes', 'democratic', '1940', 're - elected', 'robert l f sikes ( d ) unopposed'], ['florida 3', 'claude pepper', 'democratic', '1962', 're - elected', "claude pepper ( d ) 65.7 % paul j o'neill ( r ) 34.3 %"], ['florida 4', 'dante fascell', 'democratic', '1954', 're - elected', 'dante fascell ( d ) 63.9 % jay mcglon ( r ) 36.1 %'], ['florida 5', 'albert s herlong , jr', 'democratic', '1948', 're - elected', 'albert s herlong , jr ( d ) unopposed'], ['florida 6', 'paul rogers', 'democratic', '1954', 're - elected', 'paul rogers ( d ) 66.0 % john d steele ( r ) 34.0 %'], ['florida 7', 'james a haley', 'democratic', '1952', 're - elected', 'james a haley ( d ) unopposed'], ['florida 8', 'donald ray matthews', 'democratic', '1952', 're - elected', 'donald ray matthews ( d ) unopposed'], ['florida 9', 'don fuqua', 'democratic', '1962', 're - elected', 'don fuqua ( d ) unopposed']] |
1990 england rugby union tour of argentina | https://en.wikipedia.org/wiki/1990_England_rugby_union_tour_of_Argentina | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17058667-1.html.csv | unique | the 4 august 1990 match against argentina was the only one with the second test status . | {'scope': 'all', 'row': '7', 'col': '5', 'col_other': '1,3', 'criterion': 'equal', 'value': 'second test', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'status', 'second test'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose status record fuzzily matches to second test .', 'tostr': 'filter_eq { all_rows ; status ; second test }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; status ; second test } }', 'tointer': 'select the rows whose status record fuzzily matches to second test . 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', 'status', 'second test'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose status record fuzzily matches to second test .', 'tostr': 'filter_eq { all_rows ; status ; second test }'}, 'opposing team'], 'result': 'argentina', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; status ; second test } ; opposing team }'}, 'argentina'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; status ; second test } ; opposing team } ; argentina }', 'tointer': 'the opposing team record of this unqiue row is argentina .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'status', 'second test'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose status record fuzzily matches to second test .', 'tostr': 'filter_eq { all_rows ; status ; second test }'}, 'date'], 'result': '4 august 1990', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; status ; second test } ; date }'}, '4 august 1990'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; status ; second test } ; date } ; 4 august 1990 }', 'tointer': 'the date record of this unqiue row is 4 august 1990 .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; status ; second test } ; opposing team } ; argentina } ; eq { hop { filter_eq { all_rows ; status ; second test } ; date } ; 4 august 1990 } }', 'tointer': 'the opposing team record of this unqiue row is argentina . the date record of this unqiue row is 4 august 1990 .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; status ; second test } } ; and { eq { hop { filter_eq { all_rows ; status ; second test } ; opposing team } ; argentina } ; eq { hop { filter_eq { all_rows ; status ; second test } ; date } ; 4 august 1990 } } } = true', 'tointer': 'select the rows whose status record fuzzily matches to second test . there is only one such row in the table . the opposing team record of this unqiue row is argentina . the date record of this unqiue row is 4 august 1990 .'} | and { only { filter_eq { all_rows ; status ; second test } } ; and { eq { hop { filter_eq { all_rows ; status ; second test } ; opposing team } ; argentina } ; eq { hop { filter_eq { all_rows ; status ; second test } ; date } ; 4 august 1990 } } } = true | select the rows whose status record fuzzily matches to second test . there is only one such row in the table . the opposing team record of this unqiue row is argentina . the date record of this unqiue row is 4 august 1990 . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'status_10': 10, 'second test_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'opposing team_12': 12, 'argentina_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'date_14': 14, '4 august 1990_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'status_10': 'status', 'second test_11': 'second test', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'opposing team_12': 'opposing team', 'argentina_13': 'argentina', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'date_14': 'date', '4 august 1990_15': '4 august 1990'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'status_10': [0], 'second test_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'opposing team_12': [2], 'argentina_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'date_14': [4], '4 august 1990_15': [5]} | ['opposing team', 'against', 'date', 'venue', 'status'] | [['banco nación', '29', '14 july 1990', 'buenos aires', 'tour match'], ['tucumán selection', '14', '18 july 1990', 'tucumán', 'tour match'], ['buenos aires selection', '26', '21 july 1990', 'buenos aires', 'tour match'], ['cuyo selection', '22', '24 july 1990', 'mendoza', 'tour match'], ['argentina', '12', '28 july 1990', 'vélez sársfield , buenos aires', 'first test'], ['córdoba', '12', '31 july 1990', 'córdoba', 'tour match'], ['argentina', '15', '4 august 1990', 'vélez sársfield , buenos aires', 'second test']] |
andy linden ( racing driver ) | https://en.wikipedia.org/wiki/Andy_Linden_%28racing_driver%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1236025-1.html.csv | superlative | of all his races , andy lindens highest start came in the year 1952 . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'start'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; start }'}, 'year'], 'result': '1952', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; start } ; year }'}, '1952'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; start } ; year } ; 1952 } = true', 'tointer': 'select the row whose start record of all rows is minimum . the year record of this row is 1952 .'} | eq { hop { argmin { all_rows ; start } ; year } ; 1952 } = true | select the row whose start record of all rows is minimum . the year record of this row is 1952 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'start_5': 5, 'year_6': 6, '1952_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'start_5': 'start', 'year_6': 'year', '1952_7': '1952'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'start_5': [0], 'year_6': [1], '1952_7': [2]} | ['year', 'start', 'qual', 'rank', 'finish', 'laps'] | [['1951', '31', '132.226', '26', '4', '200'], ['1952', '2', '137.002', '4', '33', '20'], ['1953', '5', '136.060', '19', '33', '3'], ['1954', '23', '137.820', '28', '25', '165'], ['1955', '8', '139.098', '22', '6', '200'], ['1956', '9', '143.056', '11', '27', '90'], ['1957', '12', '143.244', '5', '5', '200']] |
bms scuderia italia | https://en.wikipedia.org/wiki/BMS_Scuderia_Italia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226647-2.html.csv | unique | the 1991 racer was the only one to use a judd engine . | {'scope': 'all', 'row': '4', 'col': '3', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': 'judd', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'engine ( s )', 'judd'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose engine ( s ) record fuzzily matches to judd .', 'tostr': 'filter_eq { all_rows ; engine ( s ) ; judd }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; engine ( s ) ; judd } }', 'tointer': 'select the rows whose engine ( s ) record fuzzily matches to judd . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'engine ( s )', 'judd'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose engine ( s ) record fuzzily matches to judd .', 'tostr': 'filter_eq { all_rows ; engine ( s ) ; judd }'}, 'year'], 'result': '1991', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; engine ( s ) ; judd } ; year }'}, '1991'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; engine ( s ) ; judd } ; year } ; 1991 }', 'tointer': 'the year record of this unqiue row is 1991 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; engine ( s ) ; judd } } ; eq { hop { filter_eq { all_rows ; engine ( s ) ; judd } ; year } ; 1991 } } = true', 'tointer': 'select the rows whose engine ( s ) record fuzzily matches to judd . there is only one such row in the table . the year record of this unqiue row is 1991 .'} | and { only { filter_eq { all_rows ; engine ( s ) ; judd } } ; eq { hop { filter_eq { all_rows ; engine ( s ) ; judd } ; year } ; 1991 } } = true | select the rows whose engine ( s ) record fuzzily matches to judd . there is only one such row in the table . the year record of this unqiue row is 1991 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'engine (s)_7': 7, 'judd_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1991_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'engine (s)_7': 'engine ( s )', 'judd_8': 'judd', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1991_10': '1991'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'engine (s)_7': [0], 'judd_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1991_10': [3]} | ['year', 'chassis', 'engine ( s )', 'tyres', 'points'] | [['1988', 'dallara 3087 dallara 188', 'ford dfz 3.5 v8', 'g', '0'], ['1989', 'dallara 189', 'ford dfr 3.5 v8', 'p', '8'], ['1990', 'dallara 190', 'ford dfr 3.5 v8', 'p', '0'], ['1991', 'dallara 191', 'judd gv 3.5 v10', 'p', '5'], ['1992', 'dallara 192', 'ferrari 037 3.5 v12', 'g', '2'], ['1993', 'lola t93 / 30', 'ferrari 040 3.5 v12', 'g', '0']] |
scottish parliament general election , 2007 | https://en.wikipedia.org/wiki/Scottish_Parliament_general_election%2C_2007 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11105214-2.html.csv | majority | the labour party was the winning party in 2003 of the majority of constituencies in the scottish parliament general election . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'labour', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'winning party 2003', 'labour'], 'result': True, 'ind': 0, 'tointer': 'for the winning party 2003 records of all rows , most of them fuzzily match to labour .', 'tostr': 'most_eq { all_rows ; winning party 2003 ; labour } = true'} | most_eq { all_rows ; winning party 2003 ; labour } = true | for the winning party 2003 records of all rows , most of them fuzzily match to labour . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'winning party 2003_3': 3, 'labour_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'winning party 2003_3': 'winning party 2003', 'labour_4': 'labour'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'winning party 2003_3': [0], 'labour_4': [0]} | ['rank', 'constituency', 'winning party 2003', 'swing to gain', "snp 's place 2003", 'result'] | [['1', 'galloway & upper nithsdale', 'conservative', '0.17', '2nd', 'con hold'], ['2', 'tweeddale , ettrick & lauderdale', 'liberal democrats', '1.01', '2nd', 'ld hold'], ['3', 'cumbernauld & kilsyth', 'labour', '1.07', '2nd', 'lab hold'], ['4', 'kilmarnock & loudoun', 'labour', '1.92', '2nd', 'snp gain'], ['5', 'dundee west', 'labour', '2.13', '2nd', 'snp gain'], ['6', 'western isles', 'labour', '2.91', '2nd', 'snp gain'], ['7', 'glasgow govan', 'labour', '2.92', '2nd', 'snp gain'], ['8', 'aberdeen central', 'labour', '2.96', '2nd', 'lab hold'], ['9', 'linlithgow', 'labour', '3.56', '2nd', 'lab hold'], ['10', 'west renfrewshire', 'labour', '4.41', '2nd', 'lab hold'], ['11', 'paisley south', 'labour', '4.91', '2nd', 'lab hold']] |
toronto raptors all - time roster | https://en.wikipedia.org/wiki/Toronto_Raptors_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10015132-3.html.csv | majority | most of the players have the united states as their nationality . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , most of them fuzzily match to united states .', 'tostr': 'most_eq { all_rows ; nationality ; united states } = true'} | most_eq { all_rows ; nationality ; united states } = true | for the nationality records of all rows , most of them fuzzily match to united states . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'united states_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'united states_4': 'united states'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'united states_4': [0]} | ['player', 'no', 'nationality', 'position', 'years in toronto', 'school / club team'] | [['josé calderón', '8', 'spain', 'guard', '2005 - 2013', 'tau cerámica ( spain )'], ['marcus camby', '21', 'united states', 'center', '1996 - 98', 'massachusetts'], ['anthony carter', '25', 'united states', 'guard', '2011 - 12', 'hawaii'], ['vince carter', '15', 'united states', 'guard - forward', '1998 - 2004', 'north carolina'], ['chris childs', '1', 'united states', 'guard', '2001 - 02', 'boise state'], ['doug christie', '13', 'united states', 'forward', '1996 - 2000', 'pepperdine'], ['keon clark', '7', 'united states', 'forward - center', '2001 - 02', 'unlv'], ['omar cook', '1', 'united states', 'guard', '2005 - 06', "st john 's"], ['tyrone corbin', '23', 'united states', 'guard - forward', '2000 - 01', 'depaul'], ['william cunningham', '54', 'united states', 'center', '1999', 'temple'], ['earl cureton', '35', 'united states', 'forward', '1996 - 97', 'detroit'], ['dell curry', '30', 'united states', 'guard', '1999 - 2002', 'virginia tech']] |
2005 u.s. open ( golf ) | https://en.wikipedia.org/wiki/2005_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14064009-4.html.csv | count | seven players in the 2008 us open for golf went +1 over par . | {'scope': 'all', 'criterion': 'equal', 'value': '+1', 'result': '7', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'to par', '+1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose to par record fuzzily matches to +1 .', 'tostr': 'filter_eq { all_rows ; to par ; +1 }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; to par ; +1 } }', 'tointer': 'select the rows whose to par record fuzzily matches to +1 . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; to par ; +1 } } ; 7 } = true', 'tointer': 'select the rows whose to par record fuzzily matches to +1 . the number of such rows is 7 .'} | eq { count { filter_eq { all_rows ; to par ; +1 } } ; 7 } = true | select the rows whose to par record fuzzily matches to +1 . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'to par_5': 5, '+1_6': 6, '7_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'to par_5': 'to par', '+1_6': '+1', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'to par_5': [0], '+1_6': [0], '7_7': [2]} | ['place', 'player', 'country', 'score', 'to par'] | [['t1', 'olin browne', 'united states', '67 + 71 = 138', '- 2'], ['t1', 'retief goosen', 'south africa', '68 + 70 = 138', '- 2'], ['t1', 'jason gore', 'united states', '71 + 67 = 138', '- 2'], ['t4', 'k j choi', 'south korea', '69 + 70 = 139', '- 1'], ['t4', 'mark hensby', 'australia', '71 + 68 = 139', '- 1'], ['t6', 'michael campbell', 'new zealand', '71 + 69 = 140', 'e'], ['t6', 'sergio garcía', 'spain', '71 + 69 = 140', 'e'], ['t6', 'vijay singh', 'fiji', '70 + 70 = 140', 'e'], ['t6', 'lee westwood', 'england', '68 + 72 = 140', 'e'], ['t10', 'stephen allan', 'australia', '72 - 69 - 141', '+ 1'], ['t10', 'keiichiro fukabori', 'japan', '74 + 67 = 141', '+ 1'], ['t10', 'jim furyk', 'united states', '71 + 70 = 141', '+ 1'], ['t10', 'brandt jobe', 'united states', '68 + 73 = 141', '+ 1'], ['t10', 'rocco mediate', 'united states', '67 + 74 = 141', '+ 1'], ['t10', 'adam scott', 'australia', '70 + 71 = 141', '+ 1'], ['t10', 'tiger woods', 'united states', '70 + 71 = 141', '+ 1']] |
i am ... ( ayumi hamasaki album ) | https://en.wikipedia.org/wiki/I_Am..._%28Ayumi_Hamasaki_album%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1754908-3.html.csv | count | there are 8 track titles in the i am ... ( ayumi hamasaki album ) . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '8', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'title'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record is arbitrary .', 'tostr': 'filter_all { all_rows ; title }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; title } }', 'tointer': 'select the rows whose title record is arbitrary . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; title } } ; 8 } = true', 'tointer': 'select the rows whose title record is arbitrary . the number of such rows is 8 .'} | eq { count { filter_all { all_rows ; title } } ; 8 } = true | select the rows whose title record is arbitrary . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'title_5': 5, '8_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'title_5': 'title', '8_6': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'title_5': [0], '8_6': [2]} | ['date', 'title', 'peak position', 'weeks', 'sales'] | [['december 13 , 2000', 'm', '1', '18 weeks', '1319070'], ['january 31 , 2001', 'evolution', '1', '17 weeks', '955250'], ['march 7 , 2001', 'never ever', '1', '12 weeks', '756980'], ['may 16 , 2001', 'endless sorrow', '1', '11 weeks', '768510'], ['july 11 , 2001', 'unite !', '1', '17 weeks', '571110'], ['september 27 , 2001', 'dearest', '1', '16 weeks', '750420'], ['december 12 , 2001', 'a song is born', '1', '10 weeks', '441410'], ['march 6 , 2002', 'daybreak ( re - cut single )', '2', '9 weeks', '197140']] |
1996 in film | https://en.wikipedia.org/wiki/1996_in_film | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-169568-1.html.csv | ordinal | " mission : impossible " was the third highest grossing film worldwide in 1996 . | {'row': '3', 'col': '5', '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', 'worldwide gross', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; worldwide gross ; 3 }'}, 'title'], 'result': 'mission : impossible', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; worldwide gross ; 3 } ; title }'}, 'mission : impossible'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; worldwide gross ; 3 } ; title } ; mission : impossible } = true', 'tointer': 'select the row whose worldwide gross record of all rows is 3rd maximum . the title record of this row is mission : impossible .'} | eq { hop { nth_argmax { all_rows ; worldwide gross ; 3 } ; title } ; mission : impossible } = true | select the row whose worldwide gross record of all rows is 3rd maximum . the title record of this row is mission : impossible . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'worldwide gross_5': 5, '3_6': 6, 'title_7': 7, 'mission : impossible_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', 'worldwide gross_5': 'worldwide gross', '3_6': '3', 'title_7': 'title', 'mission : impossible_8': 'mission : impossible'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'worldwide gross_5': [0], '3_6': [0], 'title_7': [1], 'mission : impossible_8': [2]} | ['rank', 'title', 'studio', 'director', 'worldwide gross'] | [['1', 'independence day', '20th century fox', 'roland emmerich', '817400891'], ['2', 'twister', 'warner bros / universal studios', 'jan de bont', '494471524'], ['3', 'mission : impossible', 'paramount pictures', 'brian de palma', '457696359'], ['4', 'the rock', 'hollywood pictures', 'michael bay', '335062621'], ['5', 'the hunchback of notre dame', 'walt disney pictures', 'kirk wise , gary trousdale', '325338851'], ['6', '101 dalmatians', 'walt disney pictures / great oaks', 'stephen herek', '320689294'], ['7', 'ransom', 'touchstone pictures / image entertainment', 'ron howard', '309492681'], ['8', 'the nutty professor', 'universal pictures', 'tom shadyac', '273961019'], ['9', 'jerry maguire', 'tristar pictures', 'cameron crowe', '273552592'], ['10', 'eraser', 'warner bros', 'chuck russell', '242295562'], ['11', 'the english patient', 'miramax films', 'anthony minghella', '231976425'], ['12', 'space jam', 'warner bros', 'joe pytka', '230418342'], ['13', 'the birdcage', 'united artists', 'mike nichols', '185260553'], ['14', 'the first wives club', 'paramount pictures', 'hugh wilson', '181490000'], ['15', 'scream', 'dimension films', 'wes craven', '173046663'], ['16', 'sleepers', 'warner bros', 'barry levinson', '165615285'], ['17', 'daylight', 'universal pictures', 'rob cohen', '159212469'], ['18', 'a time to kill', 'warner bros', 'joel schumacher', '152266007'], ['19', 'phenomenon', 'buena vista', 'jon turteltaub', '152036382'], ['20', 'broken arrow', '20th century fox', 'john woo', '150270147']] |
list of covert affairs episodes | https://en.wikipedia.org/wiki/List_of_Covert_Affairs_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25740548-3.html.csv | ordinal | for the list of covert affairs episodes in with an original air date in june the episode title begin the begin had the highest viewers . | {'scope': 'subset', 'row': '1', 'col': '8', 'order': '1', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '6', 'criterion': 'fuzzily_match', 'value': 'june'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'original air date', 'june'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; original air date ; june }', 'tointer': 'select the rows whose original air date record fuzzily matches to june .'}, 'us viewers ( million )', '1'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_eq { all_rows ; original air date ; june } ; us viewers ( million ) ; 1 }'}, 'title'], 'result': 'begin the begin', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_eq { all_rows ; original air date ; june } ; us viewers ( million ) ; 1 } ; title }'}, 'begin the begin'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_eq { all_rows ; original air date ; june } ; us viewers ( million ) ; 1 } ; title } ; begin the begin } = true', 'tointer': 'select the rows whose original air date record fuzzily matches to june . select the row whose us viewers ( million ) record of these rows is 1st maximum . the title record of this row is begin the begin .'} | eq { hop { nth_argmax { filter_eq { all_rows ; original air date ; june } ; us viewers ( million ) ; 1 } ; title } ; begin the begin } = true | select the rows whose original air date record fuzzily matches to june . select the row whose us viewers ( million ) record of these rows is 1st maximum . the title record of this row is begin the begin . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'original air date_6': 6, 'june_7': 7, 'us viewers (million)_8': 8, '1_9': 9, 'title_10': 10, 'begin the begin_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'original air date_6': 'original air date', 'june_7': 'june', 'us viewers (million)_8': 'us viewers ( million )', '1_9': '1', 'title_10': 'title', 'begin the begin_11': 'begin the begin'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'original air date_6': [0], 'june_7': [0], 'us viewers (million)_8': [1], '1_9': [1], 'title_10': [2], 'begin the begin_11': [3]} | ['series', 'season', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( million )'] | [['12', '1', 'begin the begin', 'kate woods', 'matt corman & chris ord', 'june 7 , 2011', 'ca201', '4.56'], ['13', '2', 'good advices', 'ken girotti', 'stephen hootstein', 'june 14 , 2011', 'ca202', '3.92'], ['14', '3', 'bang and blame', 'allan kroeker', 'erica shelton', 'june 21 , 2011', 'ca203', '4.03'], ['15', '4', 'all the right friends', 'stephen kay', 'norman morrill', 'june 28 , 2011', 'ca204', '4.01'], ['16', '5', 'around the sun', 'félix alcalá', 'dana calvo', 'july 5 , 2011', 'ca205', '4.81'], ['17', '6', 'the outsiders', 'marc roskin', 'julia ruchman', 'july 12 , 2011', 'ca206', '4.30'], ['18', '7', 'half a world away', 'félix alcalá', 'julia ruchman', 'july 19 , 2011', 'ca207', '4.55'], ['19', '8', 'welcome to the occupation', 'john fawcett', 'zak schwartz', 'july 26 , 2011', 'ca208', '4.36'], ['20', '9', 'sad professor', 'j miller tobin', 'alex berger', 'august 2 , 2011', 'ca209', '4.61'], ['21', '10', 'world leader pretend', 'kate woods', 'matt corman & chris ord', 'august 9 , 2011', 'ca210', '4.70'], ['22', '11', 'the wake - up bomb', 'stephen kay', 'stephen hootstein', 'november 1 , 2011', 'ca211', '2.70'], ['23', '12', 'uberlin', 'jonathan glassner', 'erica shelton', 'november 8 , 2011', 'ca212', '2.67'], ['24', '13', 'a girl like you', 'stephen kay', 'norman morrill', 'november 15 , 2011', 'ca213', '2.26'], ['25', '14', 'horse to water', 'rosemary rodriguez', 'alex berger', 'november 22 , 2011', 'ca214', '2.29'], ['26', '15', "what 's the frequency , kenneth", 'omar madha', 'donald joh', 'november 29 , 2011', 'ca215', '3.22']] |
