niryuu/sni-each-converted huggingface dataset:
Command: most_less { all_rows ; wins ; 20 }, interpretation: for the wins records of all rows , most of them are less than 20 .
Output: yes
Command: most_less { all_rows ; wins ; 20 }, interpretation: select the rows whose tournament record fuzzily matches to mercedes . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; tournament ; mercedes } } ; 3 }, interpretation: select the rows whose tournament record fuzzily matches to mercedes . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; tournament ; mercedes } } ; 3 }, interpretation: select the rows whose began play record fuzzily matches to 19 . for the club records of these rows , most of them fuzzily match to rochester .
Output: no
Command: most_eq { filter_eq { all_rows ; began play ; 19 } ; club ; rochester }, interpretation: select the rows whose began play record fuzzily matches to 19 . for the club records of these rows , most of them fuzzily match to rochester .
Output: yes
Command: most_eq { filter_eq { all_rows ; began play ; 19 } ; club ; rochester }, interpretation: select the rows whose winning constructor record fuzzily matches to bugatti . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; winning constructor ; bugatti } } ; 4 }, interpretation: select the rows whose winning constructor record fuzzily matches to bugatti . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; winning constructor ; bugatti } } ; 4 }, interpretation: for the notes records of all rows , most of them fuzzily match to french .
Output: no
Command: most_eq { all_rows ; notes ; french }, interpretation: for the notes records of all rows , most of them fuzzily match to french .
Output: yes
Command: most_eq { all_rows ; notes ; french }, interpretation: for the time records of all rows , most of them are less than 54.0 .
Output: no
Command: most_less { all_rows ; time ; 54.0 }, interpretation: for the time records of all rows , most of them are less than 54.0 .
Output: yes
Command: most_less { all_rows ; time ; 54.0 }, interpretation: select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60,000 . the number of such rows is 1 .
Output: no
Command: eq { count { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60,000 } } ; 1 }, interpretation: select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60,000 . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60,000 } } ; 1 }, interpretation: select the rows whose branch record fuzzily matches to us marine corps . there is only one such row in the table . the name record of this unqiue row is general james l jones .
Output: no
Command: and { only { filter_eq { all_rows ; branch ; us marine corps } } ; eq { hop { filter_eq { all_rows ; branch ; us marine corps } ; name } ; general james l jones } }, interpretation: select the rows whose branch record fuzzily matches to us marine corps . there is only one such row in the table . the name record of this unqiue row is general james l jones .
Output: yes
Command: and { only { filter_eq { all_rows ; branch ; us marine corps } } ; eq { hop { filter_eq { all_rows ; branch ; us marine corps } ; name } ; general james l jones } }, interpretation: the average of the bronze record of all rows is 1.67 .
Output: no
Command: round_eq { avg { all_rows ; bronze } ; 1.67 }, interpretation: the average of the bronze record of all rows is 1.67 .
Output: yes
Command: round_eq { avg { all_rows ; bronze } ; 1.67 }, interpretation: select the row whose enrollment record of all rows is maximum . the location record of this row is cleveland , tennessee .
Output: no
Command: eq { hop { argmax { all_rows ; enrollment } ; location } ; cleveland , tennessee }, interpretation: select the row whose enrollment record of all rows is maximum . the location record of this row is cleveland , tennessee .
Output: yes
Command: eq { hop { argmax { all_rows ; enrollment } ; location } ; cleveland , tennessee }, interpretation: select the row whose location attendance record of all rows is 1st maximum . the team record of this row is chicago .
Output: no
Command: eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; chicago }, interpretation: select the row whose location attendance record of all rows is 1st maximum . the team record of this row is chicago .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; chicago }, interpretation: select the rows whose college record fuzzily matches to houston . there is only one such row in the table . the name record of this unqiue row is jimmy herndon .
Output: no
Command: and { only { filter_eq { all_rows ; college ; houston } } ; eq { hop { filter_eq { all_rows ; college ; houston } ; name } ; jimmy herndon } }, interpretation: select the rows whose college record fuzzily matches to houston . there is only one such row in the table . the name record of this unqiue row is jimmy herndon .
Output: yes
Command: and { only { filter_eq { all_rows ; college ; houston } } ; eq { hop { filter_eq { all_rows ; college ; houston } ; name } ; jimmy herndon } }, interpretation: select the rows whose winner record fuzzily matches to rangers . among these rows , select the rows whose runner - up record fuzzily matches to morton . there is only one such row in the table . the season record of this unqiue row is 1941 - 42 .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; winner ; rangers } ; runner - up ; morton } } ; eq { hop { filter_eq { filter_eq { all_rows ; winner ; rangers } ; runner - up ; morton } ; season } ; 1941 - 42 } }, interpretation: select the rows whose winner record fuzzily matches to rangers . among these rows , select the rows whose runner - up record fuzzily matches to morton . there is only one such row in the table . the season record of this unqiue row is 1941 - 42 .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; winner ; rangers } ; runner - up ; morton } } ; eq { hop { filter_eq { filter_eq { all_rows ; winner ; rangers } ; runner - up ; morton } ; season } ; 1941 - 42 } }, interpretation: select the row whose original air date record of all rows is 1st maximum . the title record of this row is i 'll take you .
