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
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2008 - 09 in scottish football
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_in_Scottish_football
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17327458-19.html.csv
|
comparative
|
there were more points scored during the september 10th game then there were on the august 20th game .
|
{'row_1': '3', 'row_2': '1', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '10 september'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 10 september .', 'tostr': 'filter_eq { all_rows ; date ; 10 september }'}, 'score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; 10 september } ; score }', 'tointer': 'select the rows whose date record fuzzily matches to 10 september . take the score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '20 august'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 20 august .', 'tostr': 'filter_eq { all_rows ; date ; 20 august }'}, 'score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; 20 august } ; score }', 'tointer': 'select the rows whose date record fuzzily matches to 20 august . take the score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; 10 september } ; score } ; hop { filter_eq { all_rows ; date ; 20 august } ; score } } = true', 'tointer': 'select the rows whose date record fuzzily matches to 10 september . take the score record of this row . select the rows whose date record fuzzily matches to 20 august . take the score record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; date ; 10 september } ; score } ; hop { filter_eq { all_rows ; date ; 20 august } ; score } } = true
|
select the rows whose date record fuzzily matches to 10 september . take the score record of this row . select the rows whose date record fuzzily matches to 20 august . take the score record of this row . the first record is greater than the second record .
|
5
|
5
|
{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, '10 september_8': 8, 'score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, '20 august_12': 12, 'score_13': 13}
|
{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', '10 september_8': '10 september', 'score_9': 'score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', '20 august_12': '20 august', 'score_13': 'score'}
|
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], '10 september_8': [0], 'score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], '20 august_12': [1], 'score_13': [3]}
|
['date', 'venue', 'score', 'competition', 'report']
|
[['20 august', 'hampden park , glasgow ( h )', '0 - 0', 'friendly', 'bbc sport'], ['6 september', 'skopje city stadium , skopje ( a )', '0 - 1', 'wcq ( 9 )', 'bbc sport'], ['10 september', 'laugardalsvöllur , reykjavík ( a )', '2 - 1', 'wcq ( 9 )', 'bbc sport'], ['11 october', 'hampden park , glasgow ( h )', '0 - 0', 'wcq ( 9 )', 'bbc sport'], ['20 november', 'hampden park , glasgow ( h )', '0 - 1', 'friendly', 'bbc sport'], ['28 march', 'amsterdam arena , amsterdam ( a )', '0 - 3', 'wcq ( 9 )', 'bbc sport'], ['1 april', 'hampden park , glasgow ( h )', '2 - 1', 'wcq ( 9 )', 'bbc sport']]
|
forward racing
|
https://en.wikipedia.org/wiki/Forward_Racing
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22737506-1.html.csv
|
unique
|
alex de angelis riding in 2012 was the only time forward racing had 16 races .
|
{'scope': 'all', 'row': '10', 'col': '6', 'col_other': '1,5', 'criterion': 'equal', 'value': '16', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'races', '16'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose races record is equal to 16 .', 'tostr': 'filter_eq { all_rows ; races ; 16 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; races ; 16 } }', 'tointer': 'select the rows whose races record is equal to 16 . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'races', '16'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose races record is equal to 16 .', 'tostr': 'filter_eq { all_rows ; races ; 16 }'}, 'year'], 'result': '2012', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; races ; 16 } ; year }'}, '2012'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; races ; 16 } ; year } ; 2012 }', 'tointer': 'the year record of this unqiue row is 2012 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'races', '16'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose races record is equal to 16 .', 'tostr': 'filter_eq { all_rows ; races ; 16 }'}, 'riders'], 'result': 'alex de angelis', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; races ; 16 } ; riders }'}, 'alex de angelis'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; races ; 16 } ; riders } ; alex de angelis }', 'tointer': 'the riders record of this unqiue row is alex de angelis .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; races ; 16 } ; year } ; 2012 } ; eq { hop { filter_eq { all_rows ; races ; 16 } ; riders } ; alex de angelis } }', 'tointer': 'the year record of this unqiue row is 2012 . the riders record of this unqiue row is alex de angelis .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; races ; 16 } } ; and { eq { hop { filter_eq { all_rows ; races ; 16 } ; year } ; 2012 } ; eq { hop { filter_eq { all_rows ; races ; 16 } ; riders } ; alex de angelis } } } = true', 'tointer': 'select the rows whose races record is equal to 16 . there is only one such row in the table . the year record of this unqiue row is 2012 . the riders record of this unqiue row is alex de angelis .'}
|
and { only { filter_eq { all_rows ; races ; 16 } } ; and { eq { hop { filter_eq { all_rows ; races ; 16 } ; year } ; 2012 } ; eq { hop { filter_eq { all_rows ; races ; 16 } ; riders } ; alex de angelis } } } = true
|
select the rows whose races record is equal to 16 . there is only one such row in the table . the year record of this unqiue row is 2012 . the riders record of this unqiue row is alex de angelis .
|
10
|
8
|
{'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_9': 9, 'races_10': 10, '16_11': 11, 'and_6': 6, 'eq_3': 3, 'num_hop_2': 2, 'year_12': 12, '2012_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'riders_14': 14, 'alex de angelis_15': 15}
|
{'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_9': 'all_rows', 'races_10': 'races', '16_11': '16', 'and_6': 'and', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_12': 'year', '2012_13': '2012', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'riders_14': 'riders', 'alex de angelis_15': 'alex de angelis'}
|
{'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_eq_0': [1, 2, 4], 'all_rows_9': [0], 'races_10': [0], '16_11': [0], 'and_6': [7], 'eq_3': [6], 'num_hop_2': [3], 'year_12': [2], '2012_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'riders_14': [4], 'alex de angelis_15': [5]}
|
['year', 'class', 'team name', 'bike', 'riders', 'races', 'wins', 'podiums', 'poles', 'flaps', 'points', 'pos']
|
[['2009', 'motogp', 'hayate racing team', 'kawasaki zx - rr', 'marco melandri', '17', '0', '1', '0', '0', '108', '10th'], ['2010', 'moto2', 'forward racing', 'suter mmx', 'jules cluzel', '17', '1', '2', '0', '1', '106', '7th'], ['2010', 'moto2', 'forward racing', 'suter mmx', 'claudio corti', '17', '0', '0', '1', '0', '20', '25th'], ['2010', 'moto2', 'forward racing', 'suter mmx', 'ferruccio lamborghini', '1 ( 5 )', '0', '0', '0', '0', '0', 'nc'], ['2011', 'moto2', 'ngm forward racing', 'suter mmxi', 'jules cluzel', '17', '0', '0', '0', '0', '41', '21st'], ['2011', 'moto2', 'ngm forward racing', 'suter mmxi', 'alex baldolini', '10 ( 14 )', '0', '0', '0', '0', '18', '27th'], ['2011', 'moto2', 'ngm forward racing', 'suter mmxi', 'raffaele de rosa', '7 ( 13 )', '0', '0', '0', '0', '0', 'nc'], ['2012', 'motogp', 'ngm mobile forward racing', 'suter mmx1', 'colin edwards', '17', '0', '0', '0', '0', '27', '20th'], ['2012', 'motogp', 'ngm mobile forward racing', 'suter mmx1', 'chris vermeulen', '1', '0', '0', '0', '0', '0', 'nc'], ['2012', 'moto2', 'ngm mobile forward racing', 'suter mmxii ftr moto m212', 'alex de angelis', '16', '1', '2', '0', '1', '86', '10th'], ['2012', 'moto2', 'ngm mobile forward racing', 'suter mmxii ftr moto m212', 'yuki takahashi', '17', '0', '0', '0', '0', '2', '30th'], ['2012', 'moto2', 'ngm mobile forward racing', 'suter mmxii ftr moto m212', 'mattia pasini', '1', '0', '0', '0', '0', '0', 'nc'], ['2013', 'motogp', 'ngm mobile forward racing', 'ftr - kawasaki', 'colin edwards', '13', '0', '0', '0', '0', '31', '14th'], ['2013', 'motogp', 'ngm mobile forward racing', 'ftr - kawasaki', 'claudio corti', '13', '0', '0', '0', '0', '7', '19th'], ['2013', 'moto2', 'ngm mobile racing', 'speed up sf13', 'simone corsi', '12', '0', '1', '0', '0', '74', '10th'], ['2013', 'moto2', 'ngm mobile racing', 'speed up sf13', 'mattia pasini', '12', '0', '0', '0', '0', '35', '16th'], ['2013', 'moto2', 'ngm mobile forward racing', 'speed up sf13', 'alex de angelis', '12', '0', '0', '0', '0', '46', '14th']]
|
drop tower : scream zone
|
https://en.wikipedia.org/wiki/Drop_Tower%3A_Scream_Zone
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12204442-1.html.csv
|
unique
|
only one drop tower can achieve a speed higher than 70 mph .
|
{'scope': 'all', 'row': '4', 'col': '4', 'col_other': '5', 'criterion': 'greater_than', 'value': '70 mph', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'speed', '70 mph'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose speed record is greater than 70 mph .', 'tostr': 'filter_greater { all_rows ; speed ; 70 mph }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; speed ; 70 mph } }', 'tointer': 'select the rows whose speed record is greater than 70 mph . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'speed', '70 mph'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose speed record is greater than 70 mph .', 'tostr': 'filter_greater { all_rows ; speed ; 70 mph }'}, 'model'], 'result': 'gyro drop', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; speed ; 70 mph } ; model }'}, 'gyro drop'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; speed ; 70 mph } ; model } ; gyro drop }', 'tointer': 'the model record of this unqiue row is gyro drop .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; speed ; 70 mph } } ; eq { hop { filter_greater { all_rows ; speed ; 70 mph } ; model } ; gyro drop } } = true', 'tointer': 'select the rows whose speed record is greater than 70 mph . there is only one such row in the table . the model record of this unqiue row is gyro drop .'}
|
and { only { filter_greater { all_rows ; speed ; 70 mph } } ; eq { hop { filter_greater { all_rows ; speed ; 70 mph } ; model } ; gyro drop } } = true
|
select the rows whose speed record is greater than 70 mph . there is only one such row in the table . the model record of this unqiue row is gyro drop .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'speed_7': 7, '70 mph_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'model_9': 9, 'gyro drop_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'speed_7': 'speed', '70 mph_8': '70 mph', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'model_9': 'model', 'gyro drop_10': 'gyro drop'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'speed_7': [0], '70 mph_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'model_9': [2], 'gyro drop_10': [3]}
|
['park', 'tower height', 'drop height', 'speed', 'model', 'opened', 'height requirement']
|
[["canada 's wonderland", '230feet', '200feet', '62 mph', 'giant drop', '1997', 'inches ( cm )'], ['carowinds', '174feet', '100feet', '56 mph', 'giant drop', 'march 1996', 'inches ( cm )'], ["california 's great america", '224feet', '207feet', '62 mph', 'giant drop', 'march 1996', 'inches ( cm )'], ['kings dominion', '305feet', '272feet', '72 mph', 'gyro drop', 'march 22 , 2003', 'inches ( cm )'], ['kings island', '315feet', '264feet', '67 mph', 'gyro drop', '1999', 'inches ( cm )']]
|
list of swat kats : the radical squadron episodes
|
https://en.wikipedia.org/wiki/List_of_SWAT_Kats%3A_The_Radical_Squadron_episodes
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17810099-3.html.csv
|
comparative
|
of the swat kats : the radical squadron episodes , the episode titled " cry turmoil " aired 7 days before the episode titled " the deadly pyramid . " .
|
{'row_1': '5', 'row_2': '7', 'col': '6', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'yes', 'diff_result': None}
|
{'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'cry turmoil'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to cry turmoil .', 'tostr': 'filter_eq { all_rows ; title ; cry turmoil }'}, 'originalairdate'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; cry turmoil } ; originalairdate }', 'tointer': 'select the rows whose title record fuzzily matches to cry turmoil . take the originalairdate record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'the deadly pyramid'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to the deadly pyramid .', 'tostr': 'filter_eq { all_rows ; title ; the deadly pyramid }'}, 'originalairdate'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; the deadly pyramid } ; originalairdate }', 'tointer': 'select the rows whose title record fuzzily matches to the deadly pyramid . take the originalairdate record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; title ; cry turmoil } ; originalairdate } ; hop { filter_eq { all_rows ; title ; the deadly pyramid } ; originalairdate } }', 'tointer': 'select the rows whose title record fuzzily matches to cry turmoil . take the originalairdate record of this row . select the rows whose title record fuzzily matches to the deadly pyramid . take the originalairdate record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'cry turmoil'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to cry turmoil .', 'tostr': 'filter_eq { all_rows ; title ; cry turmoil }'}, 'originalairdate'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; cry turmoil } ; originalairdate }', 'tointer': 'select the rows whose title record fuzzily matches to cry turmoil . take the originalairdate record of this row .'}, 'november 5 , 1994'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; title ; cry turmoil } ; originalairdate } ; november 5 , 1994 }', 'tointer': 'the originalairdate record of the first row is november 5 , 1994 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'the deadly pyramid'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to the deadly pyramid .', 'tostr': 'filter_eq { all_rows ; title ; the deadly pyramid }'}, 'originalairdate'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; the deadly pyramid } ; originalairdate }', 'tointer': 'select the rows whose title record fuzzily matches to the deadly pyramid . take the originalairdate record of this row .'}, 'november 12 , 1994'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; title ; the deadly pyramid } ; originalairdate } ; november 12 , 1994 }', 'tointer': 'the originalairdate record of the second row is november 12 , 1994 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; title ; cry turmoil } ; originalairdate } ; november 5 , 1994 } ; eq { hop { filter_eq { all_rows ; title ; the deadly pyramid } ; originalairdate } ; november 12 , 1994 } }', 'tointer': 'the originalairdate record of the first row is november 5 , 1994 . the originalairdate record of the second row is november 12 , 1994 .'}], 'result': True, 'ind': 8, 'tostr': 'and { less { hop { filter_eq { all_rows ; title ; cry turmoil } ; originalairdate } ; hop { filter_eq { all_rows ; title ; the deadly pyramid } ; originalairdate } } ; and { eq { hop { filter_eq { all_rows ; title ; cry turmoil } ; originalairdate } ; november 5 , 1994 } ; eq { hop { filter_eq { all_rows ; title ; the deadly pyramid } ; originalairdate } ; november 12 , 1994 } } } = true', 'tointer': 'select the rows whose title record fuzzily matches to cry turmoil . take the originalairdate record of this row . select the rows whose title record fuzzily matches to the deadly pyramid . take the originalairdate record of this row . the first record is less than the second record . the originalairdate record of the first row is november 5 , 1994 . the originalairdate record of the second row is november 12 , 1994 .'}
|
and { less { hop { filter_eq { all_rows ; title ; cry turmoil } ; originalairdate } ; hop { filter_eq { all_rows ; title ; the deadly pyramid } ; originalairdate } } ; and { eq { hop { filter_eq { all_rows ; title ; cry turmoil } ; originalairdate } ; november 5 , 1994 } ; eq { hop { filter_eq { all_rows ; title ; the deadly pyramid } ; originalairdate } ; november 12 , 1994 } } } = true
|
select the rows whose title record fuzzily matches to cry turmoil . take the originalairdate record of this row . select the rows whose title record fuzzily matches to the deadly pyramid . take the originalairdate record of this row . the first record is less than the second record . the originalairdate record of the first row is november 5 , 1994 . the originalairdate record of the second row is november 12 , 1994 .
|
13
|
9
|
{'and_8': 8, 'result_9': 9, 'less_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'title_11': 11, 'cry turmoil_12': 12, 'originalairdate_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'title_15': 15, 'the deadly pyramid_16': 16, 'originalairdate_17': 17, 'and_7': 7, 'str_eq_5': 5, 'november 5 , 1994_18': 18, 'str_eq_6': 6, 'november 12 , 1994_19': 19}
|
{'and_8': 'and', 'result_9': 'true', 'less_4': 'less', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'title_11': 'title', 'cry turmoil_12': 'cry turmoil', 'originalairdate_13': 'originalairdate', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'title_15': 'title', 'the deadly pyramid_16': 'the deadly pyramid', 'originalairdate_17': 'originalairdate', 'and_7': 'and', 'str_eq_5': 'str_eq', 'november 5 , 1994_18': 'november 5 , 1994', 'str_eq_6': 'str_eq', 'november 12 , 1994_19': 'november 12 , 1994'}
|
{'and_8': [9], 'result_9': [], 'less_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'title_11': [0], 'cry turmoil_12': [0], 'originalairdate_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'title_15': [1], 'the deadly pyramid_16': [1], 'originalairdate_17': [3], 'and_7': [8], 'str_eq_5': [7], 'november 5 , 1994_18': [5], 'str_eq_6': [7], 'november 12 , 1994_19': [6]}
|
['episode', 'season', 'title', 'writer ( s )', 'director', 'originalairdate']
|
[['14', '2', 'mutation city', 'glenn leopold', 'robert alvarez', 'september 10 , 1994'], ['15', '2', 'a bright and shiny future', 'glenn leopold', 'robert alvarez', 'september 17 , 1994'], ['16', '2', 'when mutilor strikes', 'lance falk', 'robert alvarez', 'september 24 , 1994'], ['17', '2', "razor 's edge", 'mark saraceni', 'robert alvarez', 'october 29 , 1994'], ['18a', '2', 'cry turmoil', 'lance falk', 'robert alvarez', 'november 5 , 1994'], ['18b', '2', 'swat kats unplugged', 'glenn leopold', 'robert alvarez', 'november 5 , 1994'], ['19', '2', 'the deadly pyramid', 'glenn leopold', 'robert alvarez', 'november 12 , 1994'], ['20', '2', 'caverns of horror', 'glenn leopold', 'robert alvarez', 'november 19 , 1994'], ['21a', '2', 'volcanus erupts !', 'glenn leopold', 'robert alvarez', 'november 26 , 1994'], ['21b', '2', 'the origin of dr viper', 'glenn leopold', 'robert alvarez', 'november 26 , 1994'], ['22', '2', 'the dark side of the swat kats', 'jim katz', 'robert alvarez', 'december 10 , 1994']]
|
list of australian submissions for the academy award for best foreign language film
|
https://en.wikipedia.org/wiki/List_of_Australian_submissions_for_the_Academy_Award_for_Best_Foreign_Language_Film
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16254861-1.html.csv
|
majority
|
of australian submissions for the academy award for best foreign language film , most of the films were not nominated .
|
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'not nominated', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'result', 'not nominated'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to not nominated .', 'tostr': 'most_eq { all_rows ; result ; not nominated } = true'}
|
most_eq { all_rows ; result ; not nominated } = true
|
for the result records of all rows , most of them fuzzily match to not nominated .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'not nominated_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'not nominated_4': 'not nominated'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'not nominated_4': [0]}
|
['year ( ceremony )', 'film title used in nomination', 'language ( s )', 'director', 'result']
|
[['1996 ( 69th )', 'floating life', 'cantonese , english , german', 'clara law', 'not nominated'], ['2001 ( 74th )', 'la spagnola', 'spanish , english , italian', 'steve jacobs', 'not nominated'], ['2006 ( 79th )', 'ten canoes', 'yolngu matha , gunwinggu , english', 'rolf de heer', 'not nominated'], ['2007 ( 80th )', 'the home song stories', 'cantonese , english , mandarin', 'tony ayres', 'not nominated'], ['2009 ( 82nd )', 'samson and delilah', 'warlpiri , english', 'warwick thornton', 'made january shortlist'], ['2012 ( 85th )', 'lore', 'german', 'cate shortland', 'not nominated'], ['2013 ( 86th )', 'the rocket', 'lao', 'kim mordaunt', 'tbd']]
|
athletics at the 2008 summer olympics - women 's 100 metres hurdles
|
https://en.wikipedia.org/wiki/Athletics_at_the_2008_Summer_Olympics_%E2%80%93_Women%27s_100_metres_hurdles
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18578883-3.html.csv
|
ordinal
|
brigitte foster - hylton is the 2nd fastest athletic from jamaica in the women 's 100 metres hurdles competition at the 2008 summer olympics .
|
{'scope': 'subset', 'row': '7', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'jamaica'}}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'jamaica'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; nationality ; jamaica }', 'tointer': 'select the rows whose nationality record fuzzily matches to jamaica .'}, 'reaction', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; nationality ; jamaica } ; reaction ; 2 }'}, 'name'], 'result': 'brigitte foster - hylton', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; nationality ; jamaica } ; reaction ; 2 } ; name }'}, 'brigitte foster - hylton'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; nationality ; jamaica } ; reaction ; 2 } ; name } ; brigitte foster - hylton } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to jamaica . select the row whose reaction record of these rows is 2nd minimum . the name record of this row is brigitte foster - hylton .'}
|
eq { hop { nth_argmin { filter_eq { all_rows ; nationality ; jamaica } ; reaction ; 2 } ; name } ; brigitte foster - hylton } = true
|
select the rows whose nationality record fuzzily matches to jamaica . select the row whose reaction record of these rows is 2nd minimum . the name record of this row is brigitte foster - hylton .
|
4
|
4
|
{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'nationality_6': 6, 'jamaica_7': 7, 'reaction_8': 8, '2_9': 9, 'name_10': 10, 'brigitte foster - hylton_11': 11}
|
{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'nationality_6': 'nationality', 'jamaica_7': 'jamaica', 'reaction_8': 'reaction', '2_9': '2', 'name_10': 'name', 'brigitte foster - hylton_11': 'brigitte foster - hylton'}
|
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'nationality_6': [0], 'jamaica_7': [0], 'reaction_8': [1], '2_9': [1], 'name_10': [2], 'brigitte foster - hylton_11': [3]}
|
['heat', 'name', 'nationality', 'reaction', 'result']
|
[['1', 'lolo jones', 'united states', '0.172', '12.43'], ['2', 'damu cherry', 'united states', '0.189', '12.62'], ['2', 'dawn harper', 'united states', '0.191', '12.66'], ['1', 'delloreen ennis - london', 'jamaica', '0.145', '12.67'], ['1', 'priscilla lopes - schliep', 'canada', '0.159', '12.68'], ['1', 'sally mclellan', 'australia', '0.140', '12.70'], ['2', 'brigitte foster - hylton', 'jamaica', '0.162', '12.76'], ['2', 'sarah claxton', 'great britain', '0.145', '12.84'], ['2', 'vonette dixon', 'jamaica', '0.237', '12.86'], ['1', 'josephine onyia', 'spain', '0.203', '12.86'], ['1', 'aurelia trywiańska - kollasch', 'poland', '0.118', '12.96'], ['1', 'carolin nytra', 'germany', '0.144', '12.99'], ['2', 'reïna - flor okori', 'france', '0.153', '13.05'], ['1', 'nevin yanit', 'turkey', '0.201', '13.28'], ['2', 'susanna kallur', 'sweden', '0.198', 'dnf'], ['2', 'anay tejeda', 'cuba', '0.156', 'dnf']]
|
united states house of representatives elections , 1942
|
https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1942
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342256-6.html.csv
|
unique
|
california 22 is the only district with a new seat republican gain during the 1942 house of representatives elections .
|
{'scope': 'all', 'row': '7', 'col': '5', 'col_other': '4', 'criterion': 'equal', 'value': 'new seat republican gain', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'new seat republican gain'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to new seat republican gain .', 'tostr': 'filter_eq { all_rows ; result ; new seat republican gain }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; new seat republican gain } }', 'tointer': 'select the rows whose result record fuzzily matches to new seat republican gain . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'new seat republican gain'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to new seat republican gain .', 'tostr': 'filter_eq { all_rows ; result ; new seat republican gain }'}, 'first elected'], 'result': 'none ( district created )', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; new seat republican gain } ; first elected }'}, 'none ( district created )'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; new seat republican gain } ; first elected } ; none ( district created ) }', 'tointer': 'the first elected record of this unqiue row is none ( district created ) .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; new seat republican gain } } ; eq { hop { filter_eq { all_rows ; result ; new seat republican gain } ; first elected } ; none ( district created ) } } = true', 'tointer': 'select the rows whose result record fuzzily matches to new seat republican gain . there is only one such row in the table . the first elected record of this unqiue row is none ( district created ) .'}
|
and { only { filter_eq { all_rows ; result ; new seat republican gain } } ; eq { hop { filter_eq { all_rows ; result ; new seat republican gain } ; first elected } ; none ( district created ) } } = true
|
select the rows whose result record fuzzily matches to new seat republican gain . there is only one such row in the table . the first elected record of this unqiue row is none ( district created ) .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'new seat republican gain_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'first elected_9': 9, 'none (district created)_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', 'new seat republican gain_8': 'new seat republican gain', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'first elected_9': 'first elected', 'none (district created)_10': 'none ( district created )'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'new seat republican gain_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'first elected_9': [2], 'none (district created)_10': [3]}
|
['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']
|
[['california 2', 'harry lane englebright', 'republican', '1926', 're - elected', 'harry lane englebright ( r ) unopposed'], ['california 4', 'thomas rolph', 'republican', '1940', 're - elected', 'thomas rolph ( r ) 98.3 % archie brown ( w / i ) 1.7 %'], ['california 7', 'john h tolan', 'democratic', '1934', 're - elected', 'john h tolan ( d ) unopposed'], ['california 9', 'bertrand w gearhart', 'republican', '1934', 're - elected', 'bertrand w gearhart ( r ) unopposed'], ['california 10', 'alfred j elliott', 'democratic', '1937', 're - elected', 'alfred j elliott ( d ) unopposed'], ['california 17', 'cecil r king', 'democratic', 'august 25 , 1942', 're - elected', 'cecil r king ( d ) unopposed'], ['california 22', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat republican gain', 'john j phillips ( r ) 57.6 % n e west ( d ) 42.4 %']]
|
doppler spectroscopy
|
https://en.wikipedia.org/wiki/Doppler_spectroscopy
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10932739-2.html.csv
|
superlative
|
alpa centauri bb is the planet with the shortest orbital period .
|
{'scope': 'all', 'col_superlative': '4', 'row_superlative': '6', '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', 'orbital period'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; orbital period }'}, 'planet'], 'result': 'alpha centauri bb', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; orbital period } ; planet }'}, 'alpha centauri bb'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; orbital period } ; planet } ; alpha centauri bb } = true', 'tointer': 'select the row whose orbital period record of all rows is minimum . the planet record of this row is alpha centauri bb .'}
|
eq { hop { argmin { all_rows ; orbital period } ; planet } ; alpha centauri bb } = true
|
select the row whose orbital period record of all rows is minimum . the planet record of this row is alpha centauri bb .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'orbital period_5': 5, 'planet_6': 6, 'alpha centauri bb_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'orbital period_5': 'orbital period', 'planet_6': 'planet', 'alpha centauri bb_7': 'alpha centauri bb'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'orbital period_5': [0], 'planet_6': [1], 'alpha centauri bb_7': [2]}
|
['planet', 'planet type', 'semimajor axis ( au )', 'orbital period', 'radial velocity ( m / s )', 'detectable by :']
|
[['51 pegasi b', 'hot jupiter', '0.05', '4.23 days', '55.9', 'first - generation spectrograph'], ['55 cancri d', 'gas giant', '5.77', '14.29 years', '45.2', 'first - generation spectrograph'], ['jupiter', 'gas giant', '5.20', '11.86 years', '12.4', 'first - generation spectrograph'], ['gliese 581c', 'super - earth', '0.07', '12.92 days', '3.18', 'second - generation spectrograph'], ['saturn', 'gas giant', '9.58', '29.46 years', '2.75', 'second - generation spectrograph'], ['alpha centauri bb', 'terrestrial planet', '0.04', '3.23 days', '0.510', 'second - generation spectrograph'], ['neptune', 'ice giant', '30.10', '164.79 years', '0.281', 'third - generation spectrograph'], ['earth', 'habitable planet', '1.00', '365.26 days', '0.089', 'third - generation spectrograph ( likely )'], ['pluto', 'dwarf planet', '39.26', '246.04 years', '0.00003', 'not detectable']]
|
european orienteering championships
|
https://en.wikipedia.org/wiki/European_Orienteering_Championships
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17760670-3.html.csv
|
ordinal
|
the second to last year for the european orienteering championships was the year that dana brozkova won the silver medal .
|
{'row': '8', 'col': '1', 'order': '8', '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', 'year', '8'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year ; 8 }'}, 'silver'], 'result': 'dana brozkova', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year ; 8 } ; silver }'}, 'dana brozkova'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 8 } ; silver } ; dana brozkova } = true', 'tointer': 'select the row whose year record of all rows is 8th minimum . the silver record of this row is dana brozkova .'}
|
eq { hop { nth_argmin { all_rows ; year ; 8 } ; silver } ; dana brozkova } = true
|
select the row whose year record of all rows is 8th minimum . the silver record of this row is dana brozkova .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, '8_6': 6, 'silver_7': 7, 'dana brozkova_8': 8}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'year_5': 'year', '8_6': '8', 'silver_7': 'silver', 'dana brozkova_8': 'dana brozkova'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], '8_6': [0], 'silver_7': [1], 'dana brozkova_8': [2]}
|
['year', 'gold', 'silver', 'bronze', 'notes']
|
[['1962', 'ulla lindkvist', 'marit ãkern', 'emy gauffin', '7.5 km , 7controls ( individual event )'], ['1964', 'margrit thommen', 'ann - marie wallsten', 'ulla lindkvist', '8.1 km , 10controls ( individual event )'], ['2000', 'hanne staff', 'brigitte wolf', 'yvette baker', 'classic distance'], ['2002', 'simone niggli - luder', 'hanne staff', 'birgitte husebye', '6.7 km , 17controls'], ['2004', 'simone niggli - luder', 'emma engstrand', 'tatiana ryabkina', '9.6 km , 21controls'], ['2006', 'simone niggli - luder', 'heli jukkola', 'minna kauppi', '10.93 km , 25controls'], ['2008', 'anne margrethe hausken', 'tatiana ryabkina', 'emma engstrand', '11.0 km , 24controls'], ['2010', 'simone niggli - luder', 'dana brozkova', 'helena jansson', '11.0 km , 26controls'], ['2012', 'simone niggli - luder', 'tatiana ryabkina', 'minna kauppi', '9.76 km , 24controls']]
|
1982 vfl season
|
https://en.wikipedia.org/wiki/1982_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10824095-15.html.csv
|
majority
|
most of the crowds at the games had more than 15000 people in attendance .
|
{'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '15000', 'subset': None}
|
{'func': 'most_greater', 'args': ['all_rows', 'crowd', '15000'], 'result': True, 'ind': 0, 'tointer': 'for the crowd records of all rows , most of them are greater than 15000 .', 'tostr': 'most_greater { all_rows ; crowd ; 15000 } = true'}
|
most_greater { all_rows ; crowd ; 15000 } = true
|
for the crowd records of all rows , most of them are greater than 15000 .
|
1
|
1
|
{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'crowd_3': 3, '15000_4': 4}
|
{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'crowd_3': 'crowd', '15000_4': '15000'}
|
{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'crowd_3': [0], '15000_4': [0]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['essendon', '21.13 ( 139 )', 'fitzroy', '7.12 ( 54 )', 'windy hill', '20059', '3 july 1982'], ['carlton', '18.20 ( 128 )', 'melbourne', '16.15 ( 111 )', 'princes park', '21871', '3 july 1982'], ['richmond', '17.14 ( 116 )', 'hawthorn', '22.14 ( 146 )', 'mcg', '48338', '3 july 1982'], ['swans', '18.18 ( 126 )', 'geelong', '12.15 ( 87 )', 'scg', '12221', '3 july 1982'], ['st kilda', '20.11 ( 131 )', 'footscray', '18.12 ( 120 )', 'moorabbin oval', '15958', '3 july 1982'], ['north melbourne', '16.13 ( 109 )', 'collingwood', '13.11 ( 89 )', 'vfl park', '32812', '3 july 1982']]
|
todd woodbridge
|
https://en.wikipedia.org/wiki/Todd_Woodbridge
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1547951-3.html.csv
|
count
|
todd woodbridge partnered with helena suková for three tournaments .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'helena suková', 'result': '3', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partner', 'helena suková'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partner record fuzzily matches to helena suková .', 'tostr': 'filter_eq { all_rows ; partner ; helena suková }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; partner ; helena suková } }', 'tointer': 'select the rows whose partner record fuzzily matches to helena suková . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; partner ; helena suková } } ; 3 } = true', 'tointer': 'select the rows whose partner record fuzzily matches to helena suková . the number of such rows is 3 .'}
|
eq { count { filter_eq { all_rows ; partner ; helena suková } } ; 3 } = true
|
select the rows whose partner record fuzzily matches to helena suková . 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, 'partner_5': 5, 'helena suková_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', 'partner_5': 'partner', 'helena suková_6': 'helena suková', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'partner_5': [0], 'helena suková_6': [0], '3_7': [2]}
|
['outcome', 'year', 'championship', 'surface', 'partner', 'opponents', 'score']
|
[['winner', '1990', 'us open', 'hard', 'elizabeth sayers smylie', 'jim pugh natasha zvereva', '6 - 4 , 6 - 2'], ['runner - up', '1992', 'australian open', 'hard', 'arantxa sánchez vicario', 'mark woodforde nicole provis', '3 - 6 , 6 - 4 , 9 - 11'], ['winner', '1992', 'french open', 'clay', 'arantxa sánchez vicario', 'bryan shelton lori mcneil', '6 - 2 , 6 - 3'], ['winner', '1993', 'australian open', 'hard', 'arantxa sánchez vicario', 'rick leach zina garrison', '7 - 5 , 6 - 4'], ['winner', '1993', 'us open', 'hard', 'helena suková', 'mark woodforde martina navratilova', '6 - 3 , 7 - 6'], ['runner - up', '1994', 'australian open', 'hard', 'helena suková', 'andrei olhovskiy larisa savchenko neiland', '5 - 7 , 7 - 6 ( 9 - 7 ) , 2 - 6'], ['winner', '1994', 'wimbledon', 'grass', 'helena suková', 't j middleton lori mcneil', '3 - 6 , 7 - 5 , 6 - 3'], ['runner - up', '1994', 'us open', 'hard', 'jana novotná', 'patrick galbraith elna reinach', '2 - 6 , 4 - 6'], ['winner', '1995', 'french open', 'clay', 'larisa savchenko', 'john - laffnie de jager jill hetherington', '7 - 6 ( 10 - 8 ) , 7 - 6 ( 7 - 4 )'], ['runner - up', '2000', 'australian open', 'hard', 'arantxa sánchez vicario', 'jared palmer rennae stubbs', '5 - 7 , 6 - 7 ( 3 - 7 )'], ['runner - up', '2000', 'french open', 'clay', 'rennae stubbs', 'david adams mariaan de swardt', '3 - 6 , 6 - 3 , 3 - 6'], ['winner', '2001', 'us open', 'hard', 'rennae stubbs', 'leander paes lisa raymond', '6 - 4 , 5 - 7 , 7 - 6'], ['runner - up', '2003', 'australian open', 'hard', 'eleni daniilidou', 'leander paes martina navrátilová', '4 - 6 , 5 - 7'], ['runner - up', '2004', 'wimbledon', 'grass', 'alicia molik', 'wayne black cara black', '6 - 3 , 6 - 7 , 4 - 6']]
|
julian bailey
|
https://en.wikipedia.org/wiki/Julian_Bailey
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1235920-4.html.csv
|
majority
|
julian bailey had mark blundell as a co-driver in the majority of his races .