mattia pasini | https://en.wikipedia.org/wiki/Mattia_Pasini | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13985563-1.html.csv | ordinal | mattia pasini competed in his 2nd fewest races during his 2010 season . | {'row': '7', 'col': '2', 'order': '2', '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', 'races', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; races ; 2 }'}, 'season'], 'result': '2010', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; races ; 2 } ; season }'}, '2010'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; races ; 2 } ; season } ; 2010 } = true', 'tointer': 'select the row whose races record of all rows is 2nd minimum . the season record of this row is 2010 .'} | eq { hop { nth_argmin { all_rows ; races ; 2 } ; season } ; 2010 } = true | select the row whose races record of all rows is 2nd minimum . the season record of this row is 2010 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'races_5': 5, '2_6': 6, 'season_7': 7, '2010_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', 'races_5': 'races', '2_6': '2', 'season_7': 'season', '2010_8': '2010'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'races_5': [0], '2_6': [0], 'season_7': [1], '2010_8': [2]} | ['season', 'races', 'podiums', 'pole', 'flap'] | [['2004', '16', '0', '0', '0'], ['2005', '15', '6', '0', '0'], ['2006', '16', '6', '2', '2'], ['2007', '17', '5', '9', '2'], ['2008', '16', '4', '0', '0'], ['2009', '16', '5', '0', '0'], ['2010', '8', '0', '0', '0'], ['2011', '17', '0', '0', '0'], ['2012', '14', '0', '0', '0'], ['2012', '1', '0', '0', '0'], ['2013', '16', '0', '0', '0'], ['total', '152', '26', '11', '4']] |
clear lake ( oregon ) | https://en.wikipedia.org/wiki/Clear_Lake_%28Oregon%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12484336-1.html.csv | ordinal | clear lake of coos county , oregon is the body of water that has the second highest gnis id . | {'row': '12', 'col': '5', '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', 'gnis id', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; gnis id ; 2 }'}, 'name'], 'result': 'clear lake ( coos county , oregon )', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; gnis id ; 2 } ; name }'}, 'clear lake ( coos county , oregon )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; gnis id ; 2 } ; name } ; clear lake ( coos county , oregon ) } = true', 'tointer': 'select the row whose gnis id record of all rows is 2nd maximum . the name record of this row is clear lake ( coos county , oregon ) .'} | eq { hop { nth_argmax { all_rows ; gnis id ; 2 } ; name } ; clear lake ( coos county , oregon ) } = true | select the row whose gnis id record of all rows is 2nd maximum . the name record of this row is clear lake ( coos county , oregon ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'gnis id_5': 5, '2_6': 6, 'name_7': 7, 'clear lake (coos county , oregon)_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', 'gnis id_5': 'gnis id', '2_6': '2', 'name_7': 'name', 'clear lake (coos county , oregon)_8': 'clear lake ( coos county , oregon )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'gnis id_5': [0], '2_6': [0], 'name_7': [1], 'clear lake (coos county , oregon)_8': [2]} | ['name', 'type', 'elevation', 'usgs map', 'gnis id'] | [['clear lake ( douglas county , oregon )', 'lake', 'feet ( m )', 'winchester bay', '1139800'], ['clear lake ( wasco county , oregon )', 'reservoir', 'feet ( m )', 'wapinitia pass', '1139803'], ['clear lake ( amazon creek , oregon )', 'lake', 'feet ( m )', 'eugene west', '1119000'], ['clear lake ( marion county , oregon )', 'lake', 'feet ( m )', 'mission bottom', '1119001'], ['clear lake ( tillamook county , oregon )', 'lake', 'feet ( m )', 'garibaldi', '1119002'], ['clear lake ( clatsop county , oregon )', 'lake', 'feet ( m )', 'warrenton', '1119003'], ['clear lake , oregon', 'populated place', 'feet ( m )', 'mission bottom', '1119004'], ['clear lake ( florence , lane county , oregon )', 'lake', 'feet ( m )', 'mercer lake', '1139801'], ['clear lake ( clackamas county , oregon )', 'lake', 'feet ( m )', 'elwood', '1139802'], ['clear lake ( wallowa county , oregon )', 'lake', 'feet ( m )', 'clear lake ridge', '1139804'], ['clear lake ( linn county , oregon )', 'lake', 'feet ( m )', 'clear lake', '1139805'], ['clear lake ( coos county , oregon )', 'lake', 'feet ( m )', 'lakeside', '1154640'], ['malabon , oregon', 'historic locale', 'feet ( m )', 'eugene west', '1166549']] |
kansas jayhawk community college conference | https://en.wikipedia.org/wiki/Kansas_Jayhawk_Community_College_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12434380-2.html.csv | count | four of the colleges use white color as part of their school color . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'white', 'result': '4', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school colors', 'white'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school colors record fuzzily matches to white .', 'tostr': 'filter_eq { all_rows ; school colors ; white }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; school colors ; white } }', 'tointer': 'select the rows whose school colors record fuzzily matches to white . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; school colors ; white } } ; 4 } = true', 'tointer': 'select the rows whose school colors record fuzzily matches to white . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; school colors ; white } } ; 4 } = true | select the rows whose school colors record fuzzily matches to white . 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, 'school colors_5': 5, 'white_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', 'school colors_5': 'school colors', 'white_6': 'white', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'school colors_5': [0], 'white_6': [0], '4_7': [2]} | ['institution', 'main campus location', 'founded', 'mascot', 'school colors'] | [['barton community college', 'great bend', '1969', 'cougars', 'blue & gold'], ['butler community college', 'el dorado', '1927', 'grizzlies', 'purple & vegas gold'], ['cloud county community college', 'concordia', '1965', 'thunderbirds', 'black & gold'], ['colby community college', 'colby', '1964', 'trojans', 'blue & white'], ['dodge city community college', 'dodge city', '1935', 'conquistadors', 'purple & gold'], ['garden city community college', 'garden city', '1919', 'broncbusters', 'brown , gold & white'], ['hutchinson community college', 'hutchinson', '1928', 'blue dragons', 'blue & red'], ['pratt community college', 'pratt', '1938', 'beavers', 'royal blue & white'], ['seward county community college', 'liberal', '1969', 'saints', 'green & white']] |
1993 washington redskins season | https://en.wikipedia.org/wiki/1993_Washington_Redskins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14610099-1.html.csv | superlative | the game played on week 9 of the 1993 washington redskins season drew the highest attendance . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'week'], 'result': '9', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; week }'}, '9'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; week } ; 9 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the week record of this row is 9 .'} | eq { hop { argmax { all_rows ; attendance } ; week } ; 9 } = true | select the row whose attendance record of all rows is maximum . the week record of this row is 9 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'week_6': 6, '9_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'week_6': 'week', '9_7': '9'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'week_6': [1], '9_7': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 6 , 1993', 'dallas cowboys', 'w 35 - 16', '56345'], ['2', 'september 12 , 1993', 'phoenix cardinals', 'l 17 - 10', '53525'], ['3', 'september 19 , 1993', 'philadelphia eagles', 'l 34 - 31', '65435'], ['5', 'october 4 , 1993', 'miami dolphins', 'l 17 - 10', '68568'], ['6', 'october 10 , 1993', 'new york giants', 'l 41 - 7', '53715'], ['7', 'october 17 , 1993', 'phoenix cardinals', 'l 36 - 6', '48143'], ['9', 'november 1 , 1993', 'buffalo bills', 'l 24 - 10', '79106'], ['10', 'november 7 , 1993', 'indianapolis colts', 'w 30 - 24', '50523'], ['11', 'november 14 , 1993', 'new york giants', 'l 20 - 6', '76606'], ['12', 'november 21 , 1993', 'los angeles rams', 'l 10 - 6', '45546'], ['13', 'november 28 , 1993', 'philadelphia eagles', 'l 17 - 14', '46663'], ['14', 'december 5 , 1993', 'tampa bay buccaneers', 'w 23 - 17', '49035'], ['15', 'december 11 , 1993', 'new york jets', 'l 3 - 0', '47970'], ['16', 'december 19 , 1993', 'atlanta falcons', 'w 30 - 17', '50192'], ['17', 'december 26 , 1993', 'dallas cowboys', 'l 38 - 3', '64497'], ['18', 'december 31 , 1993', 'minnesota vikings', 'l 14 - 9', '42836']] |
1979 world figure skating championships | https://en.wikipedia.org/wiki/1979_World_Figure_Skating_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11312764-6.html.csv | superlative | the duo of natalia linichuk / gennadi karponosov scored the highest number of points in the 1979 world figure skating championships . | {'scope': 'all', 'col_superlative': '4', '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', 'points'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points }'}, 'name'], 'result': 'natalia linichuk / gennadi karponosov', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points } ; name }'}, 'natalia linichuk / gennadi karponosov'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points } ; name } ; natalia linichuk / gennadi karponosov } = true', 'tointer': 'select the row whose points record of all rows is maximum . the name record of this row is natalia linichuk / gennadi karponosov .'} | eq { hop { argmax { all_rows ; points } ; name } ; natalia linichuk / gennadi karponosov } = true | select the row whose points record of all rows is maximum . the name record of this row is natalia linichuk / gennadi karponosov . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, 'name_6': 6, 'natalia linichuk / gennadi karponosov_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', 'name_6': 'name', 'natalia linichuk / gennadi karponosov_7': 'natalia linichuk / gennadi karponosov'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], 'name_6': [1], 'natalia linichuk / gennadi karponosov_7': [2]} | ['rank', 'name', 'nation', 'points', 'places'] | [['1', 'natalia linichuk / gennadi karponosov', 'soviet union', '207.86', '9'], ['2', 'krisztina regőczy / andrás sallay', 'hungary', '204.10', '22'], ['3', 'irina moiseeva / andrei minenkov', 'soviet union', '203.74', '23'], ['4', 'liliana rehakova / stanislav drastich', 'czechoslovakia', '196.94', '36'], ['5', 'janet thompson / warren maxwell', 'united kingdom', '194.00', '51'], ['6', 'lorna wighton / john dowding', 'canada', '192.70', '52'], ['7', 'susi handschmann / peter handschmann', 'austria', '188.72', '65'], ['8', 'jayne torvill / christopher dean', 'united kingdom', '187.84', '71'], ['9', 'stacey smith / john summers', 'united states', '185.70', '81'], ['10', 'natalia bestemianova / andrei bukin', 'soviet union', '184.06', '87'], ['11', 'carol fox / richard dalley', 'united states', '180.30', '98'], ['12', 'henriette fröschl / christian steiner', 'west germany', '175.50', '108'], ['13', 'karen barber / nicky slater', 'united kingdom', '169.74', '121'], ['14', 'anna pisanská / jiri musil', 'czechoslovakia', '168.70', '124'], ['15', 'patricia fletcher / michael de la penotiere', 'canada', '165.24', '134'], ['16', 'martine olivier / yves tarayre', 'france', '161.44', '145'], ['17', 'jindra hola / karol foltan', 'czechoslovakia', '157.64', '157'], ['18', 'gabriella remport / sándor nagy', 'hungary', '158.26', '157'], ['19', 'yumiko kage / tadayuki takahashi', 'japan', '153.30', '175'], ['20', 'claudia koch / peter schübl', 'austria', '151.94', '174']] |
1977 - 78 coupe de france | https://en.wikipedia.org/wiki/1977%E2%80%9378_Coupe_de_France | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17905518-1.html.csv | unique | the 1st round between as angoulême and fc sochaux - montbéliard was the only one that ended with a 0-0 score . | {'scope': 'all', 'row': '6', 'col': '4', 'col_other': '1,3', 'criterion': 'equal', 'value': '0-0', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st round', '0-0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st round record fuzzily matches to 0-0 .', 'tostr': 'filter_eq { all_rows ; 1st round ; 0-0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; 1st round ; 0-0 } }', 'tointer': 'select the rows whose 1st round record fuzzily matches to 0-0 . 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', '1st round', '0-0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st round record fuzzily matches to 0-0 .', 'tostr': 'filter_eq { all_rows ; 1st round ; 0-0 }'}, 'team 1'], 'result': 'as angoulême ( d2 )', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 1 }'}, 'as angoulême ( d2 )'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 1 } ; as angoulême ( d2 ) }', 'tointer': 'the team 1 record of this unqiue row is as angoulême ( d2 ) .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st round', '0-0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st round record fuzzily matches to 0-0 .', 'tostr': 'filter_eq { all_rows ; 1st round ; 0-0 }'}, 'team 2'], 'result': 'fc sochaux - montbéliard ( d1 )', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 2 }'}, 'fc sochaux - montbéliard ( d1 )'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 2 } ; fc sochaux - montbéliard ( d1 ) }', 'tointer': 'the team 2 record of this unqiue row is fc sochaux - montbéliard ( d1 ) .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 1 } ; as angoulême ( d2 ) } ; eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 2 } ; fc sochaux - montbéliard ( d1 ) } }', 'tointer': 'the team 1 record of this unqiue row is as angoulême ( d2 ) . the team 2 record of this unqiue row is fc sochaux - montbéliard ( d1 ) .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; 1st round ; 0-0 } } ; and { eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 1 } ; as angoulême ( d2 ) } ; eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 2 } ; fc sochaux - montbéliard ( d1 ) } } } = true', 'tointer': 'select the rows whose 1st round record fuzzily matches to 0-0 . there is only one such row in the table . the team 1 record of this unqiue row is as angoulême ( d2 ) . the team 2 record of this unqiue row is fc sochaux - montbéliard ( d1 ) .'} | and { only { filter_eq { all_rows ; 1st round ; 0-0 } } ; and { eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 1 } ; as angoulême ( d2 ) } ; eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 2 } ; fc sochaux - montbéliard ( d1 ) } } } = true | select the rows whose 1st round record fuzzily matches to 0-0 . there is only one such row in the table . the team 1 record of this unqiue row is as angoulême ( d2 ) . the team 2 record of this unqiue row is fc sochaux - montbéliard ( d1 ) . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, '1st round_10': 10, '0-0_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'team 1_12': 12, 'as angoulême (d2)_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'team 2_14': 14, 'fc sochaux - montbéliard (d1)_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', '1st round_10': '1st round', '0-0_11': '0-0', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team 1_12': 'team 1', 'as angoulême (d2)_13': 'as angoulême ( d2 )', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'team 2_14': 'team 2', 'fc sochaux - montbéliard (d1)_15': 'fc sochaux - montbéliard ( d1 )'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], '1st round_10': [0], '0-0_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'team 1_12': [2], 'as angoulême (d2)_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'team 2_14': [4], 'fc sochaux - montbéliard (d1)_15': [5]} | ['team 1', 'score', 'team 2', '1st round', '2nd round'] | [['stade de reims ( d1 )', '1 - 3', 'sc bastia ( d1 )', '0 - 1', '1 - 2'], ['fc metz ( d1 )', '0 - 5', 'ogc nice ( d1 )', '0 - 2', '0 - 3'], ['olympique de marseille ( d1 )', '3 - 0', 'girondins de bordeaux ( d1 )', '1 - 0', '2 - 0'], ['as nancy ( d1 )', '3 - 1', 'fc martigues ( d2 )', '2 - 0', '1 - 1'], ['lille osc ( d2 )', '3 - 4', 'as monaco ( d1 )', '1 - 1', '2 - 3'], ['as angoulême ( d2 )', '0 - 1', 'fc sochaux - montbéliard ( d1 )', '0 - 0', '0 - 1'], ['fc nantes ( d1 )', '7 - 0', 'usl dunkerque ( d2 )', '2 - 0', '5 - 0'], ['gazélec ajaccio ( d2 )', '4 - 6', 'us valenciennes ( d1 )', '4 - 1', '0 - 5']] |
1994 - 95 philadelphia flyers season | https://en.wikipedia.org/wiki/1994%E2%80%9395_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14022127-7.html.csv | aggregation | the average attendance of the philadelphia flyers games was 16325 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '16325', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '16325', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '16325'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 16325 } = true', 'tointer': 'the average of the attendance record of all rows is 16325 .'} | round_eq { avg { all_rows ; attendance } ; 16325 } = true | the average of the attendance record of all rows is 16325 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '16325_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '16325_5': '16325'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '16325_5': [1]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'series'] | [['may 7', 'buffalo', '3 - 4', 'philadelphia', 'hextall', '17380', 'flyers lead 1 - 0'], ['may 8', 'buffalo', '1 - 3', 'philadelphia', 'hextall', '17380', 'flyers lead 2 - 0'], ['may 10', 'philadelphia', '1 - 3', 'buffalo', 'hextall', '13256', 'flyers lead 2 - 1'], ['may 12', 'philadelphia', '4 - 2', 'buffalo', 'hextall', '16230', 'flyers lead 3 - 1'], ['may 14', 'buffalo', '4 - 6', 'philadelphia', 'hextall', '17380', 'flyers win 4 - 1']] |
fred astaire chronology of performances | https://en.wikipedia.org/wiki/Fred_Astaire_chronology_of_performances | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15186990-3.html.csv | ordinal | in the chronology of fred astaire 's performances , the love letter is the title of the earliest partnered dance . | {'row': '1', 'col': '2', 'order': '1', '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', 'date', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 1 }'}, 'title'], 'result': 'the love letter', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 1 } ; title }'}, 'the love letter'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 1 } ; title } ; the love letter } = true', 'tointer': 'select the row whose date record of all rows is 1st minimum . the title record of this row is the love letter .'} | eq { hop { nth_argmin { all_rows ; date ; 1 } ; title } ; the love letter } = true | select the row whose date record of all rows is 1st minimum . the title record of this row is the love letter . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '1_6': 6, 'title_7': 7, 'the love letter_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', 'date_5': 'date', '1_6': '1', 'title_7': 'title', 'the love letter_8': 'the love letter'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '1_6': [0], 'title_7': [1], 'the love letter_8': [2]} | ['title', 'date', 'theatre', 'role', 'dance partner', 'director', 'lyrics', 'music'] | [['the love letter', 'oct 4 1921', 'globe', 'richard kolner', 'adele astaire', 'edward royce', 'william lebaron', 'victor jacobi'], ['for goodness sake', 'feb 20 1922', 'lyric', 'teddy lawrence', 'adele astaire', 'priestley morrison', 'arthur jackson', 'william daly paul lannin'], ['the bunch and judy', 'nov 28 1922', 'globe', 'gerald lane', 'adele astaire', 'fred latham', 'anne caldwell', 'jerome kern'], ['stop flirting ( ( for goodness sake ) )', 'may 30 , 1923', 'shaftsbury queens strand', 'teddy lawrence', 'adele astaire', 'felix edwardes', 'arthur jackson', 'william daly paul lannin'], ['lady , be good', 'dec 1 1924', 'liberty', 'dick trevor', 'adele astaire', 'felix edwardes', 'ira gershwin', 'george gershwin'], ['lady , be good', 'apr 14 1926', 'empire', 'dick trevor', 'adele astaire', 'felix edwardes', 'ira gershwin', 'george gershwin'], ['funny face', 'nov 22 1927', 'alvin', 'jimmie reeves', 'adele astaire', 'edward macgregor', 'ira gershwin', 'george gershwin'], ['funny face', 'nov 8 1928', "prince 's theatre", 'jimmie reeves', 'adele astaire', 'felix edwardes', 'ira gershwin', 'george gershwin'], ['smiles', 'nov 18 1930', 'ziegfeld', 'bob hastings', 'adele astaire marilyn miller', 'william anthony mcguire', 'clifford grey harold adamson ring lardner', 'vincent youmans']] |
united states house of representatives elections , 1986 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1986 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341586-19.html.csv | majority | most of the louisiana representatives in the seventies and eighties were democrats . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'democratic', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'party', 'democratic'], 'result': True, 'ind': 0, 'tointer': 'for the party records of all rows , most of them fuzzily match to democratic .', 'tostr': 'most_eq { all_rows ; party ; democratic } = true'} | most_eq { all_rows ; party ; democratic } = true | for the party records of all rows , most of them fuzzily match to democratic . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'party_3': 3, 'democratic_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'party_3': 'party', 'democratic_4': 'democratic'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'party_3': [0], 'democratic_4': [0]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['louisiana 1', 'bob livingston', 'republican', '1977', 're - elected', 'bob livingston ( r ) unopposed'], ['louisiana 2', 'lindy boggs', 'democratic', '1973', 're - elected', 'lindy boggs ( d ) unopposed'], ['louisiana 3', 'billy tauzin', 'democratic', '1980', 're - elected', 'billy tauzin ( d ) unopposed'], ['louisiana 4', 'buddy roemer', 'democratic', '1980', 're - elected', 'buddy roemer ( d ) unopposed'], ['louisiana 5', 'jerry huckaby', 'democratic', '1976', 're - elected', 'jerry huckaby ( d ) unopposed'], ['louisiana 6', 'henson moore', 'republican', '1974', 'retired to run for u s senate republican hold', 'richard baker ( r ) unopposed']] |
