niryuu/sni-each-converted huggingface dataset:
Command: and { eq { hop { argmin { all_rows ; place } ; artist } ; liam reilly } ; eq { hop { argmin { all_rows ; place } ; song } ; somewhere in europe } }, interpretation: select the rows whose pole position record fuzzily matches to chris goodwin andrew kirkaldy . there is only one such row in the table . the round record of this unqiue row is 5 .
Output: no
Command: and { only { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } } ; eq { hop { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } ; round } ; 5 } }, interpretation: select the rows whose pole position record fuzzily matches to chris goodwin andrew kirkaldy . there is only one such row in the table . the round record of this unqiue row is 5 .
Output: yes
Command: and { only { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } } ; eq { hop { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } ; round } ; 5 } }, interpretation: select the rows whose ties record is equal to 1 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; ties ; 1 } } ; 3 }, interpretation: select the rows whose ties record is equal to 1 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; ties ; 1 } } ; 3 }, interpretation: select the rows whose escorts record is equal to - . there is only one such row in the table . the nato member record of this unqiue row is belgium .
Output: no
Command: and { only { filter_eq { all_rows ; escorts ; - } } ; eq { hop { filter_eq { all_rows ; escorts ; - } ; nato member } ; belgium } }, interpretation: select the rows whose escorts record is equal to - . there is only one such row in the table . the nato member record of this unqiue row is belgium .
Output: yes
Command: and { only { filter_eq { all_rows ; escorts ; - } } ; eq { hop { filter_eq { all_rows ; escorts ; - } ; nato member } ; belgium } }, interpretation: select the rows whose proposed record fuzzily matches to 1989 . among these rows , select the rows whose proposed record fuzzily matches to 1989 . there is only one such row in the table . the county record of this unqiue row is new london .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; proposed ; 1989 } ; proposed ; 1989 } } ; eq { hop { filter_eq { filter_eq { all_rows ; proposed ; 1989 } ; proposed ; 1989 } ; county } ; new london } }, interpretation: select the rows whose proposed record fuzzily matches to 1989 . among these rows , select the rows whose proposed record fuzzily matches to 1989 . there is only one such row in the table . the county record of this unqiue row is new london .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; proposed ; 1989 } ; proposed ; 1989 } } ; eq { hop { filter_eq { filter_eq { all_rows ; proposed ; 1989 } ; proposed ; 1989 } ; county } ; new london } }, interpretation: select the rows whose year ( ceremony ) record fuzzily matches to 2006 ( 79th ) . take the year ( ceremony ) record of this row . select the rows whose year ( ceremony ) record fuzzily matches to 2007 ( 80th ) . take the year ( ceremony ) record of this row . the first record is less than the second record . the year ( ceremony ) record of the first row is 2006 ( 79th ) . the year ( ceremony ) record of the second row is 2007 ( 80th ) .
Output: no
Command: and { less { hop { filter_eq { all_rows ; year ( ceremony ) ; 2006 ( 79th ) } ; year ( ceremony ) } ; hop { filter_eq { all_rows ; year ( ceremony ) ; 2007 ( 80th ) } ; year ( ceremony ) } } ; and { eq { hop { filter_eq { all_rows ; year ( ceremony ) ; 2006 ( 79th ) } ; year ( ceremony ) } ; 2006 ( 79th ) } ; eq { hop { filter_eq { all_rows ; year ( ceremony ) ; 2007 ( 80th ) } ; year ( ceremony ) } ; 2007 ( 80th ) } } }, interpretation: select the rows whose year ( ceremony ) record fuzzily matches to 2006 ( 79th ) . take the year ( ceremony ) record of this row . select the rows whose year ( ceremony ) record fuzzily matches to 2007 ( 80th ) . take the year ( ceremony ) record of this row . the first record is less than the second record . the year ( ceremony ) record of the first row is 2006 ( 79th ) . the year ( ceremony ) record of the second row is 2007 ( 80th ) .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; year ( ceremony ) ; 2006 ( 79th ) } ; year ( ceremony ) } ; hop { filter_eq { all_rows ; year ( ceremony ) ; 2007 ( 80th ) } ; year ( ceremony ) } } ; and { eq { hop { filter_eq { all_rows ; year ( ceremony ) ; 2006 ( 79th ) } ; year ( ceremony ) } ; 2006 ( 79th ) } ; eq { hop { filter_eq { all_rows ; year ( ceremony ) ; 2007 ( 80th ) } ; year ( ceremony ) } ; 2007 ( 80th ) } } }, interpretation: select the rows whose position record fuzzily matches to punter . there is only one such row in the table . the name record of this unqiue row is adam podlesh .
