niryuu/sni-each-converted huggingface dataset:
Command: eq { nth_max { all_rows ; winnings ; 3 } ; 160261 }, interpretation: select the rows whose points record is equal to 0 . there is only one such row in the table . the artist record of this unqiue row is photogenique .
Output: no
Command: and { only { filter_eq { all_rows ; points ; 0 } } ; eq { hop { filter_eq { all_rows ; points ; 0 } ; artist } ; photogenique } }, interpretation: select the rows whose points record is equal to 0 . there is only one such row in the table . the artist record of this unqiue row is photogenique .
Output: yes
Command: and { only { filter_eq { all_rows ; points ; 0 } } ; eq { hop { filter_eq { all_rows ; points ; 0 } ; artist } ; photogenique } }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is wright patman .
Output: no
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; wright patman }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is wright patman .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; wright patman }, interpretation: select the rows whose country record fuzzily matches to united states . for the to par records of these rows , most of them are greater than -8 .
Output: no
Command: most_greater { filter_eq { all_rows ; country ; united states } ; to par ; -8 }, interpretation: select the rows whose country record fuzzily matches to united states . for the to par records of these rows , most of them are greater than -8 .
Output: yes
Command: most_greater { filter_eq { all_rows ; country ; united states } ; to par ; -8 }, interpretation: the sum of the quantity record of all rows is 10 .
Output: no
Command: round_eq { sum { all_rows ; quantity } ; 10 }, interpretation: the sum of the quantity record of all rows is 10 .
Output: yes
Command: round_eq { sum { all_rows ; quantity } ; 10 }, interpretation: for the date records of all rows , all of them fuzzily match to 1 august .
Output: no
Command: all_eq { all_rows ; date ; 1 august }, interpretation: for the date records of all rows , all of them fuzzily match to 1 august .
Output: yes
Command: all_eq { all_rows ; date ; 1 august }, interpretation: for the mlb draft records of all rows , most of them fuzzily match to draft .
Output: no
Command: most_eq { all_rows ; mlb draft ; draft }, interpretation: for the mlb draft records of all rows , most of them fuzzily match to draft .
Output: yes
Command: most_eq { all_rows ; mlb draft ; draft }, interpretation: select the rows whose tournament record fuzzily matches to us open . take the top - 10 record of this row . select the rows whose tournament record fuzzily matches to the open championship . take the top - 10 record of this row . the first record is 1 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; tournament ; us open } ; top - 10 } ; hop { filter_eq { all_rows ; tournament ; the open championship } ; top - 10 } } ; 1 }, interpretation: select the rows whose tournament record fuzzily matches to us open . take the top - 10 record of this row . select the rows whose tournament record fuzzily matches to the open championship . take the top - 10 record of this row . the first record is 1 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; tournament ; us open } ; top - 10 } ; hop { filter_eq { all_rows ; tournament ; the open championship } ; top - 10 } } ; 1 }, interpretation: for the venue records of all rows , most of them fuzzily match to twickenham , london .
Output: no
Command: most_eq { all_rows ; venue ; twickenham , london }, interpretation: for the venue records of all rows , most of them fuzzily match to twickenham , london .
Output: yes
Command: most_eq { all_rows ; venue ; twickenham , london }, interpretation: for the population gender records of all rows , most of them fuzzily match to male .
Output: no
Command: most_eq { all_rows ; population gender ; male }, interpretation: for the population gender records of all rows , most of them fuzzily match to male .
Output: yes
Command: most_eq { all_rows ; population gender ; male }, interpretation: select the rows whose agricultural panel record is equal to 0 . there is only one such row in the table . the party record of this unqiue row is independent .
Output: no
Command: and { only { filter_eq { all_rows ; agricultural panel ; 0 } } ; eq { hop { filter_eq { all_rows ; agricultural panel ; 0 } ; party } ; independent } }, interpretation: select the rows whose agricultural panel record is equal to 0 . there is only one such row in the table . the party record of this unqiue row is independent .
Output: yes
Command: and { only { filter_eq { all_rows ; agricultural panel ; 0 } } ; eq { hop { filter_eq { all_rows ; agricultural panel ; 0 } ; party } ; independent } }, interpretation: for the to par records of all rows , most of them are equal to 7 .
Output: no
Command: most_eq { all_rows ; to par ; 7 }, interpretation: for the to par records of all rows , most of them are equal to 7 .
Output: yes
Command: most_eq { all_rows ; to par ; 7 }, interpretation: select the row whose rank record of all rows is minimum . the year record of this row is 2012 .
Output: no
Command: eq { hop { argmin { all_rows ; rank } ; year } ; 2012 }, interpretation: select the row whose rank record of all rows is minimum . the year record of this row is 2012 .
