niryuu/sni-each-converted huggingface dataset:
Command: and { only { filter_eq { all_rows ; high points ; gerald wallace } } ; eq { hop { filter_eq { all_rows ; high points ; gerald wallace } ; date } ; february 8 } }, interpretation: select the row whose points record of all rows is 2nd maximum . the song record of this row is are you sure .
Output: no
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; song } ; are you sure }, interpretation: select the row whose points record of all rows is 2nd maximum . the song record of this row is are you sure .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; song } ; are you sure }, interpretation: select the rows whose champion record fuzzily matches to university of california , berkeley . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; champion ; university of california , berkeley } } ; 4 }, interpretation: select the rows whose champion record fuzzily matches to university of california , berkeley . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; champion ; university of california , berkeley } } ; 4 }, interpretation: for the qual 2 records of all rows , most of them are not equal to - .
Output: no
Command: most_not_eq { all_rows ; qual 2 ; - }, interpretation: for the qual 2 records of all rows , most of them are not equal to - .
Output: yes
Command: most_not_eq { all_rows ; qual 2 ; - }, interpretation: select the row whose height record of all rows is maximum . the player record of this row is josé luis moltó .
Output: no
Command: eq { hop { argmax { all_rows ; height } ; player } ; josé luis moltó }, interpretation: select the row whose height record of all rows is maximum . the player record of this row is josé luis moltó .
Output: yes
Command: eq { hop { argmax { all_rows ; height } ; player } ; josé luis moltó }, interpretation: select the rows whose position record fuzzily matches to ol . select the row whose pick record of these rows is minimum . the cfl team record of this row is montreal alouettes .
Output: no
Command: eq { hop { argmin { filter_eq { all_rows ; position ; ol } ; pick } ; cfl team } ; montreal alouettes }, interpretation: select the rows whose position record fuzzily matches to ol . select the row whose pick record of these rows is minimum . the cfl team record of this row is montreal alouettes .
Output: yes
Command: eq { hop { argmin { filter_eq { all_rows ; position ; ol } ; pick } ; cfl team } ; montreal alouettes }, interpretation: select the rows whose 2008 club record fuzzily matches to henan . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; 2008 club ; henan } } ; 2 }, interpretation: select the rows whose 2008 club record fuzzily matches to henan . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; 2008 club ; henan } } ; 2 }, interpretation: select the rows whose televote record fuzzily matches to 2.33 % . there is only one such row in the table . the artist record of this unqiue row is simona sivanio .
Output: no
Command: and { only { filter_eq { all_rows ; televote ; 2.33 % } } ; eq { hop { filter_eq { all_rows ; televote ; 2.33 % } ; artist } ; simona sivanio } }, interpretation: select the rows whose televote record fuzzily matches to 2.33 % . there is only one such row in the table . the artist record of this unqiue row is simona sivanio .
Output: yes
Command: and { only { filter_eq { all_rows ; televote ; 2.33 % } } ; eq { hop { filter_eq { all_rows ; televote ; 2.33 % } ; artist } ; simona sivanio } }, interpretation: select the rows whose laps record is equal to 1 . there is only one such row in the table . the year record of this unqiue row is 1964 . the finish record of this unqiue row is 30 .
Output: no
Command: and { only { filter_eq { all_rows ; laps ; 1 } } ; and { eq { hop { filter_eq { all_rows ; laps ; 1 } ; year } ; 1964 } ; eq { hop { filter_eq { all_rows ; laps ; 1 } ; finish } ; 30 } } }, interpretation: select the rows whose laps record is equal to 1 . there is only one such row in the table . the year record of this unqiue row is 1964 . the finish record of this unqiue row is 30 .
Output: yes
Command: and { only { filter_eq { all_rows ; laps ; 1 } } ; and { eq { hop { filter_eq { all_rows ; laps ; 1 } ; year } ; 1964 } ; eq { hop { filter_eq { all_rows ; laps ; 1 } ; finish } ; 30 } } }, interpretation: select the rows whose successor record fuzzily matches to vacant . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; successor ; vacant } } ; 2 }, interpretation: select the rows whose successor record fuzzily matches to vacant . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; successor ; vacant } } ; 2 }, interpretation: for the category records of all rows , most of them fuzzily match to best original score .
Output: no
Command: most_eq { all_rows ; category ; best original score }, interpretation: for the category records of all rows , most of them fuzzily match to best original score .
Output: yes
Command: most_eq { all_rows ; category ; best original score }, interpretation: the maximum home team score record of all rows is 15.17 ( 107 ) . the home team record of the row with superlative home team score record is south melbourne . the date record of the row with superlative home team score record is 7 august 1926 .
Output: no
Command: and { eq { max { all_rows ; home team score } ; 15.17 ( 107 ) } ; and { eq { hop { argmax { all_rows ; home team score } ; home team } ; south melbourne } ; eq { hop { argmax { all_rows ; home team score } ; date } ; 7 august 1926 } } }, interpretation: the maximum home team score record of all rows is 15.17 ( 107 ) . the home team record of the row with superlative home team score record is south melbourne . the date record of the row with superlative home team score record is 7 august 1926 .