list of tallest buildings in tampa | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Tampa | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17983290-2.html.csv | count | in the list of tallest buildings in tampa , 2 of the buildings in north franklin street has a height ft of more than 200 ft. | {'scope': 'subset', 'criterion': 'greater_than', 'value': '200', 'result': '2', 'col': '4', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'north franklin street'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'street address', 'north franklin street'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; street address ; north franklin street }', 'tointer': 'select the rows whose street address record fuzzily matches to north franklin street .'}, 'height ft ( m )', '200'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose street address record fuzzily matches to north franklin street . among these rows , select the rows whose height ft ( m ) record is greater than 200 .', 'tostr': 'filter_greater { filter_eq { all_rows ; street address ; north franklin street } ; height ft ( m ) ; 200 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_greater { filter_eq { all_rows ; street address ; north franklin street } ; height ft ( m ) ; 200 } }', 'tointer': 'select the rows whose street address record fuzzily matches to north franklin street . among these rows , select the rows whose height ft ( m ) record is greater than 200 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_eq { all_rows ; street address ; north franklin street } ; height ft ( m ) ; 200 } } ; 2 } = true', 'tointer': 'select the rows whose street address record fuzzily matches to north franklin street . among these rows , select the rows whose height ft ( m ) record is greater than 200 . the number of such rows is 2 .'} | eq { count { filter_greater { filter_eq { all_rows ; street address ; north franklin street } ; height ft ( m ) ; 200 } } ; 2 } = true | select the rows whose street address record fuzzily matches to north franklin street . among these rows , select the rows whose height ft ( m ) record is greater than 200 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'street address_6': 6, 'north franklin street_7': 7, 'height ft (m)_8': 8, '200_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'street address_6': 'street address', 'north franklin street_7': 'north franklin street', 'height ft (m)_8': 'height ft ( m )', '200_9': '200', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'street address_6': [0], 'north franklin street_7': [0], 'height ft (m)_8': [1], '200_9': [1], '2_10': [3]} | ['name', 'street address', 'years as tallest', 'height ft ( m )', 'floors'] | [['citizens bank building', '701 north franklin street', '1913 - 1915', '145 ( 44 )', '12'], ['tampa city hall', '315 john f kennedy boulevard', '1915 - 1926', '160 ( 49 )', '10'], ['floridan hotel', '905 franklin street', '1926 - 1966', '204 ( 62 )', '17'], ['franklin exchange building', '655 north franklin street', '1966 - 1972', '280 ( 85 )', '22'], ['park tower', '400 north tampa street', '1972 - 1981', '458 ( 140 )', '36'], ['one tampa city center', '201 north franklin street', '1981 - 1986', '537 ( 164 )', '39'], ['bank of america tower', '101 east kennedy boulevard', '1986 - 1992', '577 ( 176 )', '42'], ['100 north tampa', '100 north tampa street', '1992 - present', '579 ( 177 )', '42']] |
thierry boutsen | https://en.wikipedia.org/wiki/Thierry_Boutsen | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1114709-3.html.csv | aggregation | in thierry boutsen 's races where he finished , he completed a total of 1359 laps . | {'scope': 'subset', 'col': '6', 'type': 'sum', 'result': '1359', 'subset': {'col': '7', 'criterion': 'not_equal', 'value': 'dnf'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'pos', 'dnf'], 'result': None, 'ind': 0, 'tostr': 'filter_not_eq { all_rows ; pos ; dnf }', 'tointer': 'select the rows whose pos record does not match to dnf .'}, 'laps'], 'result': '1359', 'ind': 1, 'tostr': 'sum { filter_not_eq { all_rows ; pos ; dnf } ; laps }'}, '1359'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_not_eq { all_rows ; pos ; dnf } ; laps } ; 1359 } = true', 'tointer': 'select the rows whose pos record does not match to dnf . the sum of the laps record of these rows is 1359 .'} | round_eq { sum { filter_not_eq { all_rows ; pos ; dnf } ; laps } ; 1359 } = true | select the rows whose pos record does not match to dnf . the sum of the laps record of these rows is 1359 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_4': 4, 'pos_5': 5, 'dnf_6': 6, 'laps_7': 7, '1359_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_4': 'all_rows', 'pos_5': 'pos', 'dnf_6': 'dnf', 'laps_7': 'laps', '1359_8': '1359'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_not_eq_0': [1], 'all_rows_4': [0], 'pos_5': [0], 'dnf_6': [0], 'laps_7': [1], '1359_8': [2]} | ['year', 'class', 'tyres', 'team', 'co - drivers', 'laps', 'pos', 'class pos'] | [['1981', 'c', 'm', 'wm aerem', 'serge saulnier michel pignard', '15', 'dnf', 'dnf'], ['1983', 'c', 'm', 'ford france', 'henri pescarolo', '174', 'dnf', 'dnf'], ['1986', 'c1', 'm', 'brun motorsport', 'didier theys alain fertã', '89', 'dnf', 'dnf'], ['1993', 'c1', 'm', 'peugeot talbot sport', 'yannick dalmas teo fabi', '374', '2nd', '2nd'], ['1994', 'gt1', 'g', 'le mans porsche team joest racing', 'hans joachim stuck danny sullivan', '343', '3rd', '2nd'], ['1995', 'wsc', 'g', 'porsche kremer racing', 'hans joachim stuck christophe bouchut', '289', '6th', '2nd'], ['1996', 'gt1', 'm', 'porsche ag', 'hans joachim stuck bob wollek', '353', '2nd', '1st'], ['1997', 'gt1', 'g', 'porsche ag', 'hans joachim stuck bob wollek', '238', 'dnf', 'dnf'], ['1998', 'gt1', 'm', 'toyota motorsports toyota team europe', 'ralf kelleners geoff lees', '330', 'dnf', 'dnf'], ['1999', 'lmgtp', 'm', 'toyota motorsports toyota team europe', 'ralf kelleners allan mcnish', '173', 'dnf', 'dnf']] |
list of kentucky derby broadcasters | https://en.wikipedia.org/wiki/List_of_Kentucky_Derby_broadcasters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22514845-4.html.csv | comparative | eddie arcaro was an s analyst for the kentucky derby earlier than bill hartack . | {'row_1': '9', 'row_2': '7', 'col': '1', 'col_other': '5', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 's analyst', 'eddie arcaro'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose s analyst record fuzzily matches to eddie arcaro .', 'tostr': 'filter_eq { all_rows ; s analyst ; eddie arcaro }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; s analyst ; eddie arcaro } ; year }', 'tointer': 'select the rows whose s analyst record fuzzily matches to eddie arcaro . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 's analyst', 'bill hartack'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose s analyst record fuzzily matches to bill hartack .', 'tostr': 'filter_eq { all_rows ; s analyst ; bill hartack }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; s analyst ; bill hartack } ; year }', 'tointer': 'select the rows whose s analyst record fuzzily matches to bill hartack . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; s analyst ; eddie arcaro } ; year } ; hop { filter_eq { all_rows ; s analyst ; bill hartack } ; year } } = true', 'tointer': 'select the rows whose s analyst record fuzzily matches to eddie arcaro . take the year record of this row . select the rows whose s analyst record fuzzily matches to bill hartack . take the year record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; s analyst ; eddie arcaro } ; year } ; hop { filter_eq { all_rows ; s analyst ; bill hartack } ; year } } = true | select the rows whose s analyst record fuzzily matches to eddie arcaro . take the year record of this row . select the rows whose s analyst record fuzzily matches to bill hartack . take the year 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, 's analyst_7': 7, 'eddie arcaro_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 's analyst_11': 11, 'bill hartack_12': 12, 'year_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', 's analyst_7': 's analyst', 'eddie arcaro_8': 'eddie arcaro', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 's analyst_11': 's analyst', 'bill hartack_12': 'bill hartack', 'year_13': 'year'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 's analyst_7': [0], 'eddie arcaro_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 's analyst_11': [1], 'bill hartack_12': [1], 'year_13': [3]} | ['year', 'network', 'race caller', 's host', 's analyst', 'reporters', 'trophy presentation'] | [['1989', 'abc', 'dave johnson', 'jim mckay and al michaels', 'charlsie cantey and dave johnson', 'jack whitaker and lynn swann', 'jim mckay'], ['1988', 'abc', 'dave johnson', 'jim mckay and al michaels', 'charlsie cantey and dave johnson', 'jack whitaker and lynn swann', 'jim mckay'], ['1987', 'abc', 'dave johnson', 'jim mckay and al michaels', 'charlsie cantey and dave johnson', 'jack whitaker and lynn swann', 'jim mckay'], ['1986', 'abc', 'mike battaglia', 'jim mckay and al michaels', 'charlsie cantey and bill hartack', 'jack whitaker and lynn swann', 'jim mckay'], ['1985', 'abc', 'mike battaglia', 'jim mckay', 'bill hartack', 'howard cosell and jack whitaker', 'jim mckay'], ['1984', 'abc', 'mike battaglia', 'jim mckay', 'bill hartack', 'howard cosell and jack whitaker', 'jim mckay'], ['1983', 'abc', 'mike battaglia', 'jim mckay', 'bill hartack', 'howard cosell , frank gifford , and jack whitaker', 'jim mckay'], ['1982', 'abc', 'mike battaglia', 'jim mckay', 'john m veitch', 'howard cosell and jack whitaker', 'jim mckay'], ['1981', 'abc', 'mike battaglia', 'jim mckay', 'eddie arcaro', 'howard cosell', 'jim mckay and howard cosell']] |
list of argumental episodes | https://en.wikipedia.org/wiki/List_of_Argumental_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19930660-2.html.csv | superlative | episode 2x10 of argumental was the episode in which the blue team recorded their highest score . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '9', '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', 'winner'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; winner }'}, 'episode'], 'result': '2x10', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; winner } ; episode }'}, '2x10'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; winner } ; episode } ; 2x10 } = true', 'tointer': 'select the row whose winner record of all rows is maximum . the episode record of this row is 2x10 .'} | eq { hop { argmax { all_rows ; winner } ; episode } ; 2x10 } = true | select the row whose winner record of all rows is maximum . the episode record of this row is 2x10 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'winner_5': 5, 'episode_6': 6, '2x10_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'winner_5': 'winner', 'episode_6': 'episode', '2x10_7': '2x10'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'winner_5': [0], 'episode_6': [1], '2x10_7': [2]} | ['episode', 'first broadcast', 'rufus guest', 'marcus guest', 'winner'] | [['2x01', '23 march 2009', 'chris addison', 'dara ó briain', 'red ( 3 - 2 )'], ['2x02', '30 march 2009', 'mark watson', "ardal o'hanlon", 'red ( 2 - 2 )'], ['2x03', '6 april 2009', 'jo caulfield', 'katy brand', 'red ( 3 - 2 )'], ['2x05', '27 april 2009', 'reginald d hunter', 'sean hughes', 'blue ( 3 - 2 )'], ['2x06', '4 may 2009', 'frankie boyle', 'lucy porter', 'blue ( 3 - 2 )'], ['2x07', '13 october 2009', 'andrew maxwell', 'frankie boyle', 'blue ( 3 - 1 )'], ['2x08', '20 october 2009', 'sean lock', 'phill jupitus', 'blue ( 3 - 1 )'], ['2x09', '27 october 2009', 'dara ó briain', 'will smith', 'blue ( 4 - 1 )'], ['2x10', '3 november 2009', 'simon day', 'charlie higson', 'blue ( 5 - 0 )'], ['2x11', '10 november 2009', 'rory mcgrath', 'sean hughes', 'blue ( 3 - 1 )'], ['2x12', '17 november 2009', 'hugh dennis', 'mark watson', 'red ( 3 - 2 )'], ['2x13', '24 november 2009', 'clips show : episodes 1 - 6', 'clips show : episodes 1 - 6', 'n / a']] |
valentino rossi | https://en.wikipedia.org/wiki/Valentino_Rossi | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-180306-2.html.csv | aggregation | valentino rossi 's podium placements from 1996 to present averages 48 per race class . | {'scope': 'all', 'col': '7', 'type': 'average', 'result': '48', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'podiums'], 'result': '48', 'ind': 0, 'tostr': 'avg { all_rows ; podiums }'}, '48'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; podiums } ; 48 } = true', 'tointer': 'the average of the podiums record of all rows is 48 .'} | round_eq { avg { all_rows ; podiums } ; 48 } = true | the average of the podiums record of all rows is 48 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'podiums_4': 4, '48_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'podiums_4': 'podiums', '48_5': '48'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'podiums_4': [0], '48_5': [1]} | ['class', 'seas', '1st gp', '1st pod', '1st win', 'race', 'podiums', 'pole', 'flap', 'wchmp'] | [['125 cc', '1996 - 1997', '1996 malaysia', '1996 austria', '1996 czech rep', '30', '15', '5', '9', '1'], ['250 cc', '1998 - 1999', '1998 japan', '1998 spain', '1998 dutch', '30', '21', '5', '11', '1'], ['500 cc', '2000 - 2001', '2000 south af', '2000 spain', '2000 british', '32', '23', '4', '15', '1'], ['motogp', '2002 - present', '2002 japan', '2002 japan', '2002 japan', '201', '124', '45', '53', '6'], ['total', '1996 - present', '1996 - present', '1996 - present', '1996 - present', '293', '183', '59', '88', '9']] |
1984 winter olympics | https://en.wikipedia.org/wiki/1984_Winter_Olympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-113362-4.html.csv | count | in the 1984 winter olympics , among the nations that won 2 gold medals , 2 of them won 4 medals in total each . | {'scope': 'subset', 'criterion': 'equal', 'value': '4', 'result': '2', 'col': '6', 'subset': {'col': '3', 'criterion': 'equal', 'value': '2'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'gold', '2'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; gold ; 2 }', 'tointer': 'select the rows whose gold record is equal to 2 .'}, 'total', '4'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose gold record is equal to 2 . among these rows , select the rows whose total record is equal to 4 .', 'tostr': 'filter_eq { filter_eq { all_rows ; gold ; 2 } ; total ; 4 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; gold ; 2 } ; total ; 4 } }', 'tointer': 'select the rows whose gold record is equal to 2 . among these rows , select the rows whose total record is equal to 4 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; gold ; 2 } ; total ; 4 } } ; 2 } = true', 'tointer': 'select the rows whose gold record is equal to 2 . among these rows , select the rows whose total record is equal to 4 . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; gold ; 2 } ; total ; 4 } } ; 2 } = true | select the rows whose gold record is equal to 2 . among these rows , select the rows whose total record is equal to 4 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_eq_0': 0, 'all_rows_5': 5, 'gold_6': 6, '2_7': 7, 'total_8': 8, '4_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_eq_0': 'filter_eq', 'all_rows_5': 'all_rows', 'gold_6': 'gold', '2_7': '2', 'total_8': 'total', '4_9': '4', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_eq_0': [1], 'all_rows_5': [0], 'gold_6': [0], '2_7': [0], 'total_8': [1], '4_9': [1], '2_10': [3]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'east germany ( gdr )', '9', '9', '6', '24'], ['2', 'soviet union ( urs )', '6', '10', '9', '25'], ['3', 'united states ( usa )', '4', '4', '0', '8'], ['4', 'finland ( fin )', '4', '3', '6', '13'], ['5', 'sweden ( swe )', '4', '2', '2', '8'], ['6', 'norway ( nor )', '3', '2', '4', '9'], ['7', 'switzerland ( sui )', '2', '2', '1', '5'], ['8', 'canada ( can )', '2', '1', '1', '4'], ['8', 'west germany ( frg )', '2', '1', '1', '4'], ['10', 'italy ( ita )', '2', '0', '0', '2'], ['14', 'yugoslavia ( yug )', '0', '1', '0', '1']] |
2007 kansas lottery indy 300 | https://en.wikipedia.org/wiki/2007_Kansas_Lottery_Indy_300 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17693171-1.html.csv | majority | most of the drivers had 0 as their lap led values during the 2007 kansas lottery indy 300 . | {'scope': 'all', 'col': '8', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'laps led', '0'], 'result': True, 'ind': 0, 'tointer': 'for the laps led records of all rows , most of them are equal to 0 .', 'tostr': 'most_eq { all_rows ; laps led ; 0 } = true'} | most_eq { all_rows ; laps led ; 0 } = true | for the laps led records of all rows , most of them are equal to 0 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'laps led_3': 3, '0_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'laps led_3': 'laps led', '0_4': '0'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'laps led_3': [0], '0_4': [0]} | ['fin pos', 'car no', 'driver', 'team', 'laps', 'time / retired', 'grid', 'laps led', 'points'] | [['1', '10', 'dan wheldon', 'target chip ganassi', '200', '1:36:56.0586', '4', '177', '50 + 3'], ['2', '27', 'dario franchitti', 'andretti green', '200', '+ 18.4830', '6', '0', '40'], ['3', '3', 'hãlio castroneves', 'team penske', '200', '+ 33.2280', '3', '0', '35'], ['4', '9', 'scott dixon', 'target chip ganassi', '200', '+ 34.4208', '5', '16', '32'], ['5', '2', 'tomas scheckter', 'vision racing', '199', '+ 1 lap', '7', '0', '30'], ['6', '6', 'sam hornish , jr', 'team penske', '199', '+ 1 lap', '2', '0', '28'], ['7', '7', 'danica patrick', 'andretti green', '198', '+ 2 laps', '10', '0', '26'], ['8', '4', 'vitor meira', 'panther racing', '198', '+ 2 laps', '8', '0', '24'], ['9', '22', 'a j foyt iv', 'vision racing', '198', '+ 2 laps', '15', '0', '22'], ['10', '17', 'jeff simmons', 'rahal letterman', '198', '+ 2 laps', '16', '0', '20'], ['11', '14', 'darren manning', 'aj foyt racing', '198', '+ 2 laps', '11', '0', '19'], ['12', '5', 'sarah fisher', 'dreyer & reinbold racing', '196', '+ 4 laps', '17', '0', '18'], ['13', '8', 'scott sharp', 'rahal letterman', '195', 'accident', '14', '0', '17'], ['14', '23', 'milka duno', 'samax motorsport', '194', '+ 6 laps', '21', '0', '16'], ['15', '11', 'tony kanaan', 'andretti green racing', '192', '+ 8 laps', '1', '7', '15'], ['16', '98', 'alex barron', 'curb / agajanian / beck', '191', '+ 9 laps', '20', '0', '14'], ['17', '20', 'ed carpenter', 'vision racing', '99', 'accident', '13', '0', '13'], ['18', '55', 'kosuke matsuura', 'super aguri panther racing', '57', 'mechanical', '12', '0', '12'], ['19', '26', 'marco andretti', 'andretti green racing', '43', 'mechanical', '9', '0', '12'], ['20', '15', 'buddy rice', 'dreyer & reinbold racing', '37', 'mechanical', '18', '0', '12']] |
1947 world series | https://en.wikipedia.org/wiki/1947_World_Series | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1332364-1.html.csv | majority | most of the games in the 1947 world series were played at yankee stadium , giving the new york yankees the home field advantage during the series . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'yankee stadium', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'location', 'yankee stadium'], 'result': True, 'ind': 0, 'tointer': 'for the location records of all rows , most of them fuzzily match to yankee stadium .', 'tostr': 'most_eq { all_rows ; location ; yankee stadium } = true'} | most_eq { all_rows ; location ; yankee stadium } = true | for the location records of all rows , most of them fuzzily match to yankee stadium . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'location_3': 3, 'yankee stadium_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'location_3': 'location', 'yankee stadium_4': 'yankee stadium'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'location_3': [0], 'yankee stadium_4': [0]} | ['game', 'date', 'score', 'location', 'time', 'attendance'] | [['1', 'september 30', 'brooklyn dodgers - 3 , new york yankees - 5', 'yankee stadium ( i )', '2:20', '73365'], ['2', 'october 1', 'brooklyn dodgers - 3 , new york yankees - 10', 'yankee stadium ( i )', '2:36', '69865'], ['3', 'october 2', 'new york yankees - 8 , brooklyn dodgers - 9', 'ebbets field', '3:05', '33098'], ['4', 'october 3', 'new york yankees - 2 , brooklyn dodgers - 3', 'ebbets field', '2:20', '33443'], ['5', 'october 4', 'new york yankees - 2 , brooklyn dodgers - 1', 'ebbets field', '2:46', '34379'], ['6', 'october 5', 'brooklyn dodgers - 8 , new york yankees - 6', 'yankee stadium ( i )', '3:19', '74065'], ['7', 'october 6', 'brooklyn dodgers - 2 , new york yankees - 5', 'yankee stadium ( i )', '2:19', '71548']] |
vehicles & animals | https://en.wikipedia.org/wiki/Vehicles_%26_Animals | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1546629-3.html.csv | unique | the version of the album vehicles & animals from the label astralwerks was the only release of the album in the united states . | {'scope': 'all', 'row': '4', 'col': '1', 'col_other': '3', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; country ; united states }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; united states } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; country ; united states }'}, 'label'], 'result': 'astralwerks', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; united states } ; label }'}, 'astralwerks'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; united states } ; label } ; astralwerks }', 'tointer': 'the label record of this unqiue row is astralwerks .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; united states } } ; eq { hop { filter_eq { all_rows ; country ; united states } ; label } ; astralwerks } } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . there is only one such row in the table . the label record of this unqiue row is astralwerks .'} | and { only { filter_eq { all_rows ; country ; united states } } ; eq { hop { filter_eq { all_rows ; country ; united states } ; label } ; astralwerks } } = true | select the rows whose country record fuzzily matches to united states . there is only one such row in the table . the label record of this unqiue row is astralwerks . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'united states_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'label_9': 9, 'astralwerks_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'united states_8': 'united states', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'label_9': 'label', 'astralwerks_10': 'astralwerks'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'united states_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'label_9': [2], 'astralwerks_10': [3]} | ['country', 'date', 'label', 'format', 'catalog'] | [['united kingdom', '7 april 2003', 'parlophone', 'lp', '582 2911'], ['united kingdom', '7 april 2003', 'parlophone', 'cd', '582 2912'], ['united kingdom', '7 april 2003', 'parlophone', 'cd digipak', '584 2112'], ['united states', '18 may 2004', 'astralwerks', 'cd', 'asw 82291'], ['australia', '14 march 2005', 'capitol records', 'cd', '582 3412']] |
combined associated schools | https://en.wikipedia.org/wiki/Combined_Associated_Schools | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1553749-1.html.csv | aggregation | average enrollment of anglican combined associated schools in 1929 was 1,833.33 . | {'scope': 'subset', 'col': '3', 'type': 'average', 'result': '1,833.33', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'anglican'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'denomination', 'anglican'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; denomination ; anglican }', 'tointer': 'select the rows whose denomination record fuzzily matches to anglican .'}, 'enrolment'], 'result': '1,833.33', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; denomination ; anglican } ; enrolment }'}, '1,833.33'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; denomination ; anglican } ; enrolment } ; 1,833.33 } = true', 'tointer': 'select the rows whose denomination record fuzzily matches to anglican . the average of the enrolment record of these rows is 1,833.33 .'} | round_eq { avg { filter_eq { all_rows ; denomination ; anglican } ; enrolment } ; 1,833.33 } = true | select the rows whose denomination record fuzzily matches to anglican . the average of the enrolment record of these rows is 1,833.33 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'denomination_5': 5, 'anglican_6': 6, 'enrolment_7': 7, '1,833.33_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'denomination_5': 'denomination', 'anglican_6': 'anglican', 'enrolment_7': 'enrolment', '1,833.33_8': '1,833.33'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'denomination_5': [0], 'anglican_6': [0], 'enrolment_7': [1], '1,833.33_8': [2]} | ['school', 'location', 'enrolment', 'founded', 'denomination', 'boys / girls', 'day / boarding', 'year entered competition', 'school colors'] | [["st aloysius ' college", 'milsons point', '1200', '1879', 'catholic', 'boys', 'day', '1929', 'royal blue and gold'], ['barker college', 'hornsby', '2300', '1890', 'anglican', 'boys only to yr 9 co - ed year 10 to 12', 'day & boarding', '1929', 'red & blue'], ['cranbrook school', 'bellevue hill', '1000', '1918', 'anglican', 'boys', 'day & boarding', '1929', 'red , white & blue'], ['knox grammar school', 'wahroonga', '1850', '1924', 'uniting church', 'boys', 'day & boarding', '1929', 'black & blue'], ['trinity grammar school', 'summer hill', '2200', '1913', 'anglican', 'boys', 'day & boarding', '1929', 'green and white'], ['waverley college', 'waverley', '1430', '1903', 'catholic', 'boys', 'day', '1944', 'royal blue and gold']] |
