niryuu/sni-each-converted huggingface dataset:
Command: eq { hop { nth_argmax { all_rows ; frequency ; 1 } ; model number } ; athlon x2 5050e }, interpretation: select the rows whose home team record fuzzily matches to south melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; home team ; south melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } }, interpretation: select the rows whose home team record fuzzily matches to south melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; home team ; south melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is may 22 .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; may 22 }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is may 22 .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; may 22 }, interpretation: select the rows whose points record is less than 100 . there is only one such row in the table . the artist record of this unqiue row is giorgos gavriel .
Output: no
Command: and { only { filter_less { all_rows ; points ; 100 } } ; eq { hop { filter_less { all_rows ; points ; 100 } ; artist } ; giorgos gavriel } }, interpretation: select the rows whose points record is less than 100 . there is only one such row in the table . the artist record of this unqiue row is giorgos gavriel .
Output: yes
Command: and { only { filter_less { all_rows ; points ; 100 } } ; eq { hop { filter_less { all_rows ; points ; 100 } ; artist } ; giorgos gavriel } }, interpretation: select the row whose time record of all rows is 2nd maximum . the country record of this row is china .
Output: no
Command: eq { hop { nth_argmax { all_rows ; time ; 2 } ; country } ; china }, interpretation: select the row whose time record of all rows is 2nd maximum . the country record of this row is china .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; time ; 2 } ; country } ; china }, interpretation: for the country records of all rows , most of them fuzzily match to united states .
Output: no
Command: most_eq { all_rows ; country ; united states }, interpretation: select the row whose took office record of all rows is maximum . the delegate record of this row is nina r harper .
Output: no
Command: eq { hop { argmax { all_rows ; took office } ; delegate } ; nina r harper }, interpretation: select the row whose took office record of all rows is maximum . the delegate record of this row is nina r harper .
Output: yes
Command: eq { hop { argmax { all_rows ; took office } ; delegate } ; nina r harper }, interpretation: for the attendance records of all rows , most of them are greater than 1500 .
Output: no
Command: most_greater { all_rows ; attendance ; 1500 }, interpretation: for the attendance records of all rows , most of them are greater than 1500 .
Output: yes
Command: most_greater { all_rows ; attendance ; 1500 }, interpretation: select the row whose home team score record of all rows is maximum . the home team record of this row is carlton .
Output: no
Command: eq { hop { argmax { all_rows ; home team score } ; home team } ; carlton }, interpretation: select the row whose home team score record of all rows is maximum . the home team record of this row is carlton .
Output: yes
Command: eq { hop { argmax { all_rows ; home team score } ; home team } ; carlton }, interpretation: select the rows whose high assists record fuzzily matches to blake griffin . the average of the high assists record of these rows is 9 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; high assists ; blake griffin } ; high assists } ; 9 }, interpretation: select the rows whose high assists record fuzzily matches to blake griffin . the average of the high assists record of these rows is 9 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; high assists ; blake griffin } ; high assists } ; 9 }, interpretation: for the screen size ( inch ) records of all rows , most of them are greater than or equal to 7 .
Output: no
Command: most_greater_eq { all_rows ; screen size ( inch ) ; 7 }, interpretation: for the screen size ( inch ) records of all rows , most of them are greater than or equal to 7 .
Output: yes
Command: most_greater_eq { all_rows ; screen size ( inch ) ; 7 }, interpretation: select the rows whose position record fuzzily matches to guard . there is only one such row in the table . the name record of this unqiue row is justin anderson .
Output: no
Command: and { only { filter_eq { all_rows ; position ; guard } } ; eq { hop { filter_eq { all_rows ; position ; guard } ; name } ; justin anderson } }, interpretation: select the rows whose position record fuzzily matches to guard . there is only one such row in the table . the name record of this unqiue row is justin anderson .
Output: yes
Command: and { only { filter_eq { all_rows ; position ; guard } } ; eq { hop { filter_eq { all_rows ; position ; guard } ; name } ; justin anderson } }, interpretation: for the venue records of all rows , most of them fuzzily match to vienna , austria .
Output: no
Command: most_eq { all_rows ; venue ; vienna , austria }, interpretation: for the venue records of all rows , most of them fuzzily match to vienna , austria .