Output: no
Command: and { only { filter_eq { all_rows ; position ; punter } } ; eq { hop { filter_eq { all_rows ; position ; punter } ; name } ; adam podlesh } }, interpretation: select the rows whose position record fuzzily matches to punter . there is only one such row in the table . the name record of this unqiue row is adam podlesh .
Output: yes
Command: and { only { filter_eq { all_rows ; position ; punter } } ; eq { hop { filter_eq { all_rows ; position ; punter } ; name } ; adam podlesh } }, interpretation: for the surface records of all rows , most of them fuzzily match to hard .
Output: no
Command: most_eq { all_rows ; surface ; hard }, interpretation: for the surface records of all rows , most of them fuzzily match to hard .
Output: yes
Command: most_eq { all_rows ; surface ; hard }, interpretation: for the drivetrain records of all rows , most of them fuzzily match to rwd .
Output: no
Command: most_eq { all_rows ; drivetrain ; rwd }, interpretation: for the drivetrain records of all rows , most of them fuzzily match to rwd .
Output: yes
Command: most_eq { all_rows ; drivetrain ; rwd }, interpretation: the average of the group 's equity shareholding record of all rows is 85 % .
Output: no
Command: round_eq { avg { all_rows ; group 's equity shareholding } ; 85 % }, interpretation: the average of the group 's equity shareholding record of all rows is 85 % .
Output: yes
Command: round_eq { avg { all_rows ; group 's equity shareholding } ; 85 % }, interpretation: for the entrant records of all rows , most of them fuzzily match to osella squadra corse .
Output: no
Command: most_eq { all_rows ; entrant ; osella squadra corse }, interpretation: for the entrant records of all rows , most of them fuzzily match to osella squadra corse .
Output: yes
Command: most_eq { all_rows ; entrant ; osella squadra corse }, interpretation: for the country records of all rows , all of them fuzzily match to united states .
Output: no
Command: all_eq { all_rows ; country ; united states }, interpretation: for the country records of all rows , all of them fuzzily match to united states .
Output: yes
Command: all_eq { all_rows ; country ; united states }, interpretation: select the rows whose club record fuzzily matches to terang . take the wins record of this row . select the rows whose club record fuzzily matches to terang mortlake . take the wins record of this row . the first record is 501 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; club ; terang } ; wins } ; hop { filter_eq { all_rows ; club ; terang mortlake } ; wins } } ; 501 }, interpretation: select the rows whose club record fuzzily matches to terang . take the wins record of this row . select the rows whose club record fuzzily matches to terang mortlake . take the wins record of this row . the first record is 501 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; club ; terang } ; wins } ; hop { filter_eq { all_rows ; club ; terang mortlake } ; wins } } ; 501 }, 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: for the country records of all rows , most of them fuzzily match to united states .
Output: yes
Command: most_eq { all_rows ; country ; united states }, interpretation: for the unit records of all rows , most of them fuzzily match to colorado .
Output: no
Command: most_eq { all_rows ; unit ; colorado }, interpretation: for the unit records of all rows , most of them fuzzily match to colorado .
Output: yes
Command: most_eq { all_rows ; unit ; colorado }, interpretation: select the rows whose date record fuzzily matches to 1982 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; date ; 1982 } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to 1982 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; date ; 1982 } } ; 2 }, interpretation: select the rows whose original air date ( uk ) record fuzzily matches to july . there is only one such row in the table . the episode title record of this unqiue row is night games .
Output: no
Command: and { only { filter_eq { all_rows ; original air date ( uk ) ; july } } ; eq { hop { filter_eq { all_rows ; original air date ( uk ) ; july } ; episode title } ; night games } }, interpretation: select the rows whose original air date ( uk ) record fuzzily matches to july . there is only one such row in the table . the episode title record of this unqiue row is night games .
Output: yes
Command: and { only { filter_eq { all_rows ; original air date ( uk ) ; july } } ; eq { hop { filter_eq { all_rows ; original air date ( uk ) ; july } ; episode title } ; night games } }, interpretation: select the rows whose date record fuzzily matches to november 25 . take the rank record of this row . select the rows whose date record fuzzily matches to november 18 . take the rank record of this row . the second record is 1 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; date ; november 25 } ; rank } ; hop { filter_eq { all_rows ; date ; november 18 } ; rank } } ; -1 }, interpretation: select the rows whose date record fuzzily matches to november 25 . take the rank record of this row . select the rows whose date record fuzzily matches to november 18 . take the rank record of this row . the second record is 1 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; date ; november 25 } ; rank } ; hop { filter_eq { all_rows ; date ; november 18 } ; rank } } ; -1 }, interpretation: the maximum result record of all rows is kuwait won by 72 runs scorecard . the details record of the row with superlative result record is 2011 division seven .