Output: yes
Command: and { eq { max { all_rows ; home team score } ; 15.17 ( 107 ) } ; and { eq { hop { argmax { all_rows ; home team score } ; home team } ; south melbourne } ; eq { hop { argmax { all_rows ; home team score } ; date } ; 7 august 1926 } } }, interpretation: select the rows whose service years record fuzzily matches to 1989 . there is only one such row in the table . the ship name record of this unqiue row is kri halim perdanakususma .
Output: no
Command: and { only { filter_eq { all_rows ; service years ; 1989 } } ; eq { hop { filter_eq { all_rows ; service years ; 1989 } ; ship name } ; kri halim perdanakususma } }, interpretation: select the rows whose service years record fuzzily matches to 1989 . there is only one such row in the table . the ship name record of this unqiue row is kri halim perdanakususma .
Output: yes
Command: and { only { filter_eq { all_rows ; service years ; 1989 } } ; eq { hop { filter_eq { all_rows ; service years ; 1989 } ; ship name } ; kri halim perdanakususma } }, interpretation: select the rows whose score record fuzzily matches to 2 - 0 . there is only one such row in the table . the home team record of this unqiue row is eastern team a .
Output: no
Command: and { only { filter_eq { all_rows ; score ; 2 - 0 } } ; eq { hop { filter_eq { all_rows ; score ; 2 - 0 } ; home team } ; eastern team a } }, interpretation: select the rows whose score record fuzzily matches to 2 - 0 . there is only one such row in the table . the home team record of this unqiue row is eastern team a .
Output: yes
Command: and { only { filter_eq { all_rows ; score ; 2 - 0 } } ; eq { hop { filter_eq { all_rows ; score ; 2 - 0 } ; home team } ; eastern team a } }, interpretation: select the rows whose director record fuzzily matches to siddique . take the year record of this row . select the rows whose director record fuzzily matches to arbaaz khan . 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 ; director ; siddique } ; year } ; hop { filter_eq { all_rows ; director ; arbaaz khan } ; year } }, interpretation: select the rows whose director record fuzzily matches to siddique . take the year record of this row . select the rows whose director record fuzzily matches to arbaaz khan . 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 ; director ; siddique } ; year } ; hop { filter_eq { all_rows ; director ; arbaaz khan } ; year } }, interpretation: for the league cup records of all rows , most of them are equal to 0 .
Output: no
Command: most_eq { all_rows ; league cup ; 0 }, interpretation: for the league cup records of all rows , most of them are equal to 0 .
Output: yes
Command: most_eq { all_rows ; league cup ; 0 }, interpretation: select the rows whose state record fuzzily matches to new york . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; state ; new york } } ; 3 }, interpretation: select the rows whose state record fuzzily matches to new york . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; state ; new york } } ; 3 }, interpretation: select the row whose enrollment record of all rows is minimum . the school record of this row is cloverdale .
Output: no
Command: eq { hop { argmin { all_rows ; enrollment } ; school } ; cloverdale }, interpretation: select the row whose enrollment record of all rows is minimum . the school record of this row is cloverdale .
Output: yes
Command: eq { hop { argmin { all_rows ; enrollment } ; school } ; cloverdale }, interpretation: select the row whose date of appointment record of all rows is 4th minimum . the replaced by record of this row is carlos jurado .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date of appointment ; 4 } ; replaced by } ; carlos jurado }, interpretation: select the row whose date of appointment record of all rows is 4th minimum . the replaced by record of this row is carlos jurado .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date of appointment ; 4 } ; replaced by } ; carlos jurado }, interpretation: select the rows whose fatalities record is equal to 0 . there is only one such row in the table . the date record of this unqiue row is 2 february 1969 .
Output: no
Command: and { only { filter_eq { all_rows ; fatalities ; 0 } } ; eq { hop { filter_eq { all_rows ; fatalities ; 0 } ; date } ; 2 february 1969 } }, interpretation: select the rows whose fatalities record is equal to 0 . there is only one such row in the table . the date record of this unqiue row is 2 february 1969 .
Output: yes
Command: and { only { filter_eq { all_rows ; fatalities ; 0 } } ; eq { hop { filter_eq { all_rows ; fatalities ; 0 } ; date } ; 2 february 1969 } }, interpretation: select the rows whose opponent record fuzzily matches to usc . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; opponent ; usc } } ; 3 }, interpretation: select the rows whose opponent record fuzzily matches to usc . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; opponent ; usc } } ; 3 }, interpretation: the average of the mark record of all rows is 47.05 .
Output: no
Command: round_eq { avg { all_rows ; mark } ; 47.05 }, interpretation: the average of the mark record of all rows is 47.05 .