Output: yes
Command: most_eq { all_rows ; venue ; vienna , austria }, interpretation: select the rows whose competition record fuzzily matches to friendly . among these rows , select the rows whose venue record fuzzily matches to stadion villach lind , villach , austria . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; competition ; friendly } ; venue ; stadion villach lind , villach , austria } } ; 2 }, interpretation: select the rows whose competition record fuzzily matches to friendly . among these rows , select the rows whose venue record fuzzily matches to stadion villach lind , villach , austria . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; competition ; friendly } ; venue ; stadion villach lind , villach , austria } } ; 2 }, interpretation: select the rows whose region record fuzzily matches to arica and parinacota . take the tertiary ( 18 - 24 years ) record of this row . select the rows whose region record fuzzily matches to maule . take the tertiary ( 18 - 24 years ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; region ; arica and parinacota } ; tertiary ( 18 - 24 years ) } ; hop { filter_eq { all_rows ; region ; maule } ; tertiary ( 18 - 24 years ) } }, interpretation: select the rows whose region record fuzzily matches to arica and parinacota . take the tertiary ( 18 - 24 years ) record of this row . select the rows whose region record fuzzily matches to maule . take the tertiary ( 18 - 24 years ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; region ; arica and parinacota } ; tertiary ( 18 - 24 years ) } ; hop { filter_eq { all_rows ; region ; maule } ; tertiary ( 18 - 24 years ) } }, interpretation: the average of the total record of all rows is 284.5 .
Output: no
Command: round_eq { avg { all_rows ; total } ; 284.5 }, interpretation: the average of the total record of all rows is 284.5 .
Output: yes
Command: round_eq { avg { all_rows ; total } ; 284.5 }, interpretation: select the rows whose song title record fuzzily matches to big boss man . take the time record of this row . select the rows whose song title record fuzzily matches to us male . take the time record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; song title ; big boss man } ; time } ; hop { filter_eq { all_rows ; song title ; us male } ; time } }, interpretation: select the rows whose song title record fuzzily matches to big boss man . take the time record of this row . select the rows whose song title record fuzzily matches to us male . take the time record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; song title ; big boss man } ; time } ; hop { filter_eq { all_rows ; song title ; us male } ; time } }, interpretation: select the rows whose premiere record fuzzily matches to 14 august . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; premiere ; 14 august } } ; 4 }, interpretation: select the rows whose premiere record fuzzily matches to 14 august . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; premiere ; 14 august } } ; 4 }, interpretation: select the rows whose result record fuzzily matches to final . the sum of the jury votes record of these rows is 29 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; result ; final } ; jury votes } ; 29 }, interpretation: select the rows whose result record fuzzily matches to final . the sum of the jury votes record of these rows is 29 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; result ; final } ; jury votes } ; 29 }, interpretation: the minimum original airdate record of all rows is november 02 .
Output: no
Command: eq { min { all_rows ; original airdate } ; november 02 }, interpretation: the minimum original airdate record of all rows is november 02 .
Output: yes
Command: eq { min { all_rows ; original airdate } ; november 02 }, interpretation: select the row whose the championship record of all rows is maximum . the player record of this row is becchio .
Output: no
Command: eq { hop { argmax { all_rows ; the championship } ; player } ; becchio }, interpretation: select the row whose the championship record of all rows is maximum . the player record of this row is becchio .
Output: yes
Command: eq { hop { argmax { all_rows ; the championship } ; player } ; becchio }, interpretation: select the row whose money record of all rows is maximum . the place record of this row is 1 .
Output: no
Command: eq { hop { argmax { all_rows ; money } ; place } ; 1 }, interpretation: select the row whose money record of all rows is maximum . the place record of this row is 1 .
Output: yes
Command: eq { hop { argmax { all_rows ; money } ; place } ; 1 }, interpretation: select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is los angeles raiders .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; los angeles raiders }, interpretation: select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is los angeles raiders .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; los angeles raiders }, interpretation: select the row whose became queen record of all rows is 2nd minimum . the name record of this row is maria teresa of the two sicilies .
Output: no
Command: eq { hop { nth_argmin { all_rows ; became queen ; 2 } ; name } ; maria teresa of the two sicilies }, interpretation: select the row whose became queen record of all rows is 2nd minimum . the name record of this row is maria teresa of the two sicilies .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; became queen ; 2 } ; name } ; maria teresa of the two sicilies }, interpretation: the sum of the laps record of all rows is 535 .
Output: no
Command: round_eq { sum { all_rows ; laps } ; 535 }, interpretation: the sum of the laps record of all rows is 535 .
Output: yes
Command: round_eq { sum { all_rows ; laps } ; 535 }, interpretation: for the gold records of all rows , most of them are equal to 0 .
Output: no
Command: most_eq { all_rows ; gold ; 0 }, interpretation: for the gold records of all rows , most of them are equal to 0 .
Output: yes
Command: most_eq { all_rows ; gold ; 0 }, interpretation: for the nation records of all rows , all of them fuzzily match to usa .
Output: no
Command: all_eq { all_rows ; nation ; usa }, interpretation: for the nation records of all rows , all of them fuzzily match to usa .