Output: no
Command: and { eq { max { all_rows ; result } ; kuwait won by 72 runs scorecard } ; eq { hop { argmax { all_rows ; result } ; details } ; 2011 division seven } }, interpretation: the maximum result record of all rows is kuwait won by 72 runs scorecard . the details record of the row with superlative result record is 2011 division seven .
Output: yes
Command: and { eq { max { all_rows ; result } ; kuwait won by 72 runs scorecard } ; eq { hop { argmax { all_rows ; result } ; details } ; 2011 division seven } }, interpretation: for the series ep records of all rows , all of them fuzzily match to 13 - .
Output: no
Command: all_eq { all_rows ; series ep ; 13 - }, interpretation: for the series ep records of all rows , all of them fuzzily match to 13 - .
Output: yes
Command: all_eq { all_rows ; series ep ; 13 - }, interpretation: for the results records of all rows , most of them fuzzily match to re - elected .
Output: no
Command: most_eq { all_rows ; results ; re - elected }, interpretation: for the results records of all rows , most of them fuzzily match to re - elected .
Output: yes
Command: most_eq { all_rows ; results ; re - elected }, interpretation: select the row whose points record of all rows is maximum . the song record of this row is all kinds of everything .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; song } ; all kinds of everything }, interpretation: select the row whose points record of all rows is maximum . the song record of this row is all kinds of everything .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; song } ; all kinds of everything }, interpretation: select the rows whose opened record is equal to 2004 . there is only one such row in the table . the stadium record of this unqiue row is indiana invaders soccer complex .
Output: no
Command: and { only { filter_eq { all_rows ; opened ; 2004 } } ; eq { hop { filter_eq { all_rows ; opened ; 2004 } ; stadium } ; indiana invaders soccer complex } }, interpretation: select the rows whose opened record is equal to 2004 . there is only one such row in the table . the stadium record of this unqiue row is indiana invaders soccer complex .
Output: yes
Command: and { only { filter_eq { all_rows ; opened ; 2004 } } ; eq { hop { filter_eq { all_rows ; opened ; 2004 } ; stadium } ; indiana invaders soccer complex } }, interpretation: select the rows whose team record fuzzily matches to hespanha . take the points record of this row . select the rows whose team record fuzzily matches to ca paulista . take the points record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; team ; hespanha } ; points } ; hop { filter_eq { all_rows ; team ; ca paulista } ; points } }, interpretation: select the rows whose team record fuzzily matches to hespanha . take the points record of this row . select the rows whose team record fuzzily matches to ca paulista . take the points record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; team ; hespanha } ; points } ; hop { filter_eq { all_rows ; team ; ca paulista } ; points } }, interpretation: select the rows whose featuring record fuzzily matches to tractators . the minimum released record of these rows is february 2010 .
Output: no
Command: eq { min { filter_eq { all_rows ; featuring ; tractators } ; released } ; february 2010 }, interpretation: select the rows whose featuring record fuzzily matches to tractators . the minimum released record of these rows is february 2010 .
Output: yes
Command: eq { min { filter_eq { all_rows ; featuring ; tractators } ; released } ; february 2010 }, interpretation: the sum of the crowd record of all rows is 86100 .
Output: no
Command: round_eq { sum { all_rows ; crowd } ; 86100 }, interpretation: the sum of the crowd record of all rows is 86100 .
Output: yes
Command: round_eq { sum { all_rows ; crowd } ; 86100 }, interpretation: select the rows whose notes record fuzzily matches to 1500 m . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; notes ; 1500 m } } ; 4 }, interpretation: select the rows whose notes record fuzzily matches to 1500 m . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; notes ; 1500 m } } ; 4 }, interpretation: select the row whose entered service record of all rows is 3rd minimum . the locomotive record of this row is bl28 .
Output: no
Command: eq { hop { nth_argmin { all_rows ; entered service ; 3 } ; locomotive } ; bl28 }, interpretation: select the row whose entered service record of all rows is 3rd minimum . the locomotive record of this row is bl28 .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; entered service ; 3 } ; locomotive } ; bl28 }, interpretation: for the date made records of all rows , most of them are less than 1900 .
Output: no
Command: most_less { all_rows ; date made ; 1900 }, interpretation: for the date made records of all rows , most of them are less than 1900 .
Output: yes
Command: most_less { all_rows ; date made ; 1900 }, interpretation: select the rows whose laps record is equal to 64 . for the points records of these rows , most of them are greater than or equal to 15 .
Output: no
Command: most_greater_eq { filter_eq { all_rows ; laps ; 64 } ; points ; 15 }, interpretation: select the rows whose laps record is equal to 64 . for the points records of these rows , most of them are greater than or equal to 15 .
Output: yes
Command: most_greater_eq { filter_eq { all_rows ; laps ; 64 } ; points ; 15 }, interpretation: the average of the pick record of all rows is 4.5 .