Output: yes
Command: round_eq { avg { all_rows ; mark } ; 47.05 }, interpretation: select the row whose first elected record of all rows is 2nd maximum . the incumbent record of this row is lane evans .
Output: no
Command: eq { hop { nth_argmax { all_rows ; first elected ; 2 } ; incumbent } ; lane evans }, interpretation: select the row whose first elected record of all rows is 2nd maximum . the incumbent record of this row is lane evans .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; first elected ; 2 } ; incumbent } ; lane evans }, interpretation: select the rows whose surface record fuzzily matches to carpet ( i ) . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; surface ; carpet ( i ) } } ; 2 }, interpretation: select the rows whose title record fuzzily matches to ( part . among these rows , select the rows whose directed by record fuzzily matches to tom tataranowicz . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; title ; ( part } ; directed by ; tom tataranowicz } } ; 3 }, interpretation: select the rows whose title record fuzzily matches to ( part . among these rows , select the rows whose directed by record fuzzily matches to tom tataranowicz . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; title ; ( part } ; directed by ; tom tataranowicz } } ; 3 }, interpretation: the average of the margin ( pts ) record of all rows is 27.25 .
Output: no
Command: round_eq { avg { all_rows ; margin ( pts ) } ; 27.25 }, interpretation: the average of the margin ( pts ) record of all rows is 27.25 .
Output: yes
Command: round_eq { avg { all_rows ; margin ( pts ) } ; 27.25 }, interpretation: the average of the to par record of all rows is -3.4 .
Output: no
Command: round_eq { avg { all_rows ; to par } ; -3.4 }, interpretation: the average of the to par record of all rows is -3.4 .
Output: yes
Command: round_eq { avg { all_rows ; to par } ; -3.4 }, interpretation: select the rows whose opponent record fuzzily matches to san diego chargers . take the date record of this row . select the rows whose opponent record fuzzily matches to dallas cowboys . 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 ; san diego chargers } ; date } ; hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; date } }, interpretation: select the rows whose opponent record fuzzily matches to san diego chargers . take the date record of this row . select the rows whose opponent record fuzzily matches to dallas cowboys . 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 ; san diego chargers } ; date } ; hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; date } }, interpretation: select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is richmond .
Output: no
Command: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; richmond }, interpretation: select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is richmond .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; richmond }, interpretation: the maximum games record of all rows is 38 . the name record of the row with superlative games record is andrew panko .
Output: no
Command: and { eq { max { all_rows ; games } ; 38 } ; eq { hop { argmax { all_rows ; games } ; name } ; andrew panko } }, interpretation: the maximum games record of all rows is 38 . the name record of the row with superlative games record is andrew panko .
Output: yes
Command: and { eq { max { all_rows ; games } ; 38 } ; eq { hop { argmax { all_rows ; games } ; name } ; andrew panko } }, interpretation: select the rows whose director record fuzzily matches to j clark mathis . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; director ; j clark mathis } }, interpretation: select the rows whose director record fuzzily matches to j clark mathis . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; director ; j clark mathis } }, interpretation: select the row whose total record of all rows is maximum . the athlete record of this row is anastasia davydova & anastasiya yermakova .
Output: no
Command: eq { hop { argmax { all_rows ; total } ; athlete } ; anastasia davydova & anastasiya yermakova }, interpretation: select the row whose total record of all rows is maximum . the athlete record of this row is anastasia davydova & anastasiya yermakova .
Output: yes
Command: eq { hop { argmax { all_rows ; total } ; athlete } ; anastasia davydova & anastasiya yermakova }, interpretation: select the rows whose previous network record fuzzily matches to nbc . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; previous network ; nbc } } ; 3 }, interpretation: select the rows whose previous network record fuzzily matches to nbc . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; previous network ; nbc } } ; 3 }, interpretation: the average of the gold record of all rows is 15 .
Output: no
Command: round_eq { avg { all_rows ; gold } ; 15 }, interpretation: the average of the gold record of all rows is 15 .
Output: yes
Command: round_eq { avg { all_rows ; gold } ; 15 }, interpretation: select the rows whose opponent record fuzzily matches to andrew chappelle . take the round record of this row . select the rows whose opponent record fuzzily matches to danny payne . take the round record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opponent ; andrew chappelle } ; round } ; hop { filter_eq { all_rows ; opponent ; danny payne } ; round } }, interpretation: select the rows whose opponent record fuzzily matches to andrew chappelle . take the round record of this row . select the rows whose opponent record fuzzily matches to danny payne . take the round record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opponent ; andrew chappelle } ; round } ; hop { filter_eq { all_rows ; opponent ; danny payne } ; round } }, interpretation: the sum of the runs record of all rows is 3324 .
Output: no
Command: round_eq { sum { all_rows ; runs } ; 3324 }, interpretation: the sum of the runs record of all rows is 3324 .