Output: no
Command: eq { hop { nth_argmax { all_rows ; original air date ; 1 } ; title } ; i 'll take you }, interpretation: select the row whose original air date record of all rows is 1st maximum . the title record of this row is i 'll take you .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; original air date ; 1 } ; title } ; i 'll take you }, interpretation: select the rows whose year record fuzzily matches to 1996 . take the winnings record of this row . select the rows whose year record fuzzily matches to 1998 . take the winnings record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; year ; 1996 } ; winnings } ; hop { filter_eq { all_rows ; year ; 1998 } ; winnings } }, interpretation: select the rows whose year record fuzzily matches to 1996 . take the winnings record of this row . select the rows whose year record fuzzily matches to 1998 . take the winnings record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; year ; 1996 } ; winnings } ; hop { filter_eq { all_rows ; year ; 1998 } ; winnings } }, interpretation: select the rows whose frequency mhz record is greater than 100 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { all_rows ; frequency mhz ; 100 } } ; 3 }, interpretation: select the rows whose frequency mhz record is greater than 100 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { all_rows ; frequency mhz ; 100 } } ; 3 }, interpretation: select the rows whose shooter record fuzzily matches to cze . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; shooter ; cze } } ; 2 }, interpretation: select the rows whose shooter record fuzzily matches to cze . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; shooter ; cze } } ; 2 }, interpretation: select the row whose original air date record of all rows is 2nd maximum . the title record of this row is the understudy .
Output: no
Command: eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; title } ; the understudy }, interpretation: select the row whose original air date record of all rows is 2nd maximum . the title record of this row is the understudy .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; title } ; the understudy }, interpretation: select the rows whose score record fuzzily matches to 1-1 . there is only one such row in the table . the april record of this unqiue row is 1 .
Output: no
Command: and { only { filter_eq { all_rows ; score ; 1-1 } } ; eq { hop { filter_eq { all_rows ; score ; 1-1 } ; april } ; 1 } }, interpretation: select the rows whose score record fuzzily matches to 1-1 . there is only one such row in the table . the april record of this unqiue row is 1 .
Output: yes
Command: and { only { filter_eq { all_rows ; score ; 1-1 } } ; eq { hop { filter_eq { all_rows ; score ; 1-1 } ; april } ; 1 } }, interpretation: the average of the for record of all rows is 31 .
Output: no
Command: round_eq { avg { all_rows ; for } ; 31 }, interpretation: the average of the for record of all rows is 31 .
Output: yes
Command: round_eq { avg { all_rows ; for } ; 31 }, interpretation: select the row whose points record of all rows is 2nd maximum . the name record of this row is angelika buck / erich buck .
Output: no
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; name } ; angelika buck / erich buck }, interpretation: select the row whose points record of all rows is 2nd maximum . the name record of this row is angelika buck / erich buck .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; name } ; angelika buck / erich buck }, interpretation: select the rows whose result record fuzzily matches to win . the sum of the black knights points record of these rows is 85 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; result ; win } ; black knights points } ; 85 }, interpretation: select the rows whose result record fuzzily matches to win . the sum of the black knights points record of these rows is 85 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; result ; win } ; black knights points } ; 85 }, interpretation: select the rows whose overall pick record fuzzily matches to 9 . take the overall pick record of this row . select the rows whose overall pick record fuzzily matches to 18 . take the overall pick record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; overall pick ; 9 } ; overall pick } ; hop { filter_eq { all_rows ; overall pick ; 18 } ; overall pick } }, interpretation: select the rows whose overall pick record fuzzily matches to 9 . take the overall pick record of this row . select the rows whose overall pick record fuzzily matches to 18 . take the overall pick record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; overall pick ; 9 } ; overall pick } ; hop { filter_eq { all_rows ; overall pick ; 18 } ; overall pick } }, interpretation: the sum of the total record of all rows is 25 .
Output: no
Command: round_eq { sum { all_rows ; total } ; 25 }, interpretation: the sum of the total record of all rows is 25 .
Output: yes
Command: round_eq { sum { all_rows ; total } ; 25 }, interpretation: select the rows whose town ships record is equal to 18 . there is only one such row in the table . the state / region record of this unqiue row is kachin state .
Output: no
Command: and { only { filter_eq { all_rows ; town ships ; 18 } } ; eq { hop { filter_eq { all_rows ; town ships ; 18 } ; state / region } ; kachin state } }, interpretation: select the rows whose town ships record is equal to 18 . there is only one such row in the table . the state / region record of this unqiue row is kachin state .