Output: yes
Command: eq { hop { argmin { all_rows ; rank } ; year } ; 2012 }, interpretation: select the rows whose series result record fuzzily matches to drawn . the number of such rows is 7 .
Output: no
Command: eq { count { filter_eq { all_rows ; series result ; drawn } } ; 7 }, interpretation: select the rows whose series result record fuzzily matches to drawn . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_eq { all_rows ; series result ; drawn } } ; 7 }, interpretation: for the pl gp records of all rows , all of them are equal to 0 .
Output: no
Command: all_eq { all_rows ; pl gp ; 0 }, interpretation: for the pl gp records of all rows , all of them are equal to 0 .
Output: yes
Command: all_eq { all_rows ; pl gp ; 0 }, interpretation: select the rows whose yards record is greater than 1000 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater { all_rows ; yards ; 1000 } } ; 2 }, interpretation: select the rows whose yards record is greater than 1000 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater { all_rows ; yards ; 1000 } } ; 2 }, interpretation: for the flagship station records of all rows , most of them fuzzily match to weei .
Output: no
Command: most_eq { all_rows ; flagship station ; weei }, interpretation: for the flagship station records of all rows , most of them fuzzily match to weei .
Output: yes
Command: most_eq { all_rows ; flagship station ; weei }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is overton brooks .
Output: no
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; overton brooks }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is overton brooks .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; overton brooks }, interpretation: select the rows whose round record is greater than 3 . among these rows , select the rows whose overall record is less than 200 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_less { filter_greater { all_rows ; round ; 3 } ; overall ; 200 } } ; 3 }, interpretation: select the rows whose round record is greater than 3 . among these rows , select the rows whose overall record is less than 200 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_less { filter_greater { all_rows ; round ; 3 } ; overall ; 200 } } ; 3 }, interpretation: select the rows whose home team record is arbitrary . the number of such rows is 6 .
Output: no
Command: eq { count { filter_all { all_rows ; home team } } ; 6 }, interpretation: select the rows whose home team record is arbitrary . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_all { all_rows ; home team } } ; 6 }, interpretation: select the row whose total record of all rows is 2nd maximum . the league cup record of this row is 2 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; total ; 2 } ; league cup } ; 2 }, interpretation: select the row whose total record of all rows is 2nd maximum . the league cup record of this row is 2 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; total ; 2 } ; league cup } ; 2 }, interpretation: select the rows whose diemaco model record fuzzily matches to lsw . take the barrel length record of this row . select the rows whose diemaco model record fuzzily matches to c8 . take the barrel length record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; diemaco model ; lsw } ; barrel length } ; hop { filter_eq { all_rows ; diemaco model ; c8 } ; barrel length } }, interpretation: select the rows whose diemaco model record fuzzily matches to lsw . take the barrel length record of this row . select the rows whose diemaco model record fuzzily matches to c8 . take the barrel length record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; diemaco model ; lsw } ; barrel length } ; hop { filter_eq { all_rows ; diemaco model ; c8 } ; barrel length } }, interpretation: the average of the enrollment record of all rows is 2282 .
Output: no
Command: round_eq { avg { all_rows ; enrollment } ; 2282 }, interpretation: the average of the enrollment record of all rows is 2282 .
Output: yes
Command: round_eq { avg { all_rows ; enrollment } ; 2282 }, interpretation: for the high assists records of all rows , most of them fuzzily match to joe johnson .
Output: no
Command: most_eq { all_rows ; high assists ; joe johnson }, interpretation: for the high assists records of all rows , most of them fuzzily match to joe johnson .
Output: yes
Command: most_eq { all_rows ; high assists ; joe johnson }, interpretation: select the rows whose home team record fuzzily matches to fitzroy . take the crowd record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the crowd record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; crowd } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; crowd } }, interpretation: select the rows whose home team record fuzzily matches to fitzroy . take the crowd record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the crowd record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; crowd } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; crowd } }, interpretation: select the row whose bronze record of all rows is 2nd maximum . the nation record of this row is netherlands .
Output: no
Command: eq { hop { nth_argmax { all_rows ; bronze ; 2 } ; nation } ; netherlands }, interpretation: select the row whose bronze record of all rows is 2nd maximum . the nation record of this row is netherlands .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; bronze ; 2 } ; nation } ; netherlands }, interpretation: select the rows whose date record fuzzily matches to 12 june 1937 . select the row whose crowd record of these rows is maximum . the venue record of this row is punt road oval .
Output: no
Command: eq { hop { argmax { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd } ; venue } ; punt road oval }, interpretation: select the rows whose date record fuzzily matches to 12 june 1937 . select the row whose crowd record of these rows is maximum . the venue record of this row is punt road oval .