Output: yes
Command: round_eq { sum { all_rows ; runs } ; 3324 }, interpretation: select the rows whose name record fuzzily matches to greg merson . take the wsop cashes record of this row . select the rows whose name record fuzzily matches to michael esposito . take the wsop cashes record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; name ; greg merson } ; wsop cashes } ; hop { filter_eq { all_rows ; name ; michael esposito } ; wsop cashes } }, interpretation: select the rows whose name record fuzzily matches to greg merson . take the wsop cashes record of this row . select the rows whose name record fuzzily matches to michael esposito . take the wsop cashes record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; name ; greg merson } ; wsop cashes } ; hop { filter_eq { all_rows ; name ; michael esposito } ; wsop cashes } }, interpretation: select the rows whose points record is greater than 60 . among these rows , select the rows whose tries for record is less than 60 . there is only one such row in the table . the club record of this unqiue row is kidwelly rfc .
Output: no
Command: and { only { filter_less { filter_greater { all_rows ; points ; 60 } ; tries for ; 60 } } ; eq { hop { filter_less { filter_greater { all_rows ; points ; 60 } ; tries for ; 60 } ; club } ; kidwelly rfc } }, interpretation: select the rows whose points record is greater than 60 . among these rows , select the rows whose tries for record is less than 60 . there is only one such row in the table . the club record of this unqiue row is kidwelly rfc .
Output: yes
Command: and { only { filter_less { filter_greater { all_rows ; points ; 60 } ; tries for ; 60 } } ; eq { hop { filter_less { filter_greater { all_rows ; points ; 60 } ; tries for ; 60 } ; club } ; kidwelly rfc } }, interpretation: the average of the points record of all rows is 11 .
Output: no
Command: round_eq { avg { all_rows ; points } ; 11 }, interpretation: the average of the points record of all rows is 11 .
Output: yes
Command: round_eq { avg { all_rows ; points } ; 11 }, interpretation: select the rows whose races record is equal to 16 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; races ; 16 } } ; 4 }, interpretation: select the rows whose races record is equal to 16 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; races ; 16 } } ; 4 }, interpretation: select the row whose points ( pts ) record of all rows is 2nd maximum . the team ( equipo ) record of this row is san francisco fc .
Output: no
Command: eq { hop { nth_argmax { all_rows ; points ( pts ) ; 2 } ; team ( equipo ) } ; san francisco fc }, interpretation: select the row whose points ( pts ) record of all rows is 2nd maximum . the team ( equipo ) record of this row is san francisco fc .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; points ( pts ) ; 2 } ; team ( equipo ) } ; san francisco fc }, interpretation: select the rows whose type record fuzzily matches to shunter . select the row whose introduced record of these rows is 1st minimum . the class record of this row is class 08 .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; type ; shunter } ; introduced ; 1 } ; class } ; class 08 }, interpretation: select the rows whose type record fuzzily matches to shunter . select the row whose introduced record of these rows is 1st minimum . the class record of this row is class 08 .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; type ; shunter } ; introduced ; 1 } ; class } ; class 08 }, interpretation: select the row whose population density ( people / km square ) record of all rows is maximum . the metropolitan area record of this row is mumbai ( bombay ) .
Output: no
Command: eq { hop { argmax { all_rows ; population density ( people / km square ) } ; metropolitan area } ; mumbai ( bombay ) }, interpretation: select the row whose population density ( people / km square ) record of all rows is maximum . the metropolitan area record of this row is mumbai ( bombay ) .
Output: yes
Command: eq { hop { argmax { all_rows ; population density ( people / km square ) } ; metropolitan area } ; mumbai ( bombay ) }, interpretation: select the rows whose host record fuzzily matches to hopton - on - sea . there is only one such row in the table . the season record of this unqiue row is 1998 / 99 .
Output: no
Command: and { only { filter_eq { all_rows ; host ; hopton - on - sea } } ; eq { hop { filter_eq { all_rows ; host ; hopton - on - sea } ; season } ; 1998 / 99 } }, interpretation: select the rows whose host record fuzzily matches to hopton - on - sea . there is only one such row in the table . the season record of this unqiue row is 1998 / 99 .
Output: yes
Command: and { only { filter_eq { all_rows ; host ; hopton - on - sea } } ; eq { hop { filter_eq { all_rows ; host ; hopton - on - sea } ; season } ; 1998 / 99 } }, interpretation: for the format records of all rows , most of them fuzzily match to vinyl .
Output: no
Command: most_eq { all_rows ; format ; vinyl }, interpretation: for the format records of all rows , most of them fuzzily match to vinyl .
Output: yes
Command: most_eq { all_rows ; format ; vinyl }, interpretation: select the rows whose drawn record is equal to 0 . select the row whose won record of these rows is maximum . the club record of this row is brynmawr rfc .
Output: no
Command: eq { hop { argmax { filter_eq { all_rows ; drawn ; 0 } ; won } ; club } ; brynmawr rfc }, interpretation: select the rows whose drawn record is equal to 0 . select the row whose won record of these rows is maximum . the club record of this row is brynmawr rfc .