Output: yes
Command: all_eq { all_rows ; nation ; usa }, interpretation: select the rows whose directed by record fuzzily matches to john t kretchmer . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; directed by ; john t kretchmer } } ; 2 }, interpretation: select the rows whose directed by record fuzzily matches to john t kretchmer . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; directed by ; john t kretchmer } } ; 2 }, interpretation: select the rows whose goalkeeper record fuzzily matches to juan calatayud . take the goals record of this row . select the rows whose goalkeeper record fuzzily matches to eduardo navarro . take the goals record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; goalkeeper ; juan calatayud } ; goals } ; hop { filter_eq { all_rows ; goalkeeper ; eduardo navarro } ; goals } }, interpretation: select the rows whose goalkeeper record fuzzily matches to juan calatayud . take the goals record of this row . select the rows whose goalkeeper record fuzzily matches to eduardo navarro . take the goals record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; goalkeeper ; juan calatayud } ; goals } ; hop { filter_eq { all_rows ; goalkeeper ; eduardo navarro } ; goals } }, interpretation: for the team records of all rows , all of them fuzzily match to honda .
Output: no
Command: all_eq { all_rows ; team ; honda }, interpretation: for the team records of all rows , all of them fuzzily match to honda .
Output: yes
Command: all_eq { all_rows ; team ; honda }, interpretation: select the rows whose written by record fuzzily matches to shonda rhimes . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; written by ; shonda rhimes } } ; 4 }, interpretation: select the rows whose written by record fuzzily matches to shonda rhimes . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; written by ; shonda rhimes } } ; 4 }, interpretation: 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 .
Output: no
Command: 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 } } }, interpretation: 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 .
Output: yes
Command: 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 } } }, interpretation: select the rows whose year record fuzzily matches to 1967 . take the laps completed record of this row . select the rows whose year record fuzzily matches to 1964 . take the laps completed record of this row . the second record is 12 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; year ; 1967 } ; laps completed } ; hop { filter_eq { all_rows ; year ; 1964 } ; laps completed } } ; -12 }, interpretation: select the rows whose year record fuzzily matches to 1967 . take the laps completed record of this row . select the rows whose year record fuzzily matches to 1964 . take the laps completed record of this row . the second record is 12 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; year ; 1967 } ; laps completed } ; hop { filter_eq { all_rows ; year ; 1964 } ; laps completed } } ; -12 }, interpretation: select the rows whose status record fuzzily matches to alive after exiting the cube . there is only one such row in the table . the name record of this unqiue row is kazan .
Output: no
Command: and { only { filter_eq { all_rows ; status ; alive after exiting the cube } } ; eq { hop { filter_eq { all_rows ; status ; alive after exiting the cube } ; name } ; kazan } }, interpretation: select the rows whose status record fuzzily matches to alive after exiting the cube . there is only one such row in the table . the name record of this unqiue row is kazan .
Output: yes
Command: and { only { filter_eq { all_rows ; status ; alive after exiting the cube } } ; eq { hop { filter_eq { all_rows ; status ; alive after exiting the cube } ; name } ; kazan } }, interpretation: select the row whose gold record of all rows is maximum . the nation record of this row is canada ( can ) .
Output: no
Command: eq { hop { argmax { all_rows ; gold } ; nation } ; canada ( can ) }, interpretation: select the row whose gold record of all rows is maximum . the nation record of this row is canada ( can ) .
Output: yes
Command: eq { hop { argmax { all_rows ; gold } ; nation } ; canada ( can ) }, interpretation: select the rows whose date record is equal to 1873 . the sum of the no built record of these rows is 14 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; date ; 1873 } ; no built } ; 14 }, interpretation: select the rows whose date record is equal to 1873 . the sum of the no built record of these rows is 14 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; date ; 1873 } ; no built } ; 14 }, interpretation: select the rows whose college record fuzzily matches to ohio state . there is only one such row in the table . the player record of this unqiue row is kirk barton .
Output: no
Command: and { only { filter_eq { all_rows ; college ; ohio state } } ; eq { hop { filter_eq { all_rows ; college ; ohio state } ; player } ; kirk barton } }, interpretation: select the rows whose college record fuzzily matches to ohio state . there is only one such row in the table . the player record of this unqiue row is kirk barton .
Output: yes
Command: and { only { filter_eq { all_rows ; college ; ohio state } } ; eq { hop { filter_eq { all_rows ; college ; ohio state } ; player } ; kirk barton } }, interpretation: select the rows whose cover model record fuzzily matches to no model pictured . there is only one such row in the table . the date record of this unqiue row is 1 - 84 .