|
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'mark blundell', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'co - drivers', 'mark blundell'], 'result': True, 'ind': 0, 'tointer': 'for the co - drivers records of all rows , most of them fuzzily match to mark blundell .', 'tostr': 'most_eq { all_rows ; co - drivers ; mark blundell } = true'}
|
most_eq { all_rows ; co - drivers ; mark blundell } = true
|
for the co - drivers records of all rows , most of them fuzzily match to mark blundell .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'co - drivers_3': 3, 'mark blundell_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'co - drivers_3': 'co - drivers', 'mark blundell_4': 'mark blundell'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'co - drivers_3': [0], 'mark blundell_4': [0]}
|
['year', 'class', 'tyres', 'team', 'co - drivers', 'laps', 'pos']
|
[['1989', 'c1', 'd', 'nissan motorsports', 'mark blundell martin donnelly', '5', 'dnf'], ['1990', 'c1', 'd', 'nissan motorsports international', 'mark blundell gianfranco brancatelli', '142', 'dnf'], ['1997', 'gt1', 'd', 'newcastle united lister', 'thomas erdos mark skaife', '77', 'dnf'], ['2001', 'lmp675', 'm', 'mg sport & racing ltd', 'mark blundell kevin mcgarrity', '92', 'dnf'], ['2002', 'lmp675', 'm', 'mg sport & racing ltd', 'mark blundell kevin mcgarrity', '219', 'dnf']]
|
big brother ( albania )
|
https://en.wikipedia.org/wiki/Big_Brother_%28Albania%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15984770-1.html.csv
|
aggregation
|
the average length of each season of big brother was about 102 days .
|
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '102', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'days'], 'result': '102', 'ind': 0, 'tostr': 'avg { all_rows ; days }'}, '102'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; days } ; 102 } = true', 'tointer': 'the average of the days record of all rows is 102 .'}
|
round_eq { avg { all_rows ; days } ; 102 } = true
|
the average of the days record of all rows is 102 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'days_4': 4, '102_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'days_4': 'days', '102_5': '102'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'days_4': [0], '102_5': [1]}
|
['series', 'host', 'launch date', 'finale date', 'days', 'winner', 'prize']
|
[['season 1', 'arbana osmani', '23 february 2008', '31 may 2008', '100', 'arbër çepani', '50000'], ['season 2', 'arbana osmani', '7 february 2009', '16 may 2009', '99', 'qetsor ferunaj', '70000'], ['season 3', 'arbana osmani', '23 january 2010', '15 may 2010', '113', 'jetmir salaj', '75000'], ['season 4', 'arbana osmani', '25 december 2010', '2 april 2011', '99', 'ermela mezuraj', '75000'], ['season 5', 'arbana osmani', '18 february 2012', '26 may 2012', '99', 'arbër zeka', '100000'], ['season 6', 'arbana osmani', '23 february 2013', '1 june 2013', '99', 'anaid kaloti', '100000']]
|
list of the busiest airports in africa
|
https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_Africa
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18600121-1.html.csv
|
comparative
|
the tenerife sur aurport had a higher amount of passengers than kotoka international airport .
|
{'row_1': '3', 'row_2': '13', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'airport', 'tenerife sur'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose airport record fuzzily matches to tenerife sur .', 'tostr': 'filter_eq { all_rows ; airport ; tenerife sur }'}, '2012'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; airport ; tenerife sur } ; 2012 }', 'tointer': 'select the rows whose airport record fuzzily matches to tenerife sur . take the 2012 record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'airport', 'kotoka international airport'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose airport record fuzzily matches to kotoka international airport .', 'tostr': 'filter_eq { all_rows ; airport ; kotoka international airport }'}, '2012'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; airport ; kotoka international airport } ; 2012 }', 'tointer': 'select the rows whose airport record fuzzily matches to kotoka international airport . take the 2012 record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; airport ; tenerife sur } ; 2012 } ; hop { filter_eq { all_rows ; airport ; kotoka international airport } ; 2012 } } = true', 'tointer': 'select the rows whose airport record fuzzily matches to tenerife sur . take the 2012 record of this row . select the rows whose airport record fuzzily matches to kotoka international airport . take the 2012 record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; airport ; tenerife sur } ; 2012 } ; hop { filter_eq { all_rows ; airport ; kotoka international airport } ; 2012 } } = true
|
select the rows whose airport record fuzzily matches to tenerife sur . take the 2012 record of this row . select the rows whose airport record fuzzily matches to kotoka international airport . take the 2012 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, 'airport_7': 7, 'tenerife sur_8': 8, '2012_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'airport_11': 11, 'kotoka international airport_12': 12, '2012_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', 'airport_7': 'airport', 'tenerife sur_8': 'tenerife sur', '2012_9': '2012', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'airport_11': 'airport', 'kotoka international airport_12': 'kotoka international airport', '2012_13': '2012'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'airport_7': [0], 'tenerife sur_8': [0], '2012_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'airport_11': [1], 'kotoka international airport_12': [1], '2012_13': [3]}
|
['country', 'airport', 'city', '2012', 'change ( 12 / 11 )']
|
[['south africa', 'or tambo international airport', 'johannesburg', '18681458', '0 1.2 %'], ['spain', 'gran canaria airport', 'las palmas de gran canaria', '9892067', '0 6.1 %'], ['spain', 'tenerife sur', 'granadilla de abona', '8530729', '0 1.5 %'], ['south africa', 'cape town international airport', 'cape town', '8505563', '0 0.8 %'], ['morocco', 'mohammed v international airport', 'casablanca', '7186331', '0 1.4 %'], ['spain', 'lanzarote airport', 'san bartolomé , las palmas', '5168775', '0 6.8 %'], ['south africa', 'king shaka international airport', 'durban', '4747224', '0 5.8 %'], ['spain', 'fuerteventura airport', 'puerto del rosario', '4399023', '0 11.1 %'], ['spain', 'tenerife norte', 'san cristóbal de la laguna', '3717944', '0 9.2 %'], ['morocco', 'marrakesh menara airport', 'marrakesh', '3373475', '0 1.7 %'], ['mauritius', 'sir seewoosagur ramgoolam international airport', 'mauritius', '2490862', '0 3.7 %'], ['france', 'la réunion roland garros airport', 'saint - denis', '1997800', '0 4.2 %'], ['ghana', 'kotoka international airport', 'accra', '1726051', '0 8.9 %'], ['morocco', 'agadir - al massira airport', 'agadir', '1384931', '0 8.7 %'], ['south africa', 'port elizabeth airport', 'port elizabeth', '1316063', '0 3.7 %'], ['tunisia', 'monastir international airport', 'monastir', '1238757', '0 23.9 %']]
|
2008 copa libertadores knockout stages
|
https://en.wikipedia.org/wiki/2008_Copa_Libertadores_knockout_stages
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16795394-3.html.csv
|
unique
|
the 1st leg match between sao paulo and nacional was the only match in the 2008 copa libertadores knockout stages to end with a 0-0 score .
|
{'scope': 'all', 'row': '8', '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 leg', '0-0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st leg record fuzzily matches to 0-0 .', 'tostr': 'filter_eq { all_rows ; 1st leg ; 0-0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; 1st leg ; 0-0 } }', 'tointer': 'select the rows whose 1st leg 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 leg', '0-0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st leg record fuzzily matches to 0-0 .', 'tostr': 'filter_eq { all_rows ; 1st leg ; 0-0 }'}, 'team 1'], 'result': 'são paulo', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 1 }'}, 'são paulo'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 1 } ; são paulo }', 'tointer': 'the team 1 record of this unqiue row is são paulo .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st leg', '0-0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st leg record fuzzily matches to 0-0 .', 'tostr': 'filter_eq { all_rows ; 1st leg ; 0-0 }'}, 'team 2'], 'result': 'nacional', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 }'}, 'nacional'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 } ; nacional }', 'tointer': 'the team 2 record of this unqiue row is nacional .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 1 } ; são paulo } ; eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 } ; nacional } }', 'tointer': 'the team 1 record of this unqiue row is são paulo . the team 2 record of this unqiue row is nacional .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; 1st leg ; 0-0 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 1 } ; são paulo } ; eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 } ; nacional } } } = true', 'tointer': 'select the rows whose 1st leg record fuzzily matches to 0-0 . there is only one such row in the table . the team 1 record of this unqiue row is são paulo . the team 2 record of this unqiue row is nacional .'}
|
and { only { filter_eq { all_rows ; 1st leg ; 0-0 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 1 } ; são paulo } ; eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 } ; nacional } } } = true
|
select the rows whose 1st leg record fuzzily matches to 0-0 . there is only one such row in the table . the team 1 record of this unqiue row is são paulo . the team 2 record of this unqiue row is nacional .
|
10
|
8
|
{'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, '1st leg_10': 10, '0-0_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'team 1_12': 12, 'são paulo_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'team 2_14': 14, 'nacional_15': 15}
|
{'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', '1st leg_10': '1st leg', '0-0_11': '0-0', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team 1_12': 'team 1', 'são paulo_13': 'são paulo', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'team 2_14': 'team 2', 'nacional_15': 'nacional'}
|
{'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], '1st leg_10': [0], '0-0_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'team 1_12': [2], 'são paulo_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'team 2_14': [4], 'nacional_15': [5]}
|
['team 1', 'points', 'team 2', '1st leg', '2nd leg']
|
[['fluminense', '6 - 0', 'atlético nacional', '2 - 1', '1 - 0'], ['flamengo', '3 - 3 ( gd )', 'américa', '4 - 2', '0 - 3'], ['river plate', '1 - 4', 'san lorenzo', '1 - 2', '2 - 2'], ['atlas', '4 - 1', 'lanús', '1 - 0', '2 - 2'], ['cruzeiro', '0 - 6', 'boca juniors', '1 - 2', '1 - 2'], ['estudiantes', '3 - 3 ( gd )', 'ldu quito', '0 - 2', '2 - 1'], ['cúcuta deportivo', '0 - 6', 'santos', '0 - 2', '0 - 2'], ['são paulo', '4 - 1', 'nacional', '0 - 0', '2 - 0']]
|
indianapolis colts draft history
|
https://en.wikipedia.org/wiki/Indianapolis_Colts_draft_history
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13312898-55.html.csv
|
comparative
|
quinn pitcock was drafted in an earlier round by the indianapolis colts than roy hall .
|
{'row_1': '4', 'row_2': '7', 'col': '1', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'quinn pitcock'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to quinn pitcock .', 'tostr': 'filter_eq { all_rows ; name ; quinn pitcock }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; quinn pitcock } ; round }', 'tointer': 'select the rows whose name record fuzzily matches to quinn pitcock . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'roy hall'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to roy hall .', 'tostr': 'filter_eq { all_rows ; name ; roy hall }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; roy hall } ; round }', 'tointer': 'select the rows whose name record fuzzily matches to roy hall . take the round record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; name ; quinn pitcock } ; round } ; hop { filter_eq { all_rows ; name ; roy hall } ; round } } = true', 'tointer': 'select the rows whose name record fuzzily matches to quinn pitcock . take the round record of this row . select the rows whose name record fuzzily matches to roy hall . take the round record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; name ; quinn pitcock } ; round } ; hop { filter_eq { all_rows ; name ; roy hall } ; round } } = true
|
select the rows whose name record fuzzily matches to quinn pitcock . take the round record of this row . select the rows whose name record fuzzily matches to roy hall . take the round 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, 'name_7': 7, 'quinn pitcock_8': 8, 'round_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'roy hall_12': 12, 'round_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', 'name_7': 'name', 'quinn pitcock_8': 'quinn pitcock', 'round_9': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'roy hall_12': 'roy hall', 'round_13': 'round'}
|
{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'quinn pitcock_8': [0], 'round_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'roy hall_12': [1], 'round_13': [3]}
|
['round', 'pick', 'overall', 'name', 'position', 'college']
|
[['1', '32', '32', 'anthony gonzalez', 'wide receiver', 'ohio state'], ['2', '10', '42', 'tony ugoh', 'offensive tackle', 'arkansas'], ['3', '31', '95', 'daymeion hughes', 'cornerback', 'california'], ['3', '34', '98', 'quinn pitcock', 'defensive tackle', 'ohio state'], ['4', '32', '131', 'brannon condren', 'safety', 'troy'], ['4', '37', '136', 'clint session', 'linebacker', 'pittsburgh'], ['5', '32', '169', 'roy hall', 'wide receiver', 'ohio state'], ['5', '36', '173', 'michael coe', 'cornerback', 'alabama state'], ['7', '32', '232', 'keyunta dawson', 'linebacker', 'texas tech']]
|
wru division one north
|
https://en.wikipedia.org/wiki/WRU_Division_One_North
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14058433-3.html.csv
|
aggregation
|
in wru division one north , the average number of games won was 8.8 .
|
{'scope': 'all', 'col': '3', 'type': 'average', 'result': '8.8', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'won'], 'result': '8.8', 'ind': 0, 'tostr': 'avg { all_rows ; won }'}, '8.8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; won } ; 8.8 } = true', 'tointer': 'the average of the won record of all rows is 8.8 .'}
|
round_eq { avg { all_rows ; won } ; 8.8 } = true
|
the average of the won record of all rows is 8.8 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'won_4': 4, '8.8_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'won_4': 'won', '8.8_5': '8.8'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'won_4': [0], '8.8_5': [1]}
|
['club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points']
|
[['club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['nant conwy rfc', '18', '17', '0', '1', '578', '183', '83', '19', '11', '1', '80'], ['caernarfon rfc', '18', '17', '0', '1', '570', '179', '81', '21', '11', '0', '79'], ['mold rfc', '18', '11', '0', '7', '471', '349', '63', '46', '8', '3', '55'], ['pwllheli rfc', '18', '10', '0', '8', '479', '338', '66', '42', '7', '4', '51'], ['bro ffestiniog rfc', '18', '9', '0', '9', '346', '457', '52', '63', '5', '2', '43'], ['ruthin rfc', '18', '8', '1', '9', '352', '381', '49', '46', '4', '1', '39'], ['colwyn bay rfc', '18', '5', '1', '12', '293', '402', '37', '55', '4', '5', '31'], ['llandudno rfc', '18', '4', '2', '12', '266', '536', '30', '79', '2', '4', '26'], ['llangefni rfc', '18', '4', '0', '14', '267', '423', '27', '58', '3', '5', '24'], ['denbigh rfc', '18', '3', '0', '15', '204', '578', '24', '83', '1', '3', '16']]
|
1955 u.s. open ( golf )
|
https://en.wikipedia.org/wiki/1955_U.S._Open_%28golf%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17290150-5.html.csv
|
count
|
there were four players that tied for third with a score of 145 each .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': '145', 'result': '4', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '145'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 145 .', 'tostr': 'filter_eq { all_rows ; score ; 145 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; score ; 145 } }', 'tointer': 'select the rows whose score record fuzzily matches to 145 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; score ; 145 } } ; 4 } = true', 'tointer': 'select the rows whose score record fuzzily matches to 145 . the number of such rows is 4 .'}
|
eq { count { filter_eq { all_rows ; score ; 145 } } ; 4 } = true
|
select the rows whose score record fuzzily matches to 145 . 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, 'score_5': 5, '145_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', 'score_5': 'score', '145_6': '145', '4_7': '4'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'score_5': [0], '145_6': [0], '4_7': [2]}
|
['place', 'player', 'country', 'score', 'to par']
|
[['t1', 'harvie ward ( a )', 'united states', '74 + 70 = 144', '+ 4'], ['t1', 'tommy bolt', 'united states', '67 + 77 = 144', '+ 4'], ['t3', 'julius boros', 'united states', '76 + 69 = 145', '+ 5'], ['t3', 'jack fleck', 'united states', '76 + 69 = 145', '+ 5'], ['t3', 'ben hogan', 'united states', '72 + 73 = 145', '+ 5'], ['t3', 'walker inman , jr', 'united states', '70 + 75 = 145', '+ 5'], ['t7', 'sam snead', 'united states', '79 + 69 = 148', '+ 8'], ['t7', 'bob harris', 'united states', '79 + 69 = 148', '+ 8'], ['t7', 'jack burke , jr', 'united states', '71 + 77 = 148', '+ 8'], ['10', 'gene littler', 'united states', '76 + 73 = 149', '+ 9']]
|
nick park
|
https://en.wikipedia.org/wiki/Nick_Park
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-149052-1.html.csv
|
count
|
two of nick park 's films were co-directed with another person .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'co - directed', 'result': '2', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'notes', 'co - directed'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose notes record fuzzily matches to co - directed .', 'tostr': 'filter_eq { all_rows ; notes ; co - directed }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; notes ; co - directed } }', 'tointer': 'select the rows whose notes record fuzzily matches to co - directed . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; notes ; co - directed } } ; 2 } = true', 'tointer': 'select the rows whose notes record fuzzily matches to co - directed . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; notes ; co - directed } } ; 2 } = true
|
select the rows whose notes record fuzzily matches to co - directed . 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, 'notes_5': 5, 'co - directed_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', 'notes_5': 'notes', 'co - directed_6': 'co - directed', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'notes_5': [0], 'co - directed_6': [0], '2_7': [2]}
|
['year', 'title', 'director', 'writer', 'notes']
|
[['1989', 'creature comforts', 'yes', 'yes', 'short film'], ['1989', 'wallace & gromit : a grand day out', 'yes', 'yes', 'short film'], ['1993', 'wallace & gromit : the wrong trousers', 'yes', 'yes', 'short film'], ['1995', 'wallace & gromit : a close shave', 'yes', 'yes', 'short film'], ['2000', 'chicken run', 'yes', 'yes', 'co - directed with peter lord'], ['2005', 'wallace & gromit : the curse of the were - rabbit', 'yes', 'yes', 'co - directed with steve box'], ['2008', 'wallace & gromit : a matter of loaf and death', 'yes', 'yes', 'short film']]
|
athletics at the 2008 summer olympics - men 's 200 metres
|
https://en.wikipedia.org/wiki/Athletics_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_200_metres
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18569011-12.html.csv
|
unique
|
among the athletes at the 2008 summer olympics , only one came from north america .
|
{'scope': 'all', 'row': '2', 'col': '4', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'united states', 'subset': None}
|
{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; nationality ; united states }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; nationality ; united states } } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to united states . there is only one such row in the table .'}
|
only { filter_eq { all_rows ; nationality ; united states } } = true
|
select the rows whose nationality record fuzzily matches to united states . 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, 'nationality_4': 4, 'united states_5': 5}
|
{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'nationality_4': 'nationality', 'united states_5': 'united states'}
|
{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'nationality_4': [0], 'united states_5': [0]}
|
['rank', 'lane', 'athlete', 'nationality', 'time', 'react']
|
[['1', '4', 'brian dzingai', 'zimbabwe', '20.23', '0.182'], ['2', '7', 'walter dix', 'united states', '20.27', '0.162'], ['3', '8', 'christopher williams', 'jamaica', '20.28', '0.159'], ['4', '6', 'christian malcolm', 'great britain', '20.30', '0.188'], ['5', '9', 'stephan buckland', 'mauritius', '20.37', '0.188'], ['6', '5', 'roman smirnov', 'russia', '20.62', '0.161'], ['7', '3', 'shinji takahira', 'japan', '20.63', '0.185'], ['8', '2', 'matic osovnikar', 'slovenia', '20.95', '0.171']]
|
fish leong
|
https://en.wikipedia.org/wiki/Fish_Leong
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1893815-1.html.csv
|
superlative
|
kissing the future of love was the first album fish leong released on the b ' in music label .
|
{'scope': 'subset', 'col_superlative': '5', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2,6', 'subset': {'col': '6', 'criterion': 'equal', 'value': "b ' in music"}}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'label', "b ' in music"], 'result': None, 'ind': 0, 'tostr': "filter_eq { all_rows ; label ; b ' in music }", 'tointer': "select the rows whose label record fuzzily matches to b ' in music ."}, 'release date'], 'result': None, 'ind': 1, 'tostr': "argmin { filter_eq { all_rows ; label ; b ' in music } ; release date }"}, 'english title'], 'result': 'kissing the future of love', 'ind': 2, 'tostr': "hop { argmin { filter_eq { all_rows ; label ; b ' in music } ; release date } ; english title }"}, 'kissing the future of love'], 'result': True, 'ind': 3, 'tostr': "eq { hop { argmin { filter_eq { all_rows ; label ; b ' in music } ; release date } ; english title } ; kissing the future of love } = true", 'tointer': "select the rows whose label record fuzzily matches to b ' in music . select the row whose release date record of these rows is minimum . the english title record of this row is kissing the future of love ."}
|
eq { hop { argmin { filter_eq { all_rows ; label ; b ' in music } ; release date } ; english title } ; kissing the future of love } = true
|
select the rows whose label record fuzzily matches to b ' in music . select the row whose release date record of these rows is minimum . the english title record of this row is kissing the future of love .
|
4
|
4
|
{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'label_6': 6, "b'in music_7": 7, 'release date_8': 8, 'english title_9': 9, 'kissing the future of love_10': 10}
|
{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmin_1': 'argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'label_6': 'label', "b'in music_7": "b ' in music", 'release date_8': 'release date', 'english title_9': 'english title', 'kissing the future of love_10': 'kissing the future of love'}
|
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'label_6': [0], "b'in music_7": [0], 'release date_8': [1], 'english title_9': [2], 'kissing the future of love_10': [3]}
|
['album', 'english title', 'chinese ( traditional )', 'chinese ( simplified )', 'release date', 'label']
|
[['1st', 'grown up overnight', '一夜長大', '一夜长大', 'september 17 , 1999', 'rock records'], ['2nd', 'courage', '勇氣', '勇气', 'august 2 , 2000', 'rock records'], ['3rd', 'shining star', '閃亮的星', '闪亮的星', 'june 27 , 2001', 'rock records'], ['4th', 'sunrise', '我喜歡', '我喜欢', 'february 7 , 2002', 'rock records'], ['5th', 'beautiful', '美麗人生', '美丽人生', 'february 12 , 2003', 'rock records'], ['6th', 'wings of love', '燕尾蝶', '燕尾蝶', 'september 10 , 2004', 'rock records'], ['7th', 'silk road of love', '絲路', '丝路', 'september 16 , 2005', 'rock records'], ['8th', 'kissing the future of love', '親親', '亲亲', 'october 6 , 2006', "b ' in music"], ['9th', "j' adore", '崇拜', '崇拜', 'november 9 , 2007', "b ' in music"], ['10th', 'fall in love & songs', '靜茹 ＆ 情歌 - 別再為他流淚', '静茹 ＆ 情歌 - 别再为他流泪', 'january 16 , 2009', "b ' in music"], ['11th', "what love songs did n't tell you", '情歌沒有告訴你', '情歌没有告诉你', 'december 24 , 2010', 'universal music']]
|
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
|
unique
|
galloway & upper nithsdale was the only constituency that was won by the conservative party .
|
{'scope': 'all', 'row': '1', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'conservative', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning party 2003', 'conservative'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning party 2003 record fuzzily matches to conservative .', 'tostr': 'filter_eq { all_rows ; winning party 2003 ; conservative }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; winning party 2003 ; conservative } }', 'tointer': 'select the rows whose winning party 2003 record fuzzily matches to conservative . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning party 2003', 'conservative'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning party 2003 record fuzzily matches to conservative .', 'tostr': 'filter_eq { all_rows ; winning party 2003 ; conservative }'}, 'constituency'], 'result': 'galloway & upper nithsdale', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winning party 2003 ; conservative } ; constituency }'}, 'galloway & upper nithsdale'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; winning party 2003 ; conservative } ; constituency } ; galloway & upper nithsdale }', 'tointer': 'the constituency record of this unqiue row is galloway & upper nithsdale .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; winning party 2003 ; conservative } } ; eq { hop { filter_eq { all_rows ; winning party 2003 ; conservative } ; constituency } ; galloway & upper nithsdale } } = true', 'tointer': 'select the rows whose winning party 2003 record fuzzily matches to conservative . there is only one such row in the table . the constituency record of this unqiue row is galloway & upper nithsdale .'}
|
and { only { filter_eq { all_rows ; winning party 2003 ; conservative } } ; eq { hop { filter_eq { all_rows ; winning party 2003 ; conservative } ; constituency } ; galloway & upper nithsdale } } = true
|
select the rows whose winning party 2003 record fuzzily matches to conservative . there is only one such row in the table . the constituency record of this unqiue row is galloway & upper nithsdale .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'winning party 2003_7': 7, 'conservative_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'constituency_9': 9, 'galloway & upper nithsdale_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'winning party 2003_7': 'winning party 2003', 'conservative_8': 'conservative', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'constituency_9': 'constituency', 'galloway & upper nithsdale_10': 'galloway & upper nithsdale'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'winning party 2003_7': [0], 'conservative_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'constituency_9': [2], 'galloway & upper nithsdale_10': [3]}
|
['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']]
|
westmorland county , new brunswick
|
https://en.wikipedia.org/wiki/Westmorland_County%2C_New_Brunswick
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-176529-2.html.csv
|
count
|
three of the parishes in westmorland county , new brunswick have a census ranking that is above 1000 .
|
{'scope': 'all', 'criterion': 'greater_than', 'value': '1000', 'result': '3', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'census ranking', '1000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose census ranking record is greater than 1000 .', 'tostr': 'filter_greater { all_rows ; census ranking ; 1000 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; census ranking ; 1000 } }', 'tointer': 'select the rows whose census ranking record is greater than 1000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; census ranking ; 1000 } } ; 3 } = true', 'tointer': 'select the rows whose census ranking record is greater than 1000 . the number of such rows is 3 .'}
|
eq { count { filter_greater { all_rows ; census ranking ; 1000 } } ; 3 } = true
|
select the rows whose census ranking record is greater than 1000 . the number of such rows is 3 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'census ranking_5': 5, '1000_6': 6, '3_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'census ranking_5': 'census ranking', '1000_6': '1000', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'census ranking_5': [0], '1000_6': [0], '3_7': [2]}
|
['official name', 'status', 'area km 2', 'population', 'census ranking']
|
[['moncton', 'parish', '582.20', '8861', '427 of 5008'], ['shediac', 'parish', '238.47', '4801', '709 of 5008'], ['salisbury', 'parish', '873.55', '3425', '909 of 5008'], ['botsford', 'parish', '303.75', '1203', '1827 of 5008'], ['sackville', 'parish', '578.28', '1174', '1857 of 5008'], ['westmorland', 'parish', '173.48', '959', '2105 of 5008']]
|
canada women 's national soccer team
|
https://en.wikipedia.org/wiki/Canada_women%27s_national_soccer_team
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1818918-3.html.csv
|
count
|
canada women 's national soccer team were runner up four out of six years .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'runner - up', 'result': '4', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'runner - up'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to runner - up .', 'tostr': 'filter_eq { all_rows ; result ; runner - up }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; runner - up } }', 'tointer': 'select the rows whose result 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 ; result ; runner - up } } ; 4 } = true', 'tointer': 'select the rows whose result record fuzzily matches to runner - up . the number of such rows is 4 .'}
|
eq { count { filter_eq { all_rows ; result ; runner - up } } ; 4 } = true
|
select the rows whose result 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, 'result_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', 'result_5': 'result', '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], 'result_5': [0], 'runner - up_6': [0], '4_7': [2]}
|
['year', 'result', 'matches', 'wins', 'draws', 'losses']
|
[['1991', 'runner - up', '5', '4', '0', '1'], ['1994', 'runner - up', '4', '3', '0', '1'], ['1998', 'champions', '5', '5', '0', '0'], ['2002', 'runner - up', '5', '4', '0', '1'], ['2006', 'runner - up', '2', '1', '0', '1'], ['2010', 'champions', '5', '5', '0', '0']]
|
2010 isle of man tt
|
https://en.wikipedia.org/wiki/2010_Isle_of_Man_TT
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25220821-3.html.csv
|
majority
|
all notices for sat 29 may schedule for 2010 isle of man tt was cancelled no time .
|
{'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'cancelled no time', 'subset': None}
|
{'func': 'all_str_eq', 'args': ['all_rows', 'sat 29 may', 'cancelled no time'], 'result': True, 'ind': 0, 'tointer': 'for the sat 29 may records of all rows , all of them fuzzily match to cancelled no time .', 'tostr': 'all_eq { all_rows ; sat 29 may ; cancelled no time } = true'}
|
all_eq { all_rows ; sat 29 may ; cancelled no time } = true
|
for the sat 29 may records of all rows , all of them fuzzily match to cancelled no time .
|
1
|
1
|
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'sat 29 may_3': 3, 'cancelled no time_4': 4}
|
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'sat 29 may_3': 'sat 29 may', 'cancelled no time_4': 'cancelled no time'}
|
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'sat 29 may_3': [0], 'cancelled no time_4': [0]}
|
['rank', 'rider', 'sat 29 may', 'mon 31 may', 'tues 1 june', 'wed 2 june', 'thurs 3 june', 'fri 4 june']
|
[['2', 'klaus klaffenböck / dan sayle 600cc lcr honda', 'cancelled no time', "20 ' 15.35 111.761 mph", "20 ' 05.79 112.647 mph", "19 ' 55.92 113.576 mph", "19 ' 50.47 114.096 mph", "19 ' 56.64 113.508 mph"], ['3', 'john holden / andrew winkle 600cc lcr suzuki', 'cancelled no time', "20 ' 17.36 111.576 mph", "20 ' 09.86 112.267 mph", "20 ' 04.82 112.737 mph", "20 ' 15.90 111.710 mph", "19 ' 59.43 113.224 mph"], ['4', 'simon neary / paul knapton 600cc honda', 'cancelled no time', "20 ' 24.08 110.964 mph", "20 ' 05.64 112.661 mph", "20 ' 11.98 112.071 mph", "20 ' 00.19 113.172 mph", "20 ' 01.41 113.058 mph"], ['5', 'conrad harrison / kerry williams 600cc honda', 'cancelled no time', "20 ' 50.30 108.636 mph", "20 ' 27.78 110.629 mph", "20 ' 25.77 110.810 mph", "20 ' 13.17 111.962 mph", "20 ' 29.39 110.484 mph"], ['6', 'tim reeves / dipash chauhan 600cc honda', 'cancelled no time', '-- no time', "20 ' 59.60 107.834 mph", "20 ' 45.81 109.028 mph", "20 ' 26.35 110.758 mph", "37 ' 03.92 61.076 mph"], ['7', 'gary bryan / gary partridge 600cc honda', 'cancelled no time', "21 ' 21.24 106.013 mph", "21 ' 09.41 107.001 mph", "20 ' 47.90 108.845 mph", "20 ' 27.35 110.668 mph", "20 ' 40.91 109.459 mph"], ['8', 'roy hanks / dave wells 600cc suzuki', 'cancelled no time', "21 ' 36.43 104.771 mph", "21 ' 05.27 107.351 mph", "20 ' 50.62 108.608 mph", "20 ' 27.93 110.615 mph", '-- no time'], ['9', 'tony elmer / darren marshall 600cc ireson yamaha', 'cancelled no time', "21 ' 35.11 108.877 mph", "21 ' 02.66 107.573 mph", "20 ' 43.24 109.253 mph", "20 ' 28.72 110.554 mph", "20 ' 39.74 109.562 mph"]]
|
2001 new york giants season
|
https://en.wikipedia.org/wiki/2001_New_York_Giants_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16059626-2.html.csv
|
superlative
|
in the 2001 new york giants season , their first winning game occurred on sept 23 .