Output: yes
Command: eq { hop { argmax { filter_eq { all_rows ; drawn ; 0 } ; won } ; club } ; brynmawr rfc }, interpretation: select the rows whose format record fuzzily matches to christian . take the frequency record of this row . select the rows whose format record fuzzily matches to public broadcasting . take the frequency record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; format ; christian } ; frequency } ; hop { filter_eq { all_rows ; format ; public broadcasting } ; frequency } }, interpretation: select the rows whose format record fuzzily matches to christian . take the frequency record of this row . select the rows whose format record fuzzily matches to public broadcasting . take the frequency record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; format ; christian } ; frequency } ; hop { filter_eq { all_rows ; format ; public broadcasting } ; frequency } }, interpretation: select the rows whose directed by record fuzzily matches to allan arkush . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; directed by ; allan arkush } } ; 2 }, interpretation: select the rows whose directed by record fuzzily matches to allan arkush . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; directed by ; allan arkush } } ; 2 }, interpretation: select the rows whose man of the match record fuzzily matches to icl pakistan . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; man of the match ; icl pakistan } }, interpretation: select the rows whose man of the match record fuzzily matches to icl pakistan . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; man of the match ; icl pakistan } }, interpretation: for the ihsaa class records of all rows , all of them fuzzily match to aaaa .
Output: no
Command: all_eq { all_rows ; ihsaa class ; aaaa }, interpretation: for the ihsaa class records of all rows , all of them fuzzily match to aaaa .
Output: yes
Command: all_eq { all_rows ; ihsaa class ; aaaa }, interpretation: select the rows whose focal plane in ft ( m ) record fuzzily matches to n / a . there is only one such row in the table . the lighthouse record of this unqiue row is basco .
Output: no
Command: and { only { filter_eq { all_rows ; focal plane in ft ( m ) ; n / a } } ; eq { hop { filter_eq { all_rows ; focal plane in ft ( m ) ; n / a } ; lighthouse } ; basco } }, interpretation: select the rows whose focal plane in ft ( m ) record fuzzily matches to n / a . there is only one such row in the table . the lighthouse record of this unqiue row is basco .
Output: yes
Command: and { only { filter_eq { all_rows ; focal plane in ft ( m ) ; n / a } } ; eq { hop { filter_eq { all_rows ; focal plane in ft ( m ) ; n / a } ; lighthouse } ; basco } }, interpretation: select the rows whose city of license record fuzzily matches to lyons falls , ny . take the frequency mhz record of this row . select the rows whose city of license record fuzzily matches to norwich , ny . take the frequency mhz record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; city of license ; lyons falls , ny } ; frequency mhz } ; hop { filter_eq { all_rows ; city of license ; norwich , ny } ; frequency mhz } }, interpretation: select the rows whose city of license record fuzzily matches to lyons falls , ny . take the frequency mhz record of this row . select the rows whose city of license record fuzzily matches to norwich , ny . take the frequency mhz record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; city of license ; lyons falls , ny } ; frequency mhz } ; hop { filter_eq { all_rows ; city of license ; norwich , ny } ; frequency mhz } }, interpretation: select the rows whose home team record fuzzily matches to melbourne . the average of the crowd record of these rows is 15750 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; home team ; melbourne } ; crowd } ; 15750 }, interpretation: select the rows whose home team record fuzzily matches to melbourne . the average of the crowd record of these rows is 15750 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; home team ; melbourne } ; crowd } ; 15750 }, interpretation: select the rows whose ground record fuzzily matches to football park . among these rows , select the rows whose crowd record is less than 16000 . the number of such rows is 1 .
Output: no
Command: eq { count { filter_less { filter_eq { all_rows ; ground ; football park } ; crowd ; 16000 } } ; 1 }, interpretation: select the rows whose ground record fuzzily matches to football park . among these rows , select the rows whose crowd record is less than 16000 . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_less { filter_eq { all_rows ; ground ; football park } ; crowd ; 16000 } } ; 1 }, interpretation: the sum of the national titles record of all rows is 79 .
Output: no
Command: round_eq { sum { all_rows ; national titles } ; 79 }, interpretation: the sum of the national titles record of all rows is 79 .
Output: yes
Command: round_eq { sum { all_rows ; national titles } ; 79 }, interpretation: select the row whose date of appointment record of all rows is 3rd minimum . the replaced by record of this row is ivan pudar .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date of appointment ; 3 } ; replaced by } ; ivan pudar }, interpretation: select the row whose date of appointment record of all rows is 3rd minimum . the replaced by record of this row is ivan pudar .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date of appointment ; 3 } ; replaced by } ; ivan pudar }, interpretation: select the rows whose county record fuzzily matches to wexford . there is only one such row in the table . the player record of this unqiue row is christy kehoe .