Output: no
Command: and { only { filter_eq { all_rows ; cover model ; no model pictured } } ; eq { hop { filter_eq { all_rows ; cover model ; no model pictured } ; date } ; 1 - 84 } }, interpretation: select the rows whose cover model record fuzzily matches to no model pictured . there is only one such row in the table . the date record of this unqiue row is 1 - 84 .
Output: yes
Command: and { only { filter_eq { all_rows ; cover model ; no model pictured } } ; eq { hop { filter_eq { all_rows ; cover model ; no model pictured } ; date } ; 1 - 84 } }, interpretation: the average of the goals against record of all rows is 24 .
Output: no
Command: round_eq { avg { all_rows ; goals against } ; 24 }, interpretation: the average of the goals against record of all rows is 24 .
Output: yes
Command: round_eq { avg { all_rows ; goals against } ; 24 }, interpretation: select the rows whose habitat type record fuzzily matches to forest steppe . the number of such rows is 8 .
Output: no
Command: eq { count { filter_eq { all_rows ; habitat type ; forest steppe } } ; 8 }, interpretation: select the rows whose habitat type record fuzzily matches to forest steppe . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_eq { all_rows ; habitat type ; forest steppe } } ; 8 }, interpretation: the average of the score record of all rows is 1.67 .
Output: no
Command: round_eq { avg { all_rows ; score } ; 1.67 }, interpretation: the average of the score record of all rows is 1.67 .
Output: yes
Command: round_eq { avg { all_rows ; score } ; 1.67 }, interpretation: select the rows whose location record fuzzily matches to seoul , south korea . there is only one such row in the table . the event record of this unqiue row is hero 's 2005 in seoul .
Output: no
Command: and { only { filter_eq { all_rows ; location ; seoul , south korea } } ; eq { hop { filter_eq { all_rows ; location ; seoul , south korea } ; event } ; hero 's 2005 in seoul } }, interpretation: select the rows whose location record fuzzily matches to seoul , south korea . there is only one such row in the table . the event record of this unqiue row is hero 's 2005 in seoul .
Output: yes
Command: and { only { filter_eq { all_rows ; location ; seoul , south korea } } ; eq { hop { filter_eq { all_rows ; location ; seoul , south korea } ; event } ; hero 's 2005 in seoul } }, interpretation: the average of the central rate record of all rows is 3.393 .
Output: no
Command: round_eq { avg { all_rows ; central rate } ; 3.393 }, interpretation: the average of the central rate record of all rows is 3.393 .
Output: yes
Command: round_eq { avg { all_rows ; central rate } ; 3.393 }, interpretation: select the rows whose party record fuzzily matches to democratic . among these rows , select the rows whose result record fuzzily matches to re-elected . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; party ; democratic } ; result ; re-elected } } ; 2 }, interpretation: select the rows whose party record fuzzily matches to democratic . among these rows , select the rows whose result record fuzzily matches to re-elected . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; party ; democratic } ; result ; re-elected } } ; 2 }, interpretation: select the rows whose status record fuzzily matches to bottom 3 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; status ; bottom 3 } } ; 2 }, interpretation: select the rows whose status record fuzzily matches to bottom 3 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; status ; bottom 3 } } ; 2 }, interpretation: select the rows whose surface record fuzzily matches to clay . among these rows , select the rows whose partner record fuzzily matches to anna linkova . there is only one such row in the table . the date record of this unqiue row is august 7 , 1995 .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; surface ; clay } ; partner ; anna linkova } } ; eq { hop { filter_eq { filter_eq { all_rows ; surface ; clay } ; partner ; anna linkova } ; date } ; august 7 , 1995 } }, interpretation: select the rows whose surface record fuzzily matches to clay . among these rows , select the rows whose partner record fuzzily matches to anna linkova . there is only one such row in the table . the date record of this unqiue row is august 7 , 1995 .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; surface ; clay } ; partner ; anna linkova } } ; eq { hop { filter_eq { filter_eq { all_rows ; surface ; clay } ; partner ; anna linkova } ; date } ; august 7 , 1995 } }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is nathaniel macon .
Output: no
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; nathaniel macon }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is nathaniel macon .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; nathaniel macon }, interpretation: for the earnings records of all rows , most of them are greater than 10000000 .
Output: no
Command: most_greater { all_rows ; earnings ; 10000000 }, interpretation: for the earnings records of all rows , most of them are greater than 10000000 .
Output: yes
Command: most_greater { all_rows ; earnings ; 10000000 }, interpretation: the average of the assists record of all rows is 64.2 .
Output: no
Command: round_eq { avg { all_rows ; assists } ; 64.2 }, interpretation: the average of the assists record of all rows is 64.2 .