Output: no
Command: round_eq { avg { all_rows ; pick } ; 4.5 }, interpretation: the average of the pick record of all rows is 4.5 .
Output: yes
Command: round_eq { avg { all_rows ; pick } ; 4.5 }, interpretation: the sum of the crowd record of all rows is 149299 .
Output: no
Command: round_eq { sum { all_rows ; crowd } ; 149299 }, interpretation: the sum of the crowd record of all rows is 149299 .
Output: yes
Command: round_eq { sum { all_rows ; crowd } ; 149299 }, interpretation: select the rows whose winner record fuzzily matches to new york jets . among these rows , select the rows whose location record fuzzily matches to shea stadium . the number of such rows is 7 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; winner ; new york jets } ; location ; shea stadium } } ; 7 }, interpretation: select the rows whose winner record fuzzily matches to new york jets . among these rows , select the rows whose location record fuzzily matches to shea stadium . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; winner ; new york jets } ; location ; shea stadium } } ; 7 }, interpretation: select the rows whose model / engine record fuzzily matches to 1.8 duratec he . take the capacity record of this row . select the rows whose model / engine record fuzzily matches to 2.0 duratec he . take the capacity record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; model / engine ; 1.8 duratec he } ; capacity } ; hop { filter_eq { all_rows ; model / engine ; 2.0 duratec he } ; capacity } }, interpretation: select the rows whose model / engine record fuzzily matches to 1.8 duratec he . take the capacity record of this row . select the rows whose model / engine record fuzzily matches to 2.0 duratec he . take the capacity record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; model / engine ; 1.8 duratec he } ; capacity } ; hop { filter_eq { all_rows ; model / engine ; 2.0 duratec he } ; capacity } }, interpretation: the average of the weight ( lbs ) record of all rows is 238.05 .
Output: no
Command: round_eq { avg { all_rows ; weight ( lbs ) } ; 238.05 }, interpretation: the average of the weight ( lbs ) record of all rows is 238.05 .
Output: yes
Command: round_eq { avg { all_rows ; weight ( lbs ) } ; 238.05 }, interpretation: for the played records of all rows , all of them are equal to 14 .
Output: no
Command: all_eq { all_rows ; played ; 14 }, interpretation: for the played records of all rows , all of them are equal to 14 .
Output: yes
Command: all_eq { all_rows ; played ; 14 }, interpretation: select the rows whose audition city record fuzzily matches to são paulo . there is only one such row in the table . the guest fourth judge record of this unqiue row is peninha .
Output: no
Command: and { only { filter_eq { all_rows ; audition city ; são paulo } } ; eq { hop { filter_eq { all_rows ; audition city ; são paulo } ; guest fourth judge } ; peninha } }, interpretation: select the rows whose audition city record fuzzily matches to são paulo . there is only one such row in the table . the guest fourth judge record of this unqiue row is peninha .
Output: yes
Command: and { only { filter_eq { all_rows ; audition city ; são paulo } } ; eq { hop { filter_eq { all_rows ; audition city ; são paulo } ; guest fourth judge } ; peninha } }, interpretation: select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is jack fields .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; retired } } ; eq { hop { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; retired } ; incumbent } ; jack fields } }, interpretation: select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is jack fields .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; retired } } ; eq { hop { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; retired } ; incumbent } ; jack fields } }, interpretation: select the rows whose top division titles record is equal to 1 . there is only one such row in the table . the team record of this unqiue row is deportivo coopsol .
Output: no
Command: and { only { filter_eq { all_rows ; top division titles ; 1 } } ; eq { hop { filter_eq { all_rows ; top division titles ; 1 } ; team } ; deportivo coopsol } }, interpretation: select the rows whose top division titles record is equal to 1 . there is only one such row in the table . the team record of this unqiue row is deportivo coopsol .
Output: yes
Command: and { only { filter_eq { all_rows ; top division titles ; 1 } } ; eq { hop { filter_eq { all_rows ; top division titles ; 1 } ; team } ; deportivo coopsol } }, interpretation: the average of the enrollment record of all rows is 538 .
Output: no
Command: round_eq { avg { all_rows ; enrollment } ; 538 }, interpretation: the average of the enrollment record of all rows is 538 .
Output: yes
Command: round_eq { avg { all_rows ; enrollment } ; 538 }, interpretation: select the rows whose team ( s ) record fuzzily matches to stegell motorsports . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; team ( s ) ; stegell motorsports } } ; 4 }, interpretation: select the rows whose team ( s ) record fuzzily matches to stegell motorsports . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; team ( s ) ; stegell motorsports } } ; 4 }, interpretation: select the row whose commenced operations record of all rows is 2nd minimum . the airline record of this row is nouvelair .