|
{'scope': 'subset', 'col_superlative': '2', 'row_superlative': '2', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '4', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'win'}}
|
{'func': 'eq', 'args': [{'func': 'min', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'win'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; result ; win }', 'tointer': 'select the rows whose result record fuzzily matches to win .'}, 'date'], 'result': 'sept 23', 'ind': 1, 'tostr': 'min { filter_eq { all_rows ; result ; win } ; date }', 'tointer': 'select the rows whose result record fuzzily matches to win . the minimum date record of these rows is sept 23 .'}, 'sept 23'], 'result': True, 'ind': 2, 'tostr': 'eq { min { filter_eq { all_rows ; result ; win } ; date } ; sept 23 } = true', 'tointer': 'select the rows whose result record fuzzily matches to win . the minimum date record of these rows is sept 23 .'}
|
eq { min { filter_eq { all_rows ; result ; win } ; date } ; sept 23 } = true
|
select the rows whose result record fuzzily matches to win . the minimum date record of these rows is sept 23 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'min_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 'win_6': 6, 'date_7': 7, 'sept 23_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'min_1': 'min', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 'win_6': 'win', 'date_7': 'date', 'sept 23_8': 'sept 23'}
|
{'eq_2': [3], 'result_3': [], 'min_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'win_6': [0], 'date_7': [1], 'sept 23_8': [2]}
|
['game', 'date', 'opponent', 'result', "giants ' points", "opponents ' points", 'record', 'tv time', 'attendance']
|
[['1', 'sept 10', 'denver broncos', 'loss', '20', '31', '0 - 1', 'abc 9:00 et / 7:00 mt', '75735'], ['2', 'sept 23', 'kansas city chiefs', 'win', '13', '3', '1 - 1', 'fox 1:00 et / 12:00 ct', '77666'], ['3', 'sept 30', 'new orleans saints', 'win', '21', '13', '2 - 1', 'fox 1:00 et', '78451'], ['4', 'oct 7', 'washington redskins', 'win', '23', '9', '3 - 1', 'fox 1:00 et', '78651'], ['5', 'oct 14', 'st louis rams', 'loss', '14', '15', '3 - 2', 'fox 1:00 et / 12:00 ct', '65992'], ['6', 'oct 22', 'philadelphia eagles', 'loss', '9', '10', '3 - 3', 'abc 9:00 et', '78821'], ['7', 'oct 28', 'washington redskins', 'loss', '21', '35', '3 - 4', 'fox 4:00 et', '80316'], ['8', 'nov 4', 'dallas cowboys', 'win', '27', '24', '4 - 4', 'fox 1:00 et', '78673'], ['9', 'nov 11', 'arizona cardinals', 'win', '17', '10', '5 - 4', 'fox 4:00 et / 2:00 mt', '36917'], ['10', 'nov 19', 'minnesota vikings', 'loss', '16', '28', '5 - 5', 'abc 9:00 et / 8:00 ct', '64283'], ['11', 'nov 25', 'oakland raiders', 'loss', '10', '28', '5 - 6', 'cbs 4:00 et', '78756'], ['12', '-', '-', '-', '-', '-', '-', '-', ''], ['13', 'dec 9', 'dallas cowboys', 'loss', '13', '20', '5 - 7', 'fox 1:00 et / 12:00 ct', '61821'], ['14', 'dec 15', 'arizona cardinals', 'win', '17', '13', '6 - 7', 'fox 12:30 et', '77913'], ['15', 'dec 23', 'seattle seahawks', 'win', '27', '24', '7 - 7', 'cbs 1:00 et', '78119'], ['16', 'dec 30', 'philadelphia eagles', 'loss', '21', '24', '7 - 8', 'fox 4:00 et', '65885'], ['17', 'jan 6', 'green bay packers', 'loss', '25', '34', '7 - 9', 'fox 1:00 et', '78601']]
|
1984 - 85 philadelphia 76ers season
|
https://en.wikipedia.org/wiki/1984%E2%80%9385_Philadelphia_76ers_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14828562-1.html.csv
|
comparative
|
in 1984 , the philadelphia 76ers picked gary springer before they picked rich congo .
|
{'row_1': '8', 'row_2': '9', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'gary springer'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to gary springer .', 'tostr': 'filter_eq { all_rows ; player ; gary springer }'}, 'pick'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; gary springer } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to gary springer . take the pick record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'rich congo'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to rich congo .', 'tostr': 'filter_eq { all_rows ; player ; rich congo }'}, 'pick'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; rich congo } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to rich congo . take the pick record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; gary springer } ; pick } ; hop { filter_eq { all_rows ; player ; rich congo } ; pick } } = true', 'tointer': 'select the rows whose player record fuzzily matches to gary springer . take the pick record of this row . select the rows whose player record fuzzily matches to rich congo . take the pick record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; player ; gary springer } ; pick } ; hop { filter_eq { all_rows ; player ; rich congo } ; pick } } = true
|
select the rows whose player record fuzzily matches to gary springer . take the pick record of this row . select the rows whose player record fuzzily matches to rich congo . take the pick 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, 'player_7': 7, 'gary springer_8': 8, 'pick_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'rich congo_12': 12, 'pick_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', 'player_7': 'player', 'gary springer_8': 'gary springer', 'pick_9': 'pick', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'rich congo_12': 'rich congo', 'pick_13': 'pick'}
|
{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'gary springer_8': [0], 'pick_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'rich congo_12': [1], 'pick_13': [3]}
|
['round', 'pick', 'player', 'nationality', 'college']
|
[['1', '5', 'charles barkley', 'united states', 'auburn'], ['1', '10', 'leon wood', 'united states', 'california state - fullerton'], ['1', '22', 'tom sewell', 'united states', 'lamar'], ['3', '48', 'james banks', 'united states', 'georgia'], ['3', '68', 'butch graves', 'united states', 'yale'], ['4', '91', 'earl harrison', 'united states', 'morehead state'], ['5', '114', 'dan federman', 'united states', 'tennessee'], ['6', '137', 'gary springer', 'united states', 'iona'], ['7', '160', 'rich congo', 'united states', 'drexel'], ['8', '183', 'franks dobbs', 'united states', 'villanova'], ['9', '205', 'michael mitchell', 'united states', 'drexel'], ['10', '227', 'martin clark', 'united states', 'boston college']]
|
1960 buffalo bills season
|
https://en.wikipedia.org/wiki/1960_Buffalo_Bills_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16351892-4.html.csv
|
majority
|
most of the games resulted in a loss for the buffalo bills .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'result', 'l'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to l .', 'tostr': 'most_eq { all_rows ; result ; l } = true'}
|
most_eq { all_rows ; result ; l } = true
|
for the result records of all rows , most of them fuzzily match to l .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'l_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'l_4': 'l'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'l_4': [0]}
|
['week', 'date', 'opponent', 'result', 'attendance']
|
[['1', 'september 11 , 1960', 'new york titans', 'l 27 - 3', '10200'], ['2', 'september 18 , 1960', 'denver broncos', 'l 27 - 21', '15229'], ['3', 'september 23 , 1960', 'boston patriots', 'w 13 - 0', '20732'], ['4', 'october 2 , 1960', 'los angeles chargers', 'l 24 - 10', '15821'], ['6', 'october 16 , 1960', 'new york titans', 'l 17 - 13', '14988'], ['7', 'october 23 , 1960', 'oakland raiders', 'w 38 - 9', '8876'], ['8', 'october 30 , 1960', 'houston oilers', 'w 25 - 24', '23001'], ['9', 'november 6 , 1960', 'dallas texans', 'l 45 - 28', '19610'], ['10', 'november 13 , 1960', 'oakland raiders', 'l 20 - 7', '8800'], ['11', 'november 20 , 1960', 'los angeles chargers', 'w 32 - 3', '16161'], ['12', 'november 27 , 1960', 'denver broncos', 't 38 - 38', '7785'], ['13', 'december 4 , 1960', 'boston patriots', 'w 38 - 14', '14335'], ['14', 'december 11 , 1960', 'houston oilers', 'l 31 - 23', '25243'], ['15', 'december 18 , 1960', 'dallas texans', 'l 24 - 7', '18000']]
|
f.c. united of manchester
|
https://en.wikipedia.org/wiki/F.C._United_of_Manchester
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1895942-1.html.csv
|
aggregation
|
the f.c. united of manchester has played on an average level of eight .
|
{'scope': 'all', 'col': '3', 'type': 'average', 'result': '8', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'level'], 'result': '8', 'ind': 0, 'tostr': 'avg { all_rows ; level }'}, '8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; level } ; 8 } = true', 'tointer': 'the average of the level record of all rows is 8 .'}
|
round_eq { avg { all_rows ; level } ; 8 } = true
|
the average of the level record of all rows is 8 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'level_4': 4, '8_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'level_4': 'level', '8_5': '8'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'level_4': [0], '8_5': [1]}
|
['season', 'leaguecontested', 'level', 'leagueposition', 'avghome attendance 1', 'fa cup', 'fa trophy', 'leading scorer 1']
|
[['2005 - 06', 'north west counties league division two', '10', '1st of 19 promoted', '3056', 'n / a', 'n / a', 'rory patterson 18'], ['2006 - 07', 'north west counties league division one', '9', '1st of 22 promoted', '2581', 'n / a', 'n / a', 'stuart rudd 38'], ['2007 - 08', 'northern premier league division one north', '8', '2nd of 18 won playoffs promoted', '2086 2', '1q', 'prelim rd', 'rory patterson 34'], ['2008 - 09', 'northern premier league premier division', '7', '6th of 22', '2152', '1q', '3q', 'kyle wilson 21'], ['2009 - 10', 'northern premier league premier division', '7', '13th of 20', '1954 3', '4q', '3q', 'phil marsh 10'], ['2010 - 11', 'northern premier league premier division', '7', '4th of 22 playoffs runner - up', '1961 4', 'r2', '3q', 'michael norton 24'], ['2011 - 12', 'northern premier league premier division', '7', '6th of 22 playoffs runner - up', '1947 5', '2q', 'r1', 'matthew wolfenden 20'], ['2012 - 13', 'northern premier league premier division', '7', '3rd of 22 playoffs runner - up', '1835 6', '4q', '2q', 'matthew wolfenden 19'], ['2013 - 14', 'northern premier league premier division', '7', '4th of 24', '1713', '1q', '1q next game 19th oct', 'tom greaves 8']]
|
2007 - 08 euroleague women
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_EuroLeague_Women
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14121260-9.html.csv
|
aggregation
|
the average number of assists for players in the women 's 2007 - 08 euroleague is 64.2 .
|
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '64.2', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'assists'], 'result': '64.2', 'ind': 0, 'tostr': 'avg { all_rows ; assists }'}, '64.2'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; assists } ; 64.2 } = true', 'tointer': 'the average of the assists record of all rows is 64.2 .'}
|
round_eq { avg { all_rows ; assists } ; 64.2 } = true
|
the average of the assists record of all rows is 64.2 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'assists_4': 4, '64.2_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'assists_4': 'assists', '64.2_5': '64.2'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'assists_4': [0], '64.2_5': [1]}
|
['rank', 'name', 'team', 'games', 'assists']
|
[['1', 'dalma iványi', 'mizo pécs 2010', '13', '74'], ['2', 'caroline aubert', 'uso mondeville basket ummc ekaterinburg', '16', '80'], ['3', 'kathy wambe', 'esb lille metropole', '10', '48'], ['4', 'sue bird', 'spartak moscow region', '14', '65'], ['5', 'jelena skerovic', 'wisła can - pack kraków', '12', '54']]
|
1967 - 68 pittsburgh penguins season
|
https://en.wikipedia.org/wiki/1967%E2%80%9368_Pittsburgh_Penguins_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13931419-3.html.csv
|
count
|
in november of the 1967 - 68 season , the pittsburgh penguins had 6 away games .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'penguins', 'result': '6', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'visitor', 'penguins'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose visitor record fuzzily matches to penguins .', 'tostr': 'filter_eq { all_rows ; visitor ; penguins }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; visitor ; penguins } }', 'tointer': 'select the rows whose visitor record fuzzily matches to penguins . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; visitor ; penguins } } ; 6 } = true', 'tointer': 'select the rows whose visitor record fuzzily matches to penguins . the number of such rows is 6 .'}
|
eq { count { filter_eq { all_rows ; visitor ; penguins } } ; 6 } = true
|
select the rows whose visitor record fuzzily matches to penguins . 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, 'visitor_5': 5, 'penguins_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', 'visitor_5': 'visitor', 'penguins_6': 'penguins', '6_7': '6'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'visitor_5': [0], 'penguins_6': [0], '6_7': [2]}
|
['date', 'visitor', 'score', 'home', 'attendance', 'record', 'points']
|
[['november 1', 'penguins', '4 - 1', 'north stars', '7535', '4 - 6 - 1', '9'], ['november 4', 'penguins', '1 - 0', 'seals', '4549', '5 - 6 - 1', '11'], ['november 8', 'flyers', '1 - 1', 'penguins', '4719', '5 - 6 - 2', '12'], ['november 9', 'penguins', '1 - 5', 'red wings', '10683', '5 - 7 - 2', '12'], ['november 11', 'blues', '5 - 1', 'penguins', '7183', '5 - 8 - 2', '12'], ['november 15', 'flyers', '0 - 5', 'penguins', '6876', '6 - 8 - 2', '14'], ['november 18', 'penguins', '5 - 3', 'blues', '7715', '7 - 8 - 2', '16'], ['november 22', 'bruins', '1 - 4', 'penguins', '9701', '8 - 8 - 2', '18'], ['november 24', 'penguins', '3 - 5', 'kings', '6409', '8 - 9 - 2', '18'], ['november 25', 'penguins', '2 - 2', 'seals', '5977', '8 - 9 - 3', '19'], ['november 29', 'seals', '1 - 6', 'penguins', '4499', '9 - 9 - 3', '21']]
|
california 's great america
|
https://en.wikipedia.org/wiki/California%27s_Great_America
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1680162-1.html.csv
|
unique
|
the taxi jam ride was the only one to achieve a rating below 3 .
|
{'scope': 'all', 'row': '6', 'col': '5', 'col_other': '1', 'criterion': 'less_than', 'value': '3', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'rating', '3'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rating record is less than 3 .', 'tostr': 'filter_less { all_rows ; rating ; 3 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; rating ; 3 } }', 'tointer': 'select the rows whose rating record is less than 3 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'rating', '3'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rating record is less than 3 .', 'tostr': 'filter_less { all_rows ; rating ; 3 }'}, 'ride'], 'result': 'taxi jam', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; rating ; 3 } ; ride }'}, 'taxi jam'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; rating ; 3 } ; ride } ; taxi jam }', 'tointer': 'the ride record of this unqiue row is taxi jam .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; rating ; 3 } } ; eq { hop { filter_less { all_rows ; rating ; 3 } ; ride } ; taxi jam } } = true', 'tointer': 'select the rows whose rating record is less than 3 . there is only one such row in the table . the ride record of this unqiue row is taxi jam .'}
|
and { only { filter_less { all_rows ; rating ; 3 } } ; eq { hop { filter_less { all_rows ; rating ; 3 } ; ride } ; taxi jam } } = true
|
select the rows whose rating record is less than 3 . there is only one such row in the table . the ride record of this unqiue row is taxi jam .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'rating_7': 7, '3_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'ride_9': 9, 'taxi jam_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'rating_7': 'rating', '3_8': '3', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'ride_9': 'ride', 'taxi jam_10': 'taxi jam'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'rating_7': [0], '3_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'ride_9': [2], 'taxi jam_10': [3]}
|
['ride', 'year opened', 'ride manufacturer and type', 'minimum height', 'rating']
|
[['the demon', '1980', 'arrow dynamics', '48', '5'], ['flight deck', '1993', 'bolliger & mabillard inverted roller coaster', '54', '5'], ['gold striker', '2013', 'great coasters international wooden roller coaster', '48', '4'], ['grizzly', '1986', 'wooden roller coaster', '48', '4'], ['psycho mouse', '2001', 'arrow dynamics wild mouse roller coaster', '44', '4'], ['taxi jam', '1999', 'e & f miller industries kiddie coaster', '36', '2'], ['vortex', '1991', 'bolliger & mabillard stand - up roller coaster', '54', '5'], ['woodstock express', '1987', 'intamin family roller coaster', '40', '3']]
|
boroughs of sherbrooke
|
https://en.wikipedia.org/wiki/Boroughs_of_Sherbrooke
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14927794-1.html.csv
|
superlative
|
fleurimont has the biggest population of all the boroughs of sherbrooke .
|
{'scope': 'all', 'col_superlative': '3', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'population'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; population }'}, 'borough'], 'result': 'fleurimont', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; population } ; borough }'}, 'fleurimont'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; population } ; borough } ; fleurimont } = true', 'tointer': 'select the row whose population record of all rows is maximum . the borough record of this row is fleurimont .'}
|
eq { hop { argmax { all_rows ; population } ; borough } ; fleurimont } = true
|
select the row whose population record of all rows is maximum . the borough record of this row is fleurimont .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'population_5': 5, 'borough_6': 6, 'fleurimont_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'population_5': 'population', 'borough_6': 'borough', 'fleurimont_7': 'fleurimont'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'population_5': [0], 'borough_6': [1], 'fleurimont_7': [2]}
|
['borough', 'components', 'population', 'number of borough councilors', 'number of municipal councilors']
|
[['brompton', 'bromptonville', '5771', '3', '1'], ['fleurimont', 'eastern sherbrooke , fleurimont', '41289', '5', '5'], ['lennoxville', 'lennoxville', '4947', '3', '1'], ['mont - bellevue', 'western sherbrooke , ascot', '31373', '4', '4'], ['rock forest - saint - élie - deauville', "rock forest , saint - élie - d'orford , deauville", '26757', '4', '4'], ['jacques - cartier', 'northern sherbrooke', '29311', '4', '4']]
|
2010 - 11 atlanta thrashers season
|
https://en.wikipedia.org/wiki/2010%E2%80%9311_Atlanta_Thrashers_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27537518-7.html.csv
|
aggregation
|
in the 2010-11 atlanta thrashers season , the average number of points is 51.8 .
|
{'scope': 'all', 'col': '10', 'type': 'average', 'result': '51.8', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '51.8', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '51.8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 51.8 } = true', 'tointer': 'the average of the points record of all rows is 51.8 .'}
|
round_eq { avg { all_rows ; points } ; 51.8 } = true
|
the average of the points record of all rows is 51.8 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '51.8_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '51.8_5': '51.8'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '51.8_5': [1]}
|
['game', 'date', 'opponent', 'score', 'first star', 'decision', 'location', 'attendance', 'record', 'points']
|
[['42', 'january 2', 'montreal canadiens', '4 - 3 ot', 'd byfuglien', 'o pavelec', 'bell centre', '21273', '21 - 15 - 6', '48'], ['43', 'january 5', 'florida panthers', '3 - 2', 'r peverley', 'o pavelec', 'bankatlantic center', '12803', '22 - 15 - 6', '50'], ['44', 'january 7', 'toronto maple leafs', '3 - 9', 'm grabovski', 'o pavelec', 'philips arena', '14592', '22 - 16 - 6', '50'], ['45', 'january 9', 'carolina hurricanes', '3 - 4 ot', 't ruutu', 'o pavelec', 'rbc center', '17907', '22 - 16 - 7', '51'], ['46', 'january 14', 'philadelphia flyers', '2 - 5', 'd briere', 'o pavelec', 'philips arena', '15081', '22 - 17 - 7', '51'], ['47', 'january 15', 'dallas stars', '1 - 6', 't daley', 'o pavelec', 'american airlines center', '17702', '22 - 18 - 7', '51'], ['48', 'january 17', 'florida panthers', '3 - 2 so', 'a burmistrov', 'o pavelec', 'bankatlantic center', '11477', '23 - 18 - 7', '53'], ['49', 'january 20', 'tampa bay lightning', '2 - 3 so', 's stamkos', 'o pavelec', 'philips arena', '12314', '23 - 18 - 8', '54'], ['50', 'january 22', 'new york rangers', '2 - 3 so', 'm zuccarello', 'o pavelec', 'philips arena', '17061', '23 - 18 - 9', '55'], ['51', 'january 23', 'tampa bay lightning', '1 - 7', 's gagne', 'o pavelec', 'st pete times forum', '13916', '23 - 19 - 9', '55']]
|
denis gremelmayr
|
https://en.wikipedia.org/wiki/Denis_Gremelmayr
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15209396-2.html.csv
|
majority
|
denis gremelmayr had most of his matches after the year 2000 .
|
{'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '2000', 'subset': None}
|
{'func': 'most_greater', 'args': ['all_rows', 'date', '2000'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them are greater than 2000 .', 'tostr': 'most_greater { all_rows ; date ; 2000 } = true'}
|
most_greater { all_rows ; date ; 2000 } = true
|
for the date records of all rows , most of them are greater than 2000 .
|
1
|
1
|
{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '2000_4': 4}
|
{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '2000_4': '2000'}
|
{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '2000_4': [0]}
|
['date', 'tournament', 'surface', 'opponent', 'score']
|
[['september 25 , 2000', 'kawaguchi', 'hard', 'leigh holland', '6 - 1 , 6 - 2'], ['january 8 , 2001', 'jorhat', 'clay', 'fabio maggi', '7 - 6 , 7 - 5'], ['october 8 , 2001', 'santo domingo', 'clay', 'josé de armas', '6 - 4 , 6 - 0'], ['january 21 , 2002', 'dubai', 'hard', 'jaroslav levinský', 'w / o'], ['august 25 , 2003', 'enschede', 'clay', 'robert lindstedt', '6 - 3 , 3 - 6 , 6 - 3'], ['november 1 , 2004', 'bangkok', 'hard', 'ruben de kleijn', '6 - 4 , 6 - 0'], ['june 27 , 2005', 'heerhugowaard', 'clay', 'nicolas todero', '6 - 4 , 6 - 2'], ['july 4 , 2005', 'kassel', 'clay', 'sascha kloer', '6 - 2 , 6 - 1'], ['september 3 , 2007', 'düsseldorf', 'clay', 'andreas haider - maurer', '6 - 7 , 6 - 2 , 6 - 4'], ['november 5 , 2007', 'eckental', 'carpet', 'roko karanušić', 'w / o'], ['november 3 , 2008', 'eckental', 'carpet', 'roko karanušić', '6 - 2 , 7 - 5'], ['may 23 , 2010', 'cremona', 'hard', 'marius copil', '6 - 4 , 7 - 5'], ['july 5 , 2010', 'scheveningen', 'clay', 'thomas schoorel', '7 - 5 , 6 - 4'], ['july 19 , 2010', 'poznań', 'clay', 'andrey kuznetsov', '6 - 1 , 6 - 2']]
|
1982 senior pga tour
|
https://en.wikipedia.org/wiki/1982_Senior_PGA_Tour
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11622896-2.html.csv
|
count
|
there were 5 players who participated in the 1982 senior pga tour .
|
{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '5', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'player'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record is arbitrary .', 'tostr': 'filter_all { all_rows ; player }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; player } }', 'tointer': 'select the rows whose player record is arbitrary . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; player } } ; 5 } = true', 'tointer': 'select the rows whose player record is arbitrary . the number of such rows is 5 .'}
|
eq { count { filter_all { all_rows ; player } } ; 5 } = true
|
select the rows whose player record is arbitrary . the number of such rows is 5 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'player_5': 5, '5_6': 6}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'player_5': 'player', '5_6': '5'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'player_5': [0], '5_6': [2]}
|
['rank', 'player', 'country', 'earnings', 'events', 'wins']
|
[['1', 'miller barber', 'united states', '106890', '10', '3'], ['2', 'don january', 'united states', '99508', '8', '2'], ['3', 'bob goalby', 'united states', '94540', '10', '1'], ['4', 'arnold palmer', 'united states', '73848', '7', '2'], ['5', 'billy casper', 'united states', '71979', '8', '2']]
|
wwwa ( fm )
|
https://en.wikipedia.org/wiki/WWWA_%28FM%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14797490-1.html.csv
|
superlative
|
in the www a ( fm ) , the radio station with the highest frequency in maine has the call sign w300bn .
|
{'scope': 'all', 'col_superlative': '2', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'frequency mhz'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; frequency mhz }'}, 'call sign'], 'result': 'w300bn', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; frequency mhz } ; call sign }'}, 'w300bn'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; frequency mhz } ; call sign } ; w300bn } = true', 'tointer': 'select the row whose frequency mhz record of all rows is maximum . the call sign record of this row is w300bn .'}
|
eq { hop { argmax { all_rows ; frequency mhz } ; call sign } ; w300bn } = true
|
select the row whose frequency mhz record of all rows is maximum . the call sign record of this row is w300bn .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'frequency mhz_5': 5, 'call sign_6': 6, 'w300bn_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'frequency mhz_5': 'frequency mhz', 'call sign_6': 'call sign', 'w300bn_7': 'w300bn'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'frequency mhz_5': [0], 'call sign_6': [1], 'w300bn_7': [2]}
|
['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info']
|
[['w264bg', '100.7', 'benedicta , maine', '19', 'd', 'fcc'], ['w272cg', '102.3', 'biddeford , maine', '19', 'd', 'fcc'], ['w250bb', '97.9', 'houlton , maine', '250', 'd', 'fcc'], ['w255bi', '98.7', 'lincoln , maine', '38', 'd', 'fcc'], ['w223bh', '92.5', 'portland , maine', '250', 'd', 'fcc'], ['w300bn', '107.9', 'portland , maine', '10', 'd', 'fcc'], ['w233be', '94.5', 'richmond , maine', '250', 'd', 'fcc'], ['w246bp', '97.1', 'sanford , maine', '10', 'd', 'fcc'], ['w272bv', '102.3', 'yarmouth , maine', '80', 'd', 'fcc']]
|
bbc sessions ( led zeppelin album )
|
https://en.wikipedia.org/wiki/BBC_Sessions_%28Led_Zeppelin_album%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11533573-4.html.csv
|
count
|
for bbc sessions for a led zeppelin album , when the label is atlantic records , there were 4 times the region was the united states .
|
{'scope': 'subset', 'criterion': 'equal', 'value': 'united states', 'result': '4', 'col': '1', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'atlantic records'}}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'label', 'atlantic records'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; label ; atlantic records }', 'tointer': 'select the rows whose label record fuzzily matches to atlantic records .'}, 'region', 'united states'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose label record fuzzily matches to atlantic records . among these rows , select the rows whose region record fuzzily matches to united states .', 'tostr': 'filter_eq { filter_eq { all_rows ; label ; atlantic records } ; region ; united states }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; label ; atlantic records } ; region ; united states } }', 'tointer': 'select the rows whose label record fuzzily matches to atlantic records . among these rows , select the rows whose region record fuzzily matches to united states . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; label ; atlantic records } ; region ; united states } } ; 4 } = true', 'tointer': 'select the rows whose label record fuzzily matches to atlantic records . among these rows , select the rows whose region record fuzzily matches to united states . the number of such rows is 4 .'}
|
eq { count { filter_eq { filter_eq { all_rows ; label ; atlantic records } ; region ; united states } } ; 4 } = true
|
select the rows whose label record fuzzily matches to atlantic records . among these rows , select the rows whose region record fuzzily matches to united states . the number of such rows is 4 .
|
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, 'label_6': 6, 'atlantic records_7': 7, 'region_8': 8, 'united states_9': 9, '4_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', 'label_6': 'label', 'atlantic records_7': 'atlantic records', 'region_8': 'region', 'united states_9': 'united states', '4_10': '4'}
|
{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'label_6': [0], 'atlantic records_7': [0], 'region_8': [1], 'united states_9': [1], '4_10': [3]}
|
['region', 'date', 'label', 'format', 'catalog']
|
[['united states', '11 november 1997', 'atlantic records', '4 lp', '83061 - 1'], ['united states', '11 november 1997', 'atlantic records', '2 compact disc', '83061 - 2'], ['united states', '11 november 1997', 'atlantic records', '2 cassette', '83061 - 4'], ['united states', '11 november 1997', 'atlantic records', '3 compact disc', '83074 - 2'], ['united kingdom', '11 november 1997', 'atlantic records', '2 compact disc', '7567 - 83061 - 2'], ['japan', '11 november 1997', 'wea japan', '2 compact disc', '11756 - 7']]
|
2008 - 09 segunda división b
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_Segunda_Divisi%C3%B3n_B
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18160020-4.html.csv
|
majority
|
in 2008-09 segunda division b , all of the goalkeepers had at least 30 matches .
|
{'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'greater_than_eq', 'value': '30', 'subset': None}
|
{'func': 'all_greater_eq', 'args': ['all_rows', 'matches', '30'], 'result': True, 'ind': 0, 'tointer': 'for the matches records of all rows , all of them are greater than or equal to 30 .', 'tostr': 'all_greater_eq { all_rows ; matches ; 30 } = true'}
|
all_greater_eq { all_rows ; matches ; 30 } = true
|
for the matches records of all rows , all of them are greater than or equal to 30 .
|
1
|
1
|
{'all_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'matches_3': 3, '30_4': 4}
|
{'all_greater_eq_0': 'all_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'matches_3': 'matches', '30_4': '30'}
|
{'all_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'matches_3': [0], '30_4': [0]}
|
['goalkeeper', 'goals', 'matches', 'average', 'team']
|
[['josé bermúdez', '18', '33', '0.55', 'cultural leonesa'], ['joel rodríguez', '24', '36', '0.67', 'celta b'], ['igor etxebarrieta', '21', '30', '0.7', 'lemona'], ['daniel giménez', '34', '38', '0.89', 'zamora'], ['miguel escalona', '34', '34', '1', 'guijuelo']]
|
united states house of representatives elections , 2006
|
https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1805191-48.html.csv
|
comparative
|
adam smith has a first elected year which is earlier than that of dave reichert .
|
{'row_1': '9', 'row_2': '8', 'col': '4', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'adam smith'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to adam smith .', 'tostr': 'filter_eq { all_rows ; incumbent ; adam smith }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; adam smith } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to adam smith . take the first elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'dave reichert'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to dave reichert .', 'tostr': 'filter_eq { all_rows ; incumbent ; dave reichert }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; dave reichert } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to dave reichert . take the first elected record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; incumbent ; adam smith } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; dave reichert } ; first elected } } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to adam smith . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to dave reichert . take the first elected record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; incumbent ; adam smith } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; dave reichert } ; first elected } } = true
|
select the rows whose incumbent record fuzzily matches to adam smith . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to dave reichert . take the first elected 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, 'incumbent_7': 7, 'adam smith_8': 8, 'first elected_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'incumbent_11': 11, 'dave reichert_12': 12, 'first elected_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', 'incumbent_7': 'incumbent', 'adam smith_8': 'adam smith', 'first elected_9': 'first elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'incumbent_11': 'incumbent', 'dave reichert_12': 'dave reichert', 'first elected_13': 'first elected'}
|
{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'incumbent_7': [0], 'adam smith_8': [0], 'first elected_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'incumbent_11': [1], 'dave reichert_12': [1], 'first elected_13': [3]}
|
['district', 'incumbent', 'party', 'first elected', 'results']
|
[['washington 1', 'jay inslee', 'democratic', '1998', 're - elected'], ['washington 2', 'rick larsen', 'democratic', '2000', 're - elected'], ['washington 3', 'brian baird', 'democratic', '1998', 're - elected'], ['washington 4', 'doc hastings', 'republican', '1994', 're - elected'], ['washington 5', 'cathy mcmorris', 'republican', '2004', 're - elected'], ['washington 6', 'norm dicks', 'democratic', '1976', 're - elected'], ['washington 7', 'jim mcdermott', 'democratic', '1988', 're - elected'], ['washington 8', 'dave reichert', 'republican', '2004', 're - elected'], ['washington 9', 'adam smith', 'democratic', '1996', 're - elected']]
|
miami dade college
|
https://en.wikipedia.org/wiki/Miami_Dade_College
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1860065-2.html.csv
|
majority
|
most of the campuses of the miami dade college were opened before the year 2000 .
|
{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '2000', 'subset': None}
|
{'func': 'most_less', 'args': ['all_rows', 'year opened', '2000'], 'result': True, 'ind': 0, 'tointer': 'for the year opened records of all rows , most of them are less than 2000 .', 'tostr': 'most_less { all_rows ; year opened ; 2000 } = true'}
|
most_less { all_rows ; year opened ; 2000 } = true
|
for the year opened records of all rows , most of them are less than 2000 .