Output: yes
Command: eq { hop { argmax { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd } ; venue } ; punt road oval }, interpretation: select the rows whose location attendance record fuzzily matches to us airways center 18422 . the number of such rows is 7 .
Output: no
Command: eq { count { filter_eq { all_rows ; location attendance ; us airways center 18422 } } ; 7 }, interpretation: select the rows whose location attendance record fuzzily matches to us airways center 18422 . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_eq { all_rows ; location attendance ; us airways center 18422 } } ; 7 }, interpretation: select the rows whose venue record fuzzily matches to chile . there is only one such row in the table . the date record of this unqiue row is september 4 , 2001 .
Output: no
Command: and { only { filter_eq { all_rows ; venue ; chile } } ; eq { hop { filter_eq { all_rows ; venue ; chile } ; date } ; september 4 , 2001 } }, interpretation: select the rows whose venue record fuzzily matches to chile . there is only one such row in the table . the date record of this unqiue row is september 4 , 2001 .
Output: yes
Command: and { only { filter_eq { all_rows ; venue ; chile } } ; eq { hop { filter_eq { all_rows ; venue ; chile } ; date } ; september 4 , 2001 } }, interpretation: select the row whose against record of all rows is maximum . the opposing teams record of this row is argentina .
Output: no
Command: eq { hop { argmax { all_rows ; against } ; opposing teams } ; argentina }, interpretation: select the row whose against record of all rows is maximum . the opposing teams record of this row is argentina .
Output: yes
Command: eq { hop { argmax { all_rows ; against } ; opposing teams } ; argentina }, interpretation: select the row whose speed record of all rows is maximum . the country record of this row is italy .
Output: no
Command: eq { hop { argmax { all_rows ; speed } ; country } ; italy }, interpretation: select the row whose speed record of all rows is maximum . the country record of this row is italy .
Output: yes
Command: eq { hop { argmax { all_rows ; speed } ; country } ; italy }, interpretation: select the rows whose result record fuzzily matches to nominated . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; nominated } } ; 6 }, interpretation: select the rows whose result record fuzzily matches to nominated . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; nominated } } ; 6 }, interpretation: the 2nd minimum date record of all rows is 22 february 1998 . the tournament record of the row with 2nd minimum date record is hannover , germany .
Output: no
Command: and { eq { nth_min { all_rows ; date ; 2 } ; 22 february 1998 } ; eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; hannover , germany } }, interpretation: the 2nd minimum date record of all rows is 22 february 1998 . the tournament record of the row with 2nd minimum date record is hannover , germany .
Output: yes
Command: and { eq { nth_min { all_rows ; date ; 2 } ; 22 february 1998 } ; eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; hannover , germany } }, interpretation: for the date records of all rows , all of them fuzzily match to 6 june 1970 .
Output: no
Command: all_eq { all_rows ; date ; 6 june 1970 }, interpretation: for the date records of all rows , all of them fuzzily match to 6 june 1970 .
Output: yes
Command: all_eq { all_rows ; date ; 6 june 1970 }, interpretation: select the rows whose winner record fuzzily matches to billy casper . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; winner ; billy casper } } ; 3 }, interpretation: select the rows whose winner record fuzzily matches to billy casper . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; winner ; billy casper } } ; 3 }, interpretation: select the rows whose first elected record is equal to 1824 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; first elected ; 1824 } } ; 3 }, interpretation: select the rows whose first elected record is equal to 1824 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; first elected ; 1824 } } ; 3 }, interpretation: select the rows whose away team score record is less than 20 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_less { filter_less { all_rows ; away team score ; 20 } ; crowd ; 20000 } } ; 3 }, interpretation: select the rows whose away team score record is less than 20 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_less { filter_less { all_rows ; away team score ; 20 } ; crowd ; 20000 } } ; 3 }, interpretation: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is keith ellison .
Output: no
Command: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; keith ellison }, interpretation: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is keith ellison .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; keith ellison }, interpretation: select the rows whose played record is less than 10 . among these rows , select the rows whose palace wins record is equal to 0 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_less { all_rows ; played ; 10 } ; palace wins ; 0 } } ; 3 }, interpretation: select the rows whose played record is less than 10 . among these rows , select the rows whose palace wins record is equal to 0 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_less { all_rows ; played ; 10 } ; palace wins ; 0 } } ; 3 }, interpretation: select the rows whose us viewers ( millions ) record does not match to n/a . the average of the us viewers ( millions ) record of these rows is 2.59 .
Output: no
Command: round_eq { avg { filter_not_eq { all_rows ; us viewers ( millions ) ; n/a } ; us viewers ( millions ) } ; 2.59 }, interpretation: select the rows whose us viewers ( millions ) record does not match to n/a . the average of the us viewers ( millions ) record of these rows is 2.59 .