Output: no
Command: eq { hop { nth_argmin { all_rows ; commenced operations ; 2 } ; airline } ; nouvelair }, interpretation: select the row whose commenced operations record of all rows is 2nd minimum . the airline record of this row is nouvelair .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; commenced operations ; 2 } ; airline } ; nouvelair }, interpretation: select the rows whose 2nd position record fuzzily matches to lija . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; 2nd position ; lija } } ; 2 }, interpretation: select the rows whose 2nd position record fuzzily matches to lija . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; 2nd position ; lija } } ; 2 }, interpretation: select the rows whose year record fuzzily matches to freshman . the average of the weight record of these rows is 215 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; year ; freshman } ; weight } ; 215 }, interpretation: select the rows whose year record fuzzily matches to freshman . the average of the weight record of these rows is 215 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; year ; freshman } ; weight } ; 215 }, interpretation: select the row whose points record of all rows is maximum . the team record of this row is alianza fc .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; team } ; alianza fc }, interpretation: select the row whose points record of all rows is maximum . the team record of this row is alianza fc .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; team } ; alianza fc }, interpretation: the sum of the points record of all rows is 0 .
Output: no
Command: round_eq { sum { all_rows ; points } ; 0 }, interpretation: the sum of the points record of all rows is 0 .
Output: yes
Command: round_eq { sum { all_rows ; points } ; 0 }, interpretation: for the score records of all rows , most of them fuzzily match to l .
Output: no
Command: most_eq { all_rows ; score ; l }, interpretation: for the score records of all rows , most of them fuzzily match to l .
Output: yes
Command: most_eq { all_rows ; score ; l }, interpretation: select the rows whose crowd record is greater than 20000 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater { all_rows ; crowd ; 20000 } } ; 2 }, interpretation: select the rows whose crowd record is greater than 20000 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater { all_rows ; crowd ; 20000 } } ; 2 }, interpretation: select the row whose rank record of all rows is minimum . the nation record of this row is united states .
Output: no
Command: eq { hop { argmin { all_rows ; rank } ; nation } ; united states }, interpretation: select the row whose rank record of all rows is minimum . the nation record of this row is united states .
Output: yes
Command: eq { hop { argmin { all_rows ; rank } ; nation } ; united states }, interpretation: select the rows whose series record fuzzily matches to formula renault 2.0 italy . take the points record of this row . select the rows whose series record fuzzily matches to italian formula three . take the points record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; series ; formula renault 2.0 italy } ; points } ; hop { filter_eq { all_rows ; series ; italian formula three } ; points } }, interpretation: select the rows whose series record fuzzily matches to formula renault 2.0 italy . take the points record of this row . select the rows whose series record fuzzily matches to italian formula three . take the points record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; series ; formula renault 2.0 italy } ; points } ; hop { filter_eq { all_rows ; series ; italian formula three } ; points } }, interpretation: select the rows whose lost record fuzzily matches to 15 . take the lost record of this row . select the rows whose lost record fuzzily matches to 15 . take the lost record of this row . the first record is equal to the second record . the lost record of the first row is 15 . the lost record of the second row is 15 .
Output: no
Command: and { eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; hop { filter_eq { all_rows ; lost ; 15 } ; lost } } ; and { eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; 15 } ; eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; 15 } } }, interpretation: select the rows whose lost record fuzzily matches to 15 . take the lost record of this row . select the rows whose lost record fuzzily matches to 15 . take the lost record of this row . the first record is equal to the second record . the lost record of the first row is 15 . the lost record of the second row is 15 .
Output: yes
Command: and { eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; hop { filter_eq { all_rows ; lost ; 15 } ; lost } } ; and { eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; 15 } ; eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; 15 } } }, interpretation: select the rows whose player record fuzzily matches to fred couples . take the weeks record of this row . select the rows whose player record fuzzily matches to david duval . take the weeks record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; fred couples } ; weeks } ; hop { filter_eq { all_rows ; player ; david duval } ; weeks } }, interpretation: select the rows whose player record fuzzily matches to fred couples . take the weeks record of this row . select the rows whose player record fuzzily matches to david duval . take the weeks record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; fred couples } ; weeks } ; hop { filter_eq { all_rows ; player ; david duval } ; weeks } }, interpretation: the average of the attendance record of all rows is 56346 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 56346 }, interpretation: the average of the attendance record of all rows is 56346 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 56346 }, interpretation: select the rows whose acquisition via record fuzzily matches to rookie draft . there is only one such row in the table . the name record of this unqiue row is ronjay buenafe .
Output: no
Command: and { only { filter_eq { all_rows ; acquisition via ; rookie draft } } ; eq { hop { filter_eq { all_rows ; acquisition via ; rookie draft } ; name } ; ronjay buenafe } }, interpretation: select the rows whose acquisition via record fuzzily matches to rookie draft . there is only one such row in the table . the name record of this unqiue row is ronjay buenafe .