wuji county | https://en.wikipedia.org/wiki/Wuji_County | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12425097-1.html.csv | majority | the majority of the towns or townships have an area larger than 40km squared . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '40', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'area ( km square )', '40'], 'result': True, 'ind': 0, 'tointer': 'for the area ( km square ) records of all rows , most of them are greater than 40 .', 'tostr': 'most_greater { all_rows ; area ( km square ) ; 40 } = true'} | most_greater { all_rows ; area ( km square ) ; 40 } = true | for the area ( km square ) records of all rows , most of them are greater than 40 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'area (km square)_3': 3, '40_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'area (km square)_3': 'area ( km square )', '40_4': '40'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'area (km square)_3': [0], '40_4': [0]} | ['name', 'hanzi', 'area ( km square )', 'population', 'villages'] | [['wuji town', '无极镇', '57', '76851', '25'], ['qiji town', '七汲镇', '54', '41584', '20'], ['zhangduangu town', '张段固镇', '51', '40916', '20'], ['beisu town', '北苏镇', '54', '54639', '18'], ['guozhuang town', '郭庄镇', '43', '43636', '23'], ['dachen town', '大陈镇', '42', '31297', '13'], ['haozhuang township', '郝庄乡', '55', '37786', '19'], ['donghoufang township', '东侯坊乡', '56', '48665', '24'], ['lichengdao township', '里城道乡', '44', '40411', '19'], ['nanliu township', '南流乡', '30', '24802', '12'], ['gaotou hui autonomous township', '高头回族乡', '32', '33722', '15']] |
1965 vfl season | https://en.wikipedia.org/wiki/1965_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10788451-14.html.csv | aggregation | the average crowd attendance for the vfl games played was 23360 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '23360', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '23360', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '23360'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 23360 } = true', 'tointer': 'the average of the crowd record of all rows is 23360 .'} | round_eq { avg { all_rows ; crowd } ; 23360 } = true | the average of the crowd record of all rows is 23360 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '23360_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '23360_5': '23360'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '23360_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '10.17 ( 77 )', 'north melbourne', '5.4 ( 34 )', 'kardinia park', '19658', '31 july 1965'], ['essendon', '13.18 ( 96 )', 'footscray', '6.11 ( 47 )', 'windy hill', '16800', '31 july 1965'], ['carlton', '9.19 ( 73 )', 'south melbourne', '13.12 ( 90 )', 'princes park', '20744', '31 july 1965'], ['st kilda', '14.12 ( 96 )', 'richmond', '11.17 ( 83 )', 'moorabbin oval', '34076', '31 july 1965'], ['melbourne', '12.11 ( 83 )', 'fitzroy', '11.15 ( 81 )', 'mcg', '30381', '31 july 1965'], ['hawthorn', '8.12 ( 60 )', 'collingwood', '12.22 ( 94 )', 'glenferrie oval', '18500', '31 july 1965']] |
1935 vfl season | https://en.wikipedia.org/wiki/1935_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10790651-2.html.csv | aggregation | the average crowd in attendance at a 1935 vfl season match was 22250 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '22250', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '22250', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '22250'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 22250 } = true', 'tointer': 'the average of the crowd record of all rows is 22250 .'} | round_eq { avg { all_rows ; crowd } ; 22250 } = true | the average of the crowd record of all rows is 22250 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '22250_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '22250_5': '22250'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '22250_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '14.20 ( 104 )', 'melbourne', '16.6 ( 102 )', 'corio oval', '11000', '4 may 1935'], ['essendon', '13.17 ( 95 )', 'st kilda', '13.14 ( 92 )', 'windy hill', '21500', '4 may 1935'], ['richmond', '14.11 ( 95 )', 'north melbourne', '10.12 ( 72 )', 'punt road oval', '14000', '4 may 1935'], ['south melbourne', '17.11 ( 113 )', 'footscray', '15.9 ( 99 )', 'lake oval', '28000', '4 may 1935'], ['fitzroy', '14.9 ( 93 )', 'collingwood', '14.9 ( 93 )', 'brunswick street oval', '36000', '6 may 1935'], ['hawthorn', '9.6 ( 60 )', 'carlton', '14.27 ( 111 )', 'glenferrie oval', '23000', '6 may 1935']] |
howard county delegation | https://en.wikipedia.org/wiki/Howard_County_Delegation | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14009909-1.html.csv | ordinal | the 7th candidate to be elected represents the republican party . | {'row': '2', 'col': '5', 'order': '7', 'col_other': '4', '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', '7'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; first elected ; 7 }'}, 'party'], 'result': 'republican', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; first elected ; 7 } ; party }'}, 'republican'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; first elected ; 7 } ; party } ; republican } = true', 'tointer': 'select the row whose first elected record of all rows is 7th minimum . the party record of this row is republican .'} | eq { hop { nth_argmin { all_rows ; first elected ; 7 } ; party } ; republican } = true | select the row whose first elected record of all rows is 7th minimum . the party record of this row is republican . | 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, '7_6': 6, 'party_7': 7, 'republican_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', '7_6': '7', 'party_7': 'party', 'republican_8': 'republican'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '7_6': [0], 'party_7': [1], 'republican_8': [2]} | ['district', 'counties represented', 'delegate', 'party', 'first elected', 'committee'] | [['09.1 9a', 'howard', 'bates , gail h gail h bates', 'republican', '2002', 'appropriations'], ['09.1 9a', 'howard', 'miller , warren e warren e miller', 'republican', '2003', 'economic matters'], ['12.1 12a', 'baltimore county , howard', 'deboy , steven j sr steven j deboy , sr', 'democratic', '2002', 'appropriations'], ['12.1 12a', 'baltimore county , howard', 'malone , james e jr james e malone , jr', 'democratic', '1994', 'environmental matters ( vice - chair )'], ['12.2 12b', 'howard', 'bobo , elizabeth elizabeth bobo', 'democratic', '1994', 'environmental matters'], ['13', 'howard', 'pendergrass , shane e shane pendergrass', 'democratic', '1994', 'health and government operations'], ['13', 'howard', 'guzzone , guy guy guzzone', 'democratic', '2006', 'appropriations'], ['13', 'howard', 'turner , frank s frank s turner', 'democratic', '1994', 'ways and means']] |
list of tallest buildings in saudi arabia | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Saudi_Arabia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11606138-2.html.csv | count | 7 of the tallest buildings in saudi arabia are located in the city of jeddah . | {'scope': 'all', 'criterion': 'equal', 'value': 'jeddah', 'result': '7', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city', 'jeddah'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose city record fuzzily matches to jeddah .', 'tostr': 'filter_eq { all_rows ; city ; jeddah }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; city ; jeddah } }', 'tointer': 'select the rows whose city record fuzzily matches to jeddah . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; city ; jeddah } } ; 7 } = true', 'tointer': 'select the rows whose city record fuzzily matches to jeddah . the number of such rows is 7 .'} | eq { count { filter_eq { all_rows ; city ; jeddah } } ; 7 } = true | select the rows whose city record fuzzily matches to jeddah . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'city_5': 5, 'jeddah_6': 6, '7_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'city_5': 'city', 'jeddah_6': 'jeddah', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'city_5': [0], 'jeddah_6': [0], '7_7': [2]} | ['rank', 'name', 'city', 'height', 'floors'] | [['1', 'kingdom tower', 'jeddah', '-', '186'], ['2', 'diamond tower', 'jeddah', '-', '93'], ['3', 'capital market authority headquarters', 'riyadh', '-', '77'], ['4', 'lamar tower 1', 'jeddah', '-', '87'], ['5', 'burj rafal', 'riyadh', '-', '68'], ['6', 'kafd world trade centre', 'riyadh', '-', '67'], ['7', 'lamar tower 2', 'jeddah', '-', '84'], ['8', 'kempinski hotel', 'jeddah', '-', '69'], ['9', 'the headquarters', 'jeddah', '-', '52'], ['10', 'aqua tower', 'jeddah', '-', '59'], ['11', 'gcc bank headquarters', 'riyadh', '-', '53'], ['12', 'abraj al bait maqam tower', 'mecca', '-', '57'], ['13', 'abraj al bait qibla tower', 'mecca', '-', '57'], ['14', 'al majdoul tower', 'riyadh', '-', '54'], ['15', 'west tower at the hq business park', 'riyadh', '-', '54']] |
1997 - 98 philadelphia flyers season | https://en.wikipedia.org/wiki/1997%E2%80%9398_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14344681-14.html.csv | count | in the 1997-98 philadelphia flyers season , four of the players were from canada . | {'scope': 'all', 'criterion': 'equal', 'value': 'canada', 'result': '4', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'canada'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to canada .', 'tostr': 'filter_eq { all_rows ; nationality ; canada }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; nationality ; canada } }', 'tointer': 'select the rows whose nationality record fuzzily matches to canada . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; nationality ; canada } } ; 4 } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to canada . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; nationality ; canada } } ; 4 } = true | select the rows whose nationality record fuzzily matches to canada . 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, 'nationality_5': 5, 'canada_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', 'nationality_5': 'nationality', 'canada_6': 'canada', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nationality_5': [0], 'canada_6': [0], '4_7': [2]} | ['round', 'player', 'position', 'nationality', 'college / junior / club team ( league )'] | [['2', 'jean - marc pelletier', 'goaltender', 'united states', 'cornell big red ( ecac )'], ['2', 'pat kavanagh', 'right wing', 'canada', 'peterborough petes ( ohl )'], ['3', 'kris mallette', 'defense', 'canada', 'kelowna rockets ( whl )'], ['4', 'mikhail chernov', 'defense', 'russia', 'torpedo yaroslavl ( rus )'], ['6', 'jordon flodell', 'defense', 'canada', 'moose jaw warriors ( whl )'], ['7', 'todd fedoruk', 'left wing', 'canada', 'kelowna rockets ( whl )'], ['8', 'marko kauppinen', 'defense', 'finland', 'jyp ht juniors ( fin )'], ['9', 'par styf', 'defense', 'sweden', 'modo jrs ( swe )']] |
eurobasket 1967 | https://en.wikipedia.org/wiki/EuroBasket_1967 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13843829-3.html.csv | unique | position 7 was the only position to have a total of two points . | {'scope': 'all', 'row': '7', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': '2', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'points', '2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is equal to 2 .', 'tostr': 'filter_eq { all_rows ; points ; 2 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; points ; 2 } }', 'tointer': 'select the rows whose points record is equal to 2 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'points', '2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is equal to 2 .', 'tostr': 'filter_eq { all_rows ; points ; 2 }'}, 'pos'], 'result': '7', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; points ; 2 } ; pos }'}, '7'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; points ; 2 } ; pos } ; 7 }', 'tointer': 'the pos record of this unqiue row is 7 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; points ; 2 } } ; eq { hop { filter_eq { all_rows ; points ; 2 } ; pos } ; 7 } } = true', 'tointer': 'select the rows whose points record is equal to 2 . there is only one such row in the table . the pos record of this unqiue row is 7 .'} | and { only { filter_eq { all_rows ; points ; 2 } } ; eq { hop { filter_eq { all_rows ; points ; 2 } ; pos } ; 7 } } = true | select the rows whose points record is equal to 2 . there is only one such row in the table . the pos record of this unqiue row is 7 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'points_7': 7, '2_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'pos_9': 9, '7_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'points_7': 'points', '2_8': '2', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'pos_9': 'pos', '7_10': '7'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'points_7': [0], '2_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'pos_9': [2], '7_10': [3]} | ['pos', 'matches', 'wins', 'loses', 'results', 'points', 'diff'] | [['1', '7', '6', '1', '550:461', '12', '+ 89'], ['2', '7', '6', '1', '554:485', '12', '+ 69'], ['3', '7', '5', '2', '479:449', '10', '+ 30'], ['4', '7', '4', '3', '493:497', '8', '4'], ['5', '7', '4', '3', '523:507', '8', '+ 16'], ['6', '7', '2', '5', '526:579', '4', '53'], ['7', '7', '1', '6', '500:581', '2', '81'], ['8', '7', '0', '7', '454:570', '0', '116']] |
henlopen conference | https://en.wikipedia.org/wiki/Henlopen_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13054553-18.html.csv | unique | of all the teams at the henlopen conference , the indians are the only team that won div ii state championship . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'won div ii state championship', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season outcome', 'won div ii state championship'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose season outcome record fuzzily matches to won div ii state championship .', 'tostr': 'filter_eq { all_rows ; season outcome ; won div ii state championship }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; season outcome ; won div ii state championship } }', 'tointer': 'select the rows whose season outcome record fuzzily matches to won div ii state championship . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season outcome', 'won div ii state championship'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose season outcome record fuzzily matches to won div ii state championship .', 'tostr': 'filter_eq { all_rows ; season outcome ; won div ii state championship }'}, 'team'], 'result': 'indians', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; season outcome ; won div ii state championship } ; team }'}, 'indians'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; season outcome ; won div ii state championship } ; team } ; indians }', 'tointer': 'the team record of this unqiue row is indians .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; season outcome ; won div ii state championship } } ; eq { hop { filter_eq { all_rows ; season outcome ; won div ii state championship } ; team } ; indians } } = true', 'tointer': 'select the rows whose season outcome record fuzzily matches to won div ii state championship . there is only one such row in the table . the team record of this unqiue row is indians .'} | and { only { filter_eq { all_rows ; season outcome ; won div ii state championship } } ; eq { hop { filter_eq { all_rows ; season outcome ; won div ii state championship } ; team } ; indians } } = true | select the rows whose season outcome record fuzzily matches to won div ii state championship . there is only one such row in the table . the team record of this unqiue row is indians . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'season outcome_7': 7, 'won div ii state championship_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'indians_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'season outcome_7': 'season outcome', 'won div ii state championship_8': 'won div ii state championship', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'indians_10': 'indians'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'season outcome_7': [0], 'won div ii state championship_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'indians_10': [3]} | ['school', 'team', 'division record', 'overall record', 'season outcome'] | [['indian river', 'indians', '6 - 0', '12 - 0', 'won div ii state championship'], ['delmar', 'wildcats', '5 - 1', '9 - 2', 'loss in first round of div ii playoffs'], ['laurel', 'bulldogs', '4 - 2', '4 - 6', 'failed to make playoffs'], ['lake forest', 'spartans', '3 - 3', '5 - 5', 'failed to make playoffs'], ['polytech', 'panthers', '2 - 4', '2 - 8', 'failed to make playoffs'], ['woodbridge', 'blue raiders', '1 - 5', '2 - 8', 'failed to make playoffs'], ['seaford', 'blue jays', '0 - 6', '0 - 10', 'failed to make playoffs']] |
list of cities in the far east by population | https://en.wikipedia.org/wiki/List_of_cities_in_the_Far_East_by_population | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16478687-5.html.csv | unique | of the list of cities in the far east with the highest population , the only one in burma is yangon . | {'scope': 'all', 'row': '15', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'burma', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'burma'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to burma .', 'tostr': 'filter_eq { all_rows ; country ; burma }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; burma } }', 'tointer': 'select the rows whose country record fuzzily matches to burma . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'burma'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to burma .', 'tostr': 'filter_eq { all_rows ; country ; burma }'}, 'city'], 'result': 'yangon', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; burma } ; city }'}, 'yangon'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; burma } ; city } ; yangon }', 'tointer': 'the city record of this unqiue row is yangon .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; burma } } ; eq { hop { filter_eq { all_rows ; country ; burma } ; city } ; yangon } } = true', 'tointer': 'select the rows whose country record fuzzily matches to burma . there is only one such row in the table . the city record of this unqiue row is yangon .'} | and { only { filter_eq { all_rows ; country ; burma } } ; eq { hop { filter_eq { all_rows ; country ; burma } ; city } ; yangon } } = true | select the rows whose country record fuzzily matches to burma . there is only one such row in the table . the city record of this unqiue row is yangon . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'burma_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'city_9': 9, 'yangon_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'burma_8': 'burma', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'city_9': 'city', 'yangon_10': 'yangon'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'burma_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'city_9': [2], 'yangon_10': [3]} | ['rank', 'city', 'population', 'definition', 'country'] | [['1', 'shanghai', '13831900', 'core districts + inner suburbs', 'china'], ['2', 'seoul', '10456034', 'special city', 'south korea'], ['3', 'beijing', '10123000', 'core districts + inner suburbs', 'china'], ['4', 'tokyo', '8795000', '23 special wards area', 'japan'], ['5', 'jakarta', '8489910', 'special capital district', 'indonesia'], ['6', 'wuhan', '8001541 ( 2006 - 12 - 31 )', 'core districts', 'china'], ['7', 'ho chi minh city', '7123340', 'province - level municipality', 'vietnam'], ['8', 'bangkok', '7025000', 'administrative area', 'thailand'], ['9', 'hong kong', '7008900', 'the entire territory', 'hong kong'], ['10', 'guangzhou', '6172839 ( 2006 - 12 - 31 )', 'core districts', 'china'], ['11', 'tianjin', '5800000', 'core districts + inner suburbs', 'china'], ['12', 'singapore', '4839400', 'country', 'singapore'], ['13', 'chongqing', '4776027', 'core districts', 'china'], ['14', 'shenyang', '4101197 ( 2006 - 12 - 31 )', 'core districts', 'china'], ['15', 'yangon', '4088000', 'urban agglomeration', 'burma'], ['16', 'yokohama', '3670000', 'city proper', 'japan'], ['17', 'busan', '3596076', 'metropolitan city', 'south korea'], ['18', 'pyongyang', '3255388', 'directly governed city', 'north korea']] |
2007 volta a catalunya | https://en.wikipedia.org/wiki/2007_Volta_a_Catalunya | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11355733-15.html.csv | count | two of the competitors had a time of +40 in the 2007 volta a catalunya . | {'scope': 'all', 'criterion': 'equal', 'value': '+40', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'time', '+40'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time record fuzzily matches to +40 .', 'tostr': 'filter_eq { all_rows ; time ; +40 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; time ; +40 } }', 'tointer': 'select the rows whose time record fuzzily matches to +40 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; time ; +40 } } ; 2 } = true', 'tointer': 'select the rows whose time record fuzzily matches to +40 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; time ; +40 } } ; 2 } = true | select the rows whose time record fuzzily matches to +40 . 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, 'time_5': 5, '+40_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', 'time_5': 'time', '+40_6': '+40', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'time_5': [0], '+40_6': [0], '2_7': [2]} | ['cyclist', 'country', 'team', 'time', 'uci points'] | [['vladimir karpets', 'russia', "caisse d'epargne", "22h 21 ' 05", '50'], ['denis menchov', 'russia', 'rabobank', '+ 40', '40'], ['michael rogers', 'australia', 't - mobile team', '+ 40', '35'], ['christophe moreau', 'france', 'ag2r prévoyance', "+ 1 ' 34", '30'], ['óscar sevilla', 'spain', 'relax - gam', "+ 1 ' 34", 'n / a'], ['francisco mancebo', 'spain', 'relax - gam', "+ 1 ' 59", 'n / a'], ['john gadret', 'france', 'ag2r prévoyance', "+ 2 ' 19", '15'], ['marcos - antonio serrano', 'spain', 'karpin - galicia', "+ 2 ' 39", 'n / a'], ['laurens ten dam', 'netherlands', 'unibetcom', "+ 2 ' 44", '5'], ['janez brajkovič', 'slovenia', 'discovery channel', "+ 2 ' 47", '2']] |