Output: yes
Command: and { only { filter_eq { all_rows ; town ships ; 18 } } ; eq { hop { filter_eq { all_rows ; town ships ; 18 } ; state / region } ; kachin state } }, interpretation: select the row whose vuelta record of all rows is maximum . the name record of this row is tony rominger ( sui ) .
Output: no
Command: eq { hop { argmax { all_rows ; vuelta } ; name } ; tony rominger ( sui ) }, interpretation: select the row whose vuelta record of all rows is maximum . the name record of this row is tony rominger ( sui ) .
Output: yes
Command: eq { hop { argmax { all_rows ; vuelta } ; name } ; tony rominger ( sui ) }, interpretation: for the year opened records of all rows , most of them are less than 2000 .
Output: no
Command: most_less { all_rows ; year opened ; 2000 }, interpretation: for the year opened records of all rows , most of them are less than 2000 .
Output: yes
Command: most_less { all_rows ; year opened ; 2000 }, interpretation: select the row whose population record of all rows is 2nd maximum . the official name record of this row is hillsborough .
Output: no
Command: eq { hop { nth_argmax { all_rows ; population ; 2 } ; official name } ; hillsborough }, interpretation: select the row whose population record of all rows is 2nd maximum . the official name record of this row is hillsborough .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; population ; 2 } ; official name } ; hillsborough }, interpretation: for the venue records of all rows , all of them fuzzily match to skonto hall , riga .
Output: no
Command: all_eq { all_rows ; venue ; skonto hall , riga }, interpretation: for the venue records of all rows , all of them fuzzily match to skonto hall , riga .
Output: yes
Command: all_eq { all_rows ; venue ; skonto hall , riga }, interpretation: select the rows whose aspect record fuzzily matches to 16:9 . there is only one such row in the table . the channel record of this unqiue row is 26.1 .
Output: no
Command: and { only { filter_eq { all_rows ; aspect ; 16:9 } } ; eq { hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel } ; 26.1 } }, interpretation: select the rows whose aspect record fuzzily matches to 16:9 . there is only one such row in the table . the channel record of this unqiue row is 26.1 .
Output: yes
Command: and { only { filter_eq { all_rows ; aspect ; 16:9 } } ; eq { hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel } ; 26.1 } }, interpretation: select the rows whose winner record fuzzily matches to kiveton park . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; winner ; kiveton park } } ; 2 }, interpretation: select the rows whose winner record fuzzily matches to kiveton park . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; winner ; kiveton park } } ; 2 }, interpretation: select the rows whose tournament record fuzzily matches to fukuoka , japan . there is only one such row in the table . the date record of this unqiue row is may 11 , 2003 .
Output: no
Command: and { only { filter_eq { all_rows ; tournament ; fukuoka , japan } } ; eq { hop { filter_eq { all_rows ; tournament ; fukuoka , japan } ; date } ; may 11 , 2003 } }, interpretation: select the rows whose tournament record fuzzily matches to fukuoka , japan . there is only one such row in the table . the date record of this unqiue row is may 11 , 2003 .
Output: yes
Command: and { only { filter_eq { all_rows ; tournament ; fukuoka , japan } } ; eq { hop { filter_eq { all_rows ; tournament ; fukuoka , japan } ; date } ; may 11 , 2003 } }, interpretation: select the row whose win % record of all rows is 2nd maximum . the coach record of this row is jim larranaga .
Output: no
Command: eq { hop { nth_argmax { all_rows ; win % ; 2 } ; coach } ; jim larranaga }, interpretation: select the row whose win % record of all rows is 2nd maximum . the coach record of this row is jim larranaga .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; win % ; 2 } ; coach } ; jim larranaga }, interpretation: select the rows whose result record fuzzily matches to selected . there is only one such row in the table . the week record of this unqiue row is wild card .
Output: no
Command: and { only { filter_eq { all_rows ; result ; selected } } ; eq { hop { filter_eq { all_rows ; result ; selected } ; week } ; wild card } }, interpretation: select the rows whose result record fuzzily matches to selected . there is only one such row in the table . the week record of this unqiue row is wild card .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; selected } } ; eq { hop { filter_eq { all_rows ; result ; selected } ; week } ; wild card } }, interpretation: select the rows whose date record fuzzily matches to 2 april . select the row whose crowd record of these rows is 1st maximum . the home team record of this row is collingwood .
Output: no
Command: eq { hop { nth_argmax { filter_eq { all_rows ; date ; 2 april } ; crowd ; 1 } ; home team } ; collingwood }, interpretation: select the rows whose date record fuzzily matches to 2 april . select the row whose crowd record of these rows is 1st maximum . the home team record of this row is collingwood .