|
1
|
1
|
{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'year opened_3': 3, '2000_4': 4}
|
{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'year opened_3': 'year opened', '2000_4': '2000'}
|
{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'year opened_3': [0], '2000_4': [0]}
|
['campus', 'year opened', 'students', 'size', 'location']
|
[['hialeah campus', '1980 2005 ( official designation )', 'na', '8 acres', 'hialeah'], ['homestead campus', '1990', 'na', '18 acres', 'downtown homestead'], ['interamerican campus', '1986 2001 ( official designation )', '6500', '4 acres', 'little havana , miami'], ['kendall campus', '1967', '66500', '185 acres', 'kendall , miami'], ['medical campus', '1977', 'na', '4.3 acres', 'civic center , miami'], ['north campus', '1960', '41000', '245 acres', 'westview , miami'], ['west campus', '2005', 'na', '10 acres', 'doral , miami'], ['wolfson campus ( main campus )', '1970', '27000', '15 acres', 'downtown miami']]
|
2007 - 08 detroit pistons season
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_Detroit_Pistons_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11960944-9.html.csv
|
majority
|
the majority of games were wins for the pistons in the 2007 - 08 detroit pistons season .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'w', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'score', 'w'], 'result': True, 'ind': 0, 'tointer': 'for the score records of all rows , most of them fuzzily match to w .', 'tostr': 'most_eq { all_rows ; score ; w } = true'}
|
most_eq { all_rows ; score ; w } = true
|
for the score records of all rows , most of them fuzzily match to w .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'score_3': 3, 'w_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'score_3': 'score', 'w_4': 'w'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'score_3': [0], 'w_4': [0]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series']
|
[['1', 'april 20', 'philadelphia', 'l 90 - 86', 'wallace ( 24 )', 'maxiell ( 11 )', 'billups , hamilton ( 4 )', 'the palace of auburn hills 22076', '0 - 1'], ['2', 'april 23', 'philadelphia', 'w 105 - 88', 'hamilton ( 20 )', 'mcdyess ( 12 )', 'hamilton ( 7 )', 'the palace of auburn hills 22076', '1 - 1'], ['3', 'april 25', 'philadelphia', 'l 95 - 75', 'hamilton ( 23 )', 'hamilton ( 6 )', 'stuckey ( 5 )', 'wachovia center 18805', '1 - 2'], ['4', 'april 27', 'philadelphia', 'w 93 - 84', 'prince ( 23 )', 'wallace ( 10 )', 'billups , hamilton ( 7 )', 'wachovia center 18347', '2 - 2'], ['5', 'april 29', 'philadelphia', 'w 98 - 81', 'billups ( 21 )', 'maxiell ( 11 )', 'billups ( 12 )', 'the palace of auburn hills 22076', '3 - 2'], ['6', 'may 1', 'philadelphia', 'w 100 - 77', 'hamilton ( 24 )', 'billups ( 7 )', 'prince ( 7 )', 'wachovia center 14130', '4 - 2']]
|
1922 in brazilian football
|
https://en.wikipedia.org/wiki/1922_in_Brazilian_football
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15389424-1.html.csv
|
majority
|
all of the 1922 brazilian football clubs played a total of 18 matches .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': '18', 'subset': None}
|
{'func': 'all_eq', 'args': ['all_rows', 'played', '18'], 'result': True, 'ind': 0, 'tointer': 'for the played records of all rows , all of them are equal to 18 .', 'tostr': 'all_eq { all_rows ; played ; 18 } = true'}
|
all_eq { all_rows ; played ; 18 } = true
|
for the played records of all rows , all of them are equal to 18 .
|
1
|
1
|
{'all_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'played_3': 3, '18_4': 4}
|
{'all_eq_0': 'all_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'played_3': 'played', '18_4': '18'}
|
{'all_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'played_3': [0], '18_4': [0]}
|
['position', 'team', 'points', 'played', 'drawn', 'lost', 'against', 'difference']
|
[['1', 'corinthians', '30', '18', '2', '2', '19', '53'], ['2', 'palestra itália - sp', '29', '18', '1', '3', '24', '24'], ['3', 'sírio', '26', '18', '4', '3', '27', '17'], ['4', 'paulistano', '22', '18', '2', '6', '34', '17'], ['5', 'aa das palmeiras', '18', '18', '4', '7', '29', '8'], ['6', 'ypiranga - sp', '15', '18', '5', '8', '34', '- 2'], ['7', 'minas gerais', '14', '18', '2', '10', '54', '- 29'], ['8', 'aa são bento', '13', '18', '1', '11', '32', '- 7']]
|
salvatore bettiol
|
https://en.wikipedia.org/wiki/Salvatore_Bettiol
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15671752-1.html.csv
|
comparative
|
salvatore bettiol placed better in 1986 than he did in 1987 .
|
{'row_1': '1', 'row_2': '2', 'col': '4', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1986'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1986 .', 'tostr': 'filter_eq { all_rows ; year ; 1986 }'}, 'position'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1986 } ; position }', 'tointer': 'select the rows whose year record fuzzily matches to 1986 . take the position record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1987'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1987 .', 'tostr': 'filter_eq { all_rows ; year ; 1987 }'}, 'position'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1987 } ; position }', 'tointer': 'select the rows whose year record fuzzily matches to 1987 . take the position record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; year ; 1986 } ; position } ; hop { filter_eq { all_rows ; year ; 1987 } ; position } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1986 . take the position record of this row . select the rows whose year record fuzzily matches to 1987 . take the position record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; year ; 1986 } ; position } ; hop { filter_eq { all_rows ; year ; 1987 } ; position } } = true
|
select the rows whose year record fuzzily matches to 1986 . take the position record of this row . select the rows whose year record fuzzily matches to 1987 . take the position record of this row . the first record is less than the second record .
|
5
|
5
|
{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '1986_8': 8, 'position_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '1987_12': 12, 'position_13': 13}
|
{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '1986_8': '1986', 'position_9': 'position', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1987_12': '1987', 'position_13': 'position'}
|
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '1986_8': [0], 'position_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '1987_12': [1], 'position_13': [3]}
|
['year', 'competition', 'venue', 'position', 'event', 'notes']
|
[['1986', 'venice marathon', 'venice , italy', '1st', 'marathon', '2:18:44'], ['1987', 'world championships', 'rome , italy', '13th', 'marathon', '2:17:45'], ['1987', 'venice marathon', 'venice , italy', '1st', 'marathon', '2:10:01'], ['1990', 'european championships', 'split , fr yugoslavia', '4th', 'marathon', '2:17:45'], ['1991', 'world championships', 'tokyo , japan', '6th', 'marathon', '2:15:58'], ['1992', 'olympic games', 'barcelona , spain', '5th', 'marathon', '2:14:15'], ['1993', 'world championships', 'stuttgart , germany', 'n / a', 'marathon', 'dnf'], ['1996', 'olympic games', 'atlanta , united states', '20th', 'marathon', '2:17:27']]
|
1953 masters tournament
|
https://en.wikipedia.org/wiki/1953_Masters_Tournament
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13073611-2.html.csv
|
count
|
a total of 10 players placed in the 1953 masters tournament .
|
{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '10', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'player'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record is arbitrary .', 'tostr': 'filter_all { all_rows ; player }'}], 'result': '10', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; player } }', 'tointer': 'select the rows whose player record is arbitrary . the number of such rows is 10 .'}, '10'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; player } } ; 10 } = true', 'tointer': 'select the rows whose player record is arbitrary . the number of such rows is 10 .'}
|
eq { count { filter_all { all_rows ; player } } ; 10 } = true
|
select the rows whose player record is arbitrary . the number of such rows is 10 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'player_5': 5, '10_6': 6}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'player_5': 'player', '10_6': '10'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'player_5': [0], '10_6': [2]}
|
['place', 'player', 'country', 'score', 'to par']
|
[['1', 'ben hogan', 'united states', '70 + 69 = 139', '- 5'], ['2', 'bob hamilton', 'united states', '71 + 69 = 140', '- 4'], ['t3', 'chick harbert', 'united states', '68 + 73 = 141', '- 3'], ['t3', 'ted kroll', 'united states', '71 + 70 = 141', '- 3'], ['t5', 'lloyd mangrum', 'united states', '74 + 68 = 142', '- 2'], ['t5', 'milan marusic', 'united states', '70 + 72 = 142', '- 2'], ['t5', 'ed oliver', 'united states', '69 + 73 = 142', '- 2'], ['t8', 'al besselink', 'united states', '69 + 75 = 144', 'e'], ['t8', 'julius boros', 'united states', '73 + 71 = 144', 'e'], ['t8', 'lew worsham', 'united states', '74 + 70 = 144', 'e']]
|
lawrence peckham
|
https://en.wikipedia.org/wiki/Lawrence_Peckham
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14576140-1.html.csv
|
majority
|
all of lawrence peckham 's international competitions were high jump events .
|
{'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'high jump', 'subset': None}
|
{'func': 'all_str_eq', 'args': ['all_rows', 'event', 'high jump'], 'result': True, 'ind': 0, 'tointer': 'for the event records of all rows , all of them fuzzily match to high jump .', 'tostr': 'all_eq { all_rows ; event ; high jump } = true'}
|
all_eq { all_rows ; event ; high jump } = true
|
for the event records of all rows , all of them fuzzily match to high jump .
|
1
|
1
|
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'event_3': 3, 'high jump_4': 4}
|
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'event_3': 'event', 'high jump_4': 'high jump'}
|
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'event_3': [0], 'high jump_4': [0]}
|
['year', 'competition', 'venue', 'position', 'event']
|
[['1962', 'british empire and commonwealth games', 'perth , australia', '6th', 'high jump'], ['1964', 'olympic games', 'tokyo , japan', '10th', 'high jump'], ['1966', 'british empire and commonwealth games', 'kingston , jamaica', '1st', 'high jump'], ['1968', 'olympic games', 'mexico city , mexico', '8th', 'high jump'], ['1969', 'pacific conference games', 'tokyo , japan', '1st', 'high jump'], ['1970', 'commonwealth games', 'edinburgh , scotland', '1st', 'high jump'], ['1972', 'olympic games', 'munich , west germany', '18th', 'high jump'], ['1973', 'pacific conference games', 'toronto , canada', '3rd', 'high jump'], ['1974', 'british commonwealth games', 'christchurch , new zealand', '2nd', 'high jump']]
|
1991 buffalo bills season
|
https://en.wikipedia.org/wiki/1991_Buffalo_Bills_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15353123-3.html.csv
|
majority
|
in the 1991 buffalo bills season , for the games in december , most of them were wins for the bills .
|
{'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'w', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'december'}}
|
{'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'december'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; december }', 'tointer': 'select the rows whose date record fuzzily matches to december .'}, 'final score', 'w'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to december . for the final score records of these rows , most of them fuzzily match to w .', 'tostr': 'most_eq { filter_eq { all_rows ; date ; december } ; final score ; w } = true'}
|
most_eq { filter_eq { all_rows ; date ; december } ; final score ; w } = true
|
select the rows whose date record fuzzily matches to december . for the final score records of these rows , most of them fuzzily match to w .
|
2
|
2
|
{'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, 'december_5': 5, 'final score_6': 6, 'w_7': 7}
|
{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', 'december_5': 'december', 'final score_6': 'final score', 'w_7': 'w'}
|
{'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'december_5': [0], 'final score_6': [1], 'w_7': [1]}
|
['week', 'date', 'opponent', 'game site', 'final score', 'record', 'tv time', 'attendance']
|
[['1', 'september 1 , 1991', 'miami dolphins', 'rich stadium', 'w 35 - 31', '1 - 0', 'nbc 4:15 pm', '80252'], ['2', 'september 8 , 1991', 'pittsburgh steelers', 'rich stadium', 'w 52 - 34', '2 - 0', 'nbc 1:00 pm', '79545'], ['3', 'september 15 , 1991', 'new york jets', 'the meadowlands', 'w 23 - 20', '3 - 0', 'nbc 4:15 pm', '65309'], ['4', 'september 22 , 1991', 'tampa bay buccaneers', 'tampa stadium', 'w 17 - 10', '4 - 0', 'nbc 4:15 pm', '57323'], ['5', 'september 29 , 1991', 'chicago bears', 'rich stadium', 'w 35 - 20', '5 - 0', 'cbs 1:00 pm', '80366'], ['6', 'october 7 , 1991', 'kansas city chiefs', 'arrowhead stadium', 'l 6 - 33', '5 - 1', 'abc 9:00 pm', '76120'], ['7', 'october 13 , 1991', 'indianapolis colts', 'rich stadium', 'w 42 - 6', '6 - 1', 'nbc 1:00 pm', '79015'], ['8', 'october 21 , 1991', 'cincinnati bengals', 'rich stadium', 'w 35 - 16', '7 - 1', 'abc 9:00 pm', '80131'], ['9', '-', '-', '-', '-', '-', '-', ''], ['10', 'november 3 , 1991', 'new england patriots', 'rich stadium', 'w 22 - 17', '8 - 1', 'nbc 1:00 pm', '78278'], ['11', 'november 10 , 1991', 'green bay packers', 'milwaukee county stadium', 'w 34 - 24', '9 - 1', 'nbc 1:00 pm', '52175'], ['12', 'november 18 , 1991', 'miami dolphins', 'joe robbie stadium', 'w 41 - 27', '10 - 1', 'abc 9:00 pm', '71062'], ['13', 'november 24 , 1991', 'new england patriots', 'foxboro stadium', 'l 13 - 16', '10 - 2', 'nbc 1:00 pm', '47053'], ['14', 'december 1 , 1991', 'new york jets', 'rich stadium', 'w 24 - 13', '11 - 2', 'nbc 1:00 pm', '80243'], ['15', 'december 8 , 1991', 'los angeles raiders', 'los angeles memorial coliseum', 'w 30 - 27', '12 - 2', 'nbc 4:15 pm', '85081'], ['16', 'december 15 , 1991', 'indianapolis colts', 'hoosier dome', 'w 35 - 7', '13 - 2', 'espn 8:15 pm', '48286'], ['17', 'december 22 , 1991', 'detroit lions', 'rich stadium', 'l 14 - 17', '13 - 3', 'cbs 1:00 pm', '78059']]
|
list of members - elect of the united states house of representatives who never took their seats
|
https://en.wikipedia.org/wiki/List_of_members-elect_of_the_United_States_House_of_Representatives_who_never_took_their_seats
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14158567-1.html.csv
|
unique
|
john cantine was the only member-elect who was elected but decided to decline their office seat .
|
{'scope': 'all', 'row': '3', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': 'elected , but declined to take office', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'reason for non - seating', 'elected , but declined to take office'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose reason for non - seating record fuzzily matches to elected , but declined to take office .', 'tostr': 'filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } }', 'tointer': 'select the rows whose reason for non - seating record fuzzily matches to elected , but declined to take office . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'reason for non - seating', 'elected , but declined to take office'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose reason for non - seating record fuzzily matches to elected , but declined to take office .', 'tostr': 'filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office }'}, 'member - elect'], 'result': 'john cantine', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } ; member - elect }'}, 'john cantine'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } ; member - elect } ; john cantine }', 'tointer': 'the member - elect record of this unqiue row is john cantine .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } } ; eq { hop { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } ; member - elect } ; john cantine } } = true', 'tointer': 'select the rows whose reason for non - seating record fuzzily matches to elected , but declined to take office . there is only one such row in the table . the member - elect record of this unqiue row is john cantine .'}
|
and { only { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } } ; eq { hop { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } ; member - elect } ; john cantine } } = true
|
select the rows whose reason for non - seating record fuzzily matches to elected , but declined to take office . there is only one such row in the table . the member - elect record of this unqiue row is john cantine .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'reason for non - seating_7': 7, 'elected , but declined to take office_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'member - elect_9': 9, 'john cantine_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'reason for non - seating_7': 'reason for non - seating', 'elected , but declined to take office_8': 'elected , but declined to take office', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'member - elect_9': 'member - elect', 'john cantine_10': 'john cantine'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'reason for non - seating_7': [0], 'elected , but declined to take office_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'member - elect_9': [2], 'john cantine_10': [3]}
|
['member - elect', 'party', 'district', 'election date', 'congress', 'reason for non - seating']
|
[['augustus f allen', 'democratic', 'ny - 33', 'november 3 , 1874', '44th', 'died january 22 , 1875'], ['andrew j campbell', 'republican', 'ny - 10', 'november 5 , 1894', '54th', 'died december 6 , 1894'], ['john cantine', 'democratic - republican', 'ny - 7', 'april 27 to 29 , 1802', '8th', 'elected , but declined to take office'], ['william dowse', 'federalist', 'ny - 15', 'december 15 to 17 , 1812', '13th', 'died on february 18 , 1813'], ['richard p giles', 'democratic', 'mo - 1', 'november 3 , 1896', '55th', 'died november 17 , 1896'], ['samuel marx', 'democratic', 'ny - 19', 'november 7 , 1922', '68th', 'died november 30 , 1922'], ['washington poe', 'whig', 'ga - 3', 'november 5 , 1844', '29th', 'resigned before taking office'], ['jack swigert', 'republican', 'co - 6', 'november 2 , 1982', '98th', 'died before taking office']]
|
lark rise to candleford ( tv series )
|
https://en.wikipedia.org/wiki/Lark_Rise_to_Candleford_%28TV_series%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15026994-5.html.csv
|
aggregation
|
the average lark rise to candleford episode had a viewing figure of about 7.2 million people from episodes 1-5 .
|
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '7.2', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'viewing figure'], 'result': '7.2', 'ind': 0, 'tostr': 'avg { all_rows ; viewing figure }'}, '7.2'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; viewing figure } ; 7.2 } = true', 'tointer': 'the average of the viewing figure record of all rows is 7.2 .'}
|
round_eq { avg { all_rows ; viewing figure } ; 7.2 } = true
|
the average of the viewing figure record of all rows is 7.2 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'viewing figure_4': 4, '7.2_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'viewing figure_4': 'viewing figure', '7.2_5': '7.2'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'viewing figure_4': [0], '7.2_5': [1]}
|
['', 'episode', 'writer', 'director', 'original air date', 'viewing figure']
|
[['35', 'episode 1', 'bill gallagher', 'sue tully', '9 january 2011', '7.68 million'], ['36', 'episode 2', 'bill gallagher', 'sue tully', '16 january 2011', '7.31 million'], ['37', 'episode 3', 'bill gallagher', 'sue tully', '23 january 2011', '7.02 million'], ['38', 'episode 4', 'rachel bennette', 'patrick lau', '30 january 2011', '6.90 million'], ['39', 'episode 5', 'bill gallagher', 'sue tully', '6 february 2011', '6.96 million']]
|
2007 - 08 portland trail blazers season
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_Portland_Trail_Blazers_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11964047-9.html.csv
|
ordinal
|
the portland trail blazers ' game on march 4 recorded their highest attendance of the 2007 - 08 season .
|
{'row': '2', 'col': '6', 'order': '1', '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', 'attendance', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance ; 1 }'}, 'date'], 'result': 'march 4', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance ; 1 } ; date }'}, 'march 4'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; march 4 } = true', 'tointer': 'select the row whose attendance record of all rows is 1st maximum . the date record of this row is march 4 .'}
|
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; march 4 } = true
|
select the row whose attendance record of all rows is 1st maximum . the date record of this row is march 4 .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '1_6': 6, 'date_7': 7, 'march 4_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', '1_6': '1', 'date_7': 'date', 'march 4_8': 'march 4'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '1_6': [0], 'date_7': [1], 'march 4_8': [2]}
|
['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record', 'streak']
|
[['march 2', 'portland trail blazers', 'l 104 - 110', 'golden state warriors', 'jackson : 29', 'oracle arena 19596', '31 - 29', 'l1'], ['march 4', 'phoenix suns', 'l 97 - 92', 'portland trail blazers', 'roy : 25', 'rose garden 20595', '31 - 30', 'l2'], ['march 7', 'portland trail blazers', 'w 103 - 101', 'milwaukee bucks', 'aldridge : 29', 'bradley center 15537', '32 - 30', 'w1'], ['march 8', 'portland trail blazers', 'w 120 - 114 ot', 'new york knicks', 'robinson : 45', 'madison square garden 19763', '33 - 30', 'w2'], ['march 10', 'portland trail blazers', 'l 80 - 88', 'cleveland cavaliers', 'aldridge : 25', 'quicken loans arena 20213', '33 - 31', 'l1'], ['march 11', 'portland trail blazers', 'w 103 - 96', 'minnesota timberwolves', 'roy : 27', 'target center 13433', '34 - 31', 'w1'], ['march 13', 'portland trail blazers', 'l 85 - 96', 'sacramento kings', 'artest : 22', 'arco arena 13333', '34 - 32', 'l1'], ['march 15', 'minnesota timberwolves', 'w 96 - 107', 'portland trail blazers', 'aldridge : 26', 'rose garden 20079', '35 - 32', 'w1'], ['march 18', 'phoenix suns', 'l 111 - 98', 'portland trail blazers', 'aldridge : 31', 'rose garden 20580', '35 - 33', 'l1'], ['march 21', 'los angeles clippers', 'w 102 - 107', 'portland trail blazers', 'mobley : 24', 'rose garden 19980', '36 - 33', 'w1'], ['march 22', 'portland trail blazers', 'w 83 - 72', 'los angeles clippers', 'roy : 23', 'staples center 18248', '37 - 33', 'w2'], ['march 24', 'portland trail blazers', 'l 84 - 97', 'seattle supersonics', 'durant : 23', 'keyarena 11292', '37 - 34', 'l1'], ['march 25', 'washington wizards', 'w 82 - 102', 'portland trail blazers', 'webster : 23', 'rose garden 19980', '38 - 34', 'w1'], ['march 27', 'portland trail blazers', 'l 95 - 111', 'golden state warriors', 'jackson : 24', 'oracle arena 19732', '38 - 35', 'l1'], ['march 29', 'charlotte bobcats', 'l 93 - 85', 'portland trail blazers', 'outlaw : 26', 'rose garden 19980', '38 - 36', 'l2']]
|
yoji anjo
|
https://en.wikipedia.org/wiki/Yoji_Anjo
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17445522-3.html.csv
|
unique
|
yoji anjo 's match against gia chirragishvili is his only match that resulted in a draw .
|
{'scope': 'all', 'row': '2', 'col': '1', 'col_other': '3', 'criterion': 'equal', 'value': 'draw', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'res', 'draw'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose res record fuzzily matches to draw .', 'tostr': 'filter_eq { all_rows ; res ; draw }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; res ; draw } }', 'tointer': 'select the rows whose res record fuzzily matches to draw . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'res', 'draw'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose res record fuzzily matches to draw .', 'tostr': 'filter_eq { all_rows ; res ; draw }'}, 'opponent'], 'result': 'gia chirragishvili', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; res ; draw } ; opponent }'}, 'gia chirragishvili'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; res ; draw } ; opponent } ; gia chirragishvili }', 'tointer': 'the opponent record of this unqiue row is gia chirragishvili .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; res ; draw } } ; eq { hop { filter_eq { all_rows ; res ; draw } ; opponent } ; gia chirragishvili } } = true', 'tointer': 'select the rows whose res record fuzzily matches to draw . there is only one such row in the table . the opponent record of this unqiue row is gia chirragishvili .'}
|
and { only { filter_eq { all_rows ; res ; draw } } ; eq { hop { filter_eq { all_rows ; res ; draw } ; opponent } ; gia chirragishvili } } = true
|
select the rows whose res record fuzzily matches to draw . there is only one such row in the table . the opponent record of this unqiue row is gia chirragishvili .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'res_7': 7, 'draw_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'opponent_9': 9, 'gia chirragishvili_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'res_7': 'res', 'draw_8': 'draw', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'opponent_9': 'opponent', 'gia chirragishvili_10': 'gia chirragishvili'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'res_7': [0], 'draw_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'opponent_9': [2], 'gia chirragishvili_10': [3]}
|
['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location']
|
[['loss', '0 - 5 - 1', 'ryan gracie', 'submission ( armbar )', 'pride shockwave 2004', '1', '8:33', 'saitama , japan'], ['draw', '0 - 4 - 1', 'gia chirragishvili', 'draw', 'deep - 1st impact', '3', '5:00', 'nagoya , japan'], ['loss', '0 - 4', 'matt lindland', 'tko ( strikes )', 'ufc 29', '1', '2:58', 'tokyo , japan'], ['loss', '0 - 3', 'murilo bustamante', 'submission ( arm triangle choke )', 'ufc 25', '2', '0:31', 'tokyo , japan'], ['loss', '0 - 2', 'david abbott', 'decision', 'ufc japan', '1', '15:00', 'yokohama , japan'], ['loss', '0 - 1', 'sean alvarez', 'submission ( punches )', 'u - japan', '1', '34:26', 'japan']]
|
islands of the clyde
|
https://en.wikipedia.org/wiki/Islands_of_the_Clyde
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15252-1.html.csv
|
aggregation
|
on the islands of the clyde , the average height was 261.3 .
|
{'scope': 'all', 'col': '7', 'type': 'average', 'result': '261.3', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'height ( m )'], 'result': '261.3', 'ind': 0, 'tostr': 'avg { all_rows ; height ( m ) }'}, '261.3'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; height ( m ) } ; 261.3 } = true', 'tointer': 'the average of the height ( m ) record of all rows is 261.3 .'}
|
round_eq { avg { all_rows ; height ( m ) } ; 261.3 } = true
|
the average of the height ( m ) record of all rows is 261.3 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'height (m)_4': 4, '261.3_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'height (m)_4': 'height ( m )', '261.3_5': '261.3'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'height (m)_4': [0], '261.3_5': [1]}
|
['island', 'gaelic name', 'location', 'area ( ha )', 'population', 'last inhabited', 'height ( m )']
|
[['ailsa craig', 'creag ealasaid', 'south ayrshire', '99', '0', '1980s', '338'], ['arran', 'arainn', 'arran', '43201', '4629', '-', '874'], ['bute', 'bòid', 'bute', '12217', '6498', '-', '278'], ['davaar', 'eilean dà bhàrr', 'kintyre', '52', '0', '-', '115'], ['great cumbrae', 'cumaradh mòr', 'bute', '1168', '1376', '-', '127'], ['holy isle', 'eilean mo laise', 'arran', '253', '31', '-', '314'], ['inchmarnock', 'innis mheàrnaig', 'bute', '253', '0', '1980s', '60'], ['little cumbrae', 'cumaradh beag', 'bute', '313', '0', '1990s', '123'], ['sanda', 'àbhainn', 'kintyre', '127', '0', '-', '123']]
|
nrn
|
https://en.wikipedia.org/wiki/NRN
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1213811-1.html.csv
|
ordinal
|
the coffs harbour channel has the second earliest air date of all the channels listed .
|
{'row': '1', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'first air date', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; first air date ; 2 }'}, 'city'], 'result': 'coffs harbour', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; first air date ; 2 } ; city }'}, 'coffs harbour'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; first air date ; 2 } ; city } ; coffs harbour } = true', 'tointer': 'select the row whose first air date record of all rows is 2nd minimum . the city record of this row is coffs harbour .'}
|
eq { hop { nth_argmin { all_rows ; first air date ; 2 } ; city } ; coffs harbour } = true
|
select the row whose first air date record of all rows is 2nd minimum . the city record of this row is coffs harbour .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'first air date_5': 5, '2_6': 6, 'city_7': 7, 'coffs harbour_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 air date_5': 'first air date', '2_6': '2', 'city_7': 'city', 'coffs harbour_8': 'coffs harbour'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'first air date_5': [0], '2_6': [0], 'city_7': [1], 'coffs harbour_8': [2]}
|
['region served', 'city', 'channels ( analog / digital )', 'first air date', 'erp ( analog / digital )', 'haat ( analog / digital ) 1', 'transmitter location']
|
[['grafton / kempsey', 'coffs harbour', '11 ( vhf ) 3 38 ( uhf )', '23 january 1965', '250 kw 250 kw', '706 m 730 m', 'mount moombil'], ['manning river', 'taree', '65 ( uhf ) 3 44 ( uhf )', '31 december 1991', '600 kw 320 kw', '633 m 633 m', 'middle brother'], ['newcastle / hunter river', 'newcastle', '57 ( uhf ) 3 51 ( uhf )', '31 december 1991', '1200 kw 500 kw', '439 m 439 m', 'mount sugarloaf'], ['richmond and tweed 2', 'lismore', '8 ( vhf ) 3 32 ( uhf )', '12 may 1962', '200 kw 200 kw', '612 m 648 m', 'mount nardi'], ['upper namoi', 'tamworth', '34 ( uhf ) 3 40 ( uhf )', '31 december 1991', '600 kw 330 kw', '844 m 874 m', 'mount dowe']]
|
1948 vfl season
|
https://en.wikipedia.org/wiki/1948_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809529-10.html.csv
|
count
|
there were 6 game venues used during the 1948 vfl season .
|
{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '6', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'venue'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record is arbitrary .', 'tostr': 'filter_all { all_rows ; venue }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; venue } }', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; venue } } ; 6 } = true', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}
|
eq { count { filter_all { all_rows ; venue } } ; 6 } = true
|
select the rows whose venue record is arbitrary . the number of such rows is 6 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'venue_5': 5, '6_6': 6}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'venue_5': 'venue', '6_6': '6'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'venue_5': [0], '6_6': [2]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['north melbourne', '14.6 ( 90 )', 'richmond', '9.16 ( 70 )', 'arden street oval', '12000', '19 june 1948'], ['geelong', '21.13 ( 139 )', 'st kilda', '9.9 ( 63 )', 'kardinia park', '12300', '19 june 1948'], ['essendon', '18.12 ( 120 )', 'footscray', '4.14 ( 38 )', 'windy hill', '22000', '19 june 1948'], ['collingwood', '15.12 ( 102 )', 'fitzroy', '13.7 ( 85 )', 'victoria park', '38000', '19 june 1948'], ['melbourne', '18.12 ( 120 )', 'hawthorn', '11.13 ( 79 )', 'mcg', '12200', '19 june 1948'], ['south melbourne', '13.9 ( 87 )', 'carlton', '23.11 ( 149 )', 'lake oval', '24000', '19 june 1948']]
|
list of auto racing tracks in the united states
|
https://en.wikipedia.org/wiki/List_of_auto_racing_tracks_in_the_United_States
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14688681-4.html.csv
|
unique
|
the little valley speedway race track is the only figure eight race track in the united states with a clay surface .
|
{'scope': 'all', 'row': '5', 'col': '5', 'col_other': '1', '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 }'}, 'track'], 'result': 'little valley speedway', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; surface ; clay } ; track }'}, 'little valley speedway'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; surface ; clay } ; track } ; little valley speedway }', 'tointer': 'the track record of this unqiue row is little valley speedway .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; track } ; little valley speedway } } = true', 'tointer': 'select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the track record of this unqiue row is little valley speedway .'}
|
and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; track } ; little valley speedway } } = true
|
select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the track record of this unqiue row is little valley speedway .