nicolas lapierre | https://en.wikipedia.org/wiki/Nicolas_Lapierre | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1628448-4.html.csv | count | from 2007 to 2013 , nicolas lapierre raced two times for toyota racing . | {'scope': 'all', 'criterion': 'equal', 'value': 'toyota racing', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'toyota racing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to toyota racing .', 'tostr': 'filter_eq { all_rows ; team ; toyota racing }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; team ; toyota racing } }', 'tointer': 'select the rows whose team record fuzzily matches to toyota racing . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; team ; toyota racing } } ; 2 } = true', 'tointer': 'select the rows whose team record fuzzily matches to toyota racing . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; team ; toyota racing } } ; 2 } = true | select the rows whose team record fuzzily matches to toyota racing . 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, 'team_5': 5, 'toyota racing_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', 'team_5': 'team', 'toyota racing_6': 'toyota racing', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'team_5': [0], 'toyota racing_6': [0], '2_7': [2]} | ['year', 'team', 'co - drivers', 'class', 'laps', 'pos', 'class pos'] | [['2007', 'team oreca', 'stéphane ortelli soheil ayari', 'gt1', '318', '16th', '9th'], ['2009', 'team oreca - matmut aim', 'olivier panis soheil ayari', 'lmp1', '370', '5th', '5th'], ['2010', 'team oreca - matmut', 'olivier panis loïc duval', 'lmp1', '373', 'dnf', 'dnf'], ['2011', 'team oreca - matmut', 'olivier panis loïc duval', 'lmp1', '339', '5th', '5th'], ['2012', 'toyota racing', 'alexander wurz kazuki nakajima', 'lmp1', '134', 'dnf', 'dnf'], ['2013', 'toyota racing', 'alexander wurz kazuki nakajima', 'lmp1', '341', '4th', '4th']] |
2003 - 04 new york rangers season | https://en.wikipedia.org/wiki/2003%E2%80%9304_New_York_Rangers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14532362-7.html.csv | ordinal | the new york rangers ' game against the vancouver canucks was the earliest in the 2003 - 04 season . | {'row': '1', 'col': '2', 'order': '1', 'col_other': '3', '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', 'february', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; february ; 1 }'}, 'opponent'], 'result': 'vancouver canucks', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; february ; 1 } ; opponent }'}, 'vancouver canucks'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; february ; 1 } ; opponent } ; vancouver canucks } = true', 'tointer': 'select the row whose february record of all rows is 1st minimum . the opponent record of this row is vancouver canucks .'} | eq { hop { nth_argmin { all_rows ; february ; 1 } ; opponent } ; vancouver canucks } = true | select the row whose february record of all rows is 1st minimum . the opponent record of this row is vancouver canucks . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'february_5': 5, '1_6': 6, 'opponent_7': 7, 'vancouver canucks_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', 'february_5': 'february', '1_6': '1', 'opponent_7': 'opponent', 'vancouver canucks_8': 'vancouver canucks'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'february_5': [0], '1_6': [0], 'opponent_7': [1], 'vancouver canucks_8': [2]} | ['game', 'february', 'opponent', 'score', 'record'] | [['54', '2', 'vancouver canucks', '4 - 3', '20 - 23 - 7 - 4'], ['55', '4', 'minnesota wild', '4 - 3', '20 - 24 - 7 - 4'], ['56', '11', 'new jersey devils', '3 - 1', '21 - 24 - 7 - 4'], ['57', '12', 'philadelphia flyers', '2 - 1', '21 - 25 - 7 - 4'], ['58', '14', 'philadelphia flyers', '6 - 2', '21 - 26 - 7 - 4'], ['59', '16', 'ottawa senators', '4 - 1', '21 - 27 - 7 - 4'], ['60', '19', 'new york islanders', '6 - 2', '22 - 27 - 7 - 4'], ['61', '21', 'new jersey devils', '7 - 3', '22 - 28 - 7 - 4'], ['62', '23', 'montreal canadiens', '4 - 1', '22 - 29 - 7 - 4'], ['63', '26', 'new york islanders', '6 - 3', '23 - 29 - 7 - 4'], ['64', '28', 'nashville predators', '2 - 1 ot', '23 - 29 - 7 - 5'], ['65', '29', 'atlanta thrashers', '3 - 2', '23 - 30 - 7 - 5']] |
liselotte neumann | https://en.wikipedia.org/wiki/Liselotte_Neumann | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1710991-1.html.csv | superlative | liselotte neumann 's highest margin of victory was by 11 strokes . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '6', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'margin of victory'], 'result': '11 strokes', 'ind': 0, 'tostr': 'max { all_rows ; margin of victory }', 'tointer': 'the maximum margin of victory record of all rows is 11 strokes .'}, '11 strokes'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; margin of victory } ; 11 strokes } = true', 'tointer': 'the maximum margin of victory record of all rows is 11 strokes .'} | eq { max { all_rows ; margin of victory } ; 11 strokes } = true | the maximum margin of victory record of all rows is 11 strokes . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'max_0': 0, 'all_rows_3': 3, 'margin of victory_4': 4, '11 strokes_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'max_0': 'max', 'all_rows_3': 'all_rows', 'margin of victory_4': 'margin of victory', '11 strokes_5': '11 strokes'} | {'eq_1': [2], 'result_2': [], 'max_0': [1], 'all_rows_3': [0], 'margin of victory_4': [0], '11 strokes_5': [1]} | ['date', 'tournament', 'winning score', 'margin of victory', 'runner ( s ) - up'] | [['7 sep 1988', "us women 's open", '- 7 ( 67 + 72 + 69 + 69 = 277 )', '3 strokes', 'patty sheehan'], ['10 nov 1991', 'mazda japan classic', '- 5 ( 70 + 72 + 69 = 211 )', '2 strokes', 'caroline keggi , dottie pepper'], ['12 jun 1994', 'minnesota lpga classic', '- 11 ( 68 + 71 + 66 = 205 )', '2 strokes', 'hiromi kobayashi'], ['12 aug 1994', "weetabix women 's british open", '- 14 ( 71 + 67 + 70 + 72 = 280 )', '3 strokes', 'dottie pepper , annika sörenstam'], ['2 oct 1994', 'ghp heartland classic', '- 10 ( 70 + 71 + 67 + 70 = 278 )', '3 strokes', 'elaine crosby , pearl sinn'], ['14 jan 1996', 'chrysler - plymouth tournament of champions', '- 13 ( 67 + 66 + 72 + 70 = 275 )', '11 strokes', 'karrie webb'], ['17 mar 1996', "ping / welch 's championship ( tucson )", '- 12 ( 68 + 71 + 69 + 68 = 276 )', '1 stroke', 'cathy johnston - forbes'], ['6 jun 1996', 'edina realty lpga classic', '- 9 ( 67 + 73 + 67 = 207 )', 'playoff', 'brandie burton , carin koch , suzanne strudwick'], ['21 sep 1997', "welch 's championship", '- 12 ( 67 + 70 + 69 + 70 = 276 )', '3 strokes', 'nancy harvey'], ['9 nov 1997', 'toray japan queens cup', '- 11 ( 68 + 70 + 67 = 205 )', '1 sttroke', 'lorie kane'], ['22 mar 1998', 'standard register ping', '- 13 ( 69 + 67 + 69 + 74 = 279 )', 'playoff', 'rosie jones'], ['26 apr 1998', 'chick - fil - a charity championship', '- 14 ( 67 + 65 + 70 = 202 )', '2 strokes', 'lori kane , dottie pepper'], ['10 oct 2004', 'asahi ryokuken international championship', '- 15 ( 68 + 68 + 69 + 68 = 273 )', '3 strokes', 'grace park']] |
2003 bridgeport barrage season | https://en.wikipedia.org/wiki/2003_Bridgeport_Barrage_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12101799-1.html.csv | count | of the games in the 2003 bridgeport barrage season , 6 of them were home games . | {'scope': 'all', 'criterion': 'equal', 'value': 'home', 'result': '6', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home / away', 'home'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home / away record fuzzily matches to home .', 'tostr': 'filter_eq { all_rows ; home / away ; home }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; home / away ; home } }', 'tointer': 'select the rows whose home / away record fuzzily matches to home . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; home / away ; home } } ; 6 } = true', 'tointer': 'select the rows whose home / away record fuzzily matches to home . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; home / away ; home } } ; 6 } = true | select the rows whose home / away record fuzzily matches to home . 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, 'home / away_5': 5, 'home_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', 'home / away_5': 'home / away', 'home_6': 'home', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'home / away_5': [0], 'home_6': [0], '6_7': [2]} | ['date', 'opponent', 'home / away', 'field', 'result'] | [['may 31', 'rattlers', 'away', 'bishop kearney field', 'l 13 - 23'], ['june 6', 'cannons', 'home', 'the ballpark at harbor yard', 'l 17 - 23'], ['june 12', 'bayhawks', 'home', 'the ballpark at harbor yard', 'l 14 - 21'], ['june 14', 'pride', 'away', 'commerce bank ballpark', 'l 9 - 16'], ['june 27', 'lizards', 'away', 'mitchel athletic complex', 'l 19 - 23'], ['july 12', 'lizards', 'home', 'the ballpark at harbor yard', 'l 16 - 17'], ['july 19', 'bayhawks', 'away', 'homewood field', 'w 22 - 17'], ['july 24', 'rattlers', 'home', 'the ballpark at harbor yard', 'l 19 - 21'], ['july 31', 'pride', 'home', 'the ballpark at harbor yard', 'l 14 - 22'], ['august 2', 'rattlers', 'away', 'bishop kearney field', 'l 13 - 28'], ['august 7', 'cannons', 'away', 'cawley memorial stadium', 'l 15 - 21'], ['august 14', 'rattlers', 'home', 'the ballpark at harbor yard', 'l 18 - 23']] |
2008 wnba draft | https://en.wikipedia.org/wiki/2008_WNBA_draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14122892-3.html.csv | majority | all of the women drafted to the wnba in 2008 were from the us . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , all of them fuzzily match to united states .', 'tostr': 'all_eq { all_rows ; nationality ; united states } = true'} | all_eq { all_rows ; nationality ; united states } = true | for the nationality records of all rows , all of them fuzzily match to united states . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'united states_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'united states_4': 'united states'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'united states_4': [0]} | ['pick', 'player', 'nationality', 'wnba team', 'school / club team'] | [['1', 'candace parker', 'united states', 'los angeles sparks', 'tennessee'], ['2', 'sylvia fowles', 'united states', 'chicago sky', 'lsu'], ['3', 'candice wiggins', 'united states', 'minnesota lynx', 'stanford'], ['4', 'alexis hornbuckle', 'united states', 'detroit shock ( from atl , via sea )', 'tennessee'], ['5', 'matee ajavon', 'united states', 'houston comets', 'rutgers'], ['6', 'crystal langhorne', 'united states', 'washington mystics', 'maryland'], ['7', 'essence carson', 'united states', 'new york liberty', 'rutgers'], ['8', 'tamera young', 'united states', 'atlanta dream ( from sea )', 'james madison'], ['9', 'amber holt', 'united states', 'connecticut sun', 'middle tennessee'], ['10', 'laura harper', 'united states', 'sacramento monarchs', 'maryland'], ['11', 'tasha humphrey', 'united states', 'detroit shock ( from sa )', 'georgia'], ['12', 'ketia swanier', 'united states', 'connecticut sun ( from ind )', 'connecticut'], ['13', 'latoya pringle', 'united states', 'phoenix mercury', 'north carolina'], ['14', 'erlana larkins', 'united states', 'new york liberty ( from det )', 'north carolina']] |
1973 - 74 philadelphia flyers season | https://en.wikipedia.org/wiki/1973%E2%80%9374_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13908182-10.html.csv | aggregation | total attendance at philadelphia flyers games was 120,528 during the 1973 - 1974 season . | {'scope': 'all', 'col': '6', 'type': 'sum', 'result': '120528', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'attendance'], 'result': '120528', 'ind': 0, 'tostr': 'sum { all_rows ; attendance }'}, '120528'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; attendance } ; 120528 } = true', 'tointer': 'the sum of the attendance record of all rows is 120528 .'} | round_eq { sum { all_rows ; attendance } ; 120528 } = true | the sum of the attendance record of all rows is 120528 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '120528_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '120528_5': '120528'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '120528_5': [1]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'series'] | [['april 20', 'ny rangers', '0 - 4', 'philadelphia', 'parent', '17007', 'flyers lead 1 - 0'], ['april 23', 'ny rangers', '2 - 5', 'philadelphia', 'parent', '17007', 'flyers lead 2 - 0'], ['april 25', 'philadelphia', '3 - 5', 'ny rangers', 'parent', '17500', 'flyers lead 2 - 1'], ['april 28', 'philadelphia', '1 - 2', 'ny rangers', 'parent', '17500', 'series tied 2 - 2'], ['april 30', 'ny rangers', '1 - 4', 'philadelphia', 'parent', '17007', 'flyers lead 3 - 2'], ['may 2', 'philadelphia', '1 - 4', 'ny rangers', 'parent', '17500', 'series tied 3 - 3'], ['may 5', 'ny rangers', '3 - 4', 'philadelphia', 'parent', '17007', 'flyers win 4 - 3']] |
high - temperature superconductivity | https://en.wikipedia.org/wiki/High-temperature_superconductivity | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-101336-1.html.csv | majority | the majority of t c ( k ) is over 80 . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '80', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 't c ( k )', '80'], 'result': True, 'ind': 0, 'tointer': 'for the t c ( k ) records of all rows , most of them are greater than 80 .', 'tostr': 'most_greater { all_rows ; t c ( k ) ; 80 } = true'} | most_greater { all_rows ; t c ( k ) ; 80 } = true | for the t c ( k ) records of all rows , most of them are greater than 80 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 't c (k)_3': 3, '80_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 't c (k)_3': 't c ( k )', '80_4': '80'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 't c (k)_3': [0], '80_4': [0]} | ['formula', 'notation', 't c ( k )', 'no of cu - o planes in unit cell', 'crystal structure'] | [['yba 2 cu 3 o 7', '123', '92', '2', 'orthorhombic'], ['bi 2 sr 2 cuo 6', 'bi - 2201', '20', '1', 'tetragonal'], ['bi 2 sr 2 cacu 2 o 8', 'bi - 2212', '85', '2', 'tetragonal'], ['bi 2 sr 2 ca 2 cu 3 o 6', 'bi - 2223', '110', '3', 'tetragonal'], ['tl 2 ba 2 cuo 6', 'tl - 2201', '80', '1', 'tetragonal'], ['tl 2 ba 2 cacu 2 o 8', 'tl - 2212', '108', '2', 'tetragonal'], ['tl 2 ba 2 ca 2 cu 3 o 10', 'tl - 2223', '125', '3', 'tetragonal'], ['tlba 2 ca 3 cu 4 o 11', 'tl - 1234', '122', '4', 'tetragonal'], ['hgba 2 cuo 4', 'hg - 1201', '94', '1', 'tetragonal'], ['hgba 2 cacu 2 o 6', 'hg - 1212', '128', '2', 'tetragonal']] |
2005 japanese television dramas | https://en.wikipedia.org/wiki/2005_Japanese_television_dramas | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18540104-1.html.csv | majority | most of the 2005 japanese television dramas had eleven episodes . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': '11', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'episodes', '11'], 'result': True, 'ind': 0, 'tointer': 'for the episodes records of all rows , most of them are equal to 11 .', 'tostr': 'most_eq { all_rows ; episodes ; 11 } = true'} | most_eq { all_rows ; episodes ; 11 } = true | for the episodes records of all rows , most of them are equal to 11 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'episodes_3': 3, '11_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'episodes_3': 'episodes', '11_4': '11'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'episodes_3': [0], '11_4': [0]} | ['japanese title', 'romaji title', 'tv station', 'theme song ( s )', 'episodes', 'average ratings'] | [['恋におちたら ～ 僕の成功の秘密 ～', 'koi ni ochitara ~ boku no seikou no himitsu ~', 'fuji tv', 'crystal kay 恋におちたら ( koi ni ochitara )', '11', '16.3 %'], ['離婚弁護士ii ～ ハンサムウーマン ～', 'rikon bengoshi ii ~ handsome woman ~', 'fuji tv', 'hoshimura mai every', '11', '13.2 %'], ['エンジン', 'engine', 'fuji tv', 'jimmy cliff i can see clearly now', '11', '22.4 %'], ['曲がり角の彼女', 'magarikado no kanojo', 'fuji tv', 'shela dear my friends', '11', '14.5 %'], ['夢で逢いましょう', 'yume de aimashou', 'tbs', 'yumi matsutouya ついてゆくわ ( tsuiteyuku wa )', '11', '11.6 %'], ['汚れた舌', 'kegareta shita', 'tbs', 'dorlis 肌のすきま ( hada no sukima )', '11', '10.0 %'], ['あいくるしい', 'ai kurushii', 'tbs', 'michael jackson ben', '11', '11.6 %'], ['タイガー & ドラゴン', 'tiger & dragon', 'tbs', 'v6 utao - utao', '11', '12.8 %'], ['雨と夢のあとに', 'ame to yume no ato ni', 'tv asahi', 'miwako okuda 雨と夢のあとに ( ame to yume no ato ni )', '10', '9.8 %'], ['アタックno1', 'attack no1', 'tv asahi', 'aya ueto 夢のチカラ ( yume no chikara )', '11', '13.1 %'], ['瑠璃の島', 'ruri no shima', 'ntv', 'kobukuro ここにしか咲かない花 ( koko ni shika sakanai hana )', '10', '12.6 %']] |
amanda overmyer | https://en.wikipedia.org/wiki/Amanda_Overmyer | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15796072-1.html.csv | comparative | amanda overmyer had a lower order number for the 1960s theme week than for the 1970s theme week . | {'row_1': '3', 'row_2': '4', 'col': '5', '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', 'week', 'top 24 ( 12 women )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose week record fuzzily matches to top 24 ( 12 women ) .', 'tostr': 'filter_eq { all_rows ; week ; top 24 ( 12 women ) }'}, 'order'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; week ; top 24 ( 12 women ) } ; order }', 'tointer': 'select the rows whose week record fuzzily matches to top 24 ( 12 women ) . take the order record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'week', 'top 20 ( 10 women )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose week record fuzzily matches to top 20 ( 10 women ) .', 'tostr': 'filter_eq { all_rows ; week ; top 20 ( 10 women ) }'}, 'order'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; week ; top 20 ( 10 women ) } ; order }', 'tointer': 'select the rows whose week record fuzzily matches to top 20 ( 10 women ) . take the order record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; week ; top 24 ( 12 women ) } ; order } ; hop { filter_eq { all_rows ; week ; top 20 ( 10 women ) } ; order } } = true', 'tointer': 'select the rows whose week record fuzzily matches to top 24 ( 12 women ) . take the order record of this row . select the rows whose week record fuzzily matches to top 20 ( 10 women ) . take the order record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; week ; top 24 ( 12 women ) } ; order } ; hop { filter_eq { all_rows ; week ; top 20 ( 10 women ) } ; order } } = true | select the rows whose week record fuzzily matches to top 24 ( 12 women ) . take the order record of this row . select the rows whose week record fuzzily matches to top 20 ( 10 women ) . take the order 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, 'week_7': 7, 'top 24 (12 women)_8': 8, 'order_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'week_11': 11, 'top 20 (10 women)_12': 12, 'order_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', 'week_7': 'week', 'top 24 (12 women)_8': 'top 24 ( 12 women )', 'order_9': 'order', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'week_11': 'week', 'top 20 (10 women)_12': 'top 20 ( 10 women )', 'order_13': 'order'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'week_7': [0], 'top 24 (12 women)_8': [0], 'order_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'week_11': [1], 'top 20 (10 women)_12': [1], 'order_13': [3]} | ['week', 'theme', 'song choice', 'original artist', 'order', 'result'] | [['hollywood', 'n / a', 'light my fire', 'the doors', 'n / a', 'advanced'], ['hollywood', 'n / a', 'piece of my heart', 'erma franklin', 'n / a', 'advanced'], ['top 24 ( 12 women )', '1960s', "baby , please do n't go", 'big joe williams', '4', 'safe'], ['top 20 ( 10 women )', '1970s', 'carry on wayward son', 'kansas', '6', 'safe'], ['top 16 ( 8 women )', '1980s', 'i hate myself for loving you', 'joan jett and the blackhearts', '3', 'safe'], ['top 12', 'lennonmccartney', "you ca n't do that", 'the beatles', '9', 'safe']] |
peak water | https://en.wikipedia.org/wiki/Peak_water | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15909409-3.html.csv | aggregation | the average total freshwater withdrawal for the listed countries is 60.34 . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '60.34', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'total freshwater withdrawal'], 'result': '60.34', 'ind': 0, 'tostr': 'avg { all_rows ; total freshwater withdrawal }'}, '60.34'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; total freshwater withdrawal } ; 60.34 } = true', 'tointer': 'the average of the total freshwater withdrawal record of all rows is 60.34 .'} | round_eq { avg { all_rows ; total freshwater withdrawal } ; 60.34 } = true | the average of the total freshwater withdrawal record of all rows is 60.34 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'total freshwater withdrawal_4': 4, '60.34_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'total freshwater withdrawal_4': 'total freshwater withdrawal', '60.34_5': '60.34'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'total freshwater withdrawal_4': [0], '60.34_5': [1]} | ['', 'total freshwater withdrawal', 'per capita withdrawal', 'domestic use', 'industrial use', 'agricultural use'] | [['turkmenistan', '24.65', '5104', '2', '1', '98'], ['kazakhstan', '35', '2360', '2', '17', '82'], ['uzbekistan', '58.34', '2194', '5', '2', '93'], ['guyana', '1.64', '2187', '2', '1', '98'], ['hungary', '21.03', '2082', '9', '59', '32'], ['azerbaijan', '17.25', '2051', '5', '28', '68'], ['kyrgyzstan', '10.08', '1916', '3', '3', '94'], ['tajikistan', '11.96', '1837', '4', '5', '92'], ['usa', '477', '1600', '13', '46', '41'], ['suriname', '0.67', '1489', '4', '3', '93'], ['iraq', '42.7', '1482', '3', '5', '92'], ['canada', '44.72', '1386', '20', '69', '12'], ['thailand', '82.75', '1288', '2', '2', '95'], ['ecuador', '16.98', '1283', '12', '5', '82']] |
1992 - 93 argentine primera división | https://en.wikipedia.org/wiki/1992%E2%80%9393_Argentine_Primera_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17968282-1.html.csv | comparative | in the 1992 - 93 argentine primera división , the team vélez sársfield had more points than the team newell 's old boys . | {'row_1': '3', 'row_2': '7', 'col': '3', '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', 'team', 'vélez sársfield'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to vélez sársfield .', 'tostr': 'filter_eq { all_rows ; team ; vélez sársfield }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; vélez sársfield } ; points }', 'tointer': 'select the rows whose team record fuzzily matches to vélez sársfield . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', "newell 's old boys"], 'result': None, 'ind': 1, 'tointer': "select the rows whose team record fuzzily matches to newell 's old boys .", 'tostr': "filter_eq { all_rows ; team ; newell 's old boys }"}, 'points'], 'result': None, 'ind': 3, 'tostr': "hop { filter_eq { all_rows ; team ; newell 's old boys } ; points }", 'tointer': "select the rows whose team record fuzzily matches to newell 's old boys . take the points record of this row ."}], 'result': True, 'ind': 4, 'tostr': "greater { hop { filter_eq { all_rows ; team ; vélez sársfield } ; points } ; hop { filter_eq { all_rows ; team ; newell 's old boys } ; points } } = true", 'tointer': "select the rows whose team record fuzzily matches to vélez sársfield . take the points record of this row . select the rows whose team record fuzzily matches to newell 's old boys . take the points record of this row . the first record is greater than the second record ."} | greater { hop { filter_eq { all_rows ; team ; vélez sársfield } ; points } ; hop { filter_eq { all_rows ; team ; newell 's old boys } ; points } } = true | select the rows whose team record fuzzily matches to vélez sársfield . take the points record of this row . select the rows whose team record fuzzily matches to newell 's old boys . 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, 'team_7': 7, 'vélez sársfield_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team_11': 11, "newell 's old boys_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', 'team_7': 'team', 'vélez sársfield_8': 'vélez sársfield', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team_11': 'team', "newell 's old boys_12": "newell 's old boys", 'points_13': 'points'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team_7': [0], 'vélez sársfield_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team_11': [1], "newell 's old boys_12": [1], 'points_13': [3]} | ['team', 'average', 'points', 'played', '1991 - 92', '1992 - 93', '1993 - 94'] | [['boca juniors', '1.307', '149', '114', '51', '50', '48'], ['river plate', '1.281', '146', '114', '45', '55', '46'], ['vélez sársfield', '1.237', '141', '114', '45', '48', '48'], ['san lorenzo', '1.088', '124', '114', '45', '45', '45'], ['huracán', '1.061', '121', '114', '40', '38', '43'], ['independiente', '1.026', '117', '114', '40', '36', '41'], ["newell 's old boys", '1.026', '117', '114', '48', '44', '25'], ['racing club', '1.009', '115', '114', '40', '39', '36'], ['deportivo español', '1.000', '114', '114', '28', '45', '41'], ['ferro carril oeste', '0.991', '113', '114', '38', '37', '38'], ['rosario central', '0.982', '112', '114', '39', '34', '39'], ['lanús', '0.974', '37', '38', 'n / a', 'n / a', '37'], ['belgrano de córdoba', '0.961', '73', '76', 'n / a', '35', '38'], ['deportivo mandiyú', '0.947', '108', '114', '38', '33', '37'], ['gimnasia de la plata', '0.947', '108', '114', '33', '41', '34'], ['estudiantes de la plata', '0.930', '106', '114', '39', '29', '38'], ['platense', '0.921', '105', '114', '35', '42', '28'], ['argentinos juniors', '0.912', '104', '114', '36', '35', '33'], ['talleres de córdoba', '0.851', '97', '114', '29', '37', '31'], ['san martín de tucumán', '0.789', '30', '38', 'n / a', 'n / a', '30']] |