Output: yes
Command: eq { hop { nth_argmax { filter_eq { all_rows ; date ; 2 april } ; crowd ; 1 } ; home team } ; collingwood }, interpretation: select the rows whose nation record fuzzily matches to mexico . take the total record of this row . select the rows whose nation record fuzzily matches to canada . take the total record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; nation ; mexico } ; total } ; hop { filter_eq { all_rows ; nation ; canada } ; total } }, interpretation: select the rows whose nation record fuzzily matches to mexico . take the total record of this row . select the rows whose nation record fuzzily matches to canada . take the total record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; nation ; mexico } ; total } ; hop { filter_eq { all_rows ; nation ; canada } ; total } }, interpretation: select the rows whose result record fuzzily matches to w . select the row whose date record of these rows is 4th minimum . the attendance record of this row is 45511 .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; date ; 4 } ; attendance } ; 45511 }, interpretation: select the rows whose result record fuzzily matches to w . select the row whose date record of these rows is 4th minimum . the attendance record of this row is 45511 .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; date ; 4 } ; attendance } ; 45511 }, interpretation: the sum of the games played record of all rows is 52 .
Output: no
Command: round_eq { sum { all_rows ; games played } ; 52 }, interpretation: the sum of the games played record of all rows is 52 .
Output: yes
Command: round_eq { sum { all_rows ; games played } ; 52 }, interpretation: select the rows whose year record is equal to 1990 . the sum of the pages record of these rows is 1135 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; year ; 1990 } ; pages } ; 1135 }, interpretation: select the rows whose year record is equal to 1990 . the sum of the pages record of these rows is 1135 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; year ; 1990 } ; pages } ; 1135 }, interpretation: select the rows whose year record fuzzily matches to 1998 . take the result record of this row . select the rows whose year record fuzzily matches to 2010 . take the result record of this row . the first record fuzzily matches to the second record . the result record of the first row is champions . the result record of the second row is champions .
Output: no
Command: and { eq { hop { filter_eq { all_rows ; year ; 1998 } ; result } ; hop { filter_eq { all_rows ; year ; 2010 } ; result } } ; and { eq { hop { filter_eq { all_rows ; year ; 1998 } ; result } ; champions } ; eq { hop { filter_eq { all_rows ; year ; 2010 } ; result } ; champions } } }, interpretation: select the rows whose year record fuzzily matches to 1998 . take the result record of this row . select the rows whose year record fuzzily matches to 2010 . take the result record of this row . the first record fuzzily matches to the second record . the result record of the first row is champions . the result record of the second row is champions .
Output: yes
Command: and { eq { hop { filter_eq { all_rows ; year ; 1998 } ; result } ; hop { filter_eq { all_rows ; year ; 2010 } ; result } } ; and { eq { hop { filter_eq { all_rows ; year ; 1998 } ; result } ; champions } ; eq { hop { filter_eq { all_rows ; year ; 2010 } ; result } ; champions } } }, interpretation: for the result records of all rows , most of them fuzzily match to win .
Output: no
Command: most_eq { all_rows ; result ; win }, interpretation: for the result records of all rows , most of them fuzzily match to win .
Output: yes
Command: most_eq { all_rows ; result ; win }, interpretation: the average of the attendance record of all rows is 23036 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 23036 }, interpretation: the average of the attendance record of all rows is 23036 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 23036 }, 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 rows whose candidates record fuzzily matches to % . there is only one such row in the table . the district record of this unqiue row is arkansas 5 .
Output: no
Command: and { only { filter_eq { all_rows ; candidates ; % } } ; eq { hop { filter_eq { all_rows ; candidates ; % } ; district } ; arkansas 5 } }, interpretation: select the rows whose candidates record fuzzily matches to % . there is only one such row in the table . the district record of this unqiue row is arkansas 5 .
Output: yes
Command: and { only { filter_eq { all_rows ; candidates ; % } } ; eq { hop { filter_eq { all_rows ; candidates ; % } ; district } ; arkansas 5 } }, interpretation: select the row whose population record of all rows is maximum . the official name record of this row is saint george .
Output: no
Command: eq { hop { argmax { all_rows ; population } ; official name } ; saint george }, interpretation: select the row whose population record of all rows is maximum . the official name record of this row is saint george .
Output: yes
Command: eq { hop { argmax { all_rows ; population } ; official name } ; saint george }, interpretation: select the row whose points record of all rows is 2nd maximum . the club record of this row is cádiz cf .
Output: no
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; club } ; cádiz cf }, interpretation: select the row whose points record of all rows is 2nd maximum . the club record of this row is cádiz cf .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; club } ; cádiz cf }, interpretation: select the row whose height record of all rows is 3rd maximum . the name record of this row is will hudson .