Output: yes
Command: round_eq { avg { filter_not_eq { all_rows ; us viewers ( millions ) ; n/a } ; us viewers ( millions ) } ; 2.59 }, interpretation: for the attendance records of all rows , most of them are greater than 10,000 .
Output: no
Command: most_greater { all_rows ; attendance ; 10,000 }, interpretation: for the attendance records of all rows , most of them are greater than 10,000 .
Output: yes
Command: most_greater { all_rows ; attendance ; 10,000 }, interpretation: select the rows whose adopted record fuzzily matches to 1999 . among these rows , select the rows whose population ( 2011 - 01 - 01 ) record is less than 1000000 . there is only one such row in the table . the state record of this unqiue row is luxembourg .
Output: no
Command: and { only { filter_less { filter_eq { all_rows ; adopted ; 1999 } ; population ( 2011 - 01 - 01 ) ; 1000000 } } ; eq { hop { filter_less { filter_eq { all_rows ; adopted ; 1999 } ; population ( 2011 - 01 - 01 ) ; 1000000 } ; state } ; luxembourg } }, interpretation: select the rows whose adopted record fuzzily matches to 1999 . among these rows , select the rows whose population ( 2011 - 01 - 01 ) record is less than 1000000 . there is only one such row in the table . the state record of this unqiue row is luxembourg .
Output: yes
Command: and { only { filter_less { filter_eq { all_rows ; adopted ; 1999 } ; population ( 2011 - 01 - 01 ) ; 1000000 } } ; eq { hop { filter_less { filter_eq { all_rows ; adopted ; 1999 } ; population ( 2011 - 01 - 01 ) ; 1000000 } ; state } ; luxembourg } }, interpretation: select the rows whose channel record is equal to 40 . there is only one such row in the table . the territory record of this unqiue row is indonesia .
Output: no
Command: and { only { filter_eq { all_rows ; channel ; 40 } } ; eq { hop { filter_eq { all_rows ; channel ; 40 } ; territory } ; indonesia } }, interpretation: select the rows whose channel record is equal to 40 . there is only one such row in the table . the territory record of this unqiue row is indonesia .
Output: yes
Command: and { only { filter_eq { all_rows ; channel ; 40 } } ; eq { hop { filter_eq { all_rows ; channel ; 40 } ; territory } ; indonesia } }, interpretation: select the rows whose constellation record fuzzily matches to centaurus . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; constellation ; centaurus } } ; 4 }, interpretation: select the rows whose constellation record fuzzily matches to centaurus . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; constellation ; centaurus } } ; 4 }, interpretation: select the rows whose incumbent record fuzzily matches to wilbur mills . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to james william trimble . take the first elected record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; incumbent ; wilbur mills } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; james william trimble } ; first elected } }, interpretation: select the rows whose incumbent record fuzzily matches to wilbur mills . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to james william trimble . take the first elected record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; incumbent ; wilbur mills } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; james william trimble } ; first elected } }, interpretation: select the row whose draw record of all rows is 1st minimum . the artist record of this row is alma čardžić .
Output: no
Command: eq { hop { nth_argmin { all_rows ; draw ; 1 } ; artist } ; alma čardžić }, interpretation: select the row whose draw record of all rows is 1st minimum . the artist record of this row is alma čardžić .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; draw ; 1 } ; artist } ; alma čardžić }, interpretation: 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 ruben villareal .
Output: no
Command: and { only { filter_eq { all_rows ; res ; draw } } ; eq { hop { filter_eq { all_rows ; res ; draw } ; opponent } ; ruben villareal } }, interpretation: 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 ruben villareal .
Output: yes
Command: and { only { filter_eq { all_rows ; res ; draw } } ; eq { hop { filter_eq { all_rows ; res ; draw } ; opponent } ; ruben villareal } }, interpretation: select the rows whose average record is equal to 32 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; average ; 32 } } ; 3 }, interpretation: select the rows whose average record is equal to 32 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; average ; 32 } } ; 3 }, interpretation: for the chassis records of all rows , most of them fuzzily match to dallara .
Output: no
Command: most_eq { all_rows ; chassis ; dallara }, interpretation: for the chassis records of all rows , most of them fuzzily match to dallara .
Output: yes
Command: most_eq { all_rows ; chassis ; dallara }, interpretation: for the weight records of all rows , most of them are greater than or equal to 200 .
Output: no
Command: most_greater_eq { all_rows ; weight ; 200 }, interpretation: for the weight records of all rows , most of them are greater than or equal to 200 .