|
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, 'track_9': 9, 'little valley speedway_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', 'track_9': 'track', 'little valley speedway_10': 'little valley speedway'}
|
{'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], 'track_9': [2], 'little valley speedway_10': [3]}
|
['track', 'city', 'state', 'opened ( closing date if defunct )', 'surface', 'length']
|
[['altamont motorsports park', 'tracy', 'california', '1966 - 2008', 'asphalt', 'miles ( km )'], ['evergreen speedway', 'monroe', 'washington', '1954', 'asphalt', 'miles ( km )'], ['holland speedway', 'holland', 'new york', '1960', 'concrete', 'miles ( km )'], ['indianapolis speedrome', 'indianapolis', 'indiana', '1945', 'asphalt', 'miles ( km )'], ['little valley speedway', 'little valley', 'new york', '1932 - 2011 ( figure 8 track )', 'clay', 'miles ( km )'], ['manzanita speedway', 'phoenix', 'arizona', '1951 - 2010', 'asphalt', 'miles ( km )'], ['riverhead raceway', 'riverhead', 'new york', '1951', 'asphalt', 'miles ( km )'], ['slinger speedway', 'slinger', 'wisconsin', '1974', 'asphalt', 'miles ( km )']]
|
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/1-17323042-6.html.csv
|
count
|
elton brand was the top scorer only in the game the 76ers played against washington .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'elton brand', 'result': '1', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high points', 'elton brand'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high points record fuzzily matches to elton brand .', 'tostr': 'filter_eq { all_rows ; high points ; elton brand }'}], 'result': '1', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; high points ; elton brand } }', 'tointer': 'select the rows whose high points record fuzzily matches to elton brand . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; high points ; elton brand } } ; 1 } = true', 'tointer': 'select the rows whose high points record fuzzily matches to elton brand . the number of such rows is 1 .'}
|
eq { count { filter_eq { all_rows ; high points ; elton brand } } ; 1 } = true
|
select the rows whose high points record fuzzily matches to elton brand . the number of such rows is 1 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'high points_5': 5, 'elton brand_6': 6, '1_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'high points_5': 'high points', 'elton brand_6': 'elton brand', '1_7': '1'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high points_5': [0], 'elton brand_6': [0], '1_7': [2]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['18', 'december 2', 'chicago', 'w 103 - 95 ( ot )', 'andre miller ( 28 )', 'elton brand ( 14 )', 'andre iguodala ( 5 )', 'united center 20485', '8 - 10'], ['20', 'december 5', 'detroit', 'w 96 - 91 ( ot )', 'andre miller ( 19 )', 'andre iguodala ( 8 )', 'andre iguodala ( 5 )', 'the palace of auburn hills 22076', '9 - 11'], ['21', 'december 6', 'new jersey', 'l 84 - 95 ( ot )', 'andre iguodala ( 20 )', 'andre iguodala ( 11 )', 'andre miller ( 5 )', 'wachovia center 13096', '9 - 12'], ['22', 'december 10', 'cleveland', 'l 93 - 101 ( ot )', 'andre iguodala ( 27 )', 'elton brand ( 10 )', 'andre miller ( 8 )', 'wachovia center 15550', '9 - 13'], ['23', 'december 12', 'cleveland', 'l 72 - 88 ( ot )', 'willie green ( 19 )', 'elton brand ( 11 )', 'andre miller ( 7 )', 'quicken loans arena 20562', '9 - 14'], ['24', 'december 13', 'washington', 'w 104 - 89 ( ot )', 'elton brand ( 27 )', 'samuel dalembert ( 17 )', 'andre miller ( 12 )', 'wachovia center 15865', '10 - 14'], ['25', 'december 17', 'milwaukee', 'w 93 - 88 ( ot )', 'louis williams ( 25 )', 'reggie evans ( 9 )', 'andre iguodala ( 7 )', 'wachovia center 11538', '11 - 14'], ['26', 'december 19', 'washington', 'w 109 - 103 ( ot )', 'louis williams ( 26 )', 'andre iguodala ( 9 )', 'andre miller ( 6 )', 'verizon center 18323', '12 - 14'], ['27', 'december 20', 'indiana', 'l 94 - 95 ( ot )', 'andre iguodala ( 26 )', 'samuel dalembert ( 14 )', 'andre miller ( 12 )', 'wachovia center 14599', '12 - 15'], ['28', 'december 23', 'boston', 'l 91 - 110 ( ot )', 'louis williams , marreese speights ( 16 )', 'samuel dalembert ( 13 )', 'louis williams , andre miller ( 8 )', 'td banknorth garden 18624', '12 - 16'], ['29', 'december 26', 'denver', 'l 101 - 105 ( ot )', 'andre iguodala ( 24 )', 'samuel dalembert ( 13 )', 'andre miller ( 8 )', 'pepsi center 19155', '12 - 17'], ['30', 'december 29', 'utah', 'l 95 - 112 ( ot )', 'andre iguodala , thaddeus young ( 17 )', 'reggie evans ( 12 )', 'andre miller ( 8 )', 'energysolutions arena 19911', '12 - 18']]
|
kstp - tv
|
https://en.wikipedia.org/wiki/KSTP-TV
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1406855-1.html.csv
|
unique
|
of all the channels , only channel 5.1 is run by kstp - tv .
|
{'scope': 'all', 'row': '1', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'kstp - tv', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'station', 'kstp - tv'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose station record fuzzily matches to kstp - tv .', 'tostr': 'filter_eq { all_rows ; station ; kstp - tv }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; station ; kstp - tv } }', 'tointer': 'select the rows whose station record fuzzily matches to kstp - tv . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'station', 'kstp - tv'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose station record fuzzily matches to kstp - tv .', 'tostr': 'filter_eq { all_rows ; station ; kstp - tv }'}, 'channel'], 'result': '5.1', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; station ; kstp - tv } ; channel }'}, '5.1'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; station ; kstp - tv } ; channel } ; 5.1 }', 'tointer': 'the channel record of this unqiue row is 5.1 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; station ; kstp - tv } } ; eq { hop { filter_eq { all_rows ; station ; kstp - tv } ; channel } ; 5.1 } } = true', 'tointer': 'select the rows whose station record fuzzily matches to kstp - tv . there is only one such row in the table . the channel record of this unqiue row is 5.1 .'}
|
and { only { filter_eq { all_rows ; station ; kstp - tv } } ; eq { hop { filter_eq { all_rows ; station ; kstp - tv } ; channel } ; 5.1 } } = true
|
select the rows whose station record fuzzily matches to kstp - tv . there is only one such row in the table . the channel record of this unqiue row is 5.1 .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'station_7': 7, 'kstp - tv_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'channel_9': 9, '5.1_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'station_7': 'station', 'kstp - tv_8': 'kstp - tv', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'channel_9': 'channel', '5.1_10': '5.1'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'station_7': [0], 'kstp - tv_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'channel_9': [2], '5.1_10': [3]}
|
['channel', 'station', 'video', 'aspect', 'psip short name', 'programming']
|
[['5.1', 'kstp - tv', '720p', '16:9', 'kstpdt1', 'main kstp - tv programming / abc'], ['5.2', 'kstc - tv', '720p', '16:9', 'kstcdt2', 'main kstc - tv programming'], ['5.3', 'kstc - tv', '480i', '16:9', 'kstcdt3', 'me - tv'], ['5.4', 'kstc - tv', '480i', '16:9', 'kstcdt4', 'antenna tv'], ['5.5', 'kstc - tv', '480i', '16:9', 'kstpdt2', 'live well network'], ['5.6', 'kstc - tv', '480i', '16:9', 'kstcdt6', 'this tv']]
|
list of german skeleton champions
|
https://en.wikipedia.org/wiki/List_of_German_skeleton_champions
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11775329-4.html.csv
|
superlative
|
kerstin jürgens - szymkowiak earned the most seconds out of any of these champions .
|
{'scope': 'all', 'col_superlative': '6', 'row_superlative': '2', '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', 'seconds'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; seconds }'}, 'name'], 'result': 'kerstin jürgens - szymkowiak', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; seconds } ; name }'}, 'kerstin jürgens - szymkowiak'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; seconds } ; name } ; kerstin jürgens - szymkowiak } = true', 'tointer': 'select the row whose seconds record of all rows is maximum . the name record of this row is kerstin jürgens - szymkowiak .'}
|
eq { hop { argmax { all_rows ; seconds } ; name } ; kerstin jürgens - szymkowiak } = true
|
select the row whose seconds record of all rows is maximum . the name record of this row is kerstin jürgens - szymkowiak .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'seconds_5': 5, 'name_6': 6, 'kerstin jürgens - szymkowiak_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'seconds_5': 'seconds', 'name_6': 'name', 'kerstin jürgens - szymkowiak_7': 'kerstin jürgens - szymkowiak'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'seconds_5': [0], 'name_6': [1], 'kerstin jürgens - szymkowiak_7': [2]}
|
['place', 'name', 'from', 'until', 'titles', 'seconds', 'thirds']
|
[['1', 'steffi hanzlik - jacob', '1997', '2004', '5', '3', '-'], ['2', 'kerstin jürgens - szymkowiak', '2003', '2011', '3', '4', '-'], ['3', 'diana sartor', '1996', '2005', '3', '1', '2'], ['4', 'anja huber', '2006', '2008', '3', '1', '-'], ['5', 'monique riekewald', '1996', '2005', '1', '2', '4'], ['6', 'katharina heinz', '2009', '2010', '1', '1', '-'], ['7', 'kathleen lorenz', '2010', '2010', '1', '-', '-'], ['8', 'ramona rahnis', '1996', '1998', '-', '2', '1'], ['9', 'marion trott - thees', '2009', '2011', '-', '2', '1'], ['10', 'julia eichhorn', '2007', '2009', '-', '-', '2'], ['10', 'sophia griebel', '2010', '2011', '-', '-', '2'], ['10', 'annett köhler', '2001', '2002', '-', '-', '2'], ['13', 'kati klinzing', '2006', '2006', '-', '-', '1'], ['13', 'melanie riedl', '2000', '2000', '-', '-', '1']]
|
2006 connecticut sun season
|
https://en.wikipedia.org/wiki/2006_Connecticut_Sun_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18894744-5.html.csv
|
superlative
|
the game against new york had the highest attendance with 10180 .
|
{'scope': 'all', 'col_superlative': '8', 'row_superlative': '3', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '3', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'location / attendance'], 'result': 'madison square garden 10180', 'ind': 0, 'tostr': 'max { all_rows ; location / attendance }', 'tointer': 'the maximum location / attendance record of all rows is madison square garden 10180 .'}, 'madison square garden 10180'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; location / attendance } ; madison square garden 10180 }', 'tointer': 'the maximum location / attendance record of all rows is madison square garden 10180 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'location / attendance'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; location / attendance }'}, 'opponent'], 'result': 'new york', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; location / attendance } ; opponent }'}, 'new york'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; location / attendance } ; opponent } ; new york }', 'tointer': 'the opponent record of the row with superlative location / attendance record is new york .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; location / attendance } ; madison square garden 10180 } ; eq { hop { argmax { all_rows ; location / attendance } ; opponent } ; new york } } = true', 'tointer': 'the maximum location / attendance record of all rows is madison square garden 10180 . the opponent record of the row with superlative location / attendance record is new york .'}
|
and { eq { max { all_rows ; location / attendance } ; madison square garden 10180 } ; eq { hop { argmax { all_rows ; location / attendance } ; opponent } ; new york } } = true
|
the maximum location / attendance record of all rows is madison square garden 10180 . the opponent record of the row with superlative location / attendance record is new york .
|
6
|
6
|
{'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'location / attendance_8': 8, 'madison square garden 10180_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'location / attendance_11': 11, 'opponent_12': 12, 'new york_13': 13}
|
{'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'location / attendance_8': 'location / attendance', 'madison square garden 10180_9': 'madison square garden 10180', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'location / attendance_11': 'location / attendance', 'opponent_12': 'opponent', 'new york_13': 'new york'}
|
{'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'location / attendance_8': [0], 'madison square garden 10180_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'location / attendance_11': [2], 'opponent_12': [3], 'new york_13': [4]}
|
['game', 'date', 'opponent', 'score', 'high points', 'high rebounds', 'high assists', 'location / attendance', 'record']
|
[['4', 'june 1', 'charlotte', 'w 89 - 65', 'sales ( 18 )', 'dydek ( 15 )', 'douglas ( 5 )', 'charlotte bobcats arena 3632', '3 - 1'], ['5', 'june 3', 'charlotte', 'w 89 - 71', 'dydek ( 17 )', 'mcwilliams - franklin ( 15 )', 'sales ( 5 )', 'mohegan sun arena 7318', '4 - 1'], ['6', 'june 7', 'new york', 'w 75 - 60', 'douglas ( 17 )', 'mcwilliams - franklin ( 7 )', 'sales , phillips ( 4 )', 'madison square garden 10180', '5 - 1'], ['7', 'june 9', 'seattle', 'w 85 - 81', 'douglas ( 18 )', 'dydek ( 12 )', 'whalen ( 9 )', 'mohegan sun arena 7138', '6 - 1'], ['8', 'june 13', 'washington', 'w 85 - 71', 'douglas ( 26 )', 'douglas ( 7 )', 'whalen , jones ( 4 )', 'mohegan sun arena 6339', '7 - 1'], ['9', 'june 16', 'phoenix', 'l 86 - 91', 'douglas ( 27 )', 'mcwilliams - franklin ( 17 )', 'whalen ( 4 )', 'us airways center 6378', '7 - 2'], ['10', 'june 17', 'los angeles', 'l 70 - 82', 'jones ( 16 )', 'sales , dydek , jones ( 5 )', 'whalen ( 5 )', 'staples center 7991', '7 - 3'], ['11', 'june 20', 'charlotte', 'w 90 - 66', 'sales ( 15 )', 'mcwilliams - franklin ( 9 )', 'whalen ( 6 )', 'charlotte bobcats arena 4243', '8 - 3'], ['12', 'june 22', 'minnesota', 'w 79 - 62', 'whalen , dydek ( 16 )', 'jones ( 11 )', 'sales ( 4 )', 'mohegan sun arena 6573', '9 - 3'], ['13', 'june 23', 'chicago', 'w 84 - 79', 'sales ( 23 )', 'mcwilliams - franklin ( 14 )', 'whalen ( 6 )', 'uic pavilion 2818', '10 - 3'], ['14', 'june 25', 'washington', 'l 80 - 87', 'mcwilliams - franklin , sales , jones ( 15 )', 'mcwilliams - franklin ( 11 )', 'whalen ( 6 )', 'mci center 7216', '10 - 4'], ['15', 'june 27', 'houston', 'w 73 - 57', 'sales ( 19 )', 'dydek ( 13 )', 'whalen ( 6 )', 'mohegan sun arena 6220', '11 - 4']]
|
1992 - 93 vancouver canucks season
|
https://en.wikipedia.org/wiki/1992%E2%80%9393_Vancouver_Canucks_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11128774-7.html.csv
|
superlative
|
the game played on march 14 drew the highest crowd attendance in the 1992 - 93 vancouver canucks season .
|
{'scope': 'all', 'col_superlative': '6', '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', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'date'], 'result': 'march 14', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; date }'}, 'march 14'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; date } ; march 14 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the date record of this row is march 14 .'}
|
eq { hop { argmax { all_rows ; attendance } ; date } ; march 14 } = true
|
select the row whose attendance record of all rows is maximum . the date record of this row is march 14 .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'date_6': 6, 'march 14_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'date_6': 'date', 'march 14_7': 'march 14'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'date_6': [1], 'march 14_7': [2]}
|
['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record']
|
[['march 1', 'vancouver', '5 - 2', 'buffalo', 'whitmore', '17098', '36 - 19 - 8'], ['march 2', 'vancouver', '3 - 3', 'washington', 'mclean', '12263', '36 - 19 - 9'], ['march 4', 'vancouver', '3 - 4', 'boston', 'whitmore', '13982', '36 - 20 - 9'], ['march 6', 'vancouver', '1 - 5', 'hartford', 'mclean', '12048', '36 - 21 - 9'], ['march 9', 'new jersey', '2 - 7', 'vancouver', 'mclean', '15822', '37 - 21 - 9'], ['march 11', 'minnesota', '4 - 3', 'vancouver', 'whitmore', '12006', '37 - 22 - 9'], ['march 12', 'vancouver', '3 - 2', 'winnipeg', 'mclean', '15567', '38 - 22 - 9'], ['march 14', 'vancouver', '2 - 3', 'calgary', 'mclean', '20214', '38 - 23 - 9'], ['march 18', 'winnipeg', '5 - 2', 'vancouver', 'mclean', '16150', '38 - 24 - 9'], ['march 20', 'ny islanders', '7 - 2', 'vancouver', 'whitmore', '16150', '38 - 25 - 9'], ['march 22', 'st louis', '3 - 1', 'vancouver', 'mclean', '15871', '38 - 26 - 9'], ['march 24', 'los angeles', '2 - 6', 'vancouver', 'mclean', '16150', '39 - 26 - 9'], ['march 26', 'calgary', '3 - 1', 'vancouver', 'mclean', '16150', '39 - 27 - 9'], ['march 30', 'vancouver', '6 - 3', 'st louis', 'mclean', '17573', '40 - 27 - 9']]
|
1956 - 57 new york rangers season
|
https://en.wikipedia.org/wiki/1956%E2%80%9357_New_York_Rangers_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17323267-7.html.csv
|
comparative
|
more goals were scored in game 69 against the boston bruins than in game 66 against the boston bruins .
|
{'row_1': '9', 'row_2': '6', 'col': '4', 'col_other': '1,3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'and', 'args': [{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'game', '69'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose game record fuzzily matches to 69 .', 'tostr': 'filter_eq { all_rows ; game ; 69 }'}, 'score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; game ; 69 } ; score }', 'tointer': 'select the rows whose game record fuzzily matches to 69 . take the score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'game', '66'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose game record fuzzily matches to 66 .', 'tostr': 'filter_eq { all_rows ; game ; 66 }'}, 'score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; game ; 66 } ; score }', 'tointer': 'select the rows whose game record fuzzily matches to 66 . take the score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; game ; 69 } ; score } ; hop { filter_eq { all_rows ; game ; 66 } ; score } }', 'tointer': 'select the rows whose game record fuzzily matches to 69 . take the score record of this row . select the rows whose game record fuzzily matches to 66 . take the score record of this row . the first record is greater than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'game', '69'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose game record fuzzily matches to 69 .', 'tostr': 'filter_eq { all_rows ; game ; 69 }'}, 'opponent'], 'result': 'boston bruins', 'ind': 5, 'tostr': 'hop { filter_eq { all_rows ; game ; 69 } ; opponent }'}, 'boston bruins'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; game ; 69 } ; opponent } ; boston bruins }', 'tointer': 'the opponent record of the first row is boston bruins .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'game', '66'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose game record fuzzily matches to 66 .', 'tostr': 'filter_eq { all_rows ; game ; 66 }'}, 'opponent'], 'result': 'boston bruins', 'ind': 7, 'tostr': 'hop { filter_eq { all_rows ; game ; 66 } ; opponent }'}, 'boston bruins'], 'result': True, 'ind': 8, 'tostr': 'eq { hop { filter_eq { all_rows ; game ; 66 } ; opponent } ; boston bruins }', 'tointer': 'the opponent record of the second row is boston bruins .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { hop { filter_eq { all_rows ; game ; 69 } ; opponent } ; boston bruins } ; eq { hop { filter_eq { all_rows ; game ; 66 } ; opponent } ; boston bruins } }', 'tointer': 'the opponent record of the first row is boston bruins . the opponent record of the second row is boston bruins .'}], 'result': True, 'ind': 10, 'tostr': 'and { greater { hop { filter_eq { all_rows ; game ; 69 } ; score } ; hop { filter_eq { all_rows ; game ; 66 } ; score } } ; and { eq { hop { filter_eq { all_rows ; game ; 69 } ; opponent } ; boston bruins } ; eq { hop { filter_eq { all_rows ; game ; 66 } ; opponent } ; boston bruins } } } = true', 'tointer': 'select the rows whose game record fuzzily matches to 69 . take the score record of this row . select the rows whose game record fuzzily matches to 66 . take the score record of this row . the first record is greater than the second record . the opponent record of the first row is boston bruins . the opponent record of the second row is boston bruins .'}
|
and { greater { hop { filter_eq { all_rows ; game ; 69 } ; score } ; hop { filter_eq { all_rows ; game ; 66 } ; score } } ; and { eq { hop { filter_eq { all_rows ; game ; 69 } ; opponent } ; boston bruins } ; eq { hop { filter_eq { all_rows ; game ; 66 } ; opponent } ; boston bruins } } } = true
|
select the rows whose game record fuzzily matches to 69 . take the score record of this row . select the rows whose game record fuzzily matches to 66 . take the score record of this row . the first record is greater than the second record . the opponent record of the first row is boston bruins . the opponent record of the second row is boston bruins .
|
13
|
11
|
{'and_10': 10, 'result_11': 11, 'greater_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_12': 12, 'game_13': 13, '69_14': 14, 'score_15': 15, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_16': 16, 'game_17': 17, '66_18': 18, 'score_19': 19, 'and_9': 9, 'str_eq_6': 6, 'str_hop_5': 5, 'opponent_20': 20, 'boston bruins_21': 21, 'str_eq_8': 8, 'str_hop_7': 7, 'opponent_22': 22, 'boston bruins_23': 23}
|
{'and_10': 'and', 'result_11': 'true', 'greater_4': 'greater', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_12': 'all_rows', 'game_13': 'game', '69_14': '69', 'score_15': 'score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_16': 'all_rows', 'game_17': 'game', '66_18': '66', 'score_19': 'score', 'and_9': 'and', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'opponent_20': 'opponent', 'boston bruins_21': 'boston bruins', 'str_eq_8': 'str_eq', 'str_hop_7': 'str_hop', 'opponent_22': 'opponent', 'boston bruins_23': 'boston bruins'}
|
{'and_10': [11], 'result_11': [], 'greater_4': [10], 'str_hop_2': [4], 'filter_str_eq_0': [2, 5], 'all_rows_12': [0], 'game_13': [0], '69_14': [0], 'score_15': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3, 7], 'all_rows_16': [1], 'game_17': [1], '66_18': [1], 'score_19': [3], 'and_9': [10], 'str_eq_6': [9], 'str_hop_5': [6], 'opponent_20': [5], 'boston bruins_21': [6], 'str_eq_8': [9], 'str_hop_7': [8], 'opponent_22': [7], 'boston bruins_23': [8]}
|
['game', 'march', 'opponent', 'score', 'record']
|
[['61', '2', 'boston bruins', '3 - 2', '23 - 27 - 11'], ['62', '3', 'detroit red wings', '1 - 1', '23 - 27 - 12'], ['63', '7', 'chicago black hawks', '2 - 2', '23 - 27 - 13'], ['64', '9', 'toronto maple leafs', '2 - 1', '24 - 27 - 13'], ['65', '10', 'detroit red wings', '4 - 1', '25 - 27 - 13'], ['66', '13', 'boston bruins', '2 - 1', '25 - 28 - 13'], ['67', '16', 'toronto maple leafs', '14 - 1', '25 - 29 - 13'], ['68', '17', 'toronto maple leafs', '5 - 3', '25 - 30 - 13'], ['69', '23', 'boston bruins', '4 - 2', '26 - 30 - 13'], ['70', '24', 'chicago black hawks', '4 - 4', '26 - 30 - 14']]
|
football records in spain
|
https://en.wikipedia.org/wiki/Football_records_in_Spain
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17937080-1.html.csv
|
superlative
|
for football records in spain , the highest number of points in 2009 was for the club in barcelona .
|
{'scope': 'subset', 'col_superlative': '4', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2,3', 'subset': {'col': '3', 'criterion': 'fuzzily_match', 'value': '2009'}}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2009'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; season ; 2009 }', 'tointer': 'select the rows whose season record fuzzily matches to 2009 .'}, 'points'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_eq { all_rows ; season ; 2009 } ; points }'}, 'club'], 'result': 'barcelona', 'ind': 2, 'tostr': 'hop { argmax { filter_eq { all_rows ; season ; 2009 } ; points } ; club }'}, 'barcelona'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; season ; 2009 } ; points } ; club } ; barcelona } = true', 'tointer': 'select the rows whose season record fuzzily matches to 2009 . select the row whose points record of these rows is maximum . the club record of this row is barcelona .'}
|
eq { hop { argmax { filter_eq { all_rows ; season ; 2009 } ; points } ; club } ; barcelona } = true
|
select the rows whose season record fuzzily matches to 2009 . select the row whose points record of these rows is maximum . the club record of this row is barcelona .
|
4
|
4
|
{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'season_6': 6, '2009_7': 7, 'points_8': 8, 'club_9': 9, 'barcelona_10': 10}
|
{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmax_1': 'argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'season_6': 'season', '2009_7': '2009', 'points_8': 'points', 'club_9': 'club', 'barcelona_10': 'barcelona'}
|
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'season_6': [0], '2009_7': [0], 'points_8': [1], 'club_9': [2], 'barcelona_10': [3]}
|
['rank', 'club', 'season', 'points', 'apps']
|
[['1', 'real madrid', '2011 / 12', '100', '38'], ['1', 'barcelona', '2012 / 13', '100', '38'], ['3', 'barcelona', '2009 / 10', '99', '38'], ['4', 'real madrid', '2009 / 10', '96', '38'], ['4', 'barcelona', '2010 / 11', '96', '38'], ['6', 'real madrid', '2010 / 11', '92', '38'], ['7', 'real madrid', '1996 / 97', '92', '42'], ['8', 'barcelona', '2011 / 12', '91', '38'], ['9', 'barcelona', '1996 / 97', '90', '42']]
|
1960 - 61 primeira divisão
|
https://en.wikipedia.org/wiki/1960%E2%80%9361_Primeira_Divis%C3%A3o
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17933459-2.html.csv
|
majority
|
in the 1960 - 61 primeira divisão , for the clubs that have 27 seasons at this level , the majority of the settlements are lisbon .
|
{'scope': 'subset', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'lisbon', 'subset': {'col': '2', 'criterion': 'equal', 'value': '27 seasons'}}
|
{'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'seasons at this level', '27 seasons'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; seasons at this level ; 27 seasons }', 'tointer': 'select the rows whose seasons at this level record fuzzily matches to 27 seasons .'}, 'settlements', 'lisbon'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose seasons at this level record fuzzily matches to 27 seasons . for the settlements records of these rows , most of them fuzzily match to lisbon .', 'tostr': 'most_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; lisbon } = true'}
|
most_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; lisbon } = true
|
select the rows whose seasons at this level record fuzzily matches to 27 seasons . for the settlements records of these rows , most of them fuzzily match to lisbon .
|
2
|
2
|
{'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'seasons at this level_4': 4, '27 seasons_5': 5, 'settlements_6': 6, 'lisbon_7': 7}
|
{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'seasons at this level_4': 'seasons at this level', '27 seasons_5': '27 seasons', 'settlements_6': 'settlements', 'lisbon_7': 'lisbon'}
|
{'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'seasons at this level_4': [0], '27 seasons_5': [0], 'settlements_6': [1], 'lisbon_7': [1]}
|
['clubs', 'seasons at this level', 'settlements', 'season joined league', 'position in 1959 - 1960']
|
[['benfica', '27 seasons', 'lisbon', '1934 - 1935', '1'], ['sporting cp', '27 seasons', 'lisbon', '1934 - 1935', '2'], ['belenenses', '27 seasons', 'lisbon', '1934 - 1935', '3'], ['porto', '27 seasons', 'porto', '1934 - 1935', '4'], ['académica de coimbra', '26 seasons', 'coimbra', '1949 - 1950', '6'], ['vitória de guimarães', '17 seasons', 'guimarães', '1958 - 1959', '7'], ['atlético cp', '15 seasons', 'lisbon', '1959 - 1960', '11'], ['barreirense', '14 seasons', 'barreiro', '1960 - 1961', 'segunda divisão'], ['sporting de braga', '13 seasons', 'braga', '1957 - 1958', '12'], ['sporting da covilhã', '12 seasons', 'covilhã', '1958 - 1959', '9'], ['lusitano de évora', '9 seasons', 'évora', '1952 - 1953', '10'], ['cuf barreiro', '8 seasons', 'barreiro', '1954 - 1955', '5'], ['salgueiros', '5 seasons', 'porto', '1960 - 1961', 'segunda divisão'], ['leixões', '4 seasons', 'matosinhos', '1959 - 1960', '8']]
|
us junior open squash championship
|
https://en.wikipedia.org/wiki/US_Junior_Open_squash_championship
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26368963-2.html.csv
|
majority
|
most of the under 11 championships were not played .
|
{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'not played', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'under - 11', 'not played'], 'result': True, 'ind': 0, 'tointer': 'for the under - 11 records of all rows , most of them fuzzily match to not played .', 'tostr': 'most_eq { all_rows ; under - 11 ; not played } = true'}
|
most_eq { all_rows ; under - 11 ; not played } = true
|
for the under - 11 records of all rows , most of them fuzzily match to not played .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'under - 11_3': 3, 'not played_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'under - 11_3': 'under - 11', 'not played_4': 'not played'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'under - 11_3': [0], 'not played_4': [0]}
|
['year', 'under - 11', 'under - 13', 'under - 15', 'under - 17', 'under - 19']
|
[['1999', 'unknown', 'unknown', 'unknown', 'jacqui inward', 'leong siu lynn'], ['2000', 'was not played', 'emery maine', 'lily lorentzen', 'lauren mccrery', 'michelle quibell'], ['2001', 'was not played', 'emily park', 'alisha turner', 'jennifer blumberg', 'ruchika kumar'], ['2002', 'was not played', 'emily park', 'rebecca dudley', 'britt hebden', 'emma beddoes'], ['2003', 'was not played', 'laura gemmell', 'emily park', 'neha kumar', 'lily lorentzen'], ['2004', 'was not played', 'olivia blatchford', 'emily park', 'kristen lange', 'jenna gates'], ['2005', 'was not played', 'skyler bouchard', 'laura gemmell', 'emily park', 'neha kumar'], ['2006', 'was not played', 'amy smedira', 'vidya rajan', 'laura gemmel', 'kristen lange'], ['2007', 'reeham sedky', 'claudia regio', 'amanda sobhy', 'salma nassar', 'laura gemmell'], ['2008', 'helen teegan', 'reeham sedky', 'maria elena ubina', 'amanda sobhy', 'laura gemmell'], ['2009', 'elena wagenmans', 'reeham sedky', 'olivia fiechter', 'maria elena ubina', 'olivia blatchford'], ['2010', 'elena wagenmans', 'helen teegan', 'reeham sedky', 'olivia fiechter', 'amanda sobhy'], ['2011', 'jamila abul enin', 'malak fayed', 'reeham sedky', 'sue ann yong', 'maria elena ubina']]
|
christian population growth
|
https://en.wikipedia.org/wiki/Christian_population_growth
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28137918-5.html.csv
|
majority
|
according to the christian population growth statistics , the majority of religions that have positive number of new adherents per year ( above 0 ) have a growth rate above 1.00 % .
|
{'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '1.00 %', 'subset': {'col': '4', 'criterion': 'greater_than', 'value': '0'}}
|
{'func': 'most_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'new adherents per year', '0'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; new adherents per year ; 0 }', 'tointer': 'select the rows whose new adherents per year record is greater than 0 .'}, 'growth rate', '1.00 %'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose new adherents per year record is greater than 0 . for the growth rate records of these rows , most of them are greater than 1.00 % .', 'tostr': 'most_greater { filter_greater { all_rows ; new adherents per year ; 0 } ; growth rate ; 1.00 % } = true'}
|
most_greater { filter_greater { all_rows ; new adherents per year ; 0 } ; growth rate ; 1.00 % } = true
|
select the rows whose new adherents per year record is greater than 0 . for the growth rate records of these rows , most of them are greater than 1.00 % .
|
2
|
2
|
{'most_greater_1': 1, 'result_2': 2, 'filter_greater_0': 0, 'all_rows_3': 3, 'new adherents per year_4': 4, '0_5': 5, 'growth rate_6': 6, '1.00%_7': 7}
|
{'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'new adherents per year_4': 'new adherents per year', '0_5': '0', 'growth rate_6': 'growth rate', '1.00%_7': '1.00 %'}
|
{'most_greater_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'new adherents per year_4': [0], '0_5': [0], 'growth rate_6': [1], '1.00%_7': [1]}
|
['religion', 'births', 'conversions', 'new adherents per year', 'growth rate']
|
[['christianity', '22708799', '2501396', '25210195', '1.56 %'], ['islam', '21951118', '865558', '22588676', '1.84 %'], ['hinduism', '13194111', '- 660377', '12533734', '1.69 %'], ['buddhism', '3530918', '156609', '3687527', '1.09 %'], ['sikhism', '363677', '28961', '392638', '1.87 %'], ['judaism', '194962', '70447', '124515', '0.91 %'], ["bahá ' í", '117158', '26333', '143491', '2.28 %'], ['confucianism', '55739', '11434', '44305', '0.73 %'], ['jainism', '74539', '39588', '34951', '0.87 %'], ['shinto', '8534', '40527', '- 31993', '1.09 %'], ['taoism', '25397', '155', '25242', '1.00 %'], ['zoroastrianism', '45391', '13080', '58471', '2.65 %']]
|
1935 vfl season
|
https://en.wikipedia.org/wiki/1935_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10790651-8.html.csv
|
majority
|
the majority of games in the 1935 vfl seaosn drew over 10000 in crowd attendance .
|
{'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10000', 'subset': None}
|
{'func': 'most_greater', 'args': ['all_rows', 'crowd', '10000'], 'result': True, 'ind': 0, 'tointer': 'for the crowd records of all rows , most of them are greater than 10000 .', 'tostr': 'most_greater { all_rows ; crowd ; 10000 } = true'}
|
most_greater { all_rows ; crowd ; 10000 } = true
|
for the crowd records of all rows , most of them are greater than 10000 .
|
1
|
1
|
{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'crowd_3': 3, '10000_4': 4}
|
{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'crowd_3': 'crowd', '10000_4': '10000'}
|
{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'crowd_3': [0], '10000_4': [0]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['hawthorn', '9.23 ( 77 )', 'south melbourne', '17.14 ( 116 )', 'glenferrie oval', '12000', '15 june 1935'], ['geelong', '13.12 ( 90 )', 'richmond', '10.16 ( 76 )', 'corio oval', '13000', '15 june 1935'], ['essendon', '9.7 ( 61 )', 'fitzroy', '14.19 ( 103 )', 'windy hill', '17000', '15 june 1935'], ['collingwood', '16.30 ( 126 )', 'north melbourne', '6.10 ( 46 )', 'victoria park', '8000', '15 june 1935'], ['st kilda', '14.10 ( 94 )', 'footscray', '7.13 ( 55 )', 'junction oval', '20000', '15 june 1935'], ['melbourne', '7.12 ( 54 )', 'carlton', '9.12 ( 66 )', 'mcg', '19546', '15 june 1935']]
|
1946 in brazilian football
|
https://en.wikipedia.org/wiki/1946_in_Brazilian_football
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15319684-1.html.csv
|
unique
|
in the 1946 campeonato paulista , sao paulo were the only team unbeaten .