Output: no
Command: eq { hop { nth_argmax { all_rows ; height ; 3 } ; name } ; will hudson }, interpretation: select the row whose height record of all rows is 3rd maximum . the name record of this row is will hudson .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; height ; 3 } ; name } ; will hudson }, interpretation: the average of the us viewers ( millions ) record of all rows is 11.34 .
Output: no
Command: round_eq { avg { all_rows ; us viewers ( millions ) } ; 11.34 }, interpretation: the average of the us viewers ( millions ) record of all rows is 11.34 .
Output: yes
Command: round_eq { avg { all_rows ; us viewers ( millions ) } ; 11.34 }, interpretation: select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 2 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 2 } } ; 2 }, interpretation: select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 2 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 2 } } ; 2 }, interpretation: select the rows whose city record fuzzily matches to budapest , hungary . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; city ; budapest , hungary } }, interpretation: select the rows whose city record fuzzily matches to budapest , hungary . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; city ; budapest , hungary } }, interpretation: select the rows whose home team record fuzzily matches to manchester city . take the score record of this row . select the rows whose home team record fuzzily matches to arsenal . 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 ; home team ; manchester city } ; score } ; hop { filter_eq { all_rows ; home team ; arsenal } ; score } }, interpretation: select the rows whose home team record fuzzily matches to manchester city . take the score record of this row . select the rows whose home team record fuzzily matches to arsenal . 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 ; home team ; manchester city } ; score } ; hop { filter_eq { all_rows ; home team ; arsenal } ; score } }, interpretation: select the rows whose week record fuzzily matches to 17 . take the result record of this row . select the rows whose week record fuzzily matches to 16 . take the result record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; week ; 17 } ; result } ; hop { filter_eq { all_rows ; week ; 16 } ; result } }, interpretation: select the rows whose week record fuzzily matches to 17 . take the result record of this row . select the rows whose week record fuzzily matches to 16 . take the result record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; week ; 17 } ; result } ; hop { filter_eq { all_rows ; week ; 16 } ; result } }, interpretation: for the headquarters records of all rows , most of them fuzzily match to united states .
Output: no
Command: most_eq { all_rows ; headquarters ; united states }, interpretation: for the headquarters records of all rows , most of them fuzzily match to united states .
Output: yes
Command: most_eq { all_rows ; headquarters ; united states }, interpretation: select the rows whose born / died record fuzzily matches to present . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; born / died ; present } } ; 2 }, interpretation: select the rows whose born / died record fuzzily matches to present . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; born / died ; present } } ; 2 }, interpretation: select the rows whose club record fuzzily matches to balmain tigers . there is only one such row in the table . the name record of this unqiue row is gavin woods .
Output: no
Command: and { only { filter_eq { all_rows ; club ; balmain tigers } } ; eq { hop { filter_eq { all_rows ; club ; balmain tigers } ; name } ; gavin woods } }, interpretation: select the rows whose club record fuzzily matches to balmain tigers . there is only one such row in the table . the name record of this unqiue row is gavin woods .
Output: yes
Command: and { only { filter_eq { all_rows ; club ; balmain tigers } } ; eq { hop { filter_eq { all_rows ; club ; balmain tigers } ; name } ; gavin woods } }, interpretation: select the row whose unemployment rate record of all rows is maximum . the county record of this row is schoharie .
Output: no
Command: eq { hop { argmax { all_rows ; unemployment rate } ; county } ; schoharie }, interpretation: select the row whose unemployment rate record of all rows is maximum . the county record of this row is schoharie .
Output: yes
Command: eq { hop { argmax { all_rows ; unemployment rate } ; county } ; schoharie }, interpretation: the 1st minimum date of vacancy record of all rows is 7 sep 2010 . the outgoing head coach record of the row with 1st minimum date of vacancy record is rasoul korbekandi .
Output: no
Command: and { eq { nth_min { all_rows ; date of vacancy ; 1 } ; 7 sep 2010 } ; eq { hop { nth_argmin { all_rows ; date of vacancy ; 1 } ; outgoing head coach } ; rasoul korbekandi } }, interpretation: the 1st minimum date of vacancy record of all rows is 7 sep 2010 . the outgoing head coach record of the row with 1st minimum date of vacancy record is rasoul korbekandi .
Output: yes
Command: and { eq { nth_min { all_rows ; date of vacancy ; 1 } ; 7 sep 2010 } ; eq { hop { nth_argmin { all_rows ; date of vacancy ; 1 } ; outgoing head coach } ; rasoul korbekandi } }, interpretation: select the row whose money record of all rows is 2nd maximum . the player record of this row is tom watson .
Output: no
Command: eq { hop { nth_argmax { all_rows ; money ; 2 } ; player } ; tom watson }, interpretation: select the row whose money record of all rows is 2nd maximum . the player record of this row is tom watson .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; money ; 2 } ; player } ; tom watson }, interpretation: for the nationality records of all rows , most of them fuzzily match to aut .