Output: yes
Command: most_greater_eq { all_rows ; weight ; 200 }, interpretation: select the row whose enrollment record of all rows is 2nd maximum . the school record of this row is fort wayne carroll .
Output: no
Command: eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; school } ; fort wayne carroll }, interpretation: select the row whose enrollment record of all rows is 2nd maximum . the school record of this row is fort wayne carroll .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; school } ; fort wayne carroll }, interpretation: select the row whose year record of all rows is 2nd maximum . the womens singles record of this row is wang yihan .
Output: no
Command: eq { hop { nth_argmax { all_rows ; year ; 2 } ; womens singles } ; wang yihan }, interpretation: select the row whose year record of all rows is 2nd maximum . the womens singles record of this row is wang yihan .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; year ; 2 } ; womens singles } ; wang yihan }, interpretation: select the rows whose running time record is less than 30 minutes . there is only one such row in the table .
Output: no
Command: only { filter_less { all_rows ; running time ; 30 minutes } }, interpretation: select the rows whose running time record is less than 30 minutes . there is only one such row in the table .
Output: yes
Command: only { filter_less { all_rows ; running time ; 30 minutes } }, interpretation: select the row whose attendance record of all rows is 2nd minimum . the date record of this row is january 2 , 2005 .
Output: no
Command: eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; january 2 , 2005 }, interpretation: select the row whose attendance record of all rows is 2nd minimum . the date record of this row is january 2 , 2005 .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; january 2 , 2005 }, interpretation: select the row whose total record of all rows is maximum . the nbr class record of this row is 396 .
Output: no
Command: eq { hop { argmax { all_rows ; total } ; nbr class } ; 396 }, interpretation: select the row whose total record of all rows is maximum . the nbr class record of this row is 396 .
Output: yes
Command: eq { hop { argmax { all_rows ; total } ; nbr class } ; 396 }, interpretation: select the row whose enrolment record of all rows is maximum . the school record of this row is pembroke school .
Output: no
Command: eq { hop { argmax { all_rows ; enrolment } ; school } ; pembroke school }, interpretation: select the row whose enrolment record of all rows is maximum . the school record of this row is pembroke school .
Output: yes
Command: eq { hop { argmax { all_rows ; enrolment } ; school } ; pembroke school }, interpretation: select the rows whose int ' l debut record fuzzily matches to france . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; int ' l debut ; france } } ; 2 }, interpretation: select the rows whose int ' l debut record fuzzily matches to france . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; int ' l debut ; france } } ; 2 }, interpretation: for the laps records of all rows , most of them are greater than 60 .
Output: no
Command: most_greater { all_rows ; laps ; 60 }, interpretation: for the laps records of all rows , most of them are greater than 60 .
Output: yes
Command: most_greater { all_rows ; laps ; 60 }, interpretation: select the rows whose year record fuzzily matches to 2011 . take the purse record of this row . select the rows whose year record fuzzily matches to 2005 . take the purse record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; year ; 2011 } ; purse } ; hop { filter_eq { all_rows ; year ; 2005 } ; purse } }, interpretation: select the rows whose year record fuzzily matches to 2011 . take the purse record of this row . select the rows whose year record fuzzily matches to 2005 . take the purse record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; year ; 2011 } ; purse } ; hop { filter_eq { all_rows ; year ; 2005 } ; purse } }, interpretation: select the rows whose event record is arbitrary . the number of such rows is 14 .
Output: no
Command: eq { count { filter_all { all_rows ; event } } ; 14 }, interpretation: select the rows whose event record is arbitrary . the number of such rows is 14 .
Output: yes
Command: eq { count { filter_all { all_rows ; event } } ; 14 }, interpretation: select the rows whose date record fuzzily matches to april 1 . take the record record of this row . select the rows whose date record fuzzily matches to march 30 . take the record record of this row . the first record is greater than the second record . the record record of the first row is 3 - 2 . the record record of the second row is 2 - 2 .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; date ; april 1 } ; record } ; hop { filter_eq { all_rows ; date ; march 30 } ; record } } ; and { eq { hop { filter_eq { all_rows ; date ; april 1 } ; record } ; 3 - 2 } ; eq { hop { filter_eq { all_rows ; date ; march 30 } ; record } ; 2 - 2 } } }, interpretation: select the rows whose date record fuzzily matches to april 1 . take the record record of this row . select the rows whose date record fuzzily matches to march 30 . take the record record of this row . the first record is greater than the second record . the record record of the first row is 3 - 2 . the record record of the second row is 2 - 2 .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; date ; april 1 } ; record } ; hop { filter_eq { all_rows ; date ; march 30 } ; record } } ; and { eq { hop { filter_eq { all_rows ; date ; april 1 } ; record } ; 3 - 2 } ; eq { hop { filter_eq { all_rows ; date ; march 30 } ; record } ; 2 - 2 } } }, interpretation: for the founded records of all rows , most of them are less than 1900 .