Output: no
Command: and { only { filter_eq { all_rows ; county ; wexford } } ; eq { hop { filter_eq { all_rows ; county ; wexford } ; player } ; christy kehoe } }, interpretation: select the rows whose county record fuzzily matches to wexford . there is only one such row in the table . the player record of this unqiue row is christy kehoe .
Output: yes
Command: and { only { filter_eq { all_rows ; county ; wexford } } ; eq { hop { filter_eq { all_rows ; county ; wexford } ; player } ; christy kehoe } }, interpretation: select the row whose population record of all rows is 3rd maximum . the english name record of this row is suibin county .
Output: no
Command: eq { hop { nth_argmax { all_rows ; population ; 3 } ; english name } ; suibin county }, interpretation: select the row whose population record of all rows is 3rd maximum . the english name record of this row is suibin county .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; population ; 3 } ; english name } ; suibin county }, interpretation: the sum of the quantity record of all rows is 427 .
Output: no
Command: round_eq { sum { all_rows ; quantity } ; 427 }, interpretation: the sum of the quantity record of all rows is 427 .
Output: yes
Command: round_eq { sum { all_rows ; quantity } ; 427 }, interpretation: select the rows whose nba years record is equal to 3 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; nba years ; 3 } } ; 5 }, interpretation: select the rows whose nba years record is equal to 3 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; nba years ; 3 } } ; 5 }, interpretation: select the rows whose year record is greater than or equal to 1960 . the maximum rank record of these rows is 17 .
Output: no
Command: eq { max { filter_greater_eq { all_rows ; year ; 1960 } ; rank } ; 17 }, interpretation: select the rows whose year record is greater than or equal to 1960 . the maximum rank record of these rows is 17 .
Output: yes
Command: eq { max { filter_greater_eq { all_rows ; year ; 1960 } ; rank } ; 17 }, interpretation: select the row whose points record of all rows is 2nd maximum . the performer record of this row is honor heffernan .
Output: no
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; performer } ; honor heffernan }, interpretation: select the row whose points record of all rows is 2nd maximum . the performer record of this row is honor heffernan .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; performer } ; honor heffernan }, interpretation: select the rows whose performer 4 record fuzzily matches to john bird . there is only one such row in the table . the episode record of this unqiue row is 5 .
Output: no
Command: and { only { filter_eq { all_rows ; performer 4 ; john bird } } ; eq { hop { filter_eq { all_rows ; performer 4 ; john bird } ; episode } ; 5 } }, interpretation: select the rows whose performer 4 record fuzzily matches to john bird . there is only one such row in the table . the episode record of this unqiue row is 5 .
Output: yes
Command: and { only { filter_eq { all_rows ; performer 4 ; john bird } } ; eq { hop { filter_eq { all_rows ; performer 4 ; john bird } ; episode } ; 5 } }, interpretation: the maximum duration ( days ) record of all rows is 184.84 . the expedition record of the row with superlative duration ( days ) record is salyut 6 - eo - 4 .
Output: no
Command: and { eq { max { all_rows ; duration ( days ) } ; 184.84 } ; eq { hop { argmax { all_rows ; duration ( days ) } ; expedition } ; salyut 6 - eo - 4 } }, interpretation: the maximum duration ( days ) record of all rows is 184.84 . the expedition record of the row with superlative duration ( days ) record is salyut 6 - eo - 4 .
Output: yes
Command: and { eq { max { all_rows ; duration ( days ) } ; 184.84 } ; eq { hop { argmax { all_rows ; duration ( days ) } ; expedition } ; salyut 6 - eo - 4 } }, interpretation: for the launch records of all rows , most of them are greater than 2000 .
Output: no
Command: most_greater { all_rows ; launch ; 2000 }, interpretation: for the launch records of all rows , most of them are greater than 2000 .
Output: yes
Command: most_greater { all_rows ; launch ; 2000 }, interpretation: select the row whose strike rate record of all rows is maximum . the player record of this row is paul harris .
Output: no
Command: eq { hop { argmax { all_rows ; strike rate } ; player } ; paul harris }, interpretation: select the row whose strike rate record of all rows is maximum . the player record of this row is paul harris .
Output: yes
Command: eq { hop { argmax { all_rows ; strike rate } ; player } ; paul harris }, interpretation: select the rows whose opposition record fuzzily matches to surrey . take the score record of this row . select the rows whose opposition record fuzzily matches to somerset . 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 ; opposition ; surrey } ; score } ; hop { filter_eq { all_rows ; opposition ; somerset } ; score } }, interpretation: select the rows whose opposition record fuzzily matches to surrey . take the score record of this row . select the rows whose opposition record fuzzily matches to somerset . 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 ; opposition ; surrey } ; score } ; hop { filter_eq { all_rows ; opposition ; somerset } ; score } }, interpretation: the average of the horwood record of all rows is 8 .
Output: no
Command: round_eq { avg { all_rows ; horwood } ; 8 }, interpretation: the average of the horwood record of all rows is 8 .