netherlands at the 2008 summer paralympics | https://en.wikipedia.org/wiki/Netherlands_at_the_2008_Summer_Paralympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18602462-9.html.csv | unique | for the netherlands at the 2008 summer paralympics , in the championship test , the only athlete with the horse donna dm is sabine peters . | {'scope': 'subset', 'row': '3', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'donna dm', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'championship test'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'championship test'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; event ; championship test }', 'tointer': 'select the rows whose event record fuzzily matches to championship test .'}, 'horse', 'donna dm'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose event record fuzzily matches to championship test . among these rows , select the rows whose horse record fuzzily matches to donna dm .', 'tostr': 'filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm } }', 'tointer': 'select the rows whose event record fuzzily matches to championship test . among these rows , select the rows whose horse record fuzzily matches to donna dm . 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', 'event', 'championship test'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; event ; championship test }', 'tointer': 'select the rows whose event record fuzzily matches to championship test .'}, 'horse', 'donna dm'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose event record fuzzily matches to championship test . among these rows , select the rows whose horse record fuzzily matches to donna dm .', 'tostr': 'filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm }'}, 'athlete'], 'result': 'sabine peters', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm } ; athlete }'}, 'sabine peters'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm } ; athlete } ; sabine peters }', 'tointer': 'the athlete record of this unqiue row is sabine peters .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm } } ; eq { hop { filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm } ; athlete } ; sabine peters } } = true', 'tointer': 'select the rows whose event record fuzzily matches to championship test . among these rows , select the rows whose horse record fuzzily matches to donna dm . there is only one such row in the table . the athlete record of this unqiue row is sabine peters .'} | and { only { filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm } } ; eq { hop { filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm } ; athlete } ; sabine peters } } = true | select the rows whose event record fuzzily matches to championship test . among these rows , select the rows whose horse record fuzzily matches to donna dm . there is only one such row in the table . the athlete record of this unqiue row is sabine peters . | 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, 'event_8': 8, 'championship test_9': 9, 'horse_10': 10, 'donna dm_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'athlete_12': 12, 'sabine peters_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', 'event_8': 'event', 'championship test_9': 'championship test', 'horse_10': 'horse', 'donna dm_11': 'donna dm', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'athlete_12': 'athlete', 'sabine peters_13': 'sabine peters'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'event_8': [0], 'championship test_9': [0], 'horse_10': [1], 'donna dm_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'athlete_12': [3], 'sabine peters_13': [4]} | ['athlete', 'class', 'horse', 'event', 'result', 'rank'] | [['ineke de groot', 'grade iv', 'indo', 'championship test', '63.161', '7'], ['ineke de groot', 'grade iv', 'indo', 'freestyle test', '58.955', '13'], ['sabine peters', 'grade iv', 'donna dm', 'championship test', '62.516', '8'], ['sabine peters', 'grade iv', 'donna dm', 'freestyle test', '65.863', '9'], ['petra van der sande', 'grade ii', 'toscane', 'championship test', '66.909', '4'], ['petra van der sande', 'grade ii', 'toscane', 'freestyle test', 'withdrawn', 'withdrawn'], ['sjerstin vermeulen', 'grade iv', 'sultano', 'championship test', '66.452', '4'], ['sjerstin vermeulen', 'grade iv', 'sultano', 'freestyle test', '67.908', '7']] |
television in italy | https://en.wikipedia.org/wiki/Television_in_Italy | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15887683-16.html.csv | majority | of the television stations of italy , most have televendita as the content . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'televendita', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'content', 'televendita'], 'result': True, 'ind': 0, 'tointer': 'for the content records of all rows , most of them fuzzily match to televendita .', 'tostr': 'most_eq { all_rows ; content ; televendita } = true'} | most_eq { all_rows ; content ; televendita } = true | for the content records of all rows , most of them fuzzily match to televendita . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'content_3': 3, 'televendita_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'content_3': 'content', 'televendita_4': 'televendita'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'content_3': [0], 'televendita_4': [0]} | ['n degree', 'television service', 'country', 'language', 'content', 'dar', 'hdtv', 'package / option'] | [['861', 'telemarket', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['862', 'noello sat', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['863', 'elite shopping tv', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['864', 'juwelo', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['865', 'diprè tv', 'italy', 'italian', 'arte', '4:3', 'no', 'no ( fta )'], ['866', 'telemarket for you', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['867', 'la sorgente sat 1', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['868', 'la sorgente sat 2', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['869', 'la sorgente sat 3', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )']] |
1934 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1934_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18007167-2.html.csv | aggregation | in the 1934 u.s golf open , the total price money won by all the players was $ 4014 . | {'scope': 'all', 'col': '6', 'type': 'sum', 'result': '4014', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'money'], 'result': '4014', 'ind': 0, 'tostr': 'sum { all_rows ; money }'}, '4014'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; money } ; 4014 } = true', 'tointer': 'the sum of the money record of all rows is 4014 .'} | round_eq { sum { all_rows ; money } ; 4014 } = true | the sum of the money record of all rows is 4014 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'money_4': 4, '4014_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'money_4': 'money', '4014_5': '4014'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'money_4': [0], '4014_5': [1]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'olin dutra', 'united states', '76 + 74 + 71 + 72 = 293', '+ 13', '1000'], ['2', 'gene sarazen', 'united states', '73 + 72 + 73 + 76 = 294', '+ 14', '750'], ['t3', 'harry cooper', 'england united states', '76 + 74 + 74 + 71 = 295', '+ 15', '400'], ['t3', 'wiffy cox', 'united states', '71 + 75 + 74 + 75 = 295', '+ 15', '400'], ['t3', 'bobby cruickshank', 'scotland', '71 + 71 + 77 + 76 = 295', '+ 15', '400'], ['t6', 'billy burke', 'united states', '76 + 71 + 77 + 72 = 296', '+ 16', '300'], ['t6', 'macdonald smith', 'scotland united states', '75 + 73 + 78 + 70 = 296', '+ 16', '300'], ['t8', 'tom creavy', 'united states', '79 + 76 + 78 + 66 = 299', '+ 19', '116'], ['t8', 'ralph guldahl', 'united states', '78 + 73 + 70 + 78 = 299', '+ 19', '116'], ['t8', 'jimmy hines', 'united states', '80 + 70 + 77 + 72 = 299', '+ 19', '116'], ['t8', 'johnny revolta', 'united states', '76 + 73 + 77 + 73 = 299', '+ 19', '116']] |
1966 atlanta falcons season | https://en.wikipedia.org/wiki/1966_Atlanta_Falcons_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16710917-2.html.csv | count | in the 1966 atlanta falcons season , among the games with attendance over 50,000 , two of them were played in december . | {'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'december', 'result': '2', 'col': '2', 'subset': {'col': '5', 'criterion': 'greater_than', 'value': '50000'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'attendance', '50000'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; attendance ; 50000 }', 'tointer': 'select the rows whose attendance record is greater than 50000 .'}, 'date', 'december'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose attendance record is greater than 50000 . among these rows , select the rows whose date record fuzzily matches to december .', 'tostr': 'filter_eq { filter_greater { all_rows ; attendance ; 50000 } ; date ; december }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_greater { all_rows ; attendance ; 50000 } ; date ; december } }', 'tointer': 'select the rows whose attendance record is greater than 50000 . among these rows , select the rows whose date record fuzzily matches to december . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_greater { all_rows ; attendance ; 50000 } ; date ; december } } ; 2 } = true', 'tointer': 'select the rows whose attendance record is greater than 50000 . among these rows , select the rows whose date record fuzzily matches to december . the number of such rows is 2 .'} | eq { count { filter_eq { filter_greater { all_rows ; attendance ; 50000 } ; date ; december } } ; 2 } = true | select the rows whose attendance record is greater than 50000 . among these rows , select the rows whose date record fuzzily matches to december . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'attendance_6': 6, '50000_7': 7, 'date_8': 8, 'december_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'attendance_6': 'attendance', '50000_7': '50000', 'date_8': 'date', 'december_9': 'december', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'attendance_6': [0], '50000_7': [0], 'date_8': [1], 'december_9': [1], '2_10': [3]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 11 , 1966', 'los angeles rams', 'l 19 - 14', '54418'], ['2', 'september 18 , 1966', 'philadelphia eagles', 'l 23 - 10', '54049'], ['3', 'september 25 , 1966', 'detroit lions', 'l 28 - 10', '47615'], ['4', 'october 2 , 1966', 'dallas cowboys', 'l 47 - 14', '56990'], ['5', 'october 9 , 1966', 'washington redskins', 'l 33 - 20', '50116'], ['6', 'october 16 , 1966', 'san francisco 49ers', 'l 44 - 7', '54788'], ['7', 'october 23 , 1966', 'green bay packers', 'l 56 - 3', '48623'], ['8', 'october 30 , 1966', 'cleveland browns', 'l 49 - 17', '57235'], ['10', 'november 13 , 1966', 'baltimore colts', 'l 19 - 7', '58850'], ['11', 'november 20 , 1966', 'new york giants', 'w 27 - 16', '62746'], ['12', 'november 27 , 1966', 'chicago bears', 'l 23 - 6', '44777'], ['13', 'december 4 , 1966', 'minnesota vikings', 'w 20 - 13', '37117'], ['14', 'december 11 , 1966', 'st louis cardinals', 'w 16 - 10', '57169'], ['15', 'december 18 , 1966', 'pittsburgh steelers', 'l 57 - 33', '56229']] |
2001 cfl draft | https://en.wikipedia.org/wiki/2001_CFL_Draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15817998-2.html.csv | count | there were 2 wr 's picked from picks 9-16 in the 2001 cfl draft . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'wr', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'wr'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to wr .', 'tostr': 'filter_eq { all_rows ; position ; wr }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; position ; wr } }', 'tointer': 'select the rows whose position record fuzzily matches to wr . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; position ; wr } } ; 2 } = true', 'tointer': 'select the rows whose position record fuzzily matches to wr . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; position ; wr } } ; 2 } = true | select the rows whose position record fuzzily matches to wr . 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, 'position_5': 5, 'wr_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', 'position_5': 'position', 'wr_6': 'wr', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'wr_6': [0], '2_7': [2]} | ['pick', 'cfl team', 'player', 'position', 'college'] | [['9', 'saskatchewan roughriders', 'jason french', 'wr', 'murray state'], ['10', 'calgary stampeders', 'lawrence deck', 'db', 'fresno state'], ['11', 'montreal alouettes', 'pat woodcock', 'wr', 'syracuse'], ['12', 'hamilton tiger - cats', 'karim grant', 'lb', 'acadia'], ['13', 'edmonton eskimos', 'fabian burke', 'cb', 'toledo'], ['14', 'calgary stampeders', "duncan o'mahony", 'k', 'british columbia'], ['15', 'montreal alouettes', 'jesse palmer', 'qb', 'florida'], ['16', 'bc lions', 'jamie boreham', 'k / s', 'manitoba']] |
2005 rhein fire season | https://en.wikipedia.org/wiki/2005_Rhein_Fire_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25380472-2.html.csv | ordinal | the game on april 30th for the 2005 rhein fire season had the third highest attendance . | {'row': '5', 'col': '8', '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', 'attendance', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance ; 3 }'}, 'date'], 'result': 'saturday , april 30', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance ; 3 } ; date }'}, 'saturday , april 30'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; saturday , april 30 } = true', 'tointer': 'select the row whose attendance record of all rows is 3rd maximum . the date record of this row is saturday , april 30 .'} | eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; saturday , april 30 } = true | select the row whose attendance record of all rows is 3rd maximum . the date record of this row is saturday , april 30 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '3_6': 6, 'date_7': 7, 'saturday , april 30_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', 'attendance_5': 'attendance', '3_6': '3', 'date_7': 'date', 'saturday , april 30_8': 'saturday , april 30'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '3_6': [0], 'date_7': [1], 'saturday , april 30_8': [2]} | ['week', 'date', 'kickoff', 'opponent', 'final score', 'team record', 'game site', 'attendance'] | [['1', 'saturday , april 2', '7:00 pm', 'amsterdam admirals', 'l 14 - 24', '0 - 1', 'amsterdam arena', '10234'], ['2', 'sunday , april 10', '4:00 pm', 'cologne centurions', 'l 10 - 23', '0 - 2', 'ltu arena', '25304'], ['3', 'saturday , april 16', '7:00 pm', 'hamburg sea devils', 'l 24 - 31', '0 - 3', 'aol arena', '19865'], ['4', 'saturday , april 23', '7:00 pm', 'berlin thunder', 'l 28 - 30', '0 - 4', 'ltu arena', '20399'], ['5', 'saturday , april 30', '7:00 pm', 'frankfurt galaxy', 'l 20 - 23', '0 - 5', 'commerzbank - arena', '27439'], ['6', 'saturday , may 7', '7:00 pm', 'hamburg sea devils', 'w 24 - 19', '1 - 5', 'ltu arena', '18632'], ['7', 'saturday , may 14', '6:00 pm', 'berlin thunder', 'l 15 - 24', '1 - 6', 'olympic stadium', '16695'], ['8', 'saturday , may 21', '7:00 pm', 'frankfurt galaxy', 'l 13 - 20', '1 - 7', 'ltu arena', '28124'], ['9', 'sunday , may 29', '4:00 pm', 'cologne centurions', 'w 28 - 16', '2 - 7', 'rheinenergiestadion', '32521']] |
1969 vfl season | https://en.wikipedia.org/wiki/1969_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809157-13.html.csv | aggregation | in the 1969 vfl season , for games with a crowd size of under 10,000 , the average crowd was 7005 . | {'scope': 'subset', 'col': '6', 'type': 'average', 'result': '7005', 'subset': {'col': '6', 'criterion': 'less_than', 'value': '10000'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'crowd', '10000'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; crowd ; 10000 }', 'tointer': 'select the rows whose crowd record is less than 10000 .'}, 'crowd'], 'result': '7005', 'ind': 1, 'tostr': 'avg { filter_less { all_rows ; crowd ; 10000 } ; crowd }'}, '7005'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_less { all_rows ; crowd ; 10000 } ; crowd } ; 7005 } = true', 'tointer': 'select the rows whose crowd record is less than 10000 . the average of the crowd record of these rows is 7005 .'} | round_eq { avg { filter_less { all_rows ; crowd ; 10000 } ; crowd } ; 7005 } = true | select the rows whose crowd record is less than 10000 . the average of the crowd record of these rows is 7005 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '10000_6': 6, 'crowd_7': 7, '7005_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '10000_6': '10000', 'crowd_7': 'crowd', '7005_8': '7005'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '10000_6': [0], 'crowd_7': [1], '7005_8': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['fitzroy', '18.21 ( 129 )', 'south melbourne', '11.14 ( 80 )', 'princes park', '7540', '5 july 1969'], ['north melbourne', '19.16 ( 130 )', 'melbourne', '19.13 ( 127 )', 'arden street oval', '6470', '5 july 1969'], ['st kilda', '12.15 ( 87 )', 'footscray', '12.5 ( 77 )', 'moorabbin oval', '14995', '5 july 1969'], ['geelong', '10.14 ( 74 )', 'essendon', '13.16 ( 94 )', 'kardinia park', '20247', '5 july 1969'], ['richmond', '13.15 ( 93 )', 'collingwood', '12.19 ( 91 )', 'mcg', '45546', '5 july 1969'], ['hawthorn', '11.14 ( 80 )', 'carlton', '22.17 ( 149 )', 'glenferrie oval', '18848', '5 july 1969']] |
cryengine | https://en.wikipedia.org/wiki/CryEngine | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1241866-4.html.csv | count | bethesda softworks was the publisher of two games made using the cryengine . | {'scope': 'all', 'criterion': 'equal', 'value': 'bethesda softworks', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'publisher', 'bethesda softworks'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose publisher record fuzzily matches to bethesda softworks .', 'tostr': 'filter_eq { all_rows ; publisher ; bethesda softworks }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; publisher ; bethesda softworks } }', 'tointer': 'select the rows whose publisher record fuzzily matches to bethesda softworks . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; publisher ; bethesda softworks } } ; 2 } = true', 'tointer': 'select the rows whose publisher record fuzzily matches to bethesda softworks . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; publisher ; bethesda softworks } } ; 2 } = true | select the rows whose publisher record fuzzily matches to bethesda softworks . 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, 'publisher_5': 5, 'bethesda softworks_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', 'publisher_5': 'publisher', 'bethesda softworks_6': 'bethesda softworks', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'publisher_5': [0], 'bethesda softworks_6': [0], '2_7': [2]} | ['title', 'year', 'developer', 'publisher', 'platform'] | [['homefront 2', '2014', 'crytek uk', 'crytek', 'tba'], ['ryse : son of rome', '2013', 'crytek gmbh', 'microsoft studios', 'xbox one'], ['star citizen', '2014', 'cloud imperium games corporation', 'cloud imperium games corporation', 'microsoft windows'], ['unannounced arkane studios title', 'tba', 'arkane studios', 'bethesda softworks', 'tba'], ['unannounced battlecry studios title', 'tba', 'battlecry studios', 'bethesda softworks', 'tba']] |
pablo andújar | https://en.wikipedia.org/wiki/Pablo_And%C3%BAjar | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16949333-3.html.csv | majority | all of pablo andujar 's matches were played on a clay surface . | {'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'clay', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , all of them fuzzily match to clay .', 'tostr': 'all_eq { all_rows ; surface ; clay } = true'} | all_eq { all_rows ; surface ; clay } = true | for the surface records of all rows , all of them fuzzily match to clay . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'clay_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'clay_4': 'clay'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'clay_4': [0]} | ['outcome', 'date', 'tournament', 'surface', 'opponent', 'score'] | [['runner - up', 'september 26 , 2010', 'brd năstase ţiriac trophy , bucharest , romania', 'clay', 'juan ignacio chela', '5 - 7 , 1 - 6'], ['winner', 'april 10 , 2011', 'grand prix hassan ii , casablanca , morocco ( 1 )', 'clay', 'potito starace', '6 - 1 , 6 - 2'], ['runner - up', 'july 17 , 2011', 'mercedescup , stuttgart , germany', 'clay', 'juan carlos ferrero', '4 - 6 , 0 - 6'], ['runner - up', 'september 25 , 2011', 'brd năstase ţiriac trophy , bucharest , romania', 'clay', 'florian mayer', '3 - 6 , 1 - 6'], ['winner', 'april 15 , 2012', 'grand prix hassan ii , casablanca , morocco ( 2 )', 'clay', 'albert ramos', '6 - 1 , 7 - 6 ( 7 - 5 )']] |