Output: no
Command: most_less { all_rows ; founded ; 1900 }, interpretation: select the row whose year born record of all rows is minimum . the player record of this row is goran jagodnik .
Output: no
Command: eq { hop { argmin { all_rows ; year born } ; player } ; goran jagodnik }, interpretation: select the row whose year born record of all rows is minimum . the player record of this row is goran jagodnik .
Output: yes
Command: eq { hop { argmin { all_rows ; year born } ; player } ; goran jagodnik }, interpretation: select the rows whose mens singles record fuzzily matches to alan budi kusuma . take the year record of this row . select the rows whose mens singles record fuzzily matches to hermawan susanto . take the year record of this row . the second record is 1 year larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; mens singles ; alan budi kusuma } ; year } ; hop { filter_eq { all_rows ; mens singles ; hermawan susanto } ; year } } ; -1 year }, interpretation: select the rows whose mens singles record fuzzily matches to alan budi kusuma . take the year record of this row . select the rows whose mens singles record fuzzily matches to hermawan susanto . take the year record of this row . the second record is 1 year larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; mens singles ; alan budi kusuma } ; year } ; hop { filter_eq { all_rows ; mens singles ; hermawan susanto } ; year } } ; -1 year }, interpretation: select the rows whose venue record is arbitrary . the number of such rows is 6 .
Output: no
Command: eq { count { filter_all { all_rows ; venue } } ; 6 }, interpretation: select the rows whose ship record fuzzily matches to lützow . take the total record of this row . select the rows whose ship record fuzzily matches to seydlitz . take the total record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; ship ; lützow } ; total } ; hop { filter_eq { all_rows ; ship ; seydlitz } ; total } }, interpretation: select the rows whose ship record fuzzily matches to lützow . take the total record of this row . select the rows whose ship record fuzzily matches to seydlitz . take the total record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; ship ; lützow } ; total } ; hop { filter_eq { all_rows ; ship ; seydlitz } ; total } }, interpretation: select the rows whose tournament record fuzzily matches to the open championship . there is only one such row in the table . the wins record of this unqiue row is 1 .
Output: no
Command: and { only { filter_eq { all_rows ; tournament ; the open championship } } ; eq { hop { filter_eq { all_rows ; tournament ; the open championship } ; wins } ; 1 } }, interpretation: select the rows whose tournament record fuzzily matches to the open championship . there is only one such row in the table . the wins record of this unqiue row is 1 .
Output: yes
Command: and { only { filter_eq { all_rows ; tournament ; the open championship } } ; eq { hop { filter_eq { all_rows ; tournament ; the open championship } ; wins } ; 1 } }, interpretation: select the rows whose al - wehdat wins record is greater than 20 . there is only one such row in the table . the tournament record of this unqiue row is jordan premier league .
Output: no
Command: and { only { filter_greater { all_rows ; al - wehdat wins ; 20 } } ; eq { hop { filter_greater { all_rows ; al - wehdat wins ; 20 } ; tournament } ; jordan premier league } }, interpretation: select the rows whose al - wehdat wins record is greater than 20 . there is only one such row in the table . the tournament record of this unqiue row is jordan premier league .
Output: yes
Command: and { only { filter_greater { all_rows ; al - wehdat wins ; 20 } } ; eq { hop { filter_greater { all_rows ; al - wehdat wins ; 20 } ; tournament } ; jordan premier league } }, interpretation: the sum of the against record of all rows is 119 .
Output: no
Command: round_eq { sum { all_rows ; against } ; 119 }, interpretation: the sum of the against record of all rows is 119 .
Output: yes
Command: round_eq { sum { all_rows ; against } ; 119 }, interpretation: select the rows whose position record fuzzily matches to lb . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; position ; lb } } ; 2 }, interpretation: select the rows whose position record fuzzily matches to lb . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; position ; lb } } ; 2 }, interpretation: for the speed records of all rows , most of them are greater than 93 .
Output: no
Command: most_greater { all_rows ; speed ; 93 }, interpretation: for the speed records of all rows , most of them are greater than 93 .
Output: yes
Command: most_greater { all_rows ; speed ; 93 }, interpretation: select the rows whose township record fuzzily matches to james hill . take the water ( sqmi ) record of this row . select the rows whose township record fuzzily matches to jim river valley . take the water ( sqmi ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; township ; james hill } ; water ( sqmi ) } ; hop { filter_eq { all_rows ; township ; jim river valley } ; water ( sqmi ) } }, interpretation: select the rows whose township record fuzzily matches to james hill . take the water ( sqmi ) record of this row . select the rows whose township record fuzzily matches to jim river valley . take the water ( sqmi ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; township ; james hill } ; water ( sqmi ) } ; hop { filter_eq { all_rows ; township ; jim river valley } ; water ( sqmi ) } }, interpretation: select the rows whose result record fuzzily matches to 28 - 17 . there is only one such row in the table . the date record of this unqiue row is september 9 , 1962 .