Output: no
Command: most_eq { all_rows ; nationality ; aut }, interpretation: for the nationality records of all rows , most of them fuzzily match to aut .
Output: yes
Command: most_eq { all_rows ; nationality ; aut }, interpretation: for the ownership records of all rows , most of them do not match to state - owned .
Output: no
Command: most_not_eq { all_rows ; ownership ; state - owned }, interpretation: for the ownership records of all rows , most of them do not match to state - owned .
Output: yes
Command: most_not_eq { all_rows ; ownership ; state - owned }, interpretation: for the country records of all rows , most of them fuzzily match to belgium .
Output: no
Command: most_eq { all_rows ; country ; belgium }, interpretation: for the country records of all rows , most of them fuzzily match to belgium .
Output: yes
Command: most_eq { all_rows ; country ; belgium }, interpretation: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is pat cannon .
Output: no
Command: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; pat cannon }, interpretation: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is pat cannon .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; pat cannon }, interpretation: select the row whose max power record of all rows is minimum . the vehicle record of this row is nissan y11 ad van .
Output: no
Command: eq { hop { argmin { all_rows ; max power } ; vehicle } ; nissan y11 ad van }, interpretation: select the row whose max power record of all rows is minimum . the vehicle record of this row is nissan y11 ad van .
Output: yes
Command: eq { hop { argmin { all_rows ; max power } ; vehicle } ; nissan y11 ad van }, interpretation: the 14th minimum no record of all rows is 14 . the date record of the row with 14th minimum no record is 18 mar 2012 . the winners share record of the row with 14th minimum no record is 225000 .
Output: no
Command: and { eq { nth_min { all_rows ; no ; 14 } ; 14 } ; and { eq { hop { nth_argmin { all_rows ; no ; 14 } ; date } ; 18 mar 2012 } ; eq { hop { nth_argmin { all_rows ; no ; 14 } ; winners share } ; 225000 } } }, interpretation: the 14th minimum no record of all rows is 14 . the date record of the row with 14th minimum no record is 18 mar 2012 . the winners share record of the row with 14th minimum no record is 225000 .
Output: yes
Command: and { eq { nth_min { all_rows ; no ; 14 } ; 14 } ; and { eq { hop { nth_argmin { all_rows ; no ; 14 } ; date } ; 18 mar 2012 } ; eq { hop { nth_argmin { all_rows ; no ; 14 } ; winners share } ; 225000 } } }, interpretation: select the row whose worldwide gross record of all rows is 4th maximum . the title record of this row is the hobbit : an unexpected journey .
Output: no
Command: eq { hop { nth_argmax { all_rows ; worldwide gross ; 4 } ; title } ; the hobbit : an unexpected journey }, interpretation: select the row whose worldwide gross record of all rows is 4th maximum . the title record of this row is the hobbit : an unexpected journey .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; worldwide gross ; 4 } ; title } ; the hobbit : an unexpected journey }, interpretation: select the rows whose opponent record fuzzily matches to mississippi state . take the date record of this row . select the rows whose opponent record fuzzily matches to vanderbilt . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; opponent ; mississippi state } ; date } ; hop { filter_eq { all_rows ; opponent ; vanderbilt } ; date } }, interpretation: select the rows whose opponent record fuzzily matches to mississippi state . take the date record of this row . select the rows whose opponent record fuzzily matches to vanderbilt . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; opponent ; mississippi state } ; date } ; hop { filter_eq { all_rows ; opponent ; vanderbilt } ; date } }, interpretation: for the result records of all rows , most of them fuzzily match to l .
Output: no
Command: most_eq { all_rows ; result ; l }, interpretation: the average of the original air date record of all rows is 2009 .
Output: no
Command: round_eq { avg { all_rows ; original air date } ; 2009 }, interpretation: the average of the original air date record of all rows is 2009 .
Output: yes
Command: round_eq { avg { all_rows ; original air date } ; 2009 }, interpretation: select the rows whose transfer window record fuzzily matches to winter . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; transfer window ; winter } } ; 3 }, interpretation: select the rows whose transfer window record fuzzily matches to winter . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; transfer window ; winter } } ; 3 }, interpretation: select the rows whose categoría record fuzzily matches to rock group . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; categoría ; rock group } } ; 2 }, interpretation: select the rows whose categoría record fuzzily matches to rock group . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; categoría ; rock group } } ; 2 }, interpretation: the sum of the electorate record of all rows is 2633581 .
Output: no
Command: round_eq { sum { all_rows ; electorate } ; 2633581 }, interpretation: the sum of the electorate record of all rows is 2633581 .
Output: yes
Command: round_eq { sum { all_rows ; electorate } ; 2633581 }, interpretation: for the class records of all rows , all of them fuzzily match to d .