|
{'scope': 'all', 'row': '1', 'col': '7', 'col_other': '2', 'criterion': 'equal', 'value': '0', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'lost', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose lost record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; lost ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; lost ; 0 } }', 'tointer': 'select the rows whose lost record is equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'lost', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose lost record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; lost ; 0 }'}, 'team'], 'result': 'são paulo', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; lost ; 0 } ; team }'}, 'são paulo'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; lost ; 0 } ; team } ; são paulo }', 'tointer': 'the team record of this unqiue row is são paulo .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; lost ; 0 } } ; eq { hop { filter_eq { all_rows ; lost ; 0 } ; team } ; são paulo } } = true', 'tointer': 'select the rows whose lost record is equal to 0 . there is only one such row in the table . the team record of this unqiue row is são paulo .'}
|
and { only { filter_eq { all_rows ; lost ; 0 } } ; eq { hop { filter_eq { all_rows ; lost ; 0 } ; team } ; são paulo } } = true
|
select the rows whose lost record is equal to 0 . there is only one such row in the table . the team record of this unqiue row is são paulo .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'lost_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'são paulo_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'lost_7': 'lost', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'são paulo_10': 'são paulo'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'lost_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'são paulo_10': [3]}
|
['position', 'team', 'points', 'played', 'won', 'drawn', 'lost', 'for', 'against', 'difference']
|
[['1', 'são paulo', '37', '20', '17', '3', '0', '62', '20', '42'], ['2', 'corinthians', '36', '20', '18', '0', '2', '62', '29', '33'], ['3', 'portuguesa', '28', '20', '13', '2', '5', '46', '20', '26'], ['4', 'santos', '22', '20', '9', '4', '7', '37', '32', '5'], ['5', 'palmeiras', '20', '20', '8', '4', '8', '37', '31', '6'], ['6', 'portuguesa santista', '17', '20', '7', '3', '10', '41', '51', '- 10'], ['7', 'ypiranga - sp', '14', '20', '6', '2', '12', '35', '48', '- 13'], ['8', 'comercial - sp', '14', '20', '4', '6', '10', '38', '55', '- 17'], ['9', 'são paulo railway', '12', '20', '5', '2', '13', '27', '46', '- 19'], ['10', 'juventus', '11', '20', '4', '3', '13', '32', '60', '- 28']]
|
1981 open championship
|
https://en.wikipedia.org/wiki/1981_Open_Championship
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18169093-6.html.csv
|
superlative
|
in the 1981 open championship , the biggest prize money won by a player not from united states took bernhard langer .
|
{'scope': 'subset', 'col_superlative': '6', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2,3', 'subset': {'col': '3', 'criterion': 'not_equal', 'value': 'united states'}}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_not_eq { all_rows ; country ; united states }', 'tointer': 'select the rows whose country record does not match to united states .'}, 'money'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_not_eq { all_rows ; country ; united states } ; money }'}, 'player'], 'result': 'bernhard langer', 'ind': 2, 'tostr': 'hop { argmax { filter_not_eq { all_rows ; country ; united states } ; money } ; player }'}, 'bernhard langer'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmax { filter_not_eq { all_rows ; country ; united states } ; money } ; player } ; bernhard langer } = true', 'tointer': 'select the rows whose country record does not match to united states . select the row whose money record of these rows is maximum . the player record of this row is bernhard langer .'}
|
eq { hop { argmax { filter_not_eq { all_rows ; country ; united states } ; money } ; player } ; bernhard langer } = true
|
select the rows whose country record does not match to united states . select the row whose money record of these rows is maximum . the player record of this row is bernhard langer .
|
4
|
4
|
{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmax_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_5': 5, 'country_6': 6, 'united states_7': 7, 'money_8': 8, 'player_9': 9, 'bernhard langer_10': 10}
|
{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmax_1': 'argmax', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_5': 'all_rows', 'country_6': 'country', 'united states_7': 'united states', 'money_8': 'money', 'player_9': 'player', 'bernhard langer_10': 'bernhard langer'}
|
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_str_not_eq_0': [1], 'all_rows_5': [0], 'country_6': [0], 'united states_7': [0], 'money_8': [1], 'player_9': [2], 'bernhard langer_10': [3]}
|
['place', 'player', 'country', 'score', 'to par', 'money']
|
[['1', 'bill rogers', 'united states', '72 + 66 + 67 + 71 = 276', '- 4', '25000'], ['2', 'bernhard langer', 'west germany', '73 + 67 + 70 + 70 = 280', 'e', '17500'], ['t3', 'raymond floyd', 'united states', '74 + 70 + 69 + 70 = 283', '+ 3', '11750'], ['t3', 'mark james', 'england', '72 + 70 + 68 + 73 = 283', '+ 3', '11750'], ['5', 'sam torrance', 'scotland', '72 + 69 + 73 + 70 = 284', '+ 4', '8500'], ['t6', 'bruce lietzke', 'united states', '76 + 69 + 71 + 69 = 285', '+ 5', '7750'], ['t6', 'manuel piñero', 'spain', '73 + 74 + 68 + 70 = 285', '+ 5', '7750'], ['t8', 'howard clark', 'england', '72 + 76 + 70 + 68 = 286', '+ 6', '6500'], ['t8', 'ben crenshaw', 'united states', '72 + 67 + 76 + 71 = 286', '+ 6', '6500'], ['t8', 'brian jones', 'australia', '73 + 76 + 66 + 71 = 286', '+ 6', '6500']]
|
anton putsila
|
https://en.wikipedia.org/wiki/Anton_Putsila
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16375026-1.html.csv
|
ordinal
|
the second time anton putsila scored a goal in a friendly competition , the game ended with a tied result .
|
{'scope': 'subset', 'row': '2', 'col': '1', 'order': '2', 'col_other': '4', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'friendly'}}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', 'friendly'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; competition ; friendly }', 'tointer': 'select the rows whose competition record fuzzily matches to friendly .'}, 'date', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; competition ; friendly } ; date ; 2 }'}, 'result'], 'result': '2 - 2', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; competition ; friendly } ; date ; 2 } ; result }'}, '2 - 2'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; competition ; friendly } ; date ; 2 } ; result } ; 2 - 2 } = true', 'tointer': 'select the rows whose competition record fuzzily matches to friendly . select the row whose date record of these rows is 2nd minimum . the result record of this row is 2 - 2 .'}
|
eq { hop { nth_argmin { filter_eq { all_rows ; competition ; friendly } ; date ; 2 } ; result } ; 2 - 2 } = true
|
select the rows whose competition record fuzzily matches to friendly . select the row whose date record of these rows is 2nd minimum . the result record of this row is 2 - 2 .
|
4
|
4
|
{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'competition_6': 6, 'friendly_7': 7, 'date_8': 8, '2_9': 9, 'result_10': 10, '2 - 2_11': 11}
|
{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'competition_6': 'competition', 'friendly_7': 'friendly', 'date_8': 'date', '2_9': '2', 'result_10': 'result', '2 - 2_11': '2 - 2'}
|
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'competition_6': [0], 'friendly_7': [0], 'date_8': [1], '2_9': [1], 'result_10': [2], '2 - 2_11': [3]}
|
['date', 'venue', 'score', 'result', 'competition']
|
[['3 march 2010', 'antalya atatürk stadium , antalya , turkey', '1 - 0', '3 - 1', 'friendly'], ['27 may 2010', 'stadion villach lind , villach , austria', '1 - 1', '2 - 2', 'friendly'], ['27 may 2010', 'stadion villach lind , villach , austria', '2 - 1', '2 - 2', 'friendly'], ['7 june 2011', 'dynama stadium , minsk , belarus', '2 - 0', '2 - 0', 'uefa euro 2012 qualification'], ['11 september 2012', 'stade de france , paris , france', '1 - 2', '1 - 3', '2014 fifa world cup qualification'], ['3 june 2013', 'a le coq arena , tallinn , estonia', '1 - 0', '2 - 0', 'friendly']]
|
1948 vfl season
|
https://en.wikipedia.org/wiki/1948_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809529-1.html.csv
|
aggregation
|
during the 1948 vfl season , the average home team score was 14.2 .
|
{'scope': 'all', 'col': '4', 'type': 'average', 'result': '14.2', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'away team score'], 'result': '14.2', 'ind': 0, 'tostr': 'avg { all_rows ; away team score }'}, '14.2'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; away team score } ; 14.2 } = true', 'tointer': 'the average of the away team score record of all rows is 14.2 .'}
|
round_eq { avg { all_rows ; away team score } ; 14.2 } = true
|
the average of the away team score record of all rows is 14.2 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'away team score_4': 4, '14.2_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'away team score_4': 'away team score', '14.2_5': '14.2'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'away team score_4': [0], '14.2_5': [1]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['footscray', '21.19 ( 145 )', 'st kilda', '9.11 ( 65 )', 'western oval', '14000', '17 april 1948'], ['fitzroy', '13.14 ( 92 )', 'geelong', '9.16 ( 70 )', 'brunswick street oval', '17000', '17 april 1948'], ['south melbourne', '18.15 ( 123 )', 'richmond', '17.6 ( 108 )', 'lake oval', '28000', '17 april 1948'], ['melbourne', '12.5 ( 77 )', 'essendon', '13.18 ( 96 )', 'mcg', '29000', '17 april 1948'], ['north melbourne', '9.12 ( 66 )', 'collingwood', '11.20 ( 86 )', 'arden street oval', '20000', '17 april 1948'], ['hawthorn', '11.10 ( 76 )', 'carlton', '17.15 ( 117 )', 'glenferrie oval', '16000', '17 april 1948']]
|
fred stolle
|
https://en.wikipedia.org/wiki/Fred_Stolle
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2201724-2.html.csv
|
comparative
|
fred stolle played at wimbleton before he played at the australian championships .
|
{'row_1': '1', 'row_2': '2', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'championship', 'wimbledon'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose championship record fuzzily matches to wimbledon .', 'tostr': 'filter_eq { all_rows ; championship ; wimbledon }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; championship ; wimbledon } ; year }', 'tointer': 'select the rows whose championship record fuzzily matches to wimbledon . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'championship', 'australian championships'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose championship record fuzzily matches to australian championships .', 'tostr': 'filter_eq { all_rows ; championship ; australian championships }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; championship ; australian championships } ; year }', 'tointer': 'select the rows whose championship record fuzzily matches to australian championships . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; championship ; wimbledon } ; year } ; hop { filter_eq { all_rows ; championship ; australian championships } ; year } } = true', 'tointer': 'select the rows whose championship record fuzzily matches to wimbledon . take the year record of this row . select the rows whose championship record fuzzily matches to australian championships . take the year record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; championship ; wimbledon } ; year } ; hop { filter_eq { all_rows ; championship ; australian championships } ; year } } = true
|
select the rows whose championship record fuzzily matches to wimbledon . take the year record of this row . select the rows whose championship record fuzzily matches to australian championships . 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, 'championship_7': 7, 'wimbledon_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'championship_11': 11, 'australian championships_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', 'championship_7': 'championship', 'wimbledon_8': 'wimbledon', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'championship_11': 'championship', 'australian championships_12': 'australian championships', 'year_13': 'year'}
|
{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'championship_7': [0], 'wimbledon_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'championship_11': [1], 'australian championships_12': [1], 'year_13': [3]}
|
['outcome', 'year', 'championship', 'surface', 'partner', 'opponents in the final', 'score in the final']
|
[['runner - up', '1961', 'wimbledon', 'grass', 'bob hewitt', 'roy emerson neale fraser', '4 - 6 , 8 - 6 , 4 - 6 , 8 - 6 , 6 - 8'], ['runner - up', '1962', 'australian championships', 'grass', 'bob hewitt', 'roy emerson neale fraser', '6 - 4 , 6 - 4 , 1 - 6 , 4 - 6 , 9 - 11'], ['winner', '1962', 'wimbledon', 'grass', 'bob hewitt', 'boro jovanović nikola pilić', '6 - 2 , 5 - 7 , 6 - 2 , 6 - 4'], ['winner', '1963', 'australian championships', 'grass', 'bob hewitt', 'ken fletcher john newcombe', '6 - 2 , 3 - 6 , 6 - 3 , 3 - 6 , 6 - 3'], ['winner', '1964', 'australian championships', 'grass', 'bob hewitt', 'roy emerson ken fletcher', '6 - 4 , 7 - 5 , 3 - 6 , 4 - 6 , 14 - 12'], ['winner', '1964', 'wimbledon', 'grass', 'bob hewitt', 'roy emerson ken fletcher', '7 - 5 , 11 - 9 , 6 - 4'], ['runner - up', '1965', 'australian championships', 'grass', 'roy emerson', 'john newcombe tony roche', '6 - 3 , 6 - 4 , 11 - 13 , 3 - 6 , 4 - 6'], ['winner', '1965', 'french championships', 'clay', 'roy emerson', 'ken fletcher bob hewitt', '6 - 8 , 6 - 3 , 8 - 6 , 6 - 2'], ['winner', '1965', 'us championships', 'grass', 'roy emerson', 'frank froehling charles pasarell', '6 - 4 , 10 - 12 , 7 - 5 , 6 - 3'], ['winner', '1966', 'australian championships', 'grass', 'roy emerson', 'john newcombe tony roche', '7 - 9 , 6 - 3 , 6 - 8 , 14 - 12 , 12 - 10'], ['winner', '1966', 'us championships', 'grass', 'roy emerson', 'clark graebner dennis ralston', '6 - 4 , 6 - 4 , 6 - 4'], ['winner', '1968', 'french open', 'clay', 'ken rosewall', 'roy emerson rod laver', '6 - 3 , 6 - 4 , 6 - 3'], ['runner - up', '1968', 'wimbledon', 'grass', 'ken rosewall', 'john newcombe tony roche', '6 - 3 , 6 - 8 , 7 - 5 , 12 - 14 , 3 - 6'], ['runner - up', '1969', 'australian open', 'grass', 'ken rosewall', 'rod laver roy emerson', '4 - 6 , 4 - 6'], ['winner', '1969', 'us open', 'grass', 'ken rosewall', 'charles pasarell dennis ralston', '2 - 6 , 7 - 5 , 13 - 11 , 6 - 3']]
|
damsels in distress ( plays )
|
https://en.wikipedia.org/wiki/Damsels_in_Distress_%28plays%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17827271-1.html.csv
|
unique
|
beth tuckley is the only female older actor .
|
{'scope': 'all', 'row': '6', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'female , older', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'actor required', 'female , older'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose actor required record fuzzily matches to female , older .', 'tostr': 'filter_eq { all_rows ; actor required ; female , older }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; actor required ; female , older } }', 'tointer': 'select the rows whose actor required record fuzzily matches to female , older . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'actor required', 'female , older'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose actor required record fuzzily matches to female , older .', 'tostr': 'filter_eq { all_rows ; actor required ; female , older }'}, 'actor in original production'], 'result': 'beth tuckley', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; actor required ; female , older } ; actor in original production }'}, 'beth tuckley'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; actor required ; female , older } ; actor in original production } ; beth tuckley }', 'tointer': 'the actor in original production record of this unqiue row is beth tuckley .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; actor required ; female , older } } ; eq { hop { filter_eq { all_rows ; actor required ; female , older } ; actor in original production } ; beth tuckley } } = true', 'tointer': 'select the rows whose actor required record fuzzily matches to female , older . there is only one such row in the table . the actor in original production record of this unqiue row is beth tuckley .'}
|
and { only { filter_eq { all_rows ; actor required ; female , older } } ; eq { hop { filter_eq { all_rows ; actor required ; female , older } ; actor in original production } ; beth tuckley } } = true
|
select the rows whose actor required record fuzzily matches to female , older . there is only one such row in the table . the actor in original production record of this unqiue row is beth tuckley .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'actor required_7': 7, 'female , older_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'actor in original production_9': 9, 'beth tuckley_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'actor required_7': 'actor required', 'female , older_8': 'female , older', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'actor in original production_9': 'actor in original production', 'beth tuckley_10': 'beth tuckley'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'actor required_7': [0], 'female , older_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'actor in original production_9': [2], 'beth tuckley_10': [3]}
|
['actor in original production', 'actor required', 'gameplan', 'flatspin', 'roleplay']
|
[['bill champion', 'male , younger', 'troy stephens', 'sam berryman', 'justin lazenby'], ['saskia butler', 'female , younger', 'sorrel saxon', 'tracy taylor', 'julie - ann jobson'], ['alison pargeter', 'female , younger', 'kelly butcher', 'rosie seymour', 'paige petite'], ['tim faraday', 'male , older', 'dan endicott', 'tommy angel', 'micky rale'], ['robert austin', 'male , older', 'leo tyler', 'maurice whickett', 'derek jobson'], ['beth tuckley', 'female , older', 'grace page', 'edna stricken', 'dee jobson']]
|
2008 in paleontology
|
https://en.wikipedia.org/wiki/2008_in_paleontology
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15688561-4.html.csv
|
unique
|
proraphidia gomezi was the only fossil that was found in england .
|
{'scope': 'all', 'row': '5', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'england', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'england'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to england .', 'tostr': 'filter_eq { all_rows ; location ; england }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; location ; england } }', 'tointer': 'select the rows whose location record fuzzily matches to england . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'england'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to england .', 'tostr': 'filter_eq { all_rows ; location ; england }'}, 'name'], 'result': 'proraphidia gomezi', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; location ; england } ; name }'}, 'proraphidia gomezi'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; location ; england } ; name } ; proraphidia gomezi }', 'tointer': 'the name record of this unqiue row is proraphidia gomezi .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; location ; england } } ; eq { hop { filter_eq { all_rows ; location ; england } ; name } ; proraphidia gomezi } } = true', 'tointer': 'select the rows whose location record fuzzily matches to england . there is only one such row in the table . the name record of this unqiue row is proraphidia gomezi .'}
|
and { only { filter_eq { all_rows ; location ; england } } ; eq { hop { filter_eq { all_rows ; location ; england } ; name } ; proraphidia gomezi } } = true
|
select the rows whose location record fuzzily matches to england . there is only one such row in the table . the name record of this unqiue row is proraphidia gomezi .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'location_7': 7, 'england_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'proraphidia gomezi_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'location_7': 'location', 'england_8': 'england', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'proraphidia gomezi_10': 'proraphidia gomezi'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'location_7': [0], 'england_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'proraphidia gomezi_10': [3]}
|
['name', 'novelty', 'status', 'authors', 'location']
|
[['formosibittacus', 'gen et sp nov', 'vaild', 'li , ren & shih', 'china'], ['haidomyrmodes', 'gen et sp nov', 'vaild', 'perrichot et al', 'france'], ['jurahylobittacus', 'gen et sp nov', 'valid', 'li , ren & shih', 'china'], ['lutzomyia adiketis', 'sp nov', 'valid', 'poinar', 'dominican republic'], ['proraphidia gomezi', 'sp nov', 'valid', 'jarzembowski', 'england'], ['proraphidia hopkinsi', 'sp nov', 'valid', 'jarzembowski', 'spain'], ['sinomeganeura', 'gen et sp nov', 'valid', 'ren , nel , & prokop', 'china'], ['syndesus ambericus', 'sp nov', 'vaild', 'woodruff', 'dominican republic'], ['termitaradus avitinquilinus', 'sp nov', 'vaild', 'grimaldi & engel', 'dominican republic']]
|
1952 washington redskins season
|
https://en.wikipedia.org/wiki/1952_Washington_Redskins_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15124415-1.html.csv
|
unique
|
for the washington redskins ' 1952 season , only one game had an attendance below 10,000 .
|
{'scope': 'all', 'row': '2', 'col': '5', 'col_other': 'n/a', 'criterion': 'less_than', 'value': '10000', 'subset': None}
|
{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'attendance', '10000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose attendance record is less than 10000 .', 'tostr': 'filter_less { all_rows ; attendance ; 10000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; attendance ; 10000 } } = true', 'tointer': 'select the rows whose attendance record is less than 10000 . there is only one such row in the table .'}
|
only { filter_less { all_rows ; attendance ; 10000 } } = true
|
select the rows whose attendance record is less than 10000 . there is only one such row in the table .
|
2
|
2
|
{'only_1': 1, 'result_2': 2, 'filter_less_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '10000_5': 5}
|
{'only_1': 'only', 'result_2': 'true', 'filter_less_0': 'filter_less', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '10000_5': '10000'}
|
{'only_1': [2], 'result_2': [], 'filter_less_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '10000_5': [0]}
|
['week', 'date', 'opponent', 'result', 'attendance']
|
[['1', 'september 29 , 1952', 'chicago cardinals', 'w 23 - 7', '17837'], ['2', 'october 5 , 1952', 'green bay packers', 'l 35 - 20', '9657'], ['3', 'october 12 , 1952', 'chicago cardinals', 'l 17 - 6', '24600'], ['4', 'october 19 , 1952', 'pittsburgh steelers', 'w 28 - 24', '22604'], ['5', 'october 26 , 1952', 'cleveland browns', 'l 19 - 15', '32496'], ['6', 'november 2 , 1952', 'pittsburgh steelers', 'l 24 - 23', '25866'], ['7', 'november 9 , 1952', 'philadelphia eagles', 'l 38 - 20', '16932'], ['8', 'november 16 , 1952', 'san francisco 49ers', 'l 23 - 17', '28997'], ['9', 'november 23 , 1952', 'new york giants', 'l 14 - 10', '21125'], ['10', 'november 30 , 1952', 'cleveland browns', 'l 48 - 21', '22679'], ['11', 'december 7 , 1952', 'new york giants', 'w 27 - 17', '21237'], ['12', 'december 14 , 1952', 'philadelphia eagles', 'w 27 - 21', '22468']]
|
manila nomads f.c
|
https://en.wikipedia.org/wiki/Manila_Nomads_F.C.
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18946789-2.html.csv
|
comparative
|
there were more teams in the 2012 season than in the 2011 season .
|
{'row_1': '4', 'row_2': '3', 'col': '1', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2012'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose season record fuzzily matches to 2012 .', 'tostr': 'filter_eq { all_rows ; season ; 2012 }'}, 'season'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; season ; 2012 } ; season }', 'tointer': 'select the rows whose season record fuzzily matches to 2012 . take the season record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2011'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose season record fuzzily matches to 2011 .', 'tostr': 'filter_eq { all_rows ; season ; 2011 }'}, 'season'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; season ; 2011 } ; season }', 'tointer': 'select the rows whose season record fuzzily matches to 2011 . take the season record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; season ; 2012 } ; season } ; hop { filter_eq { all_rows ; season ; 2011 } ; season } } = true', 'tointer': 'select the rows whose season record fuzzily matches to 2012 . take the season record of this row . select the rows whose season record fuzzily matches to 2011 . take the season record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; season ; 2012 } ; season } ; hop { filter_eq { all_rows ; season ; 2011 } ; season } } = true
|
select the rows whose season record fuzzily matches to 2012 . take the season record of this row . select the rows whose season record fuzzily matches to 2011 . take the season record of this row . the first record is greater than the second record .
|
5
|
5
|
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'season_7': 7, '2012_8': 8, 'season_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'season_11': 11, '2011_12': 12, 'season_13': 13}
|
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'season_7': 'season', '2012_8': '2012', 'season_9': 'season', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'season_11': 'season', '2011_12': '2011', 'season_13': 'season'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'season_7': [0], '2012_8': [0], 'season_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'season_11': [1], '2011_12': [1], 'season_13': [3]}
|
['season', 'division', 'tms', 'pos', 'pff nmcc', 'ufl cup', 'afc pc']
|
[['no league yet', 'no league yet', 'no league yet', 'no league yet', 'no league yet', 'semi - finals', '-'], ['2010', '2', '8', '2nd', '-', 'quarter - finals', '-'], ['2011', '2', '8', '1st ( prom )', '-', 'quarter - finals', '-'], ['2012', '1', '10', '7th', '-', 'group stage', '-'], ['2013', '1', '10', '8th', 'round of 16', 'tbd', 'dnq']]
|
pete sampras career statistics
|
https://en.wikipedia.org/wiki/Pete_Sampras_career_statistics
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22834834-3.html.csv
|
unique
|
the only masters series finals singles match that pete sampras competed in on a clay surface was in 1994 in rome .
|
{'scope': 'all', 'row': '7', 'col': '4', 'col_other': '2,3', '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': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_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 }'}, 'year'], 'result': '1994', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; surface ; clay } ; year }'}, '1994'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; surface ; clay } ; year } ; 1994 }', 'tointer': 'the year record of this unqiue row is 1994 .'}, {'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 }'}, 'championship'], 'result': 'rome', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; surface ; clay } ; championship }'}, 'rome'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; surface ; clay } ; championship } ; rome }', 'tointer': 'the championship record of this unqiue row is rome .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; surface ; clay } ; year } ; 1994 } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; championship } ; rome } }', 'tointer': 'the year record of this unqiue row is 1994 . the championship record of this unqiue row is rome .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; surface ; clay } } ; and { eq { hop { filter_eq { all_rows ; surface ; clay } ; year } ; 1994 } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; championship } ; rome } } } = true', 'tointer': 'select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the year record of this unqiue row is 1994 . the championship record of this unqiue row is rome .'}
|
and { only { filter_eq { all_rows ; surface ; clay } } ; and { eq { hop { filter_eq { all_rows ; surface ; clay } ; year } ; 1994 } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; championship } ; rome } } } = true
|
select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the year record of this unqiue row is 1994 . the championship record of this unqiue row is rome .
|
10
|
8
|
{'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'surface_10': 10, 'clay_11': 11, 'and_6': 6, 'eq_3': 3, 'num_hop_2': 2, 'year_12': 12, '1994_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'championship_14': 14, 'rome_15': 15}
|
{'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'surface_10': 'surface', 'clay_11': 'clay', 'and_6': 'and', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_12': 'year', '1994_13': '1994', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'championship_14': 'championship', 'rome_15': 'rome'}
|
{'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'surface_10': [0], 'clay_11': [0], 'and_6': [7], 'eq_3': [6], 'num_hop_2': [3], 'year_12': [2], '1994_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'championship_14': [4], 'rome_15': [5]}
|
['outcome', 'year', 'championship', 'surface', 'opponent in the final', 'score in the final']
|
[['runner - up', '1991', 'cincinnati', 'hard', 'guy forget', '6 - 2 , 6 - 7 ( 4 - 7 ) , 4 - 6'], ['runner - up', '1991', 'paris', 'carpet ( i )', 'guy forget', '6 - 7 ( 9 - 11 ) , 6 - 4 , 7 - 5 , 4 - 6 , 4 - 6'], ['winner', '1992', 'cincinnati', 'hard', 'ivan lendl', '6 - 3 , 3 - 6 , 6 - 3'], ['winner', '1993', 'miami', 'hard', 'malivai washington', '6 - 3 , 6 - 2'], ['winner', '1994', 'indian wells', 'hard', 'petr korda', '4 - 6 , 6 - 3 , 3 - 6 , 6 - 3 , 6 - 2'], ['winner', '1994', 'miami ( 2 )', 'hard', 'andre agassi', '5 - 7 , 6 - 3 , 6 - 3'], ['winner', '1994', 'rome', 'clay', 'boris becker', '6 - 1 , 6 - 2 , 6 - 2'], ['winner', '1995', 'indian wells ( 2 )', 'hard', 'andre agassi', '7 - 5 , 6 - 3 , 7 - 5'], ['runner - up', '1995', 'miami', 'hard', 'andre agassi', '6 - 3 , 2 - 6 , 6 - 7 ( 6 - 8 )'], ['runner - up', '1995', 'canada ( montreal )', 'hard', 'andre agassi', '6 - 3 , 2 - 6 , 3 - 6'], ['winner', '1995', 'paris', 'carpet ( i )', 'boris becker', '7 - 6 ( 7 - 5 ) , 6 - 4 , 6 - 4'], ['runner - up', '1996', 'stuttgart', 'carpet ( i )', 'boris becker', '6 - 3 , 3 - 6 , 6 - 3 , 3 - 6 , 4 - 6'], ['winner', '1997', 'cincinnati ( 2 )', 'hard', 'thomas muster', '6 - 3 , 6 - 4'], ['winner', '1997', 'paris ( 2 )', 'carpet ( i )', 'jonas björkman', '6 - 3 , 4 - 6 , 6 - 3 , 6 - 1'], ['runner - up', '1998', 'cincinnati ( 2 )', 'hard', 'patrick rafter', '6 - 1 , 6 - 7 ( 2 - 7 ) , 4 - 6'], ['runner - up', '1998', 'paris ( 2 )', 'carpet ( i )', 'greg rusedski', '4 - 6 , 6 - 7 ( 4 - 7 ) , 3 - 6'], ['winner', '1999', 'cincinnati ( 3 )', 'hard', 'patrick rafter', '7 - 6 ( 9 - 7 ) , 6 - 3'], ['winner', '2000', 'miami ( 3 )', 'hard', 'gustavo kuerten', '6 - 1 , 6 - 7 ( 2 - 7 ) , 7 - 6 ( 7 - 5 ) , 7 - 6 ( 10 - 8 )']]
|
1981 vfl season
|
https://en.wikipedia.org/wiki/1981_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10823950-1.html.csv
|
ordinal
|
the 2nd largest crowd was at the game where essendon was the away team .
|
{'row': '4', 'col': '6', 'order': '2', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'crowd', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 2 }'}, 'away team'], 'result': 'essendon', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 2 } ; away team }'}, 'essendon'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; away team } ; essendon } = true', 'tointer': 'select the row whose crowd record of all rows is 2nd maximum . the away team record of this row is essendon .'}
|
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; away team } ; essendon } = true
|
select the row whose crowd record of all rows is 2nd maximum . the away team record of this row is essendon .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '2_6': 6, 'away team_7': 7, 'essendon_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', 'crowd_5': 'crowd', '2_6': '2', 'away team_7': 'away team', 'essendon_8': 'essendon'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '2_6': [0], 'away team_7': [1], 'essendon_8': [2]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['north melbourne', '21.19 ( 145 )', 'south melbourne', '12.25 ( 97 )', 'arden street oval', '19437', '28 march 1981'], ['footscray', '16.12 ( 108 )', 'st kilda', '23.19 ( 157 )', 'western oval', '19101', '28 march 1981'], ['melbourne', '16.16 ( 112 )', 'hawthorn', '23.15 ( 153 )', 'mcg', '32202', '28 march 1981'], ['geelong', '10.17 ( 77 )', 'essendon', '10.11 ( 71 )', 'kardinia park', '37303', '28 march 1981'], ['fitzroy', '20.13 ( 133 )', 'collingwood', '22.27 ( 159 )', 'junction oval', '27200', '28 march 1981'], ['carlton', '22.12 ( 144 )', 'richmond', '12.10 ( 82 )', 'vfl park', '56372', '28 march 1981']]
|
peter whitehead ( racing driver )
|
https://en.wikipedia.org/wiki/Peter_Whitehead_%28racing_driver%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1235868-1.html.csv
|
majority
|
the most engine used by peter whitehead ( racing driver ) was ferrari v12 .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'ferrari v12', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'engine', 'ferrari v12'], 'result': True, 'ind': 0, 'tointer': 'for the engine records of all rows , most of them fuzzily match to ferrari v12 .', 'tostr': 'most_eq { all_rows ; engine ; ferrari v12 } = true'}
|
most_eq { all_rows ; engine ; ferrari v12 } = true
|
for the engine records of all rows , most of them fuzzily match to ferrari v12 .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'engine_3': 3, 'ferrari v12_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'engine_3': 'engine', 'ferrari v12_4': 'ferrari v12'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'engine_3': [0], 'ferrari v12_4': [0]}
|
['year', 'entrant', 'chassis', 'engine', 'points']
|
[['1950', 'peter whitehead', 'ferrari 125', 'ferrari v12', '4'], ['1950', 'scuderia ferrari', 'ferrari 125', 'ferrari v12', '4'], ['1951', 'peter whitehead', 'ferrari 125', 'ferrari v12', '0'], ['1951', 'peter whitehead', 'ferrari 375', 'ferrari v12', '0'], ['1951', 'g a vandervell', 'ferrari 375 thinwall', 'ferrari v12', '0'], ['1952', 'peter whitehead', 'alta f2', 'alta straight - 4', '0'], ['1952', 'peter whitehead', 'ferrari 125', 'ferrari v12', '0'], ['1953', 'atlantic stable', 'cooper t24', 'alta straight - 4', '0'], ['1954', 'peter whitehead', 'cooper t24', 'alta straight - 4', '0']]
|
list of state leaders in 820s bc
|
https://en.wikipedia.org/wiki/List_of_state_leaders_in_820s_BC
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17338083-13.html.csv
|
count
|
three of the state leaders in the 800s bc had the title of viscount .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'viscount', 'result': '3', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'viscount'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to viscount .', 'tostr': 'filter_eq { all_rows ; title ; viscount }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; title ; viscount } }', 'tointer': 'select the rows whose title record fuzzily matches to viscount . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; title ; viscount } } ; 3 } = true', 'tointer': 'select the rows whose title record fuzzily matches to viscount . the number of such rows is 3 .'}
|
eq { count { filter_eq { all_rows ; title ; viscount } } ; 3 } = true
|
select the rows whose title record fuzzily matches to viscount . 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, 'title_5': 5, 'viscount_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', 'title_5': 'title', 'viscount_6': 'viscount', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'title_5': [0], 'viscount_6': [0], '3_7': [2]}
|
['state', 'type', 'name', 'title', 'royal house', 'from']
|
[['cai', 'sovereign', 'yi', 'marquis', 'ji', '837 bc'], ['cao', 'sovereign', 'you', 'count', '-', '835 bc'], ['cao', 'sovereign', 'dai', 'count', '-', '826 bc'], ['chen', 'sovereign', 'li', 'duke', '-', '831 bc'], ['chu', 'sovereign', 'xiong yan the younger', 'viscount', 'mi', '837 bc'], ['chu', 'sovereign', 'xiong shuang', 'viscount', 'mi', '827 bc'], ['chu', 'sovereign', 'xiong xun', 'viscount', 'mi', '821 bc'], ['jin', 'sovereign', 'xi', 'marquis', 'ji', '840 bc'], ['jin', 'sovereign', 'xian', 'marquis', 'ji', '822 bc'], ['lu', 'sovereign', 'shen', 'duke', 'ji', '854 bc'], ['lu', 'sovereign', 'wu', 'duke', 'ji', '825 bc'], ['qi', 'sovereign', 'wu', 'duke', 'jiang', '850 bc'], ['qi', 'sovereign', 'li', 'duke', 'jiang', '824 bc'], ['qin', 'sovereign', 'qin zhong', 'ruler', 'ying', '845 bc'], ['qin', 'sovereign', 'zhuang', 'duke', 'ying', '822 bc'], ['song', 'sovereign', 'hui', 'duke', '-', '830 bc'], ['wey', 'sovereign', 'li', 'marquis', '-', '855 bc'], ['yan', 'sovereign', 'hui', 'marquis', '-', '864 bc'], ['yan', 'sovereign', 'li', 'marquis', '-', '826 bc']]
|
24 ( season 8 )
|
https://en.wikipedia.org/wiki/24_%28season_8%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22181917-2.html.csv
|
count
|
according to the list of episodes of 24 ( season 8 ) , 3 of the episodes directed by brad turner were written by manny coto & brannon braga .