spaceport | https://en.wikipedia.org/wiki/Spaceport | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-179174-2.html.csv | unique | in the list of spaceport and flights the only launcher for lunar flights was the saturn v. | {'scope': 'all', 'row': '12', 'col': '3', 'col_other': '5', 'criterion': 'equal', 'value': 'saturn v', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'launcher', 'saturn v'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose launcher record fuzzily matches to saturn v .', 'tostr': 'filter_eq { all_rows ; launcher ; saturn v }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; launcher ; saturn v } }', 'tointer': 'select the rows whose launcher record fuzzily matches to saturn v . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'launcher', 'saturn v'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose launcher record fuzzily matches to saturn v .', 'tostr': 'filter_eq { all_rows ; launcher ; saturn v }'}, 'flights'], 'result': '10 lun / or', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; launcher ; saturn v } ; flights }'}, '10 lun / or'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; launcher ; saturn v } ; flights } ; 10 lun / or }', 'tointer': 'the flights record of this unqiue row is 10 lun / or .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; launcher ; saturn v } } ; eq { hop { filter_eq { all_rows ; launcher ; saturn v } ; flights } ; 10 lun / or } } = true', 'tointer': 'select the rows whose launcher record fuzzily matches to saturn v . there is only one such row in the table . the flights record of this unqiue row is 10 lun / or .'} | and { only { filter_eq { all_rows ; launcher ; saturn v } } ; eq { hop { filter_eq { all_rows ; launcher ; saturn v } ; flights } ; 10 lun / or } } = true | select the rows whose launcher record fuzzily matches to saturn v . there is only one such row in the table . the flights record of this unqiue row is 10 lun / or . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'launcher_7': 7, 'saturn v_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'flights_9': 9, '10 lun / or_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'launcher_7': 'launcher', 'saturn v_8': 'saturn v', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'flights_9': 'flights', '10 lun / or_10': '10 lun / or'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'launcher_7': [0], 'saturn v_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'flights_9': [2], '10 lun / or_10': [3]} | ['spaceport', 'launch complex', 'launcher', 'spacecraft', 'flights', 'years'] | [['baikonur cosmodrome , kazakhstan', 'site 1', 'vostok ( r )', 'vostok 1 - 6', '6 orbital', '1961 - 1963'], ['baikonur cosmodrome , kazakhstan', 'site 1', 'voskhod ( r )', 'voskhod 1 - 2', '2 orbital', '1964 - 1965'], ['baikonur cosmodrome , kazakhstan', 'site 1 , 31', 'soyuz ( r )', 'soyuz 1 - 40', '37 orbital', '1967 - 1981'], ['baikonur cosmodrome , kazakhstan', 'site 1 , 31', 'soyuz ( r )', 'soyuz - t 2 - 15', '14 orbital', '1980 - 1986'], ['baikonur cosmodrome , kazakhstan', 'site 1', 'soyuz ( r )', 'soyuz - tm 2 - 34', '33 orbital', '1987 - 2002'], ['baikonur cosmodrome , kazakhstan', 'site 1', 'soyuz ( r )', 'soyuz - tma 1 - 22', '22 orbital', '2002 - 2011'], ['baikonur cosmodrome , kazakhstan', 'site 1', 'soyuz ( r )', 'soyuz tma - m 1 - 9', '9 orbital', '2010 -'], ['cape canaveral afs , florida , usa', 'lc5', 'redstone', 'mercury 3 - 4', '2 sub - o', '1961 - 1961'], ['cape canaveral afs , florida , usa', 'lc14', 'atlas', 'mercury 6 - 9', '4 orbital', '1962 - 1963'], ['cape canaveral afs , florida , usa', 'lc19', 'titan ii', 'gemini 3 - 12', '10 orbital', '1965 - 1966'], ['cape canaveral afs , florida , usa', 'lc34', 'saturn ib', 'apollo 7', '1 orbital', '1968 - 1968'], ['kennedy space center , florida , usa', 'lc39', 'saturn v', 'apollo 8 - 17', '10 lun / or', '1968 - 1970'], ['kennedy space center , florida , usa', 'lc39', 'saturn ib', 'skylab 2 - 4', '3 orbital', '1973 - 1974'], ['kennedy space center , florida , usa', 'lc39', 'saturn ib', 'apollo - soyuz', '1 orbital', '1975 - 1975'], ['kennedy space center , florida , usa', 'lc39', 'sts 1 - 135', 'space shuttle', '134 orbital', '1981 - 2011']] |
1926 vfl season | https://en.wikipedia.org/wiki/1926_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10746808-12.html.csv | aggregation | the average crowd attendance for all the games was 16000 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '16000', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '16000', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '16000'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 16000 } = true', 'tointer': 'the average of the crowd record of all rows is 16000 .'} | round_eq { avg { all_rows ; crowd } ; 16000 } = true | the average of the crowd record of all rows is 16000 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '16000_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '16000_5': '16000'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '16000_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['richmond', '10.15 ( 75 )', 'south melbourne', '16.12 ( 108 )', 'punt road oval', '27000', '17 july 1926'], ['footscray', '7.14 ( 56 )', 'geelong', '15.17 ( 107 )', 'western oval', '17000', '17 july 1926'], ['collingwood', '18.16 ( 124 )', 'fitzroy', '11.16 ( 82 )', 'victoria park', '16000', '17 july 1926'], ['carlton', '8.17 ( 65 )', 'hawthorn', '8.9 ( 57 )', 'princes park', '12000', '17 july 1926'], ['st kilda', '3.11 ( 29 )', 'melbourne', '17.16 ( 118 )', 'junction oval', '14000', '17 july 1926'], ['north melbourne', '4.8 ( 32 )', 'essendon', '6.14 ( 50 )', 'arden street oval', '10000', '17 july 1926']] |
2008 - 09 united states network television schedule | https://en.wikipedia.org/wiki/2008%E2%80%9309_United_States_network_television_schedule | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15708593-12.html.csv | unique | parks and recreation only has one 30 minute time slot . | {'scope': 'all', 'row': '9', 'col': '2', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'parks and recreation', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '8:30', 'parks and recreation'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 8:30 record fuzzily matches to parks and recreation .', 'tostr': 'filter_eq { all_rows ; 8:30 ; parks and recreation }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; 8:30 ; parks and recreation } } = true', 'tointer': 'select the rows whose 8:30 record fuzzily matches to parks and recreation . there is only one such row in the table .'} | only { filter_eq { all_rows ; 8:30 ; parks and recreation } } = true | select the rows whose 8:30 record fuzzily matches to parks and recreation . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, '8:30_4': 4, 'parks and recreation_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', '8:30_4': '8:30', 'parks and recreation_5': 'parks and recreation'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], '8:30_4': [0], 'parks and recreation_5': [0]} | ['8:00', '8:30', '9:00', '9:30', '10:00'] | [['in the motherhood', 'samantha who', "grey 's anatomy", "grey 's anatomy", 'private practice'], ['ugly betty', 'ugly betty', "grey 's anatomy", "grey 's anatomy", 'private practice'], ['survivor : tocantins - the brazilian highlands', 'survivor : tocantins - the brazilian highlands', 'csi : crime scene investigation', 'csi : crime scene investigation', "harper 's island"], ['survivor : tocantins - the brazilian highlands', 'survivor : tocantins - the brazilian highlands', 'csi : crime scene investigation', 'csi : crime scene investigation', 'various crimetime programs ( reruns )'], ['smallville', 'smallville', 'supernatural', 'supernatural', 'local programming'], ['bones', 'bones', "hell 's kitchen", "hell 's kitchen", 'local programming'], ['my thursday night movie', 'my thursday night movie', 'my thursday night movie', 'my thursday night movie', 'local programming'], ['my name is earl', 'kath & kim', 'the office', '30 rock', 'southland'], ['my name is earl', 'parks and recreation', 'the office', '30 rock', 'southland']] |
1970 detroit lions season | https://en.wikipedia.org/wiki/1970_Detroit_Lions_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18733362-2.html.csv | aggregation | the 1970 detroit lions scored 25.5 points per game . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '25.5', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'result'], 'result': '25.5', 'ind': 0, 'tostr': 'avg { all_rows ; result }'}, '25.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; result } ; 25.5 } = true', 'tointer': 'the average of the result record of all rows is 25.5 .'} | round_eq { avg { all_rows ; result } ; 25.5 } = true | the average of the result record of all rows is 25.5 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'result_4': 4, '25.5_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'result_4': 'result', '25.5_5': '25.5'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'result_4': [0], '25.5_5': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 20 , 1970', 'green bay packers', 'w 40 - 0', '56263'], ['2', 'september 27 , 1970', 'cincinnati bengals', 'w 38 - 3', '58202'], ['3', 'october 5 , 1970', 'chicago bears', 'w 28 - 14', '58210'], ['4', 'october 11 , 1970', 'washington redskins', 'l 31 - 10', '50414'], ['5', 'october 18 , 1970', 'cleveland browns', 'w 41 - 24', '83577'], ['6', 'october 25 , 1970', 'chicago bears', 'w 16 - 10', '45632'], ['7', 'november 1 , 1970', 'minnesota vikings', 'l 30 - 17', '58210'], ['8', 'november 8 , 1970', 'new orleans saints', 'l 19 - 17', '66910'], ['9', 'november 15 , 1970', 'minnesota vikings', 'l 24 - 20', '47900'], ['10', 'november 22 , 1970', 'san francisco 49ers', 'w 28 - 7', '56232'], ['11', 'november 26 , 1970', 'oakland raiders', 'w 28 - 14', '56597'], ['12', 'december 6 , 1970', 'st louis cardinals', 'w 16 - 3', '56362'], ['13', 'december 14 , 1970', 'los angeles rams', 'w 28 - 23', '79441'], ['14', 'december 20 , 1970', 'green bay packers', 'w 20 - 0', '57387']] |
2006 - 07 coventry city f.c. season | https://en.wikipedia.org/wiki/2006%E2%80%9307_Coventry_City_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12808457-2.html.csv | superlative | in the 2006 - 07 coventry city f.c. season , kevin kyle had the most total . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', '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', 'total'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; total }'}, 'name'], 'result': 'kevin kyle', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; total } ; name }'}, 'kevin kyle'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; total } ; name } ; kevin kyle } = true', 'tointer': 'select the row whose total record of all rows is maximum . the name record of this row is kevin kyle .'} | eq { hop { argmax { all_rows ; total } ; name } ; kevin kyle } = true | select the row whose total record of all rows is maximum . the name record of this row is kevin kyle . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'total_5': 5, 'name_6': 6, 'kevin kyle_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', 'name_6': 'name', 'kevin kyle_7': 'kevin kyle'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'total_5': [0], 'name_6': [1], 'kevin kyle_7': [2]} | ['name', 'championship', 'league cup', 'fa cup', 'total'] | [['kevin kyle', '11', '0', '1', '12'], ['robert page', '10', '0', '0', '10'], ['michael doyle', '8', '0', '2', '10'], ['andrew whing', '6', '1', '0', '7'], ['david mcnamee', '6', '0', '0', '6'], ['marcus hall', '5', '0', '0', '5'], ['leon mckenzie', '5', '0', '0', '5'], ['jay tabb', '5', '0', '0', '5'], ['elliott ward', '5', '0', '0', '5'], ['richard duffy', '4', '0', '0', '4'], ['stephen hughes', '3', '1', '0', '3'], ['dele adebola', '3', '0', '0', '3'], ['isaac osbourne', '3', '0', '0', '3'], ['kevin thornton', '3', '0', '0', '3'], ['adam virgo', '2', '0', '1', '3'], ['colin cameron', '1', '0', '0', '1'], ['colin hawkins', '1', '0', '0', '1'], ['stern john', '1', '0', '0', '1']] |
the apprentice new zealand | https://en.wikipedia.org/wiki/The_Apprentice_New_Zealand | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26263322-1.html.csv | superlative | of the candidates in the apprentice new zealand , kirsty parkhill is the oldest . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '12', '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', 'age'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; age }'}, 'candidate'], 'result': 'kirsty parkhill', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; age } ; candidate }'}, 'kirsty parkhill'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; age } ; candidate } ; kirsty parkhill } = true', 'tointer': 'select the row whose age record of all rows is maximum . the candidate record of this row is kirsty parkhill .'} | eq { hop { argmax { all_rows ; age } ; candidate } ; kirsty parkhill } = true | select the row whose age record of all rows is maximum . the candidate record of this row is kirsty parkhill . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'age_5': 5, 'candidate_6': 6, 'kirsty parkhill_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'age_5': 'age', 'candidate_6': 'candidate', 'kirsty parkhill_7': 'kirsty parkhill'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'age_5': [0], 'candidate_6': [1], 'kirsty parkhill_7': [2]} | ['candidate', 'background', 'original team', 'age', 'hometown', 'result'] | [['thomas ben', 'divisional manager', 'number 8', '34', 'auckland', 'hired by serepisos'], ['david wyatt', 'self - employed - media agency', 'number 8', '27', 'auckland', 'fired in the season finale'], ['catherine livingstone', 'self - employed - concierge service', 'athena', '33', 'auckland', 'fired in week 12'], ['karen reid', 'self - employed - practices in alternative medicine', 'athena', '33', 'auckland', 'fired in week 11'], ['linda slade', 'university student', 'athena', '21', 'christchurch', 'fired in week 10'], ['nicky clarke', 'pr specialist', 'athena', '28', 'auckland', 'fired in week 9'], ['daniel phillips', 'advertising account manager', 'number 8', '31', 'auckland', 'fired in week 8'], ['meena chhagan', 'accountant', 'athena', '24', 'wellington', 'fired in week 7'], ['richard henry', 'getfrank founder', 'number 8', '26', 'auckland', 'fired in week 7'], ['paul natac', 'infringement relationship manager', 'number 8', '28', 'auckland', 'fired in week 6'], ['chris whiteside', 'accountant', 'number 8', '28', 'christchurch', 'fired in week 4'], ['kirsty parkhill', 'business development manager', 'athena', '35', 'wellington', 'fired in week 3']] |
jack mcgrath | https://en.wikipedia.org/wiki/Jack_McGrath | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1236208-1.html.csv | unique | in 1949 there was the fewest laps with only 39 total laps . | {'scope': 'all', 'row': '2', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': '39', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'laps', '39'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose laps record is equal to 39 .', 'tostr': 'filter_eq { all_rows ; laps ; 39 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; laps ; 39 } }', 'tointer': 'select the rows whose laps record is equal to 39 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'laps', '39'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose laps record is equal to 39 .', 'tostr': 'filter_eq { all_rows ; laps ; 39 }'}, 'year'], 'result': '1949', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; laps ; 39 } ; year }'}, '1949'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; laps ; 39 } ; year } ; 1949 }', 'tointer': 'the year record of this unqiue row is 1949 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; laps ; 39 } } ; eq { hop { filter_eq { all_rows ; laps ; 39 } ; year } ; 1949 } } = true', 'tointer': 'select the rows whose laps record is equal to 39 . there is only one such row in the table . the year record of this unqiue row is 1949 .'} | and { only { filter_eq { all_rows ; laps ; 39 } } ; eq { hop { filter_eq { all_rows ; laps ; 39 } ; year } ; 1949 } } = true | select the rows whose laps record is equal to 39 . there is only one such row in the table . the year record of this unqiue row is 1949 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'laps_7': 7, '39_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1949_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'laps_7': 'laps', '39_8': '39', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1949_10': '1949'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'laps_7': [0], '39_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1949_10': [3]} | ['year', 'start', 'qual', 'rank', 'finish', 'laps'] | [['1948', '13', '124.580', '16', '21', '70'], ['1949', '3', '128.884', '8', '26', '39'], ['1950', '6', '131.868', '10', '14', '131'], ['1951', '3', '134.303', '8', '3', '200'], ['1952', '3', '136.664', '5', '11', '200'], ['1953', '3', '136.602', '13', '5', '200'], ['1954', '1', '141.033', '1', '3', '200'], ['1955', '3', '142.580', '1', '26', '54']] |
2008 - 09 philadelphia 76ers season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Philadelphia_76ers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17323042-11.html.csv | count | in the 2008 - 09 philadelphia 76ers season , among the games played in amway arena , two of them featured andre iguodala as a high pointer . | {'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'andre iguodala', 'result': '2', 'col': '5', 'subset': {'col': '8', 'criterion': 'fuzzily_match', 'value': 'amway arena'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'amway arena'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location attendance ; amway arena }', 'tointer': 'select the rows whose location attendance record fuzzily matches to amway arena .'}, 'high points', 'andre iguodala'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose location attendance record fuzzily matches to amway arena . among these rows , select the rows whose high points record fuzzily matches to andre iguodala .', 'tostr': 'filter_eq { filter_eq { all_rows ; location attendance ; amway arena } ; high points ; andre iguodala }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; location attendance ; amway arena } ; high points ; andre iguodala } }', 'tointer': 'select the rows whose location attendance record fuzzily matches to amway arena . among these rows , select the rows whose high points record fuzzily matches to andre iguodala . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; location attendance ; amway arena } ; high points ; andre iguodala } } ; 2 } = true', 'tointer': 'select the rows whose location attendance record fuzzily matches to amway arena . among these rows , select the rows whose high points record fuzzily matches to andre iguodala . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; location attendance ; amway arena } ; high points ; andre iguodala } } ; 2 } = true | select the rows whose location attendance record fuzzily matches to amway arena . among these rows , select the rows whose high points record fuzzily matches to andre iguodala . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'location attendance_6': 6, 'amway arena_7': 7, 'high points_8': 8, 'andre iguodala_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'location attendance_6': 'location attendance', 'amway arena_7': 'amway arena', 'high points_8': 'high points', 'andre iguodala_9': 'andre iguodala', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'location attendance_6': [0], 'amway arena_7': [0], 'high points_8': [1], 'andre iguodala_9': [1], '2_10': [3]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['1', 'april 19', 'orlando', 'w 100 - 98 ( ot )', 'andre iguodala ( 20 )', 'andre iguodala ( 8 )', 'andre iguodala ( 8 )', 'amway arena 17461', '1 - 0'], ['2', 'april 22', 'orlando', 'l 87 - 96 ( ot )', 'andre miller ( 30 )', 'andre iguodala , theo ratliff ( 8 )', 'andre iguodala ( 7 )', 'amway arena 17461', '1 - 1'], ['3', 'april 24', 'orlando', 'w 96 - 94 ( ot )', 'andre iguodala ( 29 )', 'andre miller , samuel dalembert ( 9 )', 'andre miller ( 7 )', 'wachovia center 16492', '2 - 1'], ['4', 'april 26', 'orlando', 'l 81 - 84 ( ot )', 'andre miller ( 17 )', 'samuel dalembert ( 9 )', 'andre iguodala ( 11 )', 'wachovia center 16464', '2 - 2'], ['5', 'april 28', 'orlando', 'l 78 - 91 ( ot )', 'andre iguodala ( 26 )', 'andre miller ( 6 )', 'samuel dalembert ( 9 )', 'amway arena 17461', '2 - 3']] |
2005 - 06 toronto raptors season | https://en.wikipedia.org/wiki/2005%E2%80%9306_Toronto_Raptors_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15873014-7.html.csv | count | chris bosh had the most rebounds in 9 games in march of the 2005 - 06 toronto raptors season . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'chris bosh', 'result': '9', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high rebounds', 'chris bosh'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high rebounds record fuzzily matches to chris bosh .', 'tostr': 'filter_eq { all_rows ; high rebounds ; chris bosh }'}], 'result': '9', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; high rebounds ; chris bosh } }', 'tointer': 'select the rows whose high rebounds record fuzzily matches to chris bosh . the number of such rows is 9 .'}, '9'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; high rebounds ; chris bosh } } ; 9 } = true', 'tointer': 'select the rows whose high rebounds record fuzzily matches to chris bosh . the number of such rows is 9 .'} | eq { count { filter_eq { all_rows ; high rebounds ; chris bosh } } ; 9 } = true | select the rows whose high rebounds record fuzzily matches to chris bosh . the number of such rows is 9 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'high rebounds_5': 5, 'chris bosh_6': 6, '9_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', 'chris bosh_6': 'chris bosh', '9_7': '9'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high rebounds_5': [0], 'chris bosh_6': [0], '9_7': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['57', 'march 1', 'atlanta', 'l 111 - 113 ( ot )', 'chris bosh ( 27 )', 'charlie villanueva ( 11 )', 'chris bosh ( 5 )', 'air canada centre 15137', '20 - 37'], ['58', 'march 4', 'new jersey', 'l 100 - 105 ( ot )', 'morris peterson ( 25 )', 'chris bosh , charlie villanueva ( 11 )', 'mike james ( 7 )', 'continental airlines arena 16215', '20 - 38'], ['59', 'march 5', 'boston', 'w 111 - 105 ( ot )', 'morris peterson ( 27 )', 'chris bosh ( 10 )', 'mike james ( 6 )', 'air canada centre 16623', '21 - 38'], ['60', 'march 7', 'cleveland', 'l 99 - 106 ( ot )', 'mike james ( 31 )', 'charlie villanueva ( 11 )', 'mike james ( 8 )', 'quicken loans arena 18077', '21 - 39'], ['61', 'march 8', 'cleveland', 'l 97 - 98 ( ot )', 'morris peterson ( 31 )', 'chris bosh ( 14 )', 'mike james ( 7 )', 'air canada centre 19800', '21 - 40'], ['62', 'march 10', 'denver', 'l 97 - 108 ( ot )', 'mike james ( 26 )', 'chris bosh ( 15 )', 'josé calderón ( 5 )', 'air canada centre 17806', '21 - 41'], ['63', 'march 12', 'indiana', 'w 93 - 89 ( ot )', 'morris peterson ( 25 )', 'chris bosh ( 8 )', 'mike james ( 4 )', 'air canada centre 17573', '22 - 41'], ['64', 'march 14', 'philadelphia', 'w 111 - 97 ( ot )', 'chris bosh ( 31 )', 'charlie villanueva ( 10 )', 'darrick martin ( 12 )', 'wachovia center 14917', '23 - 41'], ['65', 'march 15', 'detroit', 'l 98 - 105 ( ot )', 'mike james ( 24 )', 'chris bosh ( 11 )', 'mike james ( 11 )', 'air canada centre 19800', '23 - 42'], ['66', 'march 17', 'milwaukee', 'w 97 - 96 ( ot )', 'chris bosh ( 27 )', 'chris bosh ( 10 )', 'mike james ( 6 )', 'air canada centre 17273', '24 - 42'], ['67', 'march 21', 'new york', 'w 114 - 109 ( ot )', 'mike james ( 37 )', 'mike james , charlie villanueva ( 8 )', 'mike james ( 5 )', 'madison square garden 18131', '25 - 42'], ['68', 'march 22', 'boston', 'l 96 - 110 ( ot )', 'mike james ( 31 )', 'chris bosh ( 11 )', 'chris bosh ( 8 )', 'td banknorth garden 18624', '25 - 43'], ['69', 'march 24', 'minnesota', 'w 97 - 77 ( ot )', 'morris peterson ( 21 )', 'chris bosh ( 15 )', 'mike james ( 5 )', 'air canada centre 17493', '26 - 43'], ['70', 'march 26', 'milwaukee', 'l 116 - 125 ( ot )', 'charlie villanueva ( 48 )', 'charlie villanueva ( 9 )', 'mike james ( 10 )', 'bradley center 16317', '26 - 44'], ['71', 'march 29', 'miami', 'l 94 - 98 ( ot )', 'morris peterson ( 28 )', 'charlie villanueva ( 13 )', 'mike james ( 12 )', 'air canada centre 19973', '26 - 45'], ['72', 'march 31', 'phoenix', 'l 126 - 140 ( ot )', 'morris peterson ( 38 )', 'pape sow ( 15 )', 'mike james ( 10 )', 'air canada centre 19800', '26 - 46']] |