Output: yes
Command: round_eq { avg { all_rows ; horwood } ; 8 }, interpretation: select the rows whose date record fuzzily matches to january . among these rows , select the rows whose crowd record is less than 4000 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_less { filter_eq { all_rows ; date ; january } ; crowd ; 4000 } } ; 3 }, interpretation: select the rows whose date record fuzzily matches to january . among these rows , select the rows whose crowd record is less than 4000 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_less { filter_eq { all_rows ; date ; january } ; crowd ; 4000 } } ; 3 }, interpretation: select the row whose time / retired record of all rows is maximum . the rider record of this row is dani pedrosa .
Output: no
Command: eq { hop { argmax { all_rows ; time / retired } ; rider } ; dani pedrosa }, interpretation: select the row whose time / retired record of all rows is maximum . the rider record of this row is dani pedrosa .
Output: yes
Command: eq { hop { argmax { all_rows ; time / retired } ; rider } ; dani pedrosa }, interpretation: for the date records of all rows , most of them fuzzily match to 03 .
Output: no
Command: most_eq { all_rows ; date ; 03 }, interpretation: for the date records of all rows , most of them fuzzily match to 03 .
Output: yes
Command: most_eq { all_rows ; date ; 03 }, interpretation: select the rows whose executive record fuzzily matches to second . among these rows , select the rows whose first minister record fuzzily matches to ian paisley . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; executive ; second } ; first minister ; ian paisley } } ; 2 }, interpretation: select the rows whose executive record fuzzily matches to second . among these rows , select the rows whose first minister record fuzzily matches to ian paisley . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; executive ; second } ; first minister ; ian paisley } } ; 2 }, interpretation: select the rows whose result record fuzzily matches to lost re-election republican gain . there is only one such row in the table . the incumbent record of this unqiue row is tom luken .
Output: no
Command: and { only { filter_eq { all_rows ; result ; lost re-election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re-election republican gain } ; incumbent } ; tom luken } }, interpretation: select the rows whose result record fuzzily matches to lost re-election republican gain . there is only one such row in the table . the incumbent record of this unqiue row is tom luken .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; lost re-election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re-election republican gain } ; incumbent } ; tom luken } }, interpretation: for the height records of all rows , most of them are greater than or equal to 6 ' 0 .
Output: no
Command: most_greater_eq { all_rows ; height ; 6 ' 0 }, interpretation: for the height records of all rows , most of them are greater than or equal to 6 ' 0 .
Output: yes
Command: most_greater_eq { all_rows ; height ; 6 ' 0 }, interpretation: select the rows whose successor record fuzzily matches to john parker hale ( r ) . take the date of successors formal installation record of this row . select the rows whose successor record fuzzily matches to william bigler ( d ) . take the date of successors formal installation record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; successor ; john parker hale ( r ) } ; date of successors formal installation } ; hop { filter_eq { all_rows ; successor ; william bigler ( d ) } ; date of successors formal installation } }, interpretation: select the rows whose successor record fuzzily matches to john parker hale ( r ) . take the date of successors formal installation record of this row . select the rows whose successor record fuzzily matches to william bigler ( d ) . take the date of successors formal installation record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; successor ; john parker hale ( r ) } ; date of successors formal installation } ; hop { filter_eq { all_rows ; successor ; william bigler ( d ) } ; date of successors formal installation } }, interpretation: select the rows whose record record fuzzily matches to 4 - 0 . take the time record of this row . select the rows whose record record fuzzily matches to 5 - 0 . take the time record of this row . the first record is less than the second record . the time record of the first row is 1:43 . the time record of the second row is 5:00 .
Output: no
Command: and { less { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } } ; and { eq { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; 1:43 } ; eq { hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } ; 5:00 } } }, interpretation: select the rows whose record record fuzzily matches to 4 - 0 . take the time record of this row . select the rows whose record record fuzzily matches to 5 - 0 . take the time record of this row . the first record is less than the second record . the time record of the first row is 1:43 . the time record of the second row is 5:00 .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } } ; and { eq { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; 1:43 } ; eq { hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } ; 5:00 } } }, interpretation: select the rows whose player record fuzzily matches to ido kozikaro . take the height record of this row . select the rows whose player record fuzzily matches to lior eliyahu . take the height record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; player ; ido kozikaro } ; height } ; hop { filter_eq { all_rows ; player ; lior eliyahu } ; height } }, interpretation: select the rows whose player record fuzzily matches to ido kozikaro . take the height record of this row . select the rows whose player record fuzzily matches to lior eliyahu . take the height record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; player ; ido kozikaro } ; height } ; hop { filter_eq { all_rows ; player ; lior eliyahu } ; height } }, interpretation: for the date records of all rows , all of them fuzzily match to 14 may 1949 .
Output: no
Command: all_eq { all_rows ; date ; 14 may 1949 }, interpretation: for the date records of all rows , all of them fuzzily match to 14 may 1949 .