Output: yes
Command: and { only { filter_eq { all_rows ; acquisition via ; rookie draft } } ; eq { hop { filter_eq { all_rows ; acquisition via ; rookie draft } ; name } ; ronjay buenafe } }, interpretation: select the row whose innings record of all rows is maximum . the player record of this row is ricky ponting .
Output: no
Command: eq { hop { argmax { all_rows ; innings } ; player } ; ricky ponting }, interpretation: select the row whose innings record of all rows is maximum . the player record of this row is ricky ponting .
Output: yes
Command: eq { hop { argmax { all_rows ; innings } ; player } ; ricky ponting }, interpretation: for the date records of all rows , all of them fuzzily match to january .
Output: no
Command: all_eq { all_rows ; date ; january }, interpretation: the average of the score record of all rows is 279 .
Output: no
Command: round_eq { avg { all_rows ; score } ; 279 }, interpretation: the average of the score record of all rows is 279 .
Output: yes
Command: round_eq { avg { all_rows ; score } ; 279 }, interpretation: for the result records of all rows , most of them fuzzily match to not nominated .
Output: no
Command: most_eq { all_rows ; result ; not nominated }, interpretation: for the result records of all rows , most of them fuzzily match to not nominated .
Output: yes
Command: most_eq { all_rows ; result ; not nominated }, interpretation: select the rows whose round record fuzzily matches to group h . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; round ; group h } } ; 3 }, interpretation: select the rows whose round record fuzzily matches to group h . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; round ; group h } } ; 3 }, interpretation: select the rows whose make record fuzzily matches to toyota . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; make ; toyota } } ; 4 }, interpretation: select the rows whose make record fuzzily matches to toyota . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; make ; toyota } } ; 4 }, interpretation: the average of the height record of all rows is 2.01 .
Output: no
Command: round_eq { avg { all_rows ; height } ; 2.01 }, interpretation: the average of the height record of all rows is 2.01 .
Output: yes
Command: round_eq { avg { all_rows ; height } ; 2.01 }, interpretation: the average of the attendance record of all rows is 15720 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 15720 }, interpretation: the average of the attendance record of all rows is 15720 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 15720 }, interpretation: for the constructor records of all rows , most of them fuzzily match to ford .
Output: no
Command: most_eq { all_rows ; constructor ; ford }, interpretation: for the constructor records of all rows , most of them fuzzily match to ford .
Output: yes
Command: most_eq { all_rows ; constructor ; ford }, interpretation: select the rows whose venue record fuzzily matches to charlotte speedway . take the closed record of this row . select the rows whose venue record fuzzily matches to belk gymnasium . take the closed record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; venue ; charlotte speedway } ; closed } ; hop { filter_eq { all_rows ; venue ; belk gymnasium } ; closed } }, interpretation: select the rows whose venue record fuzzily matches to charlotte speedway . take the closed record of this row . select the rows whose venue record fuzzily matches to belk gymnasium . take the closed record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; venue ; charlotte speedway } ; closed } ; hop { filter_eq { all_rows ; venue ; belk gymnasium } ; closed } }, interpretation: select the rows whose position record fuzzily matches to guard . the maximum height in ft record of these rows is 6 - 8 .
Output: no
Command: eq { max { filter_eq { all_rows ; position ; guard } ; height in ft } ; 6 - 8 }, interpretation: select the rows whose position record fuzzily matches to guard . the maximum height in ft record of these rows is 6 - 8 .
Output: yes
Command: eq { max { filter_eq { all_rows ; position ; guard } ; height in ft } ; 6 - 8 }, interpretation: the average of the attendance record of all rows is 2900 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 2900 }, interpretation: the average of the attendance record of all rows is 2900 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 2900 }, interpretation: select the rows whose institution record fuzzily matches to anglo irish bank . take the tier 1 capital , million record of this row . select the rows whose institution record fuzzily matches to irish life and permanent . take the tier 1 capital , million record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; institution ; anglo irish bank } ; tier 1 capital , million } ; hop { filter_eq { all_rows ; institution ; irish life and permanent } ; tier 1 capital , million } }, interpretation: select the rows whose institution record fuzzily matches to anglo irish bank . take the tier 1 capital , million record of this row . select the rows whose institution record fuzzily matches to irish life and permanent . take the tier 1 capital , million record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; institution ; anglo irish bank } ; tier 1 capital , million } ; hop { filter_eq { all_rows ; institution ; irish life and permanent } ; tier 1 capital , million } }, interpretation: select the rows whose language record fuzzily matches to english . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; language ; english } } ; 2 }, interpretation: select the rows whose language record fuzzily matches to english . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; language ; english } } ; 2 }, interpretation: select the rows whose locale record fuzzily matches to prince edward island . there is only one such row in the table . the skip record of this unqiue row is rod macdonald .