Output: yes
Command: round_eq { avg { all_rows ; assists } ; 64.2 }, interpretation: select the rows whose name record fuzzily matches to aran . take the tournaments record of this row . select the rows whose name record fuzzily matches to itai . take the tournaments record of this row . the second record is 1 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; name ; aran } ; tournaments } ; hop { filter_eq { all_rows ; name ; itai } ; tournaments } } ; -1 }, interpretation: select the rows whose name record fuzzily matches to aran . take the tournaments record of this row . select the rows whose name record fuzzily matches to itai . take the tournaments record of this row . the second record is 1 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; name ; aran } ; tournaments } ; hop { filter_eq { all_rows ; name ; itai } ; tournaments } } ; -1 }, interpretation: select the rows whose date record fuzzily matches to october 31 . take the score record of this row . select the rows whose date record fuzzily matches to october 29 . take the score record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; date ; october 31 } ; score } ; hop { filter_eq { all_rows ; date ; october 29 } ; score } }, interpretation: select the rows whose date record fuzzily matches to october 31 . take the score record of this row . select the rows whose date record fuzzily matches to october 29 . take the score record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; date ; october 31 } ; score } ; hop { filter_eq { all_rows ; date ; october 29 } ; score } }, interpretation: select the rows whose name record fuzzily matches to shareese woods . take the react record of this row . select the rows whose name record fuzzily matches to natalya nazarova . take the react record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; shareese woods } ; react } ; hop { filter_eq { all_rows ; name ; natalya nazarova } ; react } }, interpretation: select the rows whose name record fuzzily matches to shareese woods . take the react record of this row . select the rows whose name record fuzzily matches to natalya nazarova . take the react record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; shareese woods } ; react } ; hop { filter_eq { all_rows ; name ; natalya nazarova } ; react } }, interpretation: select the rows whose player record fuzzily matches to brandon bass . take the years in orlando record of this row . select the rows whose player record fuzzily matches to andre barrett . take the years in orlando record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; brandon bass } ; years in orlando } ; hop { filter_eq { all_rows ; player ; andre barrett } ; years in orlando } }, interpretation: select the rows whose player record fuzzily matches to brandon bass . take the years in orlando record of this row . select the rows whose player record fuzzily matches to andre barrett . take the years in orlando record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; brandon bass } ; years in orlando } ; hop { filter_eq { all_rows ; player ; andre barrett } ; years in orlando } }, interpretation: select the rows whose pitcher record fuzzily matches to toad ramsey . take the strikeouts record of this row . select the rows whose pitcher record fuzzily matches to old hoss radbourn . take the strikeouts record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; pitcher ; toad ramsey } ; strikeouts } ; hop { filter_eq { all_rows ; pitcher ; old hoss radbourn } ; strikeouts } }, interpretation: select the rows whose pitcher record fuzzily matches to toad ramsey . take the strikeouts record of this row . select the rows whose pitcher record fuzzily matches to old hoss radbourn . take the strikeouts record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; pitcher ; toad ramsey } ; strikeouts } ; hop { filter_eq { all_rows ; pitcher ; old hoss radbourn } ; strikeouts } }, interpretation: select the rows whose team record fuzzily matches to honda . the sum of the wins record of these rows is 6 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; team ; honda } ; wins } ; 6 }, interpretation: select the rows whose team record fuzzily matches to honda . the sum of the wins record of these rows is 6 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; team ; honda } ; wins } ; 6 }, interpretation: select the rows whose air date record fuzzily matches to 2008 . the number of such rows is 11 .
Output: no
Command: eq { count { filter_eq { all_rows ; air date ; 2008 } } ; 11 }, interpretation: select the rows whose air date record fuzzily matches to 2008 . the number of such rows is 11 .
Output: yes
Command: eq { count { filter_eq { all_rows ; air date ; 2008 } } ; 11 }, interpretation: select the row whose semifinalists record of all rows is 4th maximum . the country / territory record of this row is australia .
Output: no
Command: eq { hop { nth_argmax { all_rows ; semifinalists ; 4 } ; country / territory } ; australia }, interpretation: select the row whose semifinalists record of all rows is 4th maximum . the country / territory record of this row is australia .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; semifinalists ; 4 } ; country / territory } ; australia }, interpretation: select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is buffalo bills .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; buffalo bills }, interpretation: select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is buffalo bills .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; buffalo bills }, interpretation: select the row whose year record of all rows is 1st minimum . the arcade record of this row is space invaders . the standalone record of this row is space invaders . the console record of this row is space invaders . the computer record of this row is space invaders .
Output: no
Command: and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; arcade } ; space invaders } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; standalone } ; space invaders } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; console } ; space invaders } ; eq { hop { nth_argmin { all_rows ; year ; 1 } ; computer } ; space invaders } } } }, interpretation: select the row whose year record of all rows is 1st minimum . the arcade record of this row is space invaders . the standalone record of this row is space invaders . the console record of this row is space invaders . the computer record of this row is space invaders .