list of ngc objects ( 2001 - 3000 ) | https://en.wikipedia.org/wiki/List_of_NGC_objects_%282001%E2%80%933000%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11097664-6.html.csv | ordinal | in the list of ngc objects ( 2001 - 3000 ) hydra has the 2nd highest apparent magnitude among open cluster object type . | {'scope': 'subset', 'row': '8', 'col': '6', 'order': '2', 'col_other': '2,3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'open cluster'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'object type', 'open cluster'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; object type ; open cluster }', 'tointer': 'select the rows whose object type record fuzzily matches to open cluster .'}, 'apparent magnitude', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_eq { all_rows ; object type ; open cluster } ; apparent magnitude ; 2 }'}, 'constellation'], 'result': 'hydra', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_eq { all_rows ; object type ; open cluster } ; apparent magnitude ; 2 } ; constellation }'}, 'hydra'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_eq { all_rows ; object type ; open cluster } ; apparent magnitude ; 2 } ; constellation } ; hydra } = true', 'tointer': 'select the rows whose object type record fuzzily matches to open cluster . select the row whose apparent magnitude record of these rows is 2nd maximum . the constellation record of this row is hydra .'} | eq { hop { nth_argmax { filter_eq { all_rows ; object type ; open cluster } ; apparent magnitude ; 2 } ; constellation } ; hydra } = true | select the rows whose object type record fuzzily matches to open cluster . select the row whose apparent magnitude record of these rows is 2nd maximum . the constellation record of this row is hydra . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'object type_6': 6, 'open cluster_7': 7, 'apparent magnitude_8': 8, '2_9': 9, 'constellation_10': 10, 'hydra_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'object type_6': 'object type', 'open cluster_7': 'open cluster', 'apparent magnitude_8': 'apparent magnitude', '2_9': '2', 'constellation_10': 'constellation', 'hydra_11': 'hydra'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'object type_6': [0], 'open cluster_7': [0], 'apparent magnitude_8': [1], '2_9': [1], 'constellation_10': [2], 'hydra_11': [3]} | ['ngc number', 'object type', 'constellation', 'right ascension ( j2000 )', 'declination ( j2000 )', 'apparent magnitude'] | [['2516', 'open cluster', 'carina', '07h58 m', 'degree45 ′', '3.8'], ['2535', 'spiral galaxy', 'cancer', '08h11 m13 .6 s', 'degree12 ′ 24 ″', '13.0'], ['2536', 'spiral galaxy', 'cancer', '08h11 m16 .1 s', 'degree10 ′ 45 ″', '14.5'], ['2537', 'irregular galaxy', 'lynx', '08h13 m14 .6 s', 'degree59 ′ 30 ″', '11.7'], ['2541', 'spiral galaxy', 'lynx', '08h14 m40 .4 s', 'degree03 ′ 42 ″', '13.0'], ['2546', 'open cluster', 'puppis', '08h12 m', 'degree37 ′', '6.5'], ['2547', 'open cluster', 'vela', '08h10 m25 .7 s', 'degree10 ′ 03 ″', '4.8'], ['2548', 'open cluster', 'hydra', '08h14 m', 'degree45 ′', '6.1'], ['2549', 'lenticular galaxy', 'lynx', '08h18 m58 .4 s', 'degree48 ′ 10 ″', '12.1'], ['2550', 'spiral galaxy', 'camelopardalis', '08h24 m33 .9 s', 'degree00 ′ 43 ″', '13.1'], ['2551', 'spiral galaxy', 'camelopardalis', '08h24 m50 .5 s', 'degree24 ′ 44 ″', '12.7'], ['2552', 'irregular galaxy', 'lynx', '08h19 m19 .6 s', 'degree00 ′ 28 ″', '13.5']] |
wobbe index | https://en.wikipedia.org/wiki/Wobbe_index | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1868929-1.html.csv | superlative | the fuel with the highest upper index kcal / nm was n-butane . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '8', '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', 'upper index kcal / nm 3'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; upper index kcal / nm 3 }'}, 'fuel gas'], 'result': 'n - butane', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; upper index kcal / nm 3 } ; fuel gas }'}, 'n - butane'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; upper index kcal / nm 3 } ; fuel gas } ; n - butane } = true', 'tointer': 'select the row whose upper index kcal / nm 3 record of all rows is maximum . the fuel gas record of this row is n - butane .'} | eq { hop { argmax { all_rows ; upper index kcal / nm 3 } ; fuel gas } ; n - butane } = true | select the row whose upper index kcal / nm 3 record of all rows is maximum . the fuel gas record of this row is n - butane . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'upper index kcal / nm 3_5': 5, 'fuel gas_6': 6, 'n - butane_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'upper index kcal / nm 3_5': 'upper index kcal / nm 3', 'fuel gas_6': 'fuel gas', 'n - butane_7': 'n - butane'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'upper index kcal / nm 3_5': [0], 'fuel gas_6': [1], 'n - butane_7': [2]} | ['fuel gas', 'upper index kcal / nm 3', 'lower index kcal / nm 3', 'upper index mj / nm 3', 'lower index mj / nm 3'] | [['hydrogen', '11528', '9715', '48.23', '40.65'], ['methane', '12735', '11452', '53.28', '47.91'], ['ethane', '16298', '14931', '68.19', '62.47'], ['ethylene', '15253', '14344', '63.82', '60.01'], ['natural gas', '12837', '11597', '53.71', '48.52'], ['propane', '19376', '17817', '81.07', '74.54'], ['propylene', '18413', '17180', '77.04', '71.88'], ['n - butane', '22066', '20336', '92.32', '85.08'], ['iso - butane', '21980', '20247', '91.96', '84.71'], ['butylene - 1', '21142', '19728', '88.46', '82.54'], ['lpg', '20755', '19106', '86.84', '79.94'], ['acetylene', '14655', '14141', '61.32', '59.16']] |
usa today all - usa high school basketball team | https://en.wikipedia.org/wiki/USA_Today_All-USA_high_school_basketball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11677760-5.html.csv | majority | most of the players participated in the 1987 draft . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': '1987 draft', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'nba draft', '1987 draft'], 'result': True, 'ind': 0, 'tointer': 'for the nba draft records of all rows , most of them fuzzily match to 1987 draft .', 'tostr': 'most_eq { all_rows ; nba draft ; 1987 draft } = true'} | most_eq { all_rows ; nba draft ; 1987 draft } = true | for the nba draft records of all rows , most of them fuzzily match to 1987 draft . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nba draft_3': 3, '1987 draft_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nba draft_3': 'nba draft', '1987 draft_4': '1987 draft'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nba draft_3': [0], '1987 draft_4': [0]} | ['player', 'height', 'school', 'hometown', 'college', 'nba draft'] | [['reggie williams', '6 - 7', 'dunbar high school', 'baltimore , md', 'georgetown', '1st round - 4th pick of 1987 draft ( clippers )'], ['dwayne washington', '6 - 2', 'boys and girls high school', 'brooklyn , ny', 'syracuse', '1st round - 13th pick of 1986 draft ( nets )'], ['dave popson', '6 - 10', "bishop o ' reilly high school", 'kingston , pa', 'north carolina', '4th round - 88th pick of 1987 draft ( pistons )'], ['james blackmon', '6 - 3', 'marion high school', 'marion , in', 'kentucky', '5th round - 94th pick of 1987 draft ( nets )'], ['antoine joubert', '6 - 5', 'southwestern high school', 'detroit , mi', 'michigan', '6th round - 135th pick of 1987 draft ( pistons )']] |
list of birmingham city f.c. records and statistics | https://en.wikipedia.org/wiki/List_of_Birmingham_City_F.C._records_and_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15702100-2.html.csv | comparative | joe bradford began playing for birmingham city f. c. 50 years before trevor francis . | {'row_1': '1', 'row_2': '2', 'col': '2', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '50 years', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'joe bradford'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to joe bradford .', 'tostr': 'filter_eq { all_rows ; name ; joe bradford }'}, 'years'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; joe bradford } ; years }', 'tointer': 'select the rows whose name record fuzzily matches to joe bradford . take the years record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'trevor francis'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to trevor francis .', 'tostr': 'filter_eq { all_rows ; name ; trevor francis }'}, 'years'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; trevor francis } ; years }', 'tointer': 'select the rows whose name record fuzzily matches to trevor francis . take the years record of this row .'}], 'result': '-50 years', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; name ; joe bradford } ; years } ; hop { filter_eq { all_rows ; name ; trevor francis } ; years } }'}, '-50 years'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; name ; joe bradford } ; years } ; hop { filter_eq { all_rows ; name ; trevor francis } ; years } } ; -50 years } = true', 'tointer': 'select the rows whose name record fuzzily matches to joe bradford . take the years record of this row . select the rows whose name record fuzzily matches to trevor francis . take the years record of this row . the second record is 50 years larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; name ; joe bradford } ; years } ; hop { filter_eq { all_rows ; name ; trevor francis } ; years } } ; -50 years } = true | select the rows whose name record fuzzily matches to joe bradford . take the years record of this row . select the rows whose name record fuzzily matches to trevor francis . take the years record of this row . the second record is 50 years larger than the first record . | 6 | 6 | {'str_eq_5': 5, 'result_6': 6, 'diff_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'name_8': 8, 'joe bradford_9': 9, 'years_10': 10, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'name_12': 12, 'trevor francis_13': 13, 'years_14': 14, '-50 years_15': 15} | {'str_eq_5': 'str_eq', 'result_6': 'true', 'diff_4': 'diff', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'name_8': 'name', 'joe bradford_9': 'joe bradford', 'years_10': 'years', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'name_12': 'name', 'trevor francis_13': 'trevor francis', 'years_14': 'years', '-50 years_15': '-50 years'} | {'str_eq_5': [6], 'result_6': [], 'diff_4': [5], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'name_8': [0], 'joe bradford_9': [0], 'years_10': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'name_12': [1], 'trevor francis_13': [1], 'years_14': [3], '-50 years_15': [5]} | ['name', 'years', 'league a', 'fa cup', 'league cup', 'other b', 'total'] | [['joe bradford', '1920 - 1935', '249 ( 414 )', '18 ( 31 )', '0 ( 0 )', '0 ( 0 )', '267 ( 445 )'], ['trevor francis', '1970 - 1979', '119 ( 280 )', '6 ( 20 )', '4 ( 19 )', '4 ( 10 )', '133 ( 329 )'], ['peter murphy', '1952 - 1960', '107 ( 245 )', '16 ( 24 )', '0 ( 0 )', '4 ( 9 )', '127 ( 278 )'], ['fred wheldon', '1890 - 1896', '99 ( 155 )', '12 ( 13 )', '0 ( 0 )', '5 ( 7 )', '116 ( 175 )'], ['george briggs', '1924 - 1933', '98 ( 298 )', '9 ( 26 )', '0 ( 0 )', '0 ( 0 )', '107 ( 324 )'], ['billy jones', '1901 - 1909 1912 - 1913', '99 ( 236 )', '3 ( 17 )', '0 ( 0 )', '0 ( 0 )', '102 ( 253 )'], ['geoff vowden', '1964 - 1970', '79 ( 221 )', '8 ( 16 )', '7 ( 16 )', '0 ( 0 )', '94 ( 253 )'], ['eddy brown', '1954 - 1958', '74 ( 158 )', '13 ( 18 )', '0 ( 0 )', '3 ( 9 )', '90 ( 185 )'], ['bob latchford', '1969 - 1974', '68 ( 160 )', '6 ( 12 )', '6 ( 16 )', '4 ( 6 )', '84 ( 193 )'], ['bob mcroberts', '1898 - 1905', '70 ( 173 )', '12 ( 14 )', '0 ( 0 )', '0 ( 0 )', '82 ( 187 )']] |
2008 - 09 dallas mavericks season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Dallas_Mavericks_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17288869-9.html.csv | majority | all games of the 2008 - 09 dallas mavericks ' season were scheduled for the month of march . | {'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'march', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'date', 'march'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to march .', 'tostr': 'all_eq { all_rows ; date ; march } = true'} | all_eq { all_rows ; date ; march } = true | for the date records of all rows , all of them fuzzily match to march . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'march_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'march_4': 'march'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'march_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['59', 'march 1', 'toronto', 'w 109 - 98 ( ot )', 'dirk nowitzki ( 24 )', 'james singleton ( 16 )', 'jason kidd ( 15 )', 'american airlines center 19688', '36 - 23'], ['60', 'march 2', 'oklahoma city', 'l 87 - 96 ( ot )', 'dirk nowitzki ( 28 )', 'james singleton ( 6 )', 'dirk nowitzki ( 6 )', 'ford center 18527', '36 - 24'], ['61', 'march 4', 'san antonio', 'w 107 - 102 ( ot )', 'josh howard ( 29 )', 'dirk nowitzki ( 12 )', 'jason kidd ( 9 )', 'american airlines center 20316', '37 - 24'], ['62', 'march 5', 'new orleans', 'l 88 - 104 ( ot )', 'dirk nowitzki ( 27 )', 'erick dampier ( 9 )', 'jason terry ( 4 )', 'new orleans arena 17230', '37 - 25'], ['63', 'march 7', 'washington', 'w 119 - 103 ( ot )', 'dirk nowitzki ( 34 )', 'dirk nowitzki ( 9 )', 'jason kidd ( 11 )', 'american airlines center 20150', '38 - 25'], ['64', 'march 10', 'phoenix', 'w 122 - 117 ( ot )', 'dirk nowitzki ( 34 )', 'dirk nowitzki ( 13 )', 'dirk nowitzki , josé juan barea ( 4 )', 'us airways center 18422', '39 - 25'], ['65', 'march 11', 'portland', 'w 93 - 89 ( ot )', 'dirk nowitzki ( 29 )', 'dirk nowitzki , jason kidd ( 10 )', 'jason kidd ( 10 )', 'rose garden 20286', '40 - 25'], ['66', 'march 13', 'golden state', 'l 110 - 119 ( ot )', 'dirk nowitzki ( 27 )', 'james singleton ( 11 )', 'jason kidd ( 11 )', 'oracle arena 18751', '40 - 26'], ['67', 'march 15', 'la lakers', 'l 100 - 107 ( ot )', 'jason terry ( 29 )', 'james singleton ( 10 )', 'jason kidd ( 9 )', 'staples center 18997', '40 - 27'], ['68', 'march 17', 'detroit', 'w 103 - 101 ( ot )', 'dirk nowitzki ( 30 )', 'erick dampier ( 13 )', 'josé juan barea ( 8 )', 'american airlines center 20427', '41 - 27'], ['69', 'march 19', 'atlanta', 'l 87 - 95 ( ot )', 'dirk nowitzki ( 23 )', 'dirk nowitzki ( 12 )', 'jason kidd ( 6 )', 'philips arena 17499', '41 - 28'], ['70', 'march 20', 'indiana', 'w 94 - 92 ( ot )', 'dirk nowitzki ( 23 )', 'james singleton ( 11 )', 'josé juan barea ( 6 )', 'conseco fieldhouse 17232', '42 - 28'], ['71', 'march 25', 'golden state', 'w 128 - 106 ( ot )', 'jason terry , dirk nowitzki ( 26 )', 'erick dampier ( 10 )', 'josé juan barea , jason kidd ( 7 )', 'american airlines center 19862', '43 - 28'], ['72', 'march 27', 'denver', 'l 101 - 103 ( ot )', 'dirk nowitzki ( 26 )', 'dirk nowitzki ( 11 )', 'josé juan barea , jason terry ( 4 )', 'american airlines center 20310', '43 - 29'], ['73', 'march 29', 'cleveland', 'l 74 - 102 ( ot )', 'dirk nowitzki ( 20 )', 'ryan hollins ( 12 )', 'jason kidd ( 8 )', 'quicken loans arena 20562', '43 - 30'], ['74', 'march 31', 'minnesota', 'w 108 - 88 ( ot )', 'dirk nowitzki ( 23 )', 'dirk nowitzki ( 12 )', 'jason kidd ( 13 )', 'target center 12111', '44 - 30']] |
1986 icf canoe sprint world championships | https://en.wikipedia.org/wiki/1986_ICF_Canoe_Sprint_World_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18715280-4.html.csv | count | 5 nations competing in the 1986 icf canoe sprint world championships won exactly 2 bronze medals . | {'scope': 'all', 'criterion': 'equal', 'value': '2', 'result': '5', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'bronze', '2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose bronze record is equal to 2 .', 'tostr': 'filter_eq { all_rows ; bronze ; 2 }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; bronze ; 2 } }', 'tointer': 'select the rows whose bronze record is equal to 2 . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; bronze ; 2 } } ; 5 } = true', 'tointer': 'select the rows whose bronze record is equal to 2 . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; bronze ; 2 } } ; 5 } = true | select the rows whose bronze record is equal to 2 . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'bronze_5': 5, '2_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'bronze_5': 'bronze', '2_6': '2', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'bronze_5': [0], '2_6': [0], '5_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'hungary', '7', '3', '1', '11'], ['2', 'soviet union', '1', '6', '3', '10'], ['3', 'romania', '3', '3', '2', '8'], ['4', 'east germany', '2', '3', '2', '7'], ['5', 'poland', '1', '1', '1', '3'], ['6', 'bulgaria', '1', '0', '2', '3'], ['7', 'west germany', '1', '0', '2', '3'], ['8', 'united kingdom', '2', '0', '0', '2'], ['9', 'france', '0', '1', '1', '2'], ['10', 'yugoslavia', '0', '1', '1', '2'], ['11', 'denmark', '0', '0', '2', '2'], ['12', 'australia', '0', '0', '1', '1'], ['total', 'total', '18', '18', '18', '54']] |
kei nishikori | https://en.wikipedia.org/wiki/Kei_Nishikori | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12299543-2.html.csv | unique | of the finals that kei nishikori participated in , the one on april 10 , 2011 was the only one on a clay surface . | {'scope': 'all', 'row': '2', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'clay', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to clay .', 'tostr': 'filter_eq { all_rows ; surface ; clay }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; surface ; clay } }', 'tointer': 'select the rows whose surface record fuzzily matches to clay . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to clay .', 'tostr': 'filter_eq { all_rows ; surface ; clay }'}, 'date'], 'result': '10 april 2011', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; surface ; clay } ; date }'}, '10 april 2011'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; surface ; clay } ; date } ; 10 april 2011 }', 'tointer': 'the date record of this unqiue row is 10 april 2011 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; date } ; 10 april 2011 } } = true', 'tointer': 'select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the date record of this unqiue row is 10 april 2011 .'} | and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; date } ; 10 april 2011 } } = true | select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the date record of this unqiue row is 10 april 2011 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'surface_7': 7, 'clay_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '10 april 2011_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'surface_7': 'surface', 'clay_8': 'clay', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '10 april 2011_10': '10 april 2011'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'surface_7': [0], 'clay_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '10 april 2011_10': [3]} | ['outcome', 'date', 'surface', 'opponent in the final', 'score in the final'] | [['winner', '11 february 2008', 'hard', 'james blake', '3 - 6 , 6 - 1 , 6 - 4'], ['runner - up', '10 april 2011', 'clay', 'ryan sweeting', '4 - 6 , 6 - 7 ( 3 - 7 )'], ['runner - up', '6 november 2011', 'hard ( i )', 'roger federer', '1 - 6 , 3 - 6'], ['winner', '7 october 2012', 'hard', 'milos raonic', '7 - 6 ( 7 - 5 ) , 3 - 6 , 6 - 0'], ['winner', '24 february 2013', 'hard ( i )', 'feliciano lópez', '6 - 2 , 6 - 3']] |
list of mountains in norway by prominence | https://en.wikipedia.org/wiki/List_of_mountains_in_Norway_by_prominence | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12280396-1.html.csv | majority | all of the mountains in norway have an elevation that is higher than 1000 meters . | {'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'greater_than', 'value': '1000', 'subset': None} | {'func': 'all_greater', 'args': ['all_rows', 'elevation ( m )', '1000'], 'result': True, 'ind': 0, 'tointer': 'for the elevation ( m ) records of all rows , all of them are greater than 1000 .', 'tostr': 'all_greater { all_rows ; elevation ( m ) ; 1000 } = true'} | all_greater { all_rows ; elevation ( m ) ; 1000 } = true | for the elevation ( m ) records of all rows , all of them are greater than 1000 . | 1 | 1 | {'all_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'elevation (m)_3': 3, '1000_4': 4} | {'all_greater_0': 'all_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'elevation (m)_3': 'elevation ( m )', '1000_4': '1000'} | {'all_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'elevation (m)_3': [0], '1000_4': [0]} | ['peak', 'elevation ( m )', 'prominence ( m )', 'isolation ( km )', 'municipality', 'county'] | [['galdhøpiggen', '2469', '2372', '1570', 'lom', 'oppland'], ['jiehkkevárri', '1833', '1741', '140', 'lyngen , tromsø', 'troms'], ['snøhetta', '2286', '1675', '83', 'dovre', 'oppland'], ['store lenangstind', '1625', '1576', '47', 'lyngen', 'troms'], ['gjegnen / blånibba', '1670', '1460', '47', 'bremanger', 'sogn og fjordane'], ['hamperokken', '1404', '1396', '18', 'tromsø', 'troms'], ['skårasalen', '1542', '1385', '7', 'ørsta', 'møre og romsdal'], ['oksskolten', '1916', '1384', '185', 'hemnes', 'nordland'], ['botnafjellet', '1572', '1339', '15', 'gloppen', 'sogn og fjordane'], ['kvitegga', '1717', '1324', '23', 'stranda , ørsta', 'møre og romsdal'], ['fresvikbreen', '1660', '1310', '17', 'vik', 'sogn og fjordane'], ['smørskredtindane', '1630', '1306', '12', 'stranda , ørsta', 'møre og romsdal'], ['njunis', '1717', '1305', '53', 'målselv', 'troms'], ['store trolla', '1850', '1292', '11', 'sunndal', 'møre og romsdal'], ['langlitinden', '1276', '1276', '26', 'ibestad', 'troms'], ['indre russetind', '1527', '1268', '9', 'balsfjord', 'troms'], ['møysalen', '1262', '1262', '60', 'hinnøya', 'nordland'], ['stortind', '1320', '1242', '14', 'tromsø', 'troms'], ['folgefonna', '1660', '1233', '29', 'kvinnherad , odda', 'hordaland'], ['daurmål', '1446', '1230', '4', 'gloppen , jølster', 'sogn og fjordane']] |
no way out ( 2009 ) | https://en.wikipedia.org/wiki/No_Way_Out_%282009%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18438494-3.html.csv | superlative | the match between kane and rey mysterio was the shortest match at no way out 2009 . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2,4', 'subset': None} | {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'time'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; time }'}, 'wrestler'], 'result': 'kane', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; time } ; wrestler }'}, 'kane'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; time } ; wrestler } ; kane }', 'tointer': 'select the row whose time record of all rows is minimum . the wrestler record of this row is kane .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'time'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; time }'}, 'eliminated by'], 'result': 'rey mysterio', 'ind': 3, 'tostr': 'hop { argmin { all_rows ; time } ; eliminated by }'}, 'rey mysterio'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmin { all_rows ; time } ; eliminated by } ; rey mysterio }', 'tointer': 'the eliminated by record of this row is rey mysterio .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { hop { argmin { all_rows ; time } ; wrestler } ; kane } ; eq { hop { argmin { all_rows ; time } ; eliminated by } ; rey mysterio } } = true', 'tointer': 'select the row whose time record of all rows is minimum . the wrestler record of this row is kane . the eliminated by record of this row is rey mysterio .'} | and { eq { hop { argmin { all_rows ; time } ; wrestler } ; kane } ; eq { hop { argmin { all_rows ; time } ; eliminated by } ; rey mysterio } } = true | select the row whose time record of all rows is minimum . the wrestler record of this row is kane . the eliminated by record of this row is rey mysterio . | 7 | 6 | {'and_5': 5, 'result_6': 6, 'str_eq_2': 2, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_7': 7, 'time_8': 8, 'wrestler_9': 9, 'kane_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'eliminated by_11': 11, 'rey mysterio_12': 12} | {'and_5': 'and', 'result_6': 'true', 'str_eq_2': 'str_eq', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_7': 'all_rows', 'time_8': 'time', 'wrestler_9': 'wrestler', 'kane_10': 'kane', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'eliminated by_11': 'eliminated by', 'rey mysterio_12': 'rey mysterio'} | {'and_5': [6], 'result_6': [], 'str_eq_2': [5], 'str_hop_1': [2], 'argmin_0': [1, 3], 'all_rows_7': [0], 'time_8': [0], 'wrestler_9': [1], 'kane_10': [2], 'str_eq_4': [5], 'str_hop_3': [4], 'eliminated by_11': [3], 'rey mysterio_12': [4]} | ['eliminated', 'wrestler', 'entered', 'eliminated by', 'method of elimination', 'time'] | [['1', 'kane', '3', 'rey mysterio', 'pinned after a seated senton from the top of a pod', '09:37'], ['2', 'mike knox', '4', 'chris jericho', 'pinned after a codebreaker', '14:42'], ['3', 'cena', '6', 'edge', 'pinned after a spear', '22:22'], ['4', 'jericho', '2', 'rey mysterio', 'pinned when mysterio reversed the walls of jericho', '23:54'], ['5', 'rey mysterio', '1', 'edge', 'pinned after a spear', '29:46']] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.