Output: no
Command: all_eq { all_rows ; class ; d }, interpretation: select the row whose attendance record of all rows is maximum . the opponent record of this row is new orleans saints .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; opponent } ; new orleans saints }, interpretation: select the row whose attendance record of all rows is maximum . the opponent record of this row is new orleans saints .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; opponent } ; new orleans saints }, interpretation: select the rows whose high assists record fuzzily matches to delonte west . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; high assists ; delonte west } }, interpretation: select the rows whose high assists record fuzzily matches to delonte west . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; high assists ; delonte west } }, interpretation: select the rows whose club record fuzzily matches to lokomotiva b . take the number of seasons in prva hnl record of this row . select the rows whose club record fuzzily matches to slaven belupo b . take the number of seasons in prva hnl record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; club ; lokomotiva b } ; number of seasons in prva hnl } ; hop { filter_eq { all_rows ; club ; slaven belupo b } ; number of seasons in prva hnl } }, interpretation: select the rows whose club record fuzzily matches to lokomotiva b . take the number of seasons in prva hnl record of this row . select the rows whose club record fuzzily matches to slaven belupo b . take the number of seasons in prva hnl record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; club ; lokomotiva b } ; number of seasons in prva hnl } ; hop { filter_eq { all_rows ; club ; slaven belupo b } ; number of seasons in prva hnl } }, interpretation: select the rows whose location record fuzzily matches to victoriaville , quebec , canada . there is only one such row in the table . the opponent record of this unqiue row is brian magee .
Output: no
Command: and { only { filter_eq { all_rows ; location ; victoriaville , quebec , canada } } ; eq { hop { filter_eq { all_rows ; location ; victoriaville , quebec , canada } ; opponent } ; brian magee } }, interpretation: select the rows whose location record fuzzily matches to victoriaville , quebec , canada . there is only one such row in the table . the opponent record of this unqiue row is brian magee .
Output: yes
Command: and { only { filter_eq { all_rows ; location ; victoriaville , quebec , canada } } ; eq { hop { filter_eq { all_rows ; location ; victoriaville , quebec , canada } ; opponent } ; brian magee } }, interpretation: for the location attendance records of all rows , most of them fuzzily match to american airlines arena .
Output: no
Command: most_eq { all_rows ; location attendance ; american airlines arena }, interpretation: for the location attendance records of all rows , most of them fuzzily match to american airlines arena .
Output: yes
Command: most_eq { all_rows ; location attendance ; american airlines arena }, interpretation: the average of the pieces record of all rows is 271.6 .
Output: no
Command: round_eq { avg { all_rows ; pieces } ; 271.6 }, interpretation: the average of the pieces record of all rows is 271.6 .
Output: yes
Command: round_eq { avg { all_rows ; pieces } ; 271.6 }, interpretation: for the population ( 2011 - 01 - 01 ) records of all rows , most of them are greater than 511840 .
Output: no
Command: most_greater { all_rows ; population ( 2011 - 01 - 01 ) ; 511840 }, interpretation: for the population ( 2011 - 01 - 01 ) records of all rows , most of them are greater than 511840 .
Output: yes
Command: most_greater { all_rows ; population ( 2011 - 01 - 01 ) ; 511840 }, interpretation: the average of the money record of all rows is 1584 .
Output: no
Command: round_eq { avg { all_rows ; money } ; 1584 }, interpretation: the average of the money record of all rows is 1584 .
Output: yes
Command: round_eq { avg { all_rows ; money } ; 1584 }, interpretation: select the rows whose matches record is less than 10 . among these rows , select the rows whose innings record is greater than 14 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater { filter_less { all_rows ; matches ; 10 } ; innings ; 14 } } ; 2 }, interpretation: select the rows whose matches record is less than 10 . among these rows , select the rows whose innings record is greater than 14 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater { filter_less { all_rows ; matches ; 10 } ; innings ; 14 } } ; 2 }, interpretation: select the rows whose age record is equal to 8 . among these rows , select the rows whose sp record fuzzily matches to 20 / 1 . the number of such rows is 1 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; age ; 8 } ; sp ; 20 / 1 } } ; 1 }, interpretation: select the rows whose age record is equal to 8 . among these rows , select the rows whose sp record fuzzily matches to 20 / 1 . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; age ; 8 } ; sp ; 20 / 1 } } ; 1 }, interpretation: for the established records of all rows , most of them are greater than or equal to 2000 .
Output: no
Command: most_greater_eq { all_rows ; established ; 2000 }, interpretation: for the established records of all rows , most of them are greater than or equal to 2000 .
Output: yes
Command: most_greater_eq { all_rows ; established ; 2000 }, 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 rows whose owner record fuzzily matches to wnetorg . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; owner ; wnetorg } } ; 2 }, interpretation: select the rows whose owner record fuzzily matches to wnetorg . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; owner ; wnetorg } } ; 2 }, interpretation: select the rows whose place record is equal to 1 . there is only one such row in the table . the draw record of this unqiue row is 1 .