Output: yes
Command: and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; arcade } ; space invaders } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; standalone } ; space invaders } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; console } ; space invaders } ; eq { hop { nth_argmin { all_rows ; year ; 1 } ; computer } ; space invaders } } } }, interpretation: select the row whose round record of all rows is 2nd maximum . the player record of this row is jeffrey hunter .
Output: no
Command: eq { hop { nth_argmax { all_rows ; round ; 2 } ; player } ; jeffrey hunter }, interpretation: select the row whose round record of all rows is 2nd maximum . the player record of this row is jeffrey hunter .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; round ; 2 } ; player } ; jeffrey hunter }, interpretation: select the rows whose date record fuzzily matches to october 3 . take the attendance record of this row . select the rows whose date record fuzzily matches to september 26 . take the attendance record of this row . the first record is 8306 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; date ; october 3 } ; attendance } ; hop { filter_eq { all_rows ; date ; september 26 } ; attendance } } ; 8306 }, interpretation: select the rows whose date record fuzzily matches to october 3 . take the attendance record of this row . select the rows whose date record fuzzily matches to september 26 . take the attendance record of this row . the first record is 8306 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; date ; october 3 } ; attendance } ; hop { filter_eq { all_rows ; date ; september 26 } ; attendance } } ; 8306 }, interpretation: select the rows whose player record fuzzily matches to tim jones . take the pick record of this row . select the rows whose player record fuzzily matches to lew kamanu . take the pick record of this row . the first record is greater than the second record . the position record of the first row is quarterback . the position record of the second row is defensive end .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; player ; tim jones } ; pick } ; hop { filter_eq { all_rows ; player ; lew kamanu } ; pick } } ; and { eq { hop { filter_eq { all_rows ; player ; tim jones } ; position } ; quarterback } ; eq { hop { filter_eq { all_rows ; player ; lew kamanu } ; position } ; defensive end } } }, interpretation: select the rows whose player record fuzzily matches to tim jones . take the pick record of this row . select the rows whose player record fuzzily matches to lew kamanu . take the pick record of this row . the first record is greater than the second record . the position record of the first row is quarterback . the position record of the second row is defensive end .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; player ; tim jones } ; pick } ; hop { filter_eq { all_rows ; player ; lew kamanu } ; pick } } ; and { eq { hop { filter_eq { all_rows ; player ; tim jones } ; position } ; quarterback } ; eq { hop { filter_eq { all_rows ; player ; lew kamanu } ; position } ; defensive end } } }, interpretation: the average of the enrollment record of all rows is 1,285 .
Output: no
Command: round_eq { avg { all_rows ; enrollment } ; 1,285 }, interpretation: the average of the enrollment record of all rows is 1,285 .
Output: yes
Command: round_eq { avg { all_rows ; enrollment } ; 1,285 }, interpretation: select the rows whose career win - loss record fuzzily matches to 2 - . there is only one such row in the table . the tournament record of this unqiue row is us open .
Output: no
Command: and { only { filter_eq { all_rows ; career win - loss ; 2 - } } ; eq { hop { filter_eq { all_rows ; career win - loss ; 2 - } ; tournament } ; us open } }, interpretation: select the rows whose career win - loss record fuzzily matches to 2 - . there is only one such row in the table . the tournament record of this unqiue row is us open .
Output: yes
Command: and { only { filter_eq { all_rows ; career win - loss ; 2 - } } ; eq { hop { filter_eq { all_rows ; career win - loss ; 2 - } ; tournament } ; us open } }, interpretation: select the rows whose incumbent record fuzzily matches to none ( district created ) . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; incumbent ; none ( district created ) } } ; 2 }, interpretation: select the rows whose incumbent record fuzzily matches to none ( district created ) . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; incumbent ; none ( district created ) } } ; 2 }, interpretation: select the rows whose constructor record fuzzily matches to brm . select the row whose time / retired record of these rows is 3rd minimum . the driver record of this row is lucien bianchi .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; constructor ; brm } ; time / retired ; 3 } ; driver } ; lucien bianchi }, interpretation: select the rows whose constructor record fuzzily matches to brm . select the row whose time / retired record of these rows is 3rd minimum . the driver record of this row is lucien bianchi .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; constructor ; brm } ; time / retired ; 3 } ; driver } ; lucien bianchi }, interpretation: select the rows whose type record fuzzily matches to loan return . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; type ; loan return } } ; 2 }, interpretation: select the rows whose type record fuzzily matches to loan return . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; type ; loan return } } ; 2 }, interpretation: select the rows whose opponent record fuzzily matches to san francisco 49ers . the maximum attendance record of these rows is 65551 . the date record of the row with superlative attendance record is november 11 , 1979 .