Output: no
Command: and { only { filter_eq { all_rows ; result ; 28 - 17 } } ; eq { hop { filter_eq { all_rows ; result ; 28 - 17 } ; date } ; september 9 , 1962 } }, interpretation: select the rows whose result record fuzzily matches to 28 - 17 . there is only one such row in the table . the date record of this unqiue row is september 9 , 1962 .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; 28 - 17 } } ; eq { hop { filter_eq { all_rows ; result ; 28 - 17 } ; date } ; september 9 , 1962 } }, interpretation: select the rows whose race record fuzzily matches to italian grand prix . take the date record of this row . select the rows whose race record fuzzily matches to european grand prix . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; race ; italian grand prix } ; date } ; hop { filter_eq { all_rows ; race ; european grand prix } ; date } }, interpretation: select the rows whose race record fuzzily matches to italian grand prix . take the date record of this row . select the rows whose race record fuzzily matches to european grand prix . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; race ; italian grand prix } ; date } ; hop { filter_eq { all_rows ; race ; european grand prix } ; date } }, interpretation: select the row whose score record of all rows is maximum . the date record of this row is 9 october 2012 .
Output: no
Command: eq { hop { argmax { all_rows ; score } ; date } ; 9 october 2012 }, interpretation: select the row whose score record of all rows is maximum . the date record of this row is 9 october 2012 .
Output: yes
Command: eq { hop { argmax { all_rows ; score } ; date } ; 9 october 2012 }, interpretation: select the rows whose tournament record fuzzily matches to the open championship . take the events record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the events record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; tournament ; the open championship } ; events } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; events } }, interpretation: select the rows whose tournament record fuzzily matches to the open championship . take the events record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the events record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; tournament ; the open championship } ; events } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; events } }, interpretation: for the batting style records of all rows , most of them fuzzily match to right hand bat .
Output: no
Command: most_eq { all_rows ; batting style ; right hand bat }, interpretation: for the batting style records of all rows , most of them fuzzily match to right hand bat .
Output: yes
Command: most_eq { all_rows ; batting style ; right hand bat }, interpretation: select the rows whose game site record fuzzily matches to shea stadium . select the row whose attendance record of these rows is maximum . the date record of this row is 1983 - 10 - 16 .
Output: no
Command: eq { hop { argmax { filter_eq { all_rows ; game site ; shea stadium } ; attendance } ; date } ; 1983 - 10 - 16 }, interpretation: select the rows whose game site record fuzzily matches to shea stadium . select the row whose attendance record of these rows is maximum . the date record of this row is 1983 - 10 - 16 .
Output: yes
Command: eq { hop { argmax { filter_eq { all_rows ; game site ; shea stadium } ; attendance } ; date } ; 1983 - 10 - 16 }, interpretation: select the row whose points record of all rows is maximum . the song record of this row is living my life without you .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; song } ; living my life without you }, interpretation: select the row whose points record of all rows is maximum . the song record of this row is living my life without you .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; song } ; living my life without you }, interpretation: select the rows whose player record is arbitrary . the number of such rows is 5 .
Output: no
Command: eq { count { filter_all { all_rows ; player } } ; 5 }, interpretation: select the rows whose player record is arbitrary . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_all { all_rows ; player } } ; 5 }, interpretation: for the format records of all rows , most of them fuzzily match to cd .
Output: no
Command: most_eq { all_rows ; format ; cd }, interpretation: select the row whose average ratings record of all rows is maximum . the romaji title record of this row is karei - naru ichizoku .
Output: no
Command: eq { hop { argmax { all_rows ; average ratings } ; romaji title } ; karei - naru ichizoku }, interpretation: select the row whose average ratings record of all rows is maximum . the romaji title record of this row is karei - naru ichizoku .
Output: yes
Command: eq { hop { argmax { all_rows ; average ratings } ; romaji title } ; karei - naru ichizoku }, interpretation: select the rows whose 1st member record fuzzily matches to edmund mordaunt . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; 1st member ; edmund mordaunt } } ; 2 }, interpretation: select the rows whose 1st member record fuzzily matches to edmund mordaunt . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; 1st member ; edmund mordaunt } } ; 2 }, interpretation: select the rows whose surface record fuzzily matches to grass . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; surface ; grass } }, interpretation: select the rows whose surface record fuzzily matches to grass . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; surface ; grass } }, interpretation: for the name records of all rows , most of them fuzzily match to - .