|
{'scope': 'subset', 'criterion': 'equal', 'value': 'manny coto & brannon braga', 'result': '3', 'col': '5', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'brad turner'}}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'brad turner'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; directed by ; brad turner }', 'tointer': 'select the rows whose directed by record fuzzily matches to brad turner .'}, 'written by', 'manny coto & brannon braga'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose directed by record fuzzily matches to brad turner . among these rows , select the rows whose written by record fuzzily matches to manny coto & brannon braga .', 'tostr': 'filter_eq { filter_eq { all_rows ; directed by ; brad turner } ; written by ; manny coto & brannon braga }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; directed by ; brad turner } ; written by ; manny coto & brannon braga } }', 'tointer': 'select the rows whose directed by record fuzzily matches to brad turner . among these rows , select the rows whose written by record fuzzily matches to manny coto & brannon braga . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; directed by ; brad turner } ; written by ; manny coto & brannon braga } } ; 3 } = true', 'tointer': 'select the rows whose directed by record fuzzily matches to brad turner . among these rows , select the rows whose written by record fuzzily matches to manny coto & brannon braga . the number of such rows is 3 .'}
|
eq { count { filter_eq { filter_eq { all_rows ; directed by ; brad turner } ; written by ; manny coto & brannon braga } } ; 3 } = true
|
select the rows whose directed by record fuzzily matches to brad turner . among these rows , select the rows whose written by record fuzzily matches to manny coto & brannon braga . the number of such rows is 3 .
|
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, 'directed by_6': 6, 'brad turner_7': 7, 'written by_8': 8, 'manny coto & brannon braga_9': 9, '3_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', 'directed by_6': 'directed by', 'brad turner_7': 'brad turner', 'written by_8': 'written by', 'manny coto & brannon braga_9': 'manny coto & brannon braga', '3_10': '3'}
|
{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'directed by_6': [0], 'brad turner_7': [0], 'written by_8': [1], 'manny coto & brannon braga_9': [1], '3_10': [3]}
|
['series', 'season', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( millions )']
|
[['169', '1', 'day 8 : 4:00 pm - 5:00 pm', 'brad turner', 'howard gordon & evan katz', 'january 17 , 2010', '8aff01', '11.50'], ['171', '3', 'day 8 : 6:00 pm - 7:00 pm', 'milan cheylov', 'david fury & alex gansa', 'january 18 , 2010', '8aff03', '10.56'], ['172', '4', 'day 8 : 7:00 pm - 8:00 pm', 'milan cheylov', 'chip johannessen & patrick harbinson', 'january 18 , 2010', '8aff04', '11.45'], ['174', '6', 'day 8 : 9:00 pm - 10:00 pm', 'brad turner', 'manny coto & brannon braga', 'february 1 , 2010', '8aff06', '9.76'], ['175', '7', 'day 8 : 10:00 pm - 11:00 pm', 'milan cheylov', 'chip johannessen & patrick harbinson', 'february 8 , 2010', '8aff07', '10.18'], ['176', '8', 'day 8 : 11:00 pm - 12:00 am', 'milan cheylov', 'david fury', 'february 15 , 2010', '8aff08', '8.49'], ['178', '10', 'day 8 : 1:00 am - 2:00 am', 'brad turner', 'manny coto & brannon braga', 'march 1 , 2010', '8aff10', '8.56'], ['179', '11', 'day 8 : 2:00 am - 3:00 am', 'nelson mccormick', 'evan katz & david fury', 'march 8 , 2010', '8aff11', '8.91'], ['180', '12', 'day 8 : 3:00 am - 4:00 am', 'nelson mccormick', 'chip johannessen & patrick harbinson', 'march 15 , 2010', '8aff12', '9.03'], ['182', '14', 'day 8 : 5:00 am - 6:00 am', 'milan cheylov', 'story : evan katz teleplay : alex gansa', 'march 29 , 2010', '8aff14', '8.31'], ['183', '15', 'day 8 : 6:00 am - 7:00 am', 'brad turner', 'chip johannessen & patrick harbinson', 'april 5 , 2010', '8aff15', '6.62'], ['184', '16', 'day 8 : 7:00 am - 8:00 am', 'brad turner', 'manny coto & brannon braga', 'april 5 , 2010', '8aff16', '7.90'], ['185', '17', 'day 8 : 8:00 am - 9:00 am', 'milan cheylov', 'david fury', 'april 12 , 2010', '8aff17', '8.33'], ['186', '18', 'day 8 : 9:00 am - 10:00 am', 'milan cheylov', 'chip johannessen & patrick harbinson', 'april 19 , 2010', '8aff18', '8.94'], ['187', '19', 'day 8 : 10:00 am - 11:00 am', 'michael klick', 'manny coto & brannon braga', 'april 26 , 2010', '8aff19', '9.19'], ['188', '20', 'day 8 : 11:00 am - 12:00 pm', 'michael klick', 'story : alex gansa teleplay : evan katz & alex gansa', 'may 3 , 2010', '8aff20', '9.00'], ['189', '21', 'day 8 : 12:00 pm - 1:00 pm', 'milan cheylov', 'chip johannessen & patrick harbinson', 'may 10 , 2010', '8aff21', '8.51'], ['190', '22', 'day 8 : 1:00 pm - 2:00 pm', 'milan cheylov', 'david fury', 'may 17 , 2010', '8aff22', '8.98'], ['191', '23', 'day 8 : 2:00 pm - 3:00 pm', 'brad turner', 'shauna mcgarry & geoff aull', 'may 24 , 2010', '8aff23', '8.39']]
|
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
|
aggregation
|
the average attendance during the 1947 world series was 52733 .
|
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '55680.4', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '55680.4', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '55680.4'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 55680.4 } = true', 'tointer': 'the average of the attendance record of all rows is 55680.4 .'}
|
round_eq { avg { all_rows ; attendance } ; 55680.4 } = true
|
the average of the attendance record of all rows is 55680.4 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '55680.4_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '55680.4_5': '55680.4'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '55680.4_5': [1]}
|
['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']]
|
1989 san francisco 49ers season
|
https://en.wikipedia.org/wiki/1989_San_Francisco_49ers_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15521693-2.html.csv
|
comparative
|
the 49ers played better on november 27th than they did on november 19th .
|
{'row_1': '12', 'row_2': '11', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november 27 , 1989 ( mon )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to november 27 , 1989 ( mon ) .', 'tostr': 'filter_eq { all_rows ; date ; november 27 , 1989 ( mon ) }'}, 'result'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; november 27 , 1989 ( mon ) } ; result }', 'tointer': 'select the rows whose date record fuzzily matches to november 27 , 1989 ( mon ) . take the result record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november 19 , 1989'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to november 19 , 1989 .', 'tostr': 'filter_eq { all_rows ; date ; november 19 , 1989 }'}, 'result'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; november 19 , 1989 } ; result }', 'tointer': 'select the rows whose date record fuzzily matches to november 19 , 1989 . take the result record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; november 27 , 1989 ( mon ) } ; result } ; hop { filter_eq { all_rows ; date ; november 19 , 1989 } ; result } } = true', 'tointer': 'select the rows whose date record fuzzily matches to november 27 , 1989 ( mon ) . take the result record of this row . select the rows whose date record fuzzily matches to november 19 , 1989 . take the result record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; date ; november 27 , 1989 ( mon ) } ; result } ; hop { filter_eq { all_rows ; date ; november 19 , 1989 } ; result } } = true
|
select the rows whose date record fuzzily matches to november 27 , 1989 ( mon ) . take the result record of this row . select the rows whose date record fuzzily matches to november 19 , 1989 . take the result record of this row . the first record is greater than the second record .
|
5
|
5
|
{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, 'november 27 , 1989 (mon)_8': 8, 'result_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'november 19 , 1989_12': 12, 'result_13': 13}
|
{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', 'november 27 , 1989 (mon)_8': 'november 27 , 1989 ( mon )', 'result_9': 'result', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'november 19 , 1989_12': 'november 19 , 1989', 'result_13': 'result'}
|
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'november 27 , 1989 (mon)_8': [0], 'result_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'november 19 , 1989_12': [1], 'result_13': [3]}
|
['week', 'date', 'opponent', 'result', 'tv time', 'attendance']
|
[['1', 'september 10 , 1989', 'indianapolis colts', 'w 30 - 24', 'cbs 10:00 am', '60111'], ['2', 'september 17 , 1989', 'tampa bay buccaneers', 'w 20 - 16', 'cbs 1:00 pm', '64087'], ['3', 'september 24 , 1989', 'philadelphia eagles', 'w 38 - 28', 'cbs 10:00 am', '66042'], ['4', 'october 1 , 1989', 'los angeles rams', 'l 13 - 12', 'cbs 1:00 pm', '64250'], ['5', 'october 8 , 1989', 'new orleans saints', 'w 24 - 20', 'cbs 10:00 am', '60488'], ['6', 'october 15 , 1989', 'dallas cowboys', 'w 31 - 14', 'cbs 10:00 am', '61077'], ['7', 'october 22 , 1989', 'new england patriots ( at stanford )', 'w 37 - 20', 'nbc 1:00 pm', '51781'], ['8', 'october 29 , 1989', 'new york jets', 'w 23 - 10', 'cbs 1:00 pm', '62805'], ['9', 'november 6 , 1989 ( mon )', 'new orleans saints', 'w 31 - 13', 'abc 6:00 pm', '60667'], ['10', 'november 12 , 1989', 'atlanta falcons', 'w 45 - 3', 'cbs 1:00 pm', '59914'], ['11', 'november 19 , 1989', 'green bay packers', 'l 21 - 17', 'cbs 1:00 pm', '62219'], ['12', 'november 27 , 1989 ( mon )', 'new york giants', 'w 34 - 24', 'abc 6:00 pm', '63461'], ['13', 'december 3 , 1989', 'atlanta falcons', 'w 23 - 10', 'cbs 10:00 am', '43128'], ['14', 'december 11 , 1989 ( mon )', 'los angeles rams', 'w 30 - 27', 'abc 6:00 pm', '67959'], ['15', 'december 17 , 1989', 'buffalo bills', 'w 23 - 10', 'nbc 1:00 pm', '60927'], ['16', 'december 24 , 1989', 'chicago bears', 'w 26 - 0', 'cbs 1:00 pm', '60207']]
|
1974 buffalo bills season
|
https://en.wikipedia.org/wiki/1974_Buffalo_Bills_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16677874-2.html.csv
|
majority
|
in 1974 , the buffalo bills had at least 60000 people at most of their games .
|
{'scope': 'all', 'col': '9', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '60000', 'subset': None}
|
{'func': 'most_greater_eq', 'args': ['all_rows', 'attendance', '60000'], 'result': True, 'ind': 0, 'tointer': 'for the attendance records of all rows , most of them are greater than or equal to 60000 .', 'tostr': 'most_greater_eq { all_rows ; attendance ; 60000 } = true'}
|
most_greater_eq { all_rows ; attendance ; 60000 } = true
|
for the attendance records of all rows , most of them are greater than or equal to 60000 .
|
1
|
1
|
{'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'attendance_3': 3, '60000_4': 4}
|
{'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'attendance_3': 'attendance', '60000_4': '60000'}
|
{'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'attendance_3': [0], '60000_4': [0]}
|
['game', 'date', 'opponent', 'result', 'bills points', 'opponents', 'bills first downs', 'record', 'attendance']
|
[['1', 'sept 16', 'oakland raiders', 'win', '21', '20', '22', '1 - 0', '80020'], ['2', 'sept 22', 'miami dolphins', 'loss', '16', '24', '16', '1 - 1', '80020'], ['3', 'sept 29', 'new york jets', 'win', '16', '12', '17', '2 - 1', '76978'], ['4', 'oct 6', 'green bay packers', 'win', '27', '7', '22', '3 - 1', '56267'], ['5', 'oct 13', 'baltimore colts', 'win', '27', '14', '15', '4 - 1', '40626'], ['6', 'oct 20', 'new england patriots', 'win', '30', '28', '19', '5 - 1', '78935'], ['7', 'oct 27', 'chicago bears', 'win', '16', '6', '16', '6 - 1', '78084'], ['8', 'nov 3', 'new england patriots', 'win', '29', '28', '22', '7 - 1', '61279'], ['9', 'nov 10', 'houston oilers', 'loss', '9', '21', '16', '7 - 2', '79144'], ['10', 'nov 17', 'miami dolphins', 'loss', '28', '35', '16', '7 - 3', '69313'], ['11', 'nov 24', 'cleveland browns', 'win', '15', '10', '10', '8 - 3', '66504'], ['12', 'dec 1', 'baltimore colts', 'win', '6', '0', '9', '9 - 3', '75325'], ['13', 'dec 8', 'new york jets', 'loss', '10', '20', '12', '9 - 4', '61091']]
|
1944 vfl season
|
https://en.wikipedia.org/wiki/1944_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809142-14.html.csv
|
superlative
|
among the matches on 5 august 1944 , ricmond vs essendon match has the most spectators .
|
{'scope': 'all', 'col_superlative': '6', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1,3', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'home team'], 'result': 'richmond', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; crowd } ; home team }'}, 'richmond'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; home team } ; richmond }', 'tointer': 'select the row whose crowd record of all rows is maximum . the home team record of this row is richmond .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'away team'], 'result': 'essendon', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; crowd } ; away team }'}, 'essendon'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; away team } ; essendon }', 'tointer': 'the away team record of this row is essendon .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { hop { argmax { all_rows ; crowd } ; home team } ; richmond } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; essendon } } = true', 'tointer': 'select the row whose crowd record of all rows is maximum . the home team record of this row is richmond . the away team record of this row is essendon .'}
|
and { eq { hop { argmax { all_rows ; crowd } ; home team } ; richmond } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; essendon } } = true
|
select the row whose crowd record of all rows is maximum . the home team record of this row is richmond . the away team record of this row is essendon .
|
7
|
6
|
{'and_5': 5, 'result_6': 6, 'str_eq_2': 2, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_7': 7, 'crowd_8': 8, 'home team_9': 9, 'richmond_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'away team_11': 11, 'essendon_12': 12}
|
{'and_5': 'and', 'result_6': 'true', 'str_eq_2': 'str_eq', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_7': 'all_rows', 'crowd_8': 'crowd', 'home team_9': 'home team', 'richmond_10': 'richmond', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'away team_11': 'away team', 'essendon_12': 'essendon'}
|
{'and_5': [6], 'result_6': [], 'str_eq_2': [5], 'str_hop_1': [2], 'argmax_0': [1, 3], 'all_rows_7': [0], 'crowd_8': [0], 'home team_9': [1], 'richmond_10': [2], 'str_eq_4': [5], 'str_hop_3': [4], 'away team_11': [3], 'essendon_12': [4]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['st kilda', '7.12 ( 54 )', 'south melbourne', '10.19 ( 79 )', 'junction oval', '8000', '5 august 1944'], ['geelong', '11.20 ( 86 )', 'hawthorn', '9.7 ( 61 )', 'kardinia park', '7000', '5 august 1944'], ['collingwood', '8.12 ( 60 )', 'footscray', '15.9 ( 99 )', 'victoria park', '9000', '5 august 1944'], ['carlton', '4.14 ( 38 )', 'melbourne', '8.6 ( 54 )', 'princes park', '10000', '5 august 1944'], ['north melbourne', '11.12 ( 78 )', 'fitzroy', '15.11 ( 101 )', 'arden street oval', '14000', '5 august 1944'], ['richmond', '11.17 ( 83 )', 'essendon', '12.15 ( 87 )', 'punt road oval', '26000', '5 august 1944']]
|
yoji anjo
|
https://en.wikipedia.org/wiki/Yoji_Anjo
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17445522-3.html.csv
|
comparative
|
yoji anjo 's match against murilo bustamante lasted more rounds than his match against david abbott .
|
{'row_1': '4', 'row_2': '5', 'col': '6', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'murilo bustamante'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to murilo bustamante .', 'tostr': 'filter_eq { all_rows ; opponent ; murilo bustamante }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; murilo bustamante } ; round }', 'tointer': 'select the rows whose opponent record fuzzily matches to murilo bustamante . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'david abbott'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to david abbott .', 'tostr': 'filter_eq { all_rows ; opponent ; david abbott }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; david abbott } ; round }', 'tointer': 'select the rows whose opponent record fuzzily matches to david abbott . take the round record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opponent ; murilo bustamante } ; round } ; hop { filter_eq { all_rows ; opponent ; david abbott } ; round } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to murilo bustamante . take the round record of this row . select the rows whose opponent record fuzzily matches to david abbott . take the round record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; opponent ; murilo bustamante } ; round } ; hop { filter_eq { all_rows ; opponent ; david abbott } ; round } } = true
|
select the rows whose opponent record fuzzily matches to murilo bustamante . take the round record of this row . select the rows whose opponent record fuzzily matches to david abbott . take the round 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, 'opponent_7': 7, 'murilo bustamante_8': 8, 'round_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'david abbott_12': 12, 'round_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', 'opponent_7': 'opponent', 'murilo bustamante_8': 'murilo bustamante', 'round_9': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'david abbott_12': 'david abbott', 'round_13': 'round'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'murilo bustamante_8': [0], 'round_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'david abbott_12': [1], 'round_13': [3]}
|
['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location']
|
[['loss', '0 - 5 - 1', 'ryan gracie', 'submission ( armbar )', 'pride shockwave 2004', '1', '8:33', 'saitama , japan'], ['draw', '0 - 4 - 1', 'gia chirragishvili', 'draw', 'deep - 1st impact', '3', '5:00', 'nagoya , japan'], ['loss', '0 - 4', 'matt lindland', 'tko ( strikes )', 'ufc 29', '1', '2:58', 'tokyo , japan'], ['loss', '0 - 3', 'murilo bustamante', 'submission ( arm triangle choke )', 'ufc 25', '2', '0:31', 'tokyo , japan'], ['loss', '0 - 2', 'david abbott', 'decision', 'ufc japan', '1', '15:00', 'yokohama , japan'], ['loss', '0 - 1', 'sean alvarez', 'submission ( punches )', 'u - japan', '1', '34:26', 'japan']]
|
norwegian european communities membership referendum , 1972
|
https://en.wikipedia.org/wiki/Norwegian_European_Communities_membership_referendum%2C_1972
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1289762-1.html.csv
|
superlative
|
oslo was the constituency in the 1972 nowegian european communities membership referendum that had the highest total polling number .
|
{'scope': 'all', 'col_superlative': '4', 'row_superlative': '3', '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 poll ( % )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; total poll ( % ) }'}, 'constituency'], 'result': 'oslo', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; total poll ( % ) } ; constituency }'}, 'oslo'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; total poll ( % ) } ; constituency } ; oslo } = true', 'tointer': 'select the row whose total poll ( % ) record of all rows is maximum . the constituency record of this row is oslo .'}
|
eq { hop { argmax { all_rows ; total poll ( % ) } ; constituency } ; oslo } = true
|
select the row whose total poll ( % ) record of all rows is maximum . the constituency record of this row is oslo .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'total poll (%)_5': 5, 'constituency_6': 6, 'oslo_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'total poll (%)_5': 'total poll ( % )', 'constituency_6': 'constituency', 'oslo_7': 'oslo'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'total poll (%)_5': [0], 'constituency_6': [1], 'oslo_7': [2]}
|
['constituency', 'electorate', 's spoilt vote', 'total poll ( % )', 'for ( % )', 'against ( % )']
|
[['østfold', '152837', '392', '121498 ( 80 )', '58931 ( 49 )', '62567 ( 51 )'], ['akershus', '217851', '542', '180503 ( 83 )', '102521 ( 57 )', '77982 ( 43 )'], ['oslo', '356153', '619', '291654 ( 82 )', '193980 ( 67 )', '97674 ( 33 )'], ['hedmark', '124960', '519', '99508 ( 80 )', '44150 ( 44 )', '55358 ( 56 )'], ['oppland', '120082', '314', '94114 ( 79 )', '37550 ( 40 )', '56564 ( 60 )'], ['buskerud', '139999', '400', '110387 ( 79 )', '59532 ( 54 )', '50855 ( 46 )'], ['vestfold', '155338', '247', '94355 ( 79 )', '53515 ( 57 )', '40840 ( 43 )'], ['telemark', '108485', '211', '84056 ( 78 )', '32284 ( 38 )', '51772 ( 62 )'], ['aust - agder', '55276', '138', '40909 ( 74 )', '18659 ( 46 )', '22250 ( 54 )'], ['vest - agder', '81707', '177', '64100 ( 79 )', '27510 ( 43 )', '36590 ( 57 )'], ['rogaland', '174925', '309', '138601 ( 79 )', '62096 ( 45 )', '76505 ( 55 )'], ['hordaland', '248675', '511', '198095 ( 80 )', '96996 ( 49 )', '101099 ( 51 )'], ['sogn og fjordane', '67335', '153', '51705 ( 77 )', '15923 ( 31 )', '35782 ( 69 )'], ['møre og romsdal', '146917', '240', '114709 ( 78 )', '33504 ( 29 )', '81205 ( 71 )'], ['sør - trøndelag', '159730', '248', '122092 ( 77 )', '51827 ( 42 )', '70265 ( 58 )'], ['nord - trøndelag', '77954', '107', '60495 ( 78 )', '19101 ( 32 )', '41394 ( 68 )'], ['nordland', '157183', '549', '120979 ( 77 )', '33228 ( 27 )', '87751 ( 73 )'], ['troms', '88174', '385', '66499 ( 76 )', '19820 ( 30 )', '46679 ( 70 )']]
|
1959 formula one season
|
https://en.wikipedia.org/wiki/1959_Formula_One_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1140108-6.html.csv
|
superlative
|
in the 1959 formula one season , the earliest race with a cooper-climax constructor was the vii glover trophy .
|
{'scope': 'subset', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'cooper - climax'}}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'constructor', 'cooper - climax'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; constructor ; cooper - climax }', 'tointer': 'select the rows whose constructor record fuzzily matches to cooper - climax .'}, 'date'], 'result': None, 'ind': 1, 'tostr': 'argmin { filter_eq { all_rows ; constructor ; cooper - climax } ; date }'}, 'race name'], 'result': 'vii glover trophy', 'ind': 2, 'tostr': 'hop { argmin { filter_eq { all_rows ; constructor ; cooper - climax } ; date } ; race name }'}, 'vii glover trophy'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmin { filter_eq { all_rows ; constructor ; cooper - climax } ; date } ; race name } ; vii glover trophy } = true', 'tointer': 'select the rows whose constructor record fuzzily matches to cooper - climax . select the row whose date record of these rows is minimum . the race name record of this row is vii glover trophy .'}
|
eq { hop { argmin { filter_eq { all_rows ; constructor ; cooper - climax } ; date } ; race name } ; vii glover trophy } = true
|
select the rows whose constructor record fuzzily matches to cooper - climax . select the row whose date record of these rows is minimum . the race name record of this row is vii glover trophy .
|
4
|
4
|
{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'constructor_6': 6, 'cooper - climax_7': 7, 'date_8': 8, 'race name_9': 9, 'vii glover trophy_10': 10}
|
{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmin_1': 'argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'constructor_6': 'constructor', 'cooper - climax_7': 'cooper - climax', 'date_8': 'date', 'race name_9': 'race name', 'vii glover trophy_10': 'vii glover trophy'}
|
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'constructor_6': [0], 'cooper - climax_7': [0], 'date_8': [1], 'race name_9': [2], 'vii glover trophy_10': [3]}
|
['race name', 'circuit', 'date', 'winning driver', 'constructor', 'report']
|
[['vii glover trophy', 'goodwood', '30 march', 'stirling moss', 'cooper - climax', 'report'], ['xiv barc aintree 200', 'aintree', '18 april', 'jean behra', 'ferrari', 'report'], ['xi brdc international trophy', 'silverstone', '2 may', 'jack brabham', 'cooper - climax', 'report'], ['vi international gold cup', 'oulton park', '26 september', 'stirling moss', 'cooper - climax', 'report'], ['iv silver city trophy', 'snetterton', '10 october', 'ron flockhart', 'brm', 'report']]
|
2008 - 09 nbl season
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_NBL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16653153-13.html.csv
|
majority
|
the majority of games in the 2008-2009 nbl season took place on october 25th .
|
{'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': '25 october', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'date', '25 october'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them fuzzily match to 25 october .', 'tostr': 'most_eq { all_rows ; date ; 25 october } = true'}
|
most_eq { all_rows ; date ; 25 october } = true
|
for the date records of all rows , most of them fuzzily match to 25 october .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '25 october_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '25 october_4': '25 october'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '25 october_4': [0]}
|
['date', 'home team', 'score', 'away team', 'venue', 'box score', 'report']
|
[['22 october', 'townsville crocodiles', '103 - 101', 'sydney spirit', 'townsville entertainment centre', 'box score', '-'], ['22 october', 'cairns taipans', '101 - 92', 'new zealand breakers', 'cairns convention centre', 'box score', '-'], ['24 october', 'wollongong hawks', '98 - 96', 'gold coast blaze', 'win entertainment centre', 'box score', '-'], ['25 october', 'adelaide 36ers', '93 - 104', 'perth wildcats', 'distinctive homes dome', 'box score', '-'], ['25 october', 'gold coast blaze', '113 - 116', 'new zealand breakers', 'gold coast convention centre', 'box score', '-'], ['25 october', 'melbourne tigers', '110 - 97', 'townsville crocodiles', 'state netball and hockey centre', 'box score', '-'], ['25 october', 'south dragons', '94 - 65', 'cairns taipans', 'hisense arena', 'box score', '-'], ['26 october', 'sydney spirit', '99 - 86', 'wollongong hawks', 'state sports centre', 'box score', '-']]
|
target house 200
|
https://en.wikipedia.org/wiki/Target_House_200
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17801022-1.html.csv
|
count
|
two of the drivers in the target house 200 used pontiacs .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'pontiac', 'result': '2', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manufacturer', 'pontiac'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose manufacturer record fuzzily matches to pontiac .', 'tostr': 'filter_eq { all_rows ; manufacturer ; pontiac }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; manufacturer ; pontiac } }', 'tointer': 'select the rows whose manufacturer record fuzzily matches to pontiac . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; manufacturer ; pontiac } } ; 2 } = true', 'tointer': 'select the rows whose manufacturer record fuzzily matches to pontiac . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; manufacturer ; pontiac } } ; 2 } = true
|
select the rows whose manufacturer record fuzzily matches to pontiac . 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, 'manufacturer_5': 5, 'pontiac_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', 'manufacturer_5': 'manufacturer', 'pontiac_6': 'pontiac', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'manufacturer_5': [0], 'pontiac_6': [0], '2_7': [2]}
|
['year', 'date', 'driver', 'manufacturer', 'laps', '-', 'race time', 'average speed ( mph )']
|
[['1984', 'october 20', 'geoffrey bodine', 'pontiac', '197', '200.349 ( 322.43 )', '2:06:51', '94.765'], ['1985', 'october 19', 'brett bodine', 'pontiac', '197', '200.349 ( 322.43 )', '1:56:00', '103.629'], ['1986', 'october 18', 'morgan shepherd', 'buick', '197', '200.349 ( 322.43 )', '1:39:08', '101.177'], ['1987', 'october 24', 'morgan shepherd', 'buick', '197', '200.349 ( 322.43 )', '1:52:29', '106.396'], ['1988', 'october 22', 'harry gant', 'buick', '197', '200.349 ( 322.43 )', '1:50:09', '109.132'], ['1989', 'october 21', 'harry gant', 'buick', '197', '200.349 ( 322.43 )', '1:47:32', '111.788'], ['1990', 'october 20', 'steve grissom', 'oldsmobile', '197', '200.349 ( 322.43 )', '1:53:31', '105.896'], ['1991', 'october 19', 'ernie irvan', 'chevrolet', '197', '200.349 ( 322.43 )', '1:55:13', '104.333'], ['1992', 'october 24', 'mark martin', 'ford', '197', '200.349 ( 322.43 )', '1:41:30', '118.433'], ['1993', 'october 23', 'mark martin', 'ford', '197', '200.349 ( 322.43 )', '1:42:37', '117.144'], ['1994', 'october 22', 'mark martin', 'ford', '197', '200.349 ( 322.43 )', '1:49:15', '110.032'], ['1995', 'october 21', 'todd bodine', 'chevrolet', '197', '200.349 ( 322.43 )', '2:01:48', '98.694'], ['1996', 'october 19', 'mark martin', 'ford', '197', '200.349 ( 322.43 )', '1:36:38', '124.397'], ['1997', 'october 25', 'mark martin', 'ford', '197', '200.349 ( 322.43 )', '1:59:42', '100.426'], ['1998', 'october 31', 'elliott sadler', 'chevrolet', '197', '200.349 ( 322.43 )', '1:43:31', '116.126'], ['1999', 'october 23', 'mark martin', 'ford', '197', '200.349 ( 322.43 )', '1:45:36', '113.835'], ['2000', 'october 21', 'jeff green', 'chevrolet', '197', '200.349 ( 322.43 )', '1:46:15', '113.138'], ['2001', 'november 3', 'kenny wallace', 'chevrolet', '197', '200.349 ( 322.43 )', '1:36:56', '124.012'], ['2002', 'november 2', 'jamie mcmurray', 'chevrolet', '197', '200.349 ( 322.43 )', '1:41:18', '118.667']]
|
anaprof 2004
|
https://en.wikipedia.org/wiki/ANAPROF_2004
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18704095-8.html.csv
|
count
|
two teams scored 67 goals at the anaprof 2004 games .
|
{'scope': 'all', 'criterion': 'equal', 'value': '67', 'result': '2', 'col': '8', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'points', '67'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is equal to 67 .', 'tostr': 'filter_eq { all_rows ; points ; 67 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; points ; 67 } }', 'tointer': 'select the rows whose points record is equal to 67 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; points ; 67 } } ; 2 } = true', 'tointer': 'select the rows whose points record is equal to 67 . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; points ; 67 } } ; 2 } = true
|
select the rows whose points record is equal to 67 . the number of such rows is 2 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'points_5': 5, '67_6': 6, '2_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'points_5': 'points', '67_6': '67', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'points_5': [0], '67_6': [0], '2_7': [2]}
|
['place', 'team', 'played', 'draw', 'lost', 'goals scored', 'goals conceded', 'points']
|
[['1', 'árabe unido', '36', '5', '5', '63', '30', '83'], ['2', 'tauro', '36', '6', '9', '61', '22', '73'], ['3', 'san francisco', '36', '7', '9', '70', '31', '67'], ['4', 'el chorrillo', '36', '10', '7', '58', '51', '67'], ['5', 'plaza amador', '35', '8', '9', '59', '33', '62'], ['6', 'alianza', '36', '6', '18', '38', '53', '42'], ['7', 'atlético veragüense', '35', '7', '18', '38', '52', '37'], ['8', 'sporting coclé', '36', '9', '18', '42', '60', '36'], ['9', 'colón river', '36', '7', '22', '41', '78', '32'], ['10', 'pan de azúcar', '36', '7', '28', '22', '92', '10']]
|
list of apollo astronauts
|
https://en.wikipedia.org/wiki/List_of_Apollo_astronauts
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-129540-2.html.csv
|
ordinal
|
in the list of apollo astronauts jim lovell was the oldest man to take part in a mission .