Output: no
Command: and { eq { max { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance } ; 65551 } ; eq { hop { argmax { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance } ; date } ; november 11 , 1979 } }, interpretation: select the rows whose opponent record fuzzily matches to san francisco 49ers . the maximum attendance record of these rows is 65551 . the date record of the row with superlative attendance record is november 11 , 1979 .
Output: yes
Command: and { eq { max { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance } ; 65551 } ; eq { hop { argmax { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance } ; date } ; november 11 , 1979 } }, interpretation: select the row whose thurs 4 june record of all rows is minimum . the rider record of this row is cameron donald 1000cc suzuki .
Output: no
Command: eq { hop { argmin { all_rows ; thurs 4 june } ; rider } ; cameron donald 1000cc suzuki }, interpretation: select the row whose thurs 4 june record of all rows is minimum . the rider record of this row is cameron donald 1000cc suzuki .
Output: yes
Command: eq { hop { argmin { all_rows ; thurs 4 june } ; rider } ; cameron donald 1000cc suzuki }, interpretation: for the current status records of all rows , most of them fuzzily match to active .
Output: no
Command: most_eq { all_rows ; current status ; active }, interpretation: for the current status records of all rows , most of them fuzzily match to active .
Output: yes
Command: most_eq { all_rows ; current status ; active }, interpretation: select the rows whose gpu frequency record fuzzily matches to 320 mhz . for the release price ( usd ) records of these rows , all of them are greater than 60 .
Output: no
Command: all_greater { filter_eq { all_rows ; gpu frequency ; 320 mhz } ; release price ( usd ) ; 60 }, interpretation: select the rows whose gpu frequency record fuzzily matches to 320 mhz . for the release price ( usd ) records of these rows , all of them are greater than 60 .
Output: yes
Command: all_greater { filter_eq { all_rows ; gpu frequency ; 320 mhz } ; release price ( usd ) ; 60 }, interpretation: select the rows whose nba draft record fuzzily matches to pick . there is only one such row in the table . the player record of this unqiue row is marquis teague .
Output: no
Command: and { only { filter_eq { all_rows ; nba draft ; pick } } ; eq { hop { filter_eq { all_rows ; nba draft ; pick } ; player } ; marquis teague } }, interpretation: select the rows whose nba draft record fuzzily matches to pick . there is only one such row in the table . the player record of this unqiue row is marquis teague .
Output: yes
Command: and { only { filter_eq { all_rows ; nba draft ; pick } } ; eq { hop { filter_eq { all_rows ; nba draft ; pick } ; player } ; marquis teague } }, interpretation: for the district records of all rows , all of them fuzzily match to pennsylvania .
Output: no
Command: all_eq { all_rows ; district ; pennsylvania }, interpretation: for the district records of all rows , all of them fuzzily match to pennsylvania .
Output: yes
Command: all_eq { all_rows ; district ; pennsylvania }, interpretation: select the rows whose venue record fuzzily matches to oval . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; venue ; oval } } ; 3 }, interpretation: select the rows whose venue record fuzzily matches to oval . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; venue ; oval } } ; 3 }, interpretation: the average of the viewers ( in millions ) record of all rows is 12.56 .
Output: no
Command: round_eq { avg { all_rows ; viewers ( in millions ) } ; 12.56 }, interpretation: the average of the viewers ( in millions ) record of all rows is 12.56 .
Output: yes
Command: round_eq { avg { all_rows ; viewers ( in millions ) } ; 12.56 }, interpretation: select the rows whose attendance record is less than 40000 . the number of such rows is 1 .
Output: no
Command: eq { count { filter_less { all_rows ; attendance ; 40000 } } ; 1 }, interpretation: select the rows whose attendance record is less than 40000 . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_less { all_rows ; attendance ; 40000 } } ; 1 }, interpretation: select the rows whose location attendance record fuzzily matches to bryce jordan center , state college , pa ( 9833 ) . there is only one such row in the table . the date record of this unqiue row is december 6 .
Output: no
Command: and { only { filter_eq { all_rows ; location attendance ; bryce jordan center , state college , pa ( 9833 ) } } ; eq { hop { filter_eq { all_rows ; location attendance ; bryce jordan center , state college , pa ( 9833 ) } ; date } ; december 6 } }, interpretation: select the rows whose location attendance record fuzzily matches to bryce jordan center , state college , pa ( 9833 ) . there is only one such row in the table . the date record of this unqiue row is december 6 .