Output: no
Command: and { only { filter_eq { all_rows ; place ; 1 } } ; eq { hop { filter_eq { all_rows ; place ; 1 } ; draw } ; 1 } }, interpretation: select the rows whose place record is equal to 1 . there is only one such row in the table . the draw record of this unqiue row is 1 .
Output: yes
Command: and { only { filter_eq { all_rows ; place ; 1 } } ; eq { hop { filter_eq { all_rows ; place ; 1 } ; draw } ; 1 } }, interpretation: the average of the attendance record of all rows is 6999 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 6999 }, interpretation: the average of the attendance record of all rows is 6999 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 6999 }, interpretation: select the rows whose team record fuzzily matches to new orleans . the sum of the location attendance record of these rows is 27053 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; team ; new orleans } ; location attendance } ; 27053 }, interpretation: select the rows whose team record fuzzily matches to new orleans . the sum of the location attendance record of these rows is 27053 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; team ; new orleans } ; location attendance } ; 27053 }, interpretation: the average of the crowd record of all rows is 18978 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 18978 }, interpretation: the average of the crowd record of all rows is 18978 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 18978 }, interpretation: for the distance / duration records of all rows , all of them are greater than 40 laps .
Output: no
Command: all_greater { all_rows ; distance / duration ; 40 laps }, interpretation: for the distance / duration records of all rows , all of them are greater than 40 laps .
Output: yes
Command: all_greater { all_rows ; distance / duration ; 40 laps }, interpretation: select the rows whose code name record fuzzily matches to surfer . take the serial number record of this row . select the rows whose code name record fuzzily matches to shark . take the serial number record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; code name ; surfer } ; serial number } ; hop { filter_eq { all_rows ; code name ; shark } ; serial number } }, interpretation: select the rows whose code name record fuzzily matches to surfer . take the serial number record of this row . select the rows whose code name record fuzzily matches to shark . take the serial number record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; code name ; surfer } ; serial number } ; hop { filter_eq { all_rows ; code name ; shark } ; serial number } }, interpretation: for the prize records of all rows , most of them are greater than 500,000 .
Output: no
Command: most_greater { all_rows ; prize ; 500,000 }, interpretation: for the prize records of all rows , most of them are greater than 500,000 .
Output: yes
Command: most_greater { all_rows ; prize ; 500,000 }, interpretation: select the rows whose type record fuzzily matches to embankment . the maximum height ( m ) record of these rows is 67.0 .
Output: no
Command: eq { max { filter_eq { all_rows ; type ; embankment } ; height ( m ) } ; 67.0 }, interpretation: select the rows whose type record fuzzily matches to embankment . the maximum height ( m ) record of these rows is 67.0 .
Output: yes
Command: eq { max { filter_eq { all_rows ; type ; embankment } ; height ( m ) } ; 67.0 }, interpretation: select the row whose location attendance record of all rows is maximum . the date record of this row is june 9 .
Output: no
Command: eq { hop { argmax { all_rows ; location attendance } ; date } ; june 9 }, interpretation: select the row whose location attendance record of all rows is maximum . the date record of this row is june 9 .
Output: yes
Command: eq { hop { argmax { all_rows ; location attendance } ; date } ; june 9 }, interpretation: for the result records of all rows , most of them fuzzily match to l .
Output: no
Command: most_eq { all_rows ; result ; l }, interpretation: select the rows whose track record fuzzily matches to mono 45upm - romance adieu ( weltklang remix ) . take the year record of this row . select the rows whose track record fuzzily matches to kinder aus asbest - hey engel ( weltklang remix ) . take the year record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; track ; mono 45upm - romance adieu ( weltklang remix ) } ; year } ; hop { filter_eq { all_rows ; track ; kinder aus asbest - hey engel ( weltklang remix ) } ; year } }, interpretation: select the rows whose track record fuzzily matches to mono 45upm - romance adieu ( weltklang remix ) . take the year record of this row . select the rows whose track record fuzzily matches to kinder aus asbest - hey engel ( weltklang remix ) . take the year record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; track ; mono 45upm - romance adieu ( weltklang remix ) } ; year } ; hop { filter_eq { all_rows ; track ; kinder aus asbest - hey engel ( weltklang remix ) } ; year } }, interpretation: for the nationality records of all rows , most of them fuzzily match to spain .
Output: no
Command: most_eq { all_rows ; nationality ; spain }, interpretation: for the nationality records of all rows , most of them fuzzily match to spain .
Output: yes
Command: most_eq { all_rows ; nationality ; spain }, interpretation: for the winning party 2003 records of all rows , most of them fuzzily match to labour .
Output: no
Command: most_eq { all_rows ; winning party 2003 ; labour }, interpretation: for the winning party 2003 records of all rows , most of them fuzzily match to labour .
Output: yes