|
{'row': '9', 'col': '3', 'order': '1', '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', 'age on mission', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; age on mission ; 1 }'}, 'name'], 'result': 'jim lovell', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; age on mission ; 1 } ; name }'}, 'jim lovell'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; age on mission ; 1 } ; name } ; jim lovell } = true', 'tointer': 'select the row whose age on mission record of all rows is 1st maximum . the name record of this row is jim lovell .'}
|
eq { hop { nth_argmax { all_rows ; age on mission ; 1 } ; name } ; jim lovell } = true
|
select the row whose age on mission record of all rows is 1st maximum . the name record of this row is jim lovell .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'age on mission_5': 5, '1_6': 6, 'name_7': 7, 'jim lovell_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', 'age on mission_5': 'age on mission', '1_6': '1', 'name_7': 'name', 'jim lovell_8': 'jim lovell'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'age on mission_5': [0], '1_6': [0], 'name_7': [1], 'jim lovell_8': [2]}
|
['name', 'born', 'age on mission', 'mission', 'mission dates', 'service']
|
[['frank borman', 'march 14 , 1928 ( age85 )', '40', 'apollo 8', 'december 21 - 27 , 1968', 'air force'], ['jim lovell', 'march 25 , 1928 ( age85 )', '40', 'apollo 8', 'december 21 - 27 , 1968', 'navy'], ['bill anders', 'october 17 , 1933 ( age80 )', '35', 'apollo 8', 'december 21 - 27 , 1968', 'air force'], ['tom stafford', 'september 17 , 1930 ( age83 )', '38', 'apollo 10', 'may 18 - 26 , 1969', 'air force'], ['john young', 'september 24 , 1930 ( age83 )', '38', 'apollo 10', 'may 18 - 26 , 1969', 'navy'], ['eugene cernan', 'march 14 , 1934 ( age79 )', '35', 'apollo 10', 'may 18 - 26 , 1969', 'navy'], ['mike collins', 'october 31 , 1930 ( age83 )', '38', 'apollo 11', 'july 16 - 24 , 1969', 'air force'], ['dick gordon', 'october 5 , 1929 ( age84 )', '40', 'apollo 12', 'november 14 - 24 , 1969', 'navy'], ['jim lovell', 'march 25 , 1928 ( age85 )', '42', 'apollo 13', 'april 11 - 17 , 1970', 'navy'], ['jack swigert', 'august 30 , 1931', '38', 'apollo 13', 'april 11 - 17 , 1970', 'nasa'], ['fred haise', 'november 14 , 1933 ( age80 )', '36', 'apollo 13', 'april 11 - 17 , 1970', 'nasa'], ['stu roosa', 'august 16 , 1933', '37', 'apollo 14', 'january 31 - february 9 , 1971', 'air force'], ['al worden', 'february 7 , 1932 ( age81 )', '39', 'apollo 15', 'july 26 - august 7 , 1971', 'air force'], ['ken mattingly', 'march 17 , 1936 ( age77 )', '36', 'apollo 16', 'april 16 - 27 , 1972', 'navy'], ['ron evans', 'november 10 , 1933', '39', 'apollo 17', 'december 7 - 19 , 1972', 'navy']]
|
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
|
unique
|
only one of the players was drafted to the clippers .
|
{'scope': 'all', 'row': '1', 'col': '6', 'col_other': 'n/a', 'criterion': 'fuzzily_match', 'value': 'clippers', 'subset': None}
|
{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nba draft', 'clippers'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nba draft record fuzzily matches to clippers .', 'tostr': 'filter_eq { all_rows ; nba draft ; clippers }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; nba draft ; clippers } } = true', 'tointer': 'select the rows whose nba draft record fuzzily matches to clippers . there is only one such row in the table .'}
|
only { filter_eq { all_rows ; nba draft ; clippers } } = true
|
select the rows whose nba draft record fuzzily matches to clippers . 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, 'nba draft_4': 4, 'clippers_5': 5}
|
{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'nba draft_4': 'nba draft', 'clippers_5': 'clippers'}
|
{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'nba draft_4': [0], 'clippers_5': [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 )']]
|
international softball congress
|
https://en.wikipedia.org/wiki/International_Softball_Congress
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18618672-1.html.csv
|
count
|
in the international softball congress results shown mary star all stars won in five occasions .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'mary star all - stars', 'result': '5', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st place team', 'mary star all - stars'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st place team record fuzzily matches to mary star all - stars .', 'tostr': 'filter_eq { all_rows ; 1st place team ; mary star all - stars }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; 1st place team ; mary star all - stars } }', 'tointer': 'select the rows whose 1st place team record fuzzily matches to mary star all - stars . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; 1st place team ; mary star all - stars } } ; 5 } = true', 'tointer': 'select the rows whose 1st place team record fuzzily matches to mary star all - stars . the number of such rows is 5 .'}
|
eq { count { filter_eq { all_rows ; 1st place team ; mary star all - stars } } ; 5 } = true
|
select the rows whose 1st place team record fuzzily matches to mary star all - stars . the number of such rows is 5 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, '1st place team_5': 5, 'mary star all - stars_6': 6, '5_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', '1st place team_5': '1st place team', 'mary star all - stars_6': 'mary star all - stars', '5_7': '5'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], '1st place team_5': [0], 'mary star all - stars_6': [0], '5_7': [2]}
|
['year', '1st place team', '2nd place team', '3rd place team', '4th place team', 'host location']
|
[['1947', 'farm fresh market , phoenix , az', 'palomar foods , san diego , ca', 'andrews motors , rome , ga', 'anderson sporting goods , oklahoma city , ok', 'phoenix , az'], ['1948', 'merchants , taft , ca', 'clark - smith autos , phoenix , az', 'bluebonnet laundry , lubbock , tx', 'wheeler realty , greeley , co', 'oklahoma city , ok'], ['1949', 'hanford kings , hanford , ca', 'roundup bar , somerton , az', 'merchants , taft , ca', 'grever truck lines , tulsa , ok', 'greeley , co'], ['1950', 'hoak packers , fresno , ca', 'hanford kings , hanford , ca', 'grever truck lines , tulsa , ok', 'wells motors , greeley , co', 'greeley , co'], ['1951', 'softball club , calvert , tx', 'fike plumbers , phoenix , az', 'hanford kings , hanford , ca', 'colonials , springfield , mo', 'phoenix , az'], ['1952', 'mary star all - stars , san pedro , ca', 'softball club , calvert , tx', 'wheeler general tires , salt lake city , ut', 'shawver bros , phoenix , az', 'phoenix , az'], ['1953', 'mary star all - stars , san pedro , ca', 'fike plumbers , phoenix , az', 'softball club , calvert , tx', 'b & b freight , tulsa , ok', 'salt lake city , ut'], ['1954', 'mary star all - stars , san pedro , ca', 'fike plumbers , phoenix , az', 'berry - carter plumbers , tulsa , ok', 'mcginnis equipment , phoenix , az', 'phoenix , az'], ['1955', 'sapulpa brick & tile , sapulpa , ok', 'mary star all - stars , san pedro , ca', 'all - stars , ralls , tx', 'berry - carter plumbers , tulsa , ok', 'tulsa , ok'], ['1956', 'mary star all - stars , san pedro , ca', 'sapulpa brick & tile , sapulpa , ok', 'elks , oxnard , ca', 'berry - carter plumbers , tulsa , ok', 'tulsa , ok'], ['1957', 'mary star all - stars , san pedro , ca', 'sapulpa brick & tile , sapulpa , ok', 'elks , oxnard , ca', 'frank phillips men ™ s club , phillips , tx', 'el paso , tx']]
|
giovanni cornacchia
|
https://en.wikipedia.org/wiki/Giovanni_Cornacchia
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11881177-1.html.csv
|
ordinal
|
in 1967 at the mediterranean games , giovanni cornacchia came in 1st place in the 110m hurdles .
|
{'scope': 'all', 'row': '5', 'col': '4', 'order': '1', 'col_other': '1,2,5', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'result', '1'], 'result': '1st', 'ind': 0, 'tostr': 'nth_min { all_rows ; result ; 1 }', 'tointer': 'the 1st minimum result record of all rows is 1st .'}, '1st'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; result ; 1 } ; 1st }', 'tointer': 'the 1st minimum result record of all rows is 1st .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'result', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; result ; 1 }'}, 'year'], 'result': '1967', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; result ; 1 } ; year }'}, '1967'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; result ; 1 } ; year } ; 1967 }', 'tointer': 'the year record of the row with 1st minimum result record is 1967 .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'result', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; result ; 1 }'}, 'tournament'], 'result': 'mediterranean games', 'ind': 5, 'tostr': 'hop { nth_argmin { all_rows ; result ; 1 } ; tournament }'}, 'mediterranean games'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { nth_argmin { all_rows ; result ; 1 } ; tournament } ; mediterranean games }', 'tointer': 'the tournament record of the row with 1st minimum result record is mediterranean games .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'result', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; result ; 1 }'}, 'event'], 'result': '110 m hurdles', 'ind': 7, 'tostr': 'hop { nth_argmin { all_rows ; result ; 1 } ; event }'}, '110 m hurdles'], 'result': True, 'ind': 8, 'tostr': 'eq { hop { nth_argmin { all_rows ; result ; 1 } ; event } ; 110 m hurdles }', 'tointer': 'the event record of the row with 1st minimum result record is 110 m hurdles .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; result ; 1 } ; tournament } ; mediterranean games } ; eq { hop { nth_argmin { all_rows ; result ; 1 } ; event } ; 110 m hurdles } }', 'tointer': 'the tournament record of the row with 1st minimum result record is mediterranean games . the event record of the row with 1st minimum result record is 110 m hurdles .'}], 'result': True, 'ind': 10, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; result ; 1 } ; year } ; 1967 } ; and { eq { hop { nth_argmin { all_rows ; result ; 1 } ; tournament } ; mediterranean games } ; eq { hop { nth_argmin { all_rows ; result ; 1 } ; event } ; 110 m hurdles } } }', 'tointer': 'the year record of the row with 1st minimum result record is 1967 . the tournament record of the row with 1st minimum result record is mediterranean games . the event record of the row with 1st minimum result record is 110 m hurdles .'}], 'result': True, 'ind': 11, 'tostr': 'and { eq { nth_min { all_rows ; result ; 1 } ; 1st } ; and { eq { hop { nth_argmin { all_rows ; result ; 1 } ; year } ; 1967 } ; and { eq { hop { nth_argmin { all_rows ; result ; 1 } ; tournament } ; mediterranean games } ; eq { hop { nth_argmin { all_rows ; result ; 1 } ; event } ; 110 m hurdles } } } } = true', 'tointer': 'the 1st minimum result record of all rows is 1st . the year record of the row with 1st minimum result record is 1967 . the tournament record of the row with 1st minimum result record is mediterranean games . the event record of the row with 1st minimum result record is 110 m hurdles .'}
|
and { eq { nth_min { all_rows ; result ; 1 } ; 1st } ; and { eq { hop { nth_argmin { all_rows ; result ; 1 } ; year } ; 1967 } ; and { eq { hop { nth_argmin { all_rows ; result ; 1 } ; tournament } ; mediterranean games } ; eq { hop { nth_argmin { all_rows ; result ; 1 } ; event } ; 110 m hurdles } } } } = true
|
the 1st minimum result record of all rows is 1st . the year record of the row with 1st minimum result record is 1967 . the tournament record of the row with 1st minimum result record is mediterranean games . the event record of the row with 1st minimum result record is 110 m hurdles .
|
14
|
12
|
{'and_11': 11, 'result_12': 12, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_13': 13, 'result_14': 14, '1_15': 15, '1st_16': 16, 'and_10': 10, 'eq_4': 4, 'num_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_17': 17, 'result_18': 18, '1_19': 19, 'year_20': 20, '1967_21': 21, 'and_9': 9, 'str_eq_6': 6, 'str_hop_5': 5, 'tournament_22': 22, 'mediterranean games_23': 23, 'str_eq_8': 8, 'str_hop_7': 7, 'event_24': 24, '110 m hurdles_25': 25}
|
{'and_11': 'and', 'result_12': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_13': 'all_rows', 'result_14': 'result', '1_15': '1', '1st_16': '1st', 'and_10': 'and', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_17': 'all_rows', 'result_18': 'result', '1_19': '1', 'year_20': 'year', '1967_21': '1967', 'and_9': 'and', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'tournament_22': 'tournament', 'mediterranean games_23': 'mediterranean games', 'str_eq_8': 'str_eq', 'str_hop_7': 'str_hop', 'event_24': 'event', '110 m hurdles_25': '110 m hurdles'}
|
{'and_11': [12], 'result_12': [], 'eq_1': [11], 'nth_min_0': [1], 'all_rows_13': [0], 'result_14': [0], '1_15': [0], '1st_16': [1], 'and_10': [11], 'eq_4': [10], 'num_hop_3': [4], 'nth_argmin_2': [3, 5, 7], 'all_rows_17': [2], 'result_18': [2], '1_19': [2], 'year_20': [3], '1967_21': [4], 'and_9': [10], 'str_eq_6': [9], 'str_hop_5': [6], 'tournament_22': [5], 'mediterranean games_23': [6], 'str_eq_8': [9], 'str_hop_7': [8], 'event_24': [7], '110 m hurdles_25': [8]}
|
['year', 'tournament', 'venue', 'result', 'event']
|
[['1962', 'european championships', 'belgrade , yugoslavia', '2nd', '110 m hurdles'], ['1964', 'olympic games', 'tokyo , japan', '7th', '110 m hurdles'], ['1965', 'universiade', 'budapest , hungary', '2nd', '110 m hurdles'], ['1966', 'european championships', 'budapest , hungary', '5th', '110 m hurdles'], ['1967', 'mediterranean games', 'tunis , tunisia', '1st', '110 m hurdles']]
|
kathleen horvath
|
https://en.wikipedia.org/wiki/Kathleen_Horvath
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17727652-3.html.csv
|
ordinal
|
of the tournaments that kathleen horvath participated in , the 2nd to earliest was in nashville .
|
{'row': '2', 'col': '2', 'order': '2', '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', 'date', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 2 }'}, 'tournament'], 'result': 'nashville', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 2 } ; tournament }'}, 'nashville'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; nashville } = true', 'tointer': 'select the row whose date record of all rows is 2nd minimum . the tournament record of this row is nashville .'}
|
eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; nashville } = true
|
select the row whose date record of all rows is 2nd minimum . the tournament record of this row is nashville .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '2_6': 6, 'tournament_7': 7, 'nashville_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', '2_6': '2', 'tournament_7': 'tournament', 'nashville_8': 'nashville'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '2_6': [0], 'tournament_7': [1], 'nashville_8': [2]}
|
['outcome', 'date', 'tournament', 'surface', 'opponent', 'score']
|
[['winner', 'january 19 , 1981', 'montreal', 'carpet ( i )', 'candy reynolds', '6 - 4 , 7 - 6'], ['winner', 'february 28 , 1983', 'nashville', 'carpet ( i )', 'marcela skuherská', '6 - 4 , 6 - 3'], ['runner - up', 'may 16 , 1983', 'berlin', 'clay', 'chris evert - lloyd', '4 - 6 , 6 - 7 ( 1 )'], ['winner', 'november 7 , 1983', 'honolulu', 'carpet ( i )', 'carling bassett', '4 - 6 , 6 - 2 , 7 - 6 ( 1 )'], ['runner - up', 'january 23 , 1984', 'marco island', 'clay', 'bonnie gadusek', '6 - 3 , 0 - 6 , 4 - 6'], ['runner - up', 'may 14 , 1984', 'berlin', 'clay', 'claudia kohde - kilsch', '6 - 7 ( 8 ) , 1 - 6'], ['winner', 'march 4 , 1985', 'indianapolis', 'carpet ( i )', 'elise burgin', '6 - 2 , 6 - 4'], ['winner', 'march 25 , 1985', 'palm beach gardens', 'clay', 'petra delhees - jauch', '3 - 6 , 6 - 3 , 6 - 3'], ['winner', 'july 6 , 1987', 'knokke', 'clay', 'bettina bunge', '6 - 1 , 7 - 6 ( 5 )']]
|
mori no asagao
|
https://en.wikipedia.org/wiki/Mori_no_Asagao
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29039942-1.html.csv
|
comparative
|
for mori no asagao , the 2nd highest ratings were for episode four .
|
{'row_1': '3', 'row_2': '2', 'col': '6', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'yes', 'diff_result': None}
|
{'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'episode', '4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose episode record fuzzily matches to 4 .', 'tostr': 'filter_eq { all_rows ; episode ; 4 }'}, 'ratings ( kanto )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) }', 'tointer': 'select the rows whose episode record fuzzily matches to 4 . take the ratings ( kanto ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'episode', '3'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose episode record fuzzily matches to 3 .', 'tostr': 'filter_eq { all_rows ; episode ; 3 }'}, 'ratings ( kanto )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) }', 'tointer': 'select the rows whose episode record fuzzily matches to 3 . take the ratings ( kanto ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } }', 'tointer': 'select the rows whose episode record fuzzily matches to 4 . take the ratings ( kanto ) record of this row . select the rows whose episode record fuzzily matches to 3 . take the ratings ( kanto ) record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'episode', '4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose episode record fuzzily matches to 4 .', 'tostr': 'filter_eq { all_rows ; episode ; 4 }'}, 'ratings ( kanto )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) }', 'tointer': 'select the rows whose episode record fuzzily matches to 4 . take the ratings ( kanto ) record of this row .'}, '4.3'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; 4.3 }', 'tointer': 'the ratings ( kanto ) record of the first row is 4.3 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'episode', '3'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose episode record fuzzily matches to 3 .', 'tostr': 'filter_eq { all_rows ; episode ; 3 }'}, 'ratings ( kanto )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) }', 'tointer': 'select the rows whose episode record fuzzily matches to 3 . take the ratings ( kanto ) record of this row .'}, '4.6'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } ; 4.6 }', 'tointer': 'the ratings ( kanto ) record of the second row is 4.6 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; 4.3 } ; eq { hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } ; 4.6 } }', 'tointer': 'the ratings ( kanto ) record of the first row is 4.3 . the ratings ( kanto ) record of the second row is 4.6 .'}], 'result': True, 'ind': 8, 'tostr': 'and { less { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } } ; and { eq { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; 4.3 } ; eq { hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } ; 4.6 } } } = true', 'tointer': 'select the rows whose episode record fuzzily matches to 4 . take the ratings ( kanto ) record of this row . select the rows whose episode record fuzzily matches to 3 . take the ratings ( kanto ) record of this row . the first record is less than the second record . the ratings ( kanto ) record of the first row is 4.3 . the ratings ( kanto ) record of the second row is 4.6 .'}
|
and { less { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } } ; and { eq { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; 4.3 } ; eq { hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } ; 4.6 } } } = true
|
select the rows whose episode record fuzzily matches to 4 . take the ratings ( kanto ) record of this row . select the rows whose episode record fuzzily matches to 3 . take the ratings ( kanto ) record of this row . the first record is less than the second record . the ratings ( kanto ) record of the first row is 4.3 . the ratings ( kanto ) record of the second row is 4.6 .
|
13
|
9
|
{'and_8': 8, 'result_9': 9, 'less_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'episode_11': 11, '4_12': 12, 'ratings (kanto)_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'episode_15': 15, '3_16': 16, 'ratings (kanto)_17': 17, 'and_7': 7, 'eq_5': 5, '4.3_18': 18, 'eq_6': 6, '4.6_19': 19}
|
{'and_8': 'and', 'result_9': 'true', 'less_4': 'less', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'episode_11': 'episode', '4_12': '4', 'ratings (kanto)_13': 'ratings ( kanto )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'episode_15': 'episode', '3_16': '3', 'ratings (kanto)_17': 'ratings ( kanto )', 'and_7': 'and', 'eq_5': 'eq', '4.3_18': '4.3', 'eq_6': 'eq', '4.6_19': '4.6'}
|
{'and_8': [9], 'result_9': [], 'less_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'episode_11': [0], '4_12': [0], 'ratings (kanto)_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'episode_15': [1], '3_16': [1], 'ratings (kanto)_17': [3], 'and_7': [8], 'eq_5': [7], '4.3_18': [5], 'eq_6': [7], '4.6_19': [6]}
|
['episode', 'title', 'writer', 'director', 'original airdate', 'ratings ( kanto )']
|
[['2', 'instruction execution ( 死刑執行命令 )', 'daisuke habara', 'akimitsu sasaki', 'oct 25 , 2010 22.00 - 22.54', '3.8'], ['3', 'give flowers to the condemned ( 死刑囚へ贈る花 )', 'shizuka oki', 'makito murakami', 'nov 1 , 2010 22.00 - 22.54', '4.6'], ['4', 'wedding bride prison ( 獄中結婚の花嫁 )', 'daisuke habara', 'makito murakami', 'nov 8 , 2010 22.00 - 22.54', '4.3'], ['6', 'gray man 33 years of false accusation ( 冤罪33年の白髪男 )', 'daisuke habara', 'munenobu yamauchi', 'nov 22 , 2010 22.00 - 22.54', '3.2'], ['8', 'visits last miracle ( 最期の面会の奇跡 )', 'shizuka oki', 'tomoyuki furumaya', 'dec 6 , 2010 22.00 - 22.54', '3.0']]
|
iran at the 1998 asian games
|
https://en.wikipedia.org/wiki/Iran_at_the_1998_Asian_Games
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10831471-37.html.csv
|
count
|
6 of the athletes were n/a at the round 5 in iran at the 1998 asian games .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'n / a', 'result': '6', 'col': '7', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'round 5', 'n / a'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose round 5 record fuzzily matches to n / a .', 'tostr': 'filter_eq { all_rows ; round 5 ; n / a }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; round 5 ; n / a } }', 'tointer': 'select the rows whose round 5 record fuzzily matches to n / a . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; round 5 ; n / a } } ; 6 } = true', 'tointer': 'select the rows whose round 5 record fuzzily matches to n / a . the number of such rows is 6 .'}
|
eq { count { filter_eq { all_rows ; round 5 ; n / a } } ; 6 } = true
|
select the rows whose round 5 record fuzzily matches to n / a . 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, 'round 5_5': 5, 'n / a_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', 'round 5_5': 'round 5', 'n / a_6': 'n / a', '6_7': '6'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'round 5_5': [0], 'n / a_6': [0], '6_7': [2]}
|
['athlete', 'event', 'round 1', 'round 2', 'round 3', 'round 4', 'round 5', 'final']
|
[['ali ashkani', '54 kg', 'suwanna w 10 - 0', 'wang l 4 - 9', 'repechage aripov l 2 - 3', 'did not advance', 'did not advance', 'did not advance'], ['sardar pashaei', '58 kg', 'tumasis w 12 - 0', '-', 'khudaiberdiev l 5 - 8', 'repechage nishimi w 3 - 2', 'n / a', '3rd place match sheng l 0 - 3'], ['parviz zeidvand', '63 kg', 'mamedov w 3 - 0', 'yi l 1 - 2 , dsq', 'did not advance', 'did not advance', 'did not advance', 'did not advance'], ['gholam hossein pezeshki', '69 kg', 'al - saleh w 5 - 0', '-', 'manukyan l 0 - 10', 'repechage jong l 2 - 3', 'n / a', 'did not advance'], ['mehdi rahimi', '76 kg', 'baiseitov l 0 - 4', 'repechage al - ken l 3 - 11', 'did not advance', 'did not advance', 'n / a', 'did not advance'], ['behrouz jamshidi', '85 kg', 'park l 2 - 4', 'repechage redjepov w 4 - 0', '-', 'repechage achilov w 7 - 0', 'n / a', '3rd place match yokoyama l 2 - 4'], ['mohammad sharabiani', '97 kg', 'matvienko l 0 - 3', 'repechage iwabuchi w 7 - 0', '-', 'repechage park l 0 - 4', 'n / a', 'did not advance'], ['mehdi sabzali', '130 kg', '-', 'zhao w 0 - 0', 'hamaue w 3 - 0', 'n / a', 'n / a', 'quziev w 6 - 3']]
|
racquetball at the world games
|
https://en.wikipedia.org/wiki/Racquetball_at_the_World_Games
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16142354-5.html.csv
|
comparative
|
mexico won more medals in racquetball in the world games than canada did .
|
{'row_1': '2', 'row_2': '3', 'col': '6', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'mexico'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to mexico .', 'tostr': 'filter_eq { all_rows ; nation ; mexico }'}, 'total'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; mexico } ; total }', 'tointer': 'select the rows whose nation record fuzzily matches to mexico . take the total record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'canada'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to canada .', 'tostr': 'filter_eq { all_rows ; nation ; canada }'}, 'total'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; canada } ; total }', 'tointer': 'select the rows whose nation record fuzzily matches to canada . take the total record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; nation ; mexico } ; total } ; hop { filter_eq { all_rows ; nation ; canada } ; total } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to mexico . take the total record of this row . select the rows whose nation record fuzzily matches to canada . take the total record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; nation ; mexico } ; total } ; hop { filter_eq { all_rows ; nation ; canada } ; total } } = true
|
select the rows whose nation record fuzzily matches to mexico . take the total record of this row . select the rows whose nation record fuzzily matches to canada . take the total record of this row . the first record is greater than the second record .
|
5
|
5
|
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nation_7': 7, 'mexico_8': 8, 'total_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'nation_11': 11, 'canada_12': 12, 'total_13': 13}
|
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nation_7': 'nation', 'mexico_8': 'mexico', 'total_9': 'total', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'canada_12': 'canada', 'total_13': 'total'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'nation_7': [0], 'mexico_8': [0], 'total_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'nation_11': [1], 'canada_12': [1], 'total_13': [3]}
|
['rank', 'nation', 'gold', 'silver', 'bronze', 'total']
|
[['1', 'united states', '9', '6', '4', '19'], ['2', 'mexico', '3', '2', '2', '7'], ['3', 'canada', '0', '2', '4', '6'], ['5', 'netherlands', '0', '1', '1', '2'], ['4', 'colombia', '0', '1', '0', '1'], ['5', 'chile', '0', '0', '1', '1'], ['total', 'total', '12', '12', '12', '36']]
|
2008 - 09 toronto raptors season
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_Toronto_Raptors_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17323092-7.html.csv
|
count
|
in february of the 2008 - 09 season , the toronto raptors won 4 games .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'w', 'result': '4', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', 'w'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to w .', 'tostr': 'filter_eq { all_rows ; score ; w }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; score ; w } }', 'tointer': 'select the rows whose score record fuzzily matches to w . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; score ; w } } ; 4 } = true', 'tointer': 'select the rows whose score record fuzzily matches to w . the number of such rows is 4 .'}
|
eq { count { filter_eq { all_rows ; score ; w } } ; 4 } = true
|
select the rows whose score record fuzzily matches to w . 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, 'score_5': 5, 'w_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', 'score_5': 'score', 'w_6': 'w', '4_7': '4'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'score_5': [0], 'w_6': [0], '4_7': [2]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['49', 'february 1', 'orlando', 'l 90 - 113 ( ot )', 'josé calderón ( 16 )', 'joey graham ( 12 )', 'josé calderón , will solomon ( 5 )', 'air canada centre 19800', '19 - 30'], ['50', 'february 3', 'cleveland', 'l 83 - 101 ( ot )', 'chris bosh ( 29 )', 'andrea bargnani ( 10 )', 'anthony parker ( 8 )', 'quicken loans arena 20562', '19 - 31'], ['51', 'february 4', 'la lakers', 'l 107 - 115 ( ot )', 'joey graham ( 24 )', "andrea bargnani , jermaine o'neal ( 9 )", 'anthony parker ( 9 )', 'air canada centre 19800', '19 - 32'], ['52', 'february 6', 'new orleans', 'l 92 - 101 ( ot )', "jermaine o'neal ( 24 )", 'jamario moon ( 7 )', 'josé calderón ( 9 )', 'new orleans arena 17319', '19 - 33'], ['53', 'february 7', 'memphis', 'l 70 - 78 ( ot )', 'josé calderón ( 18 )', 'andrea bargnani , jamario moon ( 9 )', 'josé calderón ( 5 )', 'fedexforum 11498', '19 - 34'], ['54', 'february 10', 'minnesota', 'w 110 - 102 ( ot )', 'joey graham ( 24 )', 'jamario moon ( 9 )', 'josé calderón ( 9 )', 'target center 12722', '20 - 34'], ['55', 'february 11', 'san antonio', 'w 91 - 89 ( ot )', 'andrea bargnani ( 23 )', "jermaine o'neal ( 10 )", 'anthony parker ( 4 )', 'air canada centre 18909', '21 - 34'], ['56', 'february 18', 'cleveland', 'l 76 - 93 ( ot )', 'joey graham ( 15 )', 'anthony parker ( 7 )', 'shawn marion ( 6 )', 'air canada centre 19800', '21 - 35'], ['57', 'february 20', 'new york', 'l 97 - 127 ( ot )', 'joey graham ( 19 )', 'shawn marion ( 12 )', 'josé calderón ( 10 )', 'madison square garden 19763', '21 - 36'], ['58', 'february 22', 'new york', 'w 111 - 100 ( ot )', 'andrea bargnani ( 28 )', 'shawn marion ( 15 )', 'josé calderón ( 11 )', 'air canada centre 19800', '22 - 36'], ['59', 'february 24', 'minnesota', 'w 118 - 110 ( ot )', 'andrea bargnani , chris bosh ( 26 )', 'shawn marion ( 8 )', 'josé calderón ( 13 )', 'air canada centre 17457', '23 - 36']]
|
1969 boston patriots season
|
https://en.wikipedia.org/wiki/1969_Boston_Patriots_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10646790-2.html.csv
|
aggregation
|
in the 1969 season of boston patriots , average attendance for games against the san diego chargers was 25,746 .
|
{'scope': 'subset', 'col': '7', 'type': 'average', 'result': '25746', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'san diego chargers'}}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'san diego chargers'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; opponent ; san diego chargers }', 'tointer': 'select the rows whose opponent record fuzzily matches to san diego chargers .'}, 'attendance'], 'result': '25746', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; opponent ; san diego chargers } ; attendance }'}, '25746'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; opponent ; san diego chargers } ; attendance } ; 25746 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to san diego chargers . the average of the attendance record of these rows is 25746 .'}
|
round_eq { avg { filter_eq { all_rows ; opponent ; san diego chargers } ; attendance } ; 25746 } = true
|
select the rows whose opponent record fuzzily matches to san diego chargers . the average of the attendance record of these rows is 25746 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opponent_5': 5, 'san diego chargers_6': 6, 'attendance_7': 7, '25746_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opponent_5': 'opponent', 'san diego chargers_6': 'san diego chargers', 'attendance_7': 'attendance', '25746_8': '25746'}
|
{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'san diego chargers_6': [0], 'attendance_7': [1], '25746_8': [2]}
|
['week', 'date', 'opponent', 'result', 'stadium', 'record', 'attendance']
|
[['1', 'september 14 , 1969', 'denver broncos', 'l 35 - 7', 'mile high stadium', '0 - 1', '43679'], ['2', 'september 21 , 1969', 'kansas city chiefs', 'l 31 - 0', 'alumni stadium', '0 - 2', '22002'], ['3', 'september 28 , 1969', 'oakland raiders', 'l 38 - 23', 'alumni stadium', '0 - 3', '19069'], ['4', 'october 5 , 1969', 'new york jets', 'l 23 - 14', 'alumni stadium', '0 - 4', '25584'], ['5', 'october 11 , 1969', 'buffalo bills', 'l 23 - 16', 'war memorial stadium', '0 - 5', '46201'], ['6', 'october 19 , 1969', 'san diego chargers', 'l 13 - 10', 'alumni stadium', '0 - 6', '18346'], ['7', 'october 26 , 1969', 'new york jets', 'l 23 - 17', 'shea stadium', '0 - 7', '62298'], ['8', 'november 2 , 1969', 'houston oilers', 'w 24 - 0', 'alumni stadium', '1 - 7', '19006'], ['9', 'november 9 , 1969', 'miami dolphins', 'l 17 - 16', 'alumni stadium', '1 - 8', '19821'], ['10', 'november 16 , 1969', 'cincinnati bengals', 'w 25 - 14', 'nippert stadium', '2 - 8', '27927'], ['11', 'november 23 , 1969', 'buffalo bills', 'w 35 - 21', 'alumni stadium', '3 - 8', '25584'], ['12', 'november 30 , 1969', 'miami dolphins', 'w 38 - 23', 'miami orange bowl', '4 - 8', '32121'], ['13', 'december 7 , 1969', 'san diego chargers', 'l 28 - 18', 'san diego stadium', '4 - 9', '33146']]
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.