Output: yes
Command: and { only { filter_eq { all_rows ; location attendance ; bryce jordan center , state college , pa ( 9833 ) } } ; eq { hop { filter_eq { all_rows ; location attendance ; bryce jordan center , state college , pa ( 9833 ) } ; date } ; december 6 } }, interpretation: select the row whose position record of all rows is minimum . the year record of this row is 2009 .
Output: no
Command: eq { hop { argmin { all_rows ; position } ; year } ; 2009 }, interpretation: select the row whose position record of all rows is minimum . the year record of this row is 2009 .
Output: yes
Command: eq { hop { argmin { all_rows ; position } ; year } ; 2009 }, interpretation: select the rows whose round record is equal to 7 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; round ; 7 } } ; 2 }, interpretation: select the rows whose round record is equal to 7 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; round ; 7 } } ; 2 }, interpretation: the 1st minimum date record of all rows is 18 may 1993 .
Output: no
Command: eq { nth_min { all_rows ; date ; 1 } ; 18 may 1993 }, interpretation: the 1st minimum date record of all rows is 18 may 1993 .
Output: yes
Command: eq { nth_min { all_rows ; date ; 1 } ; 18 may 1993 }, interpretation: select the rows whose annual ridership ( 2012 ) record fuzzily matches to 280904200 . take the stations record of this row . select the rows whose annual ridership ( 2012 ) record fuzzily matches to 231154300 . take the stations record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; annual ridership ( 2012 ) ; 280904200 } ; stations } ; hop { filter_eq { all_rows ; annual ridership ( 2012 ) ; 231154300 } ; stations } }, interpretation: select the rows whose annual ridership ( 2012 ) record fuzzily matches to 280904200 . take the stations record of this row . select the rows whose annual ridership ( 2012 ) record fuzzily matches to 231154300 . take the stations record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; annual ridership ( 2012 ) ; 280904200 } ; stations } ; hop { filter_eq { all_rows ; annual ridership ( 2012 ) ; 231154300 } ; stations } }, interpretation: select the rows whose year record is greater than or equal to 2011 . the sum of the tournaments played record of these rows is 33 .
Output: no
Command: round_eq { sum { filter_greater_eq { all_rows ; year ; 2011 } ; tournaments played } ; 33 }, interpretation: select the rows whose year record is greater than or equal to 2011 . the sum of the tournaments played record of these rows is 33 .
Output: yes
Command: round_eq { sum { filter_greater_eq { all_rows ; year ; 2011 } ; tournaments played } ; 33 }, interpretation: select the rows whose turbines record is equal to 1 . there is only one such row in the table . the wind farm record of this unqiue row is glenough extension .
Output: no
Command: and { only { filter_eq { all_rows ; turbines ; 1 } } ; eq { hop { filter_eq { all_rows ; turbines ; 1 } ; wind farm } ; glenough extension } }, interpretation: select the rows whose turbines record is equal to 1 . there is only one such row in the table . the wind farm record of this unqiue row is glenough extension .
Output: yes
Command: and { only { filter_eq { all_rows ; turbines ; 1 } } ; eq { hop { filter_eq { all_rows ; turbines ; 1 } ; wind farm } ; glenough extension } }, interpretation: select the row whose location attendance record of all rows is maximum . the date record of this row is april 2 .
Output: no
Command: eq { hop { argmax { all_rows ; location attendance } ; date } ; april 2 }, interpretation: select the row whose location attendance record of all rows is maximum . the date record of this row is april 2 .
Output: yes
Command: eq { hop { argmax { all_rows ; location attendance } ; date } ; april 2 }, 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 engine record fuzzily matches to 4.7 l powertech v8 . among these rows , select the rows whose years record fuzzily matches to 2005-2007 . the number of such rows is 1 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; engine ; 4.7 l powertech v8 } ; years ; 2005-2007 } } ; 1 }, interpretation: select the rows whose engine record fuzzily matches to 4.7 l powertech v8 . among these rows , select the rows whose years record fuzzily matches to 2005-2007 . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; engine ; 4.7 l powertech v8 } ; years ; 2005-2007 } } ; 1 }, interpretation: select the row whose us air date record of all rows is minimum . the storyteller record of this row is kristen .
Output: no
Command: eq { hop { argmin { all_rows ; us air date } ; storyteller } ; kristen }, interpretation: select the row whose us air date record of all rows is minimum . the storyteller record of this row is kristen .
Output: yes
Command: eq { hop { argmin { all_rows ; us air date } ; storyteller } ; kristen }, interpretation: select the rows whose brand record fuzzily matches to gmc . take the year founded record of this row . select the rows whose brand record fuzzily matches to chevrolet . take the year founded record of this row . the second record is 10 larger than the first record .
Output: no