Output: yes
Command: all_eq { all_rows ; date ; 14 may 1949 }, interpretation: select the rows whose jersey number ( s ) record fuzzily matches to , . there is only one such row in the table . the player record of this unqiue row is reggie evans .
Output: no
Command: and { only { filter_eq { all_rows ; jersey number ( s ) ; , } } ; eq { hop { filter_eq { all_rows ; jersey number ( s ) ; , } ; player } ; reggie evans } }, interpretation: select the rows whose jersey number ( s ) record fuzzily matches to , . there is only one such row in the table . the player record of this unqiue row is reggie evans .
Output: yes
Command: and { only { filter_eq { all_rows ; jersey number ( s ) ; , } } ; eq { hop { filter_eq { all_rows ; jersey number ( s ) ; , } ; player } ; reggie evans } }, interpretation: for the origin of programming records of all rows , most of them fuzzily match to india .
Output: no
Command: most_eq { all_rows ; origin of programming ; india }, interpretation: for the origin of programming records of all rows , most of them fuzzily match to india .
Output: yes
Command: most_eq { all_rows ; origin of programming ; india }, interpretation: select the rows whose song title record fuzzily matches to he thinks i still care . take the length record of this row . select the rows whose song title record fuzzily matches to the pain of loving you . take the length record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; song title ; he thinks i still care } ; length } ; hop { filter_eq { all_rows ; song title ; the pain of loving you } ; length } }, interpretation: select the rows whose song title record fuzzily matches to he thinks i still care . take the length record of this row . select the rows whose song title record fuzzily matches to the pain of loving you . take the length record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; song title ; he thinks i still care } ; length } ; hop { filter_eq { all_rows ; song title ; the pain of loving you } ; length } }, interpretation: the average of the floor record of all rows is 60.589 .
Output: no
Command: round_eq { avg { all_rows ; floor } ; 60.589 }, interpretation: the average of the floor record of all rows is 60.589 .
Output: yes
Command: round_eq { avg { all_rows ; floor } ; 60.589 }, interpretation: select the rows whose season record is arbitrary . the number of such rows is 10 .
Output: no
Command: eq { count { filter_all { all_rows ; season } } ; 10 }, interpretation: select the rows whose season record is arbitrary . the number of such rows is 10 .
Output: yes
Command: eq { count { filter_all { all_rows ; season } } ; 10 }, interpretation: select the rows whose opposing team record fuzzily matches to fiji . take the against record of this row . select the rows whose opposing team record fuzzily matches to taranaki . take the against record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opposing team ; fiji } ; against } ; hop { filter_eq { all_rows ; opposing team ; taranaki } ; against } }, interpretation: select the rows whose opposing team record fuzzily matches to fiji . take the against record of this row . select the rows whose opposing team record fuzzily matches to taranaki . take the against record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opposing team ; fiji } ; against } ; hop { filter_eq { all_rows ; opposing team ; taranaki } ; against } }, interpretation: select the row whose caps record of all rows is maximum . the name record of this row is ronaldo .
Output: no
Command: eq { hop { argmax { all_rows ; caps } ; name } ; ronaldo }, interpretation: select the row whose caps record of all rows is maximum . the name record of this row is ronaldo .
Output: yes
Command: eq { hop { argmax { all_rows ; caps } ; name } ; ronaldo }, interpretation: select the rows whose name record fuzzily matches to jelena skerovic . take the assists record of this row . select the rows whose name record fuzzily matches to kathy wambe . take the assists record of this row . the first record is greater than the second record . the assists record of the first row is 54 . the assists record of the second row is 48 .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; name ; jelena skerovic } ; assists } ; hop { filter_eq { all_rows ; name ; kathy wambe } ; assists } } ; and { eq { hop { filter_eq { all_rows ; name ; jelena skerovic } ; assists } ; 54 } ; eq { hop { filter_eq { all_rows ; name ; kathy wambe } ; assists } ; 48 } } }, interpretation: select the rows whose name record fuzzily matches to jelena skerovic . take the assists record of this row . select the rows whose name record fuzzily matches to kathy wambe . take the assists record of this row . the first record is greater than the second record . the assists record of the first row is 54 . the assists record of the second row is 48 .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; name ; jelena skerovic } ; assists } ; hop { filter_eq { all_rows ; name ; kathy wambe } ; assists } } ; and { eq { hop { filter_eq { all_rows ; name ; jelena skerovic } ; assists } ; 54 } ; eq { hop { filter_eq { all_rows ; name ; kathy wambe } ; assists } ; 48 } } }, interpretation: select the rows whose position record fuzzily matches to tight end . there is only one such row in the table . the player record of this unqiue row is spencer nead .
Output: no
Command: and { only { filter_eq { all_rows ; position ; tight end } } ; eq { hop { filter_eq { all_rows ; position ; tight end } ; player } ; spencer nead } }, interpretation: select the rows whose position record fuzzily matches to tight end . there is only one such row in the table . the player record of this unqiue row is spencer nead .
Output: yes