Output: no
Command: most_eq { all_rows ; name ; - }, interpretation: for the name records of all rows , most of them fuzzily match to - .
Output: yes
Command: most_eq { all_rows ; name ; - }, interpretation: select the row whose year record of all rows is minimum . the winner record of this row is deep gold .
Output: no
Command: eq { hop { argmin { all_rows ; year } ; winner } ; deep gold }, interpretation: select the row whose year record of all rows is minimum . the winner record of this row is deep gold .
Output: yes
Command: eq { hop { argmin { all_rows ; year } ; winner } ; deep gold }, interpretation: select the rows whose bronze record is greater than 0 . there is only one such row in the table . the nation record of this unqiue row is austria .
Output: no
Command: and { only { filter_greater { all_rows ; bronze ; 0 } } ; eq { hop { filter_greater { all_rows ; bronze ; 0 } ; nation } ; austria } }, interpretation: select the rows whose bronze record is greater than 0 . there is only one such row in the table . the nation record of this unqiue row is austria .
Output: yes
Command: and { only { filter_greater { all_rows ; bronze ; 0 } } ; eq { hop { filter_greater { all_rows ; bronze ; 0 } ; nation } ; austria } }, interpretation: select the rows whose date record fuzzily matches to september . for the result records of these rows , all of them fuzzily match to l .
Output: no
Command: all_eq { filter_eq { all_rows ; date ; september } ; result ; l }, interpretation: select the rows whose date record fuzzily matches to september . for the result records of these rows , all of them fuzzily match to l .
Output: yes
Command: all_eq { filter_eq { all_rows ; date ; september } ; result ; l }, interpretation: select the rows whose visitor record fuzzily matches to cavaliers . take the date record of this row . select the rows whose visitor record fuzzily matches to hornets . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; visitor ; cavaliers } ; date } ; hop { filter_eq { all_rows ; visitor ; hornets } ; date } }, interpretation: select the rows whose visitor record fuzzily matches to cavaliers . take the date record of this row . select the rows whose visitor record fuzzily matches to hornets . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; visitor ; cavaliers } ; date } ; hop { filter_eq { all_rows ; visitor ; hornets } ; date } }, interpretation: select the rows whose incumbent record fuzzily matches to chauncey w reed . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to sid simpson . take the first elected record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; incumbent ; chauncey w reed } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; sid simpson } ; first elected } }, interpretation: select the rows whose incumbent record fuzzily matches to chauncey w reed . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to sid simpson . take the first elected record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; incumbent ; chauncey w reed } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; sid simpson } ; first elected } }, interpretation: select the rows whose venue record fuzzily matches to etihad stadium . there is only one such row in the table . the premier record of this unqiue row is geelong .
Output: no
Command: and { only { filter_eq { all_rows ; venue ; etihad stadium } } ; eq { hop { filter_eq { all_rows ; venue ; etihad stadium } ; premier } ; geelong } }, interpretation: select the rows whose venue record fuzzily matches to etihad stadium . there is only one such row in the table . the premier record of this unqiue row is geelong .
Output: yes
Command: and { only { filter_eq { all_rows ; venue ; etihad stadium } } ; eq { hop { filter_eq { all_rows ; venue ; etihad stadium } ; premier } ; geelong } }, interpretation: for the class records of all rows , most of them fuzzily match to a .
Output: no
Command: most_eq { all_rows ; class ; a }, interpretation: for the class records of all rows , most of them fuzzily match to a .
Output: yes
Command: most_eq { all_rows ; class ; a }, interpretation: select the rows whose easa ( eu ) record fuzzily matches to yes . there is only one such row in the table . the airline record of this unqiue row is premiair .
Output: no
Command: and { only { filter_eq { all_rows ; easa ( eu ) ; yes } } ; eq { hop { filter_eq { all_rows ; easa ( eu ) ; yes } ; airline } ; premiair } }, interpretation: select the rows whose easa ( eu ) record fuzzily matches to yes . there is only one such row in the table . the airline record of this unqiue row is premiair .
Output: yes
Command: and { only { filter_eq { all_rows ; easa ( eu ) ; yes } } ; eq { hop { filter_eq { all_rows ; easa ( eu ) ; yes } ; airline } ; premiair } }, interpretation: select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is melbourne .
Output: no
Command: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; melbourne }, interpretation: select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is melbourne .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; melbourne }, interpretation: select the rows whose points record is greater than 0 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater { all_rows ; points ; 0 } } ; 2 }, interpretation: select the rows whose points record is greater than 0 . the number of such rows is 2 .
Output: yes