Output: no
Command: and { only { filter_eq { all_rows ; locale ; prince edward island } } ; eq { hop { filter_eq { all_rows ; locale ; prince edward island } ; skip } ; rod macdonald } }, interpretation: select the rows whose locale record fuzzily matches to prince edward island . there is only one such row in the table . the skip record of this unqiue row is rod macdonald .
Output: yes
Command: and { only { filter_eq { all_rows ; locale ; prince edward island } } ; eq { hop { filter_eq { all_rows ; locale ; prince edward island } ; skip } ; rod macdonald } }, interpretation: select the rows whose name record fuzzily matches to chopra . take the started record of this row . select the rows whose name record fuzzily matches to routledge . take the started record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; chopra } ; started } ; hop { filter_eq { all_rows ; name ; routledge } ; started } }, interpretation: select the rows whose name record fuzzily matches to chopra . take the started record of this row . select the rows whose name record fuzzily matches to routledge . take the started record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; chopra } ; started } ; hop { filter_eq { all_rows ; name ; routledge } ; started } }, interpretation: select the row whose first broadcast record of all rows is maximum . the episode record of this row is 3x08 .
Output: no
Command: eq { hop { argmax { all_rows ; first broadcast } ; episode } ; 3x08 }, interpretation: select the row whose first broadcast record of all rows is maximum . the episode record of this row is 3x08 .
Output: yes
Command: eq { hop { argmax { all_rows ; first broadcast } ; episode } ; 3x08 }, interpretation: for the day / boarding records of all rows , most of them fuzzily match to day & boarding .
Output: no
Command: most_eq { all_rows ; day / boarding ; day & boarding }, interpretation: for the day / boarding records of all rows , most of them fuzzily match to day & boarding .
Output: yes
Command: most_eq { all_rows ; day / boarding ; day & boarding }, interpretation: select the rows whose opponent record fuzzily matches to oakland raiders . the sum of the attendance record of these rows is 123032 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; opponent ; oakland raiders } ; attendance } ; 123032 }, interpretation: select the rows whose opponent record fuzzily matches to oakland raiders . the sum of the attendance record of these rows is 123032 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; opponent ; oakland raiders } ; attendance } ; 123032 }, interpretation: select the rows whose 2011 record fuzzily matches to 2r . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; 2011 ; 2r } } ; 2 }, interpretation: select the rows whose 2011 record fuzzily matches to 2r . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; 2011 ; 2r } } ; 2 }, interpretation: select the row whose goal difference record of all rows is 3rd maximum . the club record of this row is real oviedo .
Output: no
Command: eq { hop { nth_argmax { all_rows ; goal difference ; 3 } ; club } ; real oviedo }, interpretation: select the row whose goal difference record of all rows is 3rd maximum . the club record of this row is real oviedo .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; goal difference ; 3 } ; club } ; real oviedo }, interpretation: select the row whose population served record of all rows is maximum . the location record of this row is marcala , hon .
Output: no
Command: eq { hop { argmax { all_rows ; population served } ; location } ; marcala , hon }, interpretation: select the row whose population served record of all rows is maximum . the location record of this row is marcala , hon .
Output: yes
Command: eq { hop { argmax { all_rows ; population served } ; location } ; marcala , hon }, interpretation: select the row whose points record of all rows is maximum . the driver record of this row is bruno junqueira .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; driver } ; bruno junqueira }, interpretation: select the row whose points record of all rows is maximum . the driver record of this row is bruno junqueira .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; driver } ; bruno junqueira }, interpretation: select the row whose agg record of all rows is maximum . the team 1 record of this row is manzini wanderers .
Output: no
Command: eq { hop { argmax { all_rows ; agg } ; team 1 } ; manzini wanderers }, interpretation: select the row whose agg record of all rows is maximum . the team 1 record of this row is manzini wanderers .
Output: yes
Command: eq { hop { argmax { all_rows ; agg } ; team 1 } ; manzini wanderers }, interpretation: select the rows whose class record fuzzily matches to 250 cc . take the race record of this row . select the rows whose class record fuzzily matches to moto2 . take the race record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; class ; 250 cc } ; race } ; hop { filter_eq { all_rows ; class ; moto2 } ; race } }, interpretation: select the rows whose class record fuzzily matches to 250 cc . take the race record of this row . select the rows whose class record fuzzily matches to moto2 . take the race record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; class ; 250 cc } ; race } ; hop { filter_eq { all_rows ; class ; moto2 } ; race } }, interpretation: select the rows whose year record is equal to 2006 . the average of the margin record of these rows is 3.5 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; year ; 2006 } ; margin } ; 3.5 }, interpretation: select the rows whose year record is equal to 2006 . the average of the margin record of these rows is 3.5 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; year ; 2006 } ; margin } ; 3.5 }, interpretation: the 1st minimum year completed record of all rows is 1955 .
Output: no
Command: eq { nth_min { all_rows ; year completed ; 1 } ; 1955 }, interpretation: the 1st minimum year completed record of all rows is 1955 .
Output: yes
Command: eq { nth_min { all_rows ; year completed ; 1 } ; 1955 }, interpretation: select the rows whose round record is equal to 2 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; round ; 2 } } ; 2 }, interpretation: select the rows whose round record is equal to 2 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; round ; 2 } } ; 2 }, interpretation: select the rows whose ngc number record fuzzily matches to 2736 . take the object type record of this row . select the rows whose ngc number record fuzzily matches to 2770 . take the object type record of this row . the first record does not match to the second record . the object type record of the first row is diffuse nebula . the object type record of the second row is spiral galaxy .
Output: no
Command: and { not_eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } } ; and { eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; diffuse nebula } ; eq { hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } ; spiral galaxy } } }, interpretation: select the rows whose ngc number record fuzzily matches to 2736 . take the object type record of this row . select the rows whose ngc number record fuzzily matches to 2770 . take the object type record of this row . the first record does not match to the second record . the object type record of the first row is diffuse nebula . the object type record of the second row is spiral galaxy .
Output: yes
Command: and { not_eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } } ; and { eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; diffuse nebula } ; eq { hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } ; spiral galaxy } } }, interpretation: the average of the 1 - year peak record of all rows is 2869 .
Output: no
Command: round_eq { avg { all_rows ; 1 - year peak } ; 2869 }, interpretation: the average of the 1 - year peak record of all rows is 2869 .
Output: yes
Command: round_eq { avg { all_rows ; 1 - year peak } ; 2869 }, interpretation: the sum of the quantity made record of all rows is 151 .
Output: no
Command: round_eq { sum { all_rows ; quantity made } ; 151 }, interpretation: the sum of the quantity made record of all rows is 151 .
Output: yes
Command: round_eq { sum { all_rows ; quantity made } ; 151 }, interpretation: select the row whose attendance record of all rows is 1st maximum . the date record of this row is march 19 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; march 19 }, interpretation: select the row whose attendance record of all rows is 1st maximum . the date record of this row is march 19 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; march 19 }, interpretation: select the rows whose school / club team record fuzzily matches to brigham young university . there is only one such row in the table . the player record of this unqiue row is tevita ofahengaue .
Output: no
Command: and { only { filter_eq { all_rows ; school / club team ; brigham young university } } ; eq { hop { filter_eq { all_rows ; school / club team ; brigham young university } ; player } ; tevita ofahengaue } }, interpretation: select the rows whose school / club team record fuzzily matches to brigham young university . there is only one such row in the table . the player record of this unqiue row is tevita ofahengaue .
Output: yes
Command: and { only { filter_eq { all_rows ; school / club team ; brigham young university } } ; eq { hop { filter_eq { all_rows ; school / club team ; brigham young university } ; player } ; tevita ofahengaue } }, interpretation: select the row whose year of intro record of all rows is 2nd minimum . the name / designation record of this row is onorati smg .
Output: no
Command: eq { hop { nth_argmin { all_rows ; year of intro ; 2 } ; name / designation } ; onorati smg }, interpretation: select the row whose year of intro record of all rows is 2nd minimum . the name / designation record of this row is onorati smg .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; year of intro ; 2 } ; name / designation } ; onorati smg }, interpretation: select the rows whose directed by record fuzzily matches to jon cassar . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; directed by ; jon cassar } } ; 5 }, interpretation: select the rows whose directed by record fuzzily matches to jon cassar . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; directed by ; jon cassar } } ; 5 }, interpretation: select the rows whose division record fuzzily matches to on hiatus . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; division ; on hiatus } } ; 2 }, interpretation: select the rows whose division record fuzzily matches to on hiatus . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; division ; on hiatus } } ; 2 }, interpretation: select the row whose launched record of all rows is 2nd minimum . the ship record of this row is faulknor ( flotilla leader ) .
Output: no
Command: eq { hop { nth_argmin { all_rows ; launched ; 2 } ; ship } ; faulknor ( flotilla leader ) }, interpretation: select the row whose launched record of all rows is 2nd minimum . the ship record of this row is faulknor ( flotilla leader ) .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; launched ; 2 } ; ship } ; faulknor ( flotilla leader ) }, interpretation: select the rows whose call sign record fuzzily matches to k297al . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to k239ax . take the frequency mhz record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; call sign ; k297al } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; k239ax } ; frequency mhz } }, interpretation: select the rows whose call sign record fuzzily matches to k297al . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to k239ax . take the frequency mhz record of this row . the first record is greater than the second record .
Output: yes