niryuu/sni-each-converted huggingface dataset:
Command: less { hop { filter_eq { all_rows ; builder ; north british } ; date } ; hop { filter_eq { all_rows ; builder ; g & swr kilmarnock } ; date } }, interpretation: select the rows whose builder record fuzzily matches to north british . take the date record of this row . select the rows whose builder record fuzzily matches to g & swr kilmarnock . 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 ; builder ; north british } ; date } ; hop { filter_eq { all_rows ; builder ; g & swr kilmarnock } ; date } }, interpretation: for the surface records of all rows , most of them fuzzily match to hard .
Output: no
Command: most_eq { all_rows ; surface ; hard }, interpretation: select the rows whose movie record fuzzily matches to agneepath . take the lifetime india distributor share record of this row . select the rows whose movie record fuzzily matches to dabangg 2 . take the lifetime india distributor share record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; movie ; agneepath } ; lifetime india distributor share } ; hop { filter_eq { all_rows ; movie ; dabangg 2 } ; lifetime india distributor share } }, interpretation: select the rows whose movie record fuzzily matches to agneepath . take the lifetime india distributor share record of this row . select the rows whose movie record fuzzily matches to dabangg 2 . take the lifetime india distributor share record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; movie ; agneepath } ; lifetime india distributor share } ; hop { filter_eq { all_rows ; movie ; dabangg 2 } ; lifetime india distributor share } }, interpretation: select the rows whose score record is equal to 69 . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; score ; 69 } } ; 6 }, interpretation: select the rows whose score record is equal to 69 . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; score ; 69 } } ; 6 }, interpretation: select the rows whose player record is arbitrary . the number of such rows is 10 .
Output: no
Command: eq { count { filter_all { all_rows ; player } } ; 10 }, interpretation: select the rows whose player record is arbitrary . the number of such rows is 10 .
Output: yes
Command: eq { count { filter_all { all_rows ; player } } ; 10 }, interpretation: select the row whose time record of all rows is 2nd minimum . the athletes record of this row is mathieu goubel .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 2 } ; athletes } ; mathieu goubel }, interpretation: select the row whose time record of all rows is 2nd minimum . the athletes record of this row is mathieu goubel .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 2 } ; athletes } ; mathieu goubel }, interpretation: select the rows whose opponent record fuzzily matches to yukiya naito . take the round record of this row . select the rows whose opponent record fuzzily matches to maxim tarasov . 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 ; yukiya naito } ; round } ; hop { filter_eq { all_rows ; opponent ; maxim tarasov } ; round } }, interpretation: select the rows whose opponent record fuzzily matches to yukiya naito . take the round record of this row . select the rows whose opponent record fuzzily matches to maxim tarasov . 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 ; yukiya naito } ; round } ; hop { filter_eq { all_rows ; opponent ; maxim tarasov } ; round } }, interpretation: select the rows whose date record fuzzily matches to 7 march 2008 . take the attendance record of this row . select the rows whose date record fuzzily matches to 8 march 2008 . take the attendance record of this row . the first record is less than the second record . the attendance record of the first row is 17225 . the attendance record of the second row is 18279 .
Output: no
Command: and { less { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; 17225 } ; eq { hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } ; 18279 } } }, interpretation: select the rows whose date record fuzzily matches to 7 march 2008 . take the attendance record of this row . select the rows whose date record fuzzily matches to 8 march 2008 . take the attendance record of this row . the first record is less than the second record . the attendance record of the first row is 17225 . the attendance record of the second row is 18279 .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; 17225 } ; eq { hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } ; 18279 } } }, interpretation: select the rows whose date record fuzzily matches to december . among these rows , select the rows whose result record fuzzily matches to l . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; date ; december } ; result ; l } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to december . among these rows , select the rows whose result record fuzzily matches to l . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; date ; december } ; result ; l } } ; 2 }, interpretation: select the rows whose original channel record fuzzily matches to bbc one . there is only one such row in the table . the programme record of this unqiue row is superstars .
Output: no
Command: and { only { filter_eq { all_rows ; original channel ; bbc one } } ; eq { hop { filter_eq { all_rows ; original channel ; bbc one } ; programme } ; superstars } }, interpretation: select the rows whose original channel record fuzzily matches to bbc one . there is only one such row in the table . the programme record of this unqiue row is superstars .
Output: yes
Command: and { only { filter_eq { all_rows ; original channel ; bbc one } } ; eq { hop { filter_eq { all_rows ; original channel ; bbc one } ; programme } ; superstars } }, interpretation: select the row whose original air date record of all rows is 2nd maximum . the production code record of this row is 407 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; production code } ; 407 }, interpretation: select the row whose original air date record of all rows is 2nd maximum . the production code record of this row is 407 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; production code } ; 407 }, interpretation: the average of the time record of all rows is 3:29 .
Output: no
Command: round_eq { avg { all_rows ; time } ; 3:29 }, interpretation: the average of the time record of all rows is 3:29 .
Output: yes
Command: round_eq { avg { all_rows ; time } ; 3:29 }, interpretation: select the row whose round record of all rows is 3rd minimum . the player record of this row is glen irwin .
Output: no
Command: eq { hop { nth_argmin { all_rows ; round ; 3 } ; player } ; glen irwin }, interpretation: select the row whose round record of all rows is 3rd minimum . the player record of this row is glen irwin .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; round ; 3 } ; player } ; glen irwin }, interpretation: the average of the tournaments played record of all rows is 16.1 .
Output: no
Command: round_eq { avg { all_rows ; tournaments played } ; 16.1 }, interpretation: the average of the tournaments played record of all rows is 16.1 .
Output: yes
Command: round_eq { avg { all_rows ; tournaments played } ; 16.1 }, interpretation: for the sport records of all rows , all of them fuzzily match to football .
Output: no
Command: all_eq { all_rows ; sport ; football }, interpretation: for the sport records of all rows , all of them fuzzily match to football .
Output: yes
Command: all_eq { all_rows ; sport ; football }, 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: for the platform ( s ) records of all rows , most of them fuzzily match to windows .
Output: no
Command: most_eq { all_rows ; platform ( s ) ; windows }, interpretation: for the platform ( s ) records of all rows , most of them fuzzily match to windows .
Output: yes
Command: most_eq { all_rows ; platform ( s ) ; windows }, interpretation: select the rows whose gold record fuzzily matches to wu yanan . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; gold ; wu yanan } } ; 2 }, interpretation: select the rows whose gold record fuzzily matches to wu yanan . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; gold ; wu yanan } } ; 2 }, interpretation: select the rows whose classification record fuzzily matches to absurdism . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; classification ; absurdism } } ; 2 }, interpretation: select the rows whose classification record fuzzily matches to absurdism . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; classification ; absurdism } } ; 2 }, interpretation: select the row whose population record of all rows is maximum . the official name record of this row is quispamsis .
Output: no
Command: eq { hop { argmax { all_rows ; population } ; official name } ; quispamsis }, interpretation: select the row whose population record of all rows is maximum . the official name record of this row is quispamsis .
Output: yes
Command: eq { hop { argmax { all_rows ; population } ; official name } ; quispamsis }, interpretation: for the capacity records of all rows , most of them are less than 10000 .
Output: no
Command: most_less { all_rows ; capacity ; 10000 }, interpretation: for the capacity records of all rows , most of them are less than 10000 .
Output: yes
Command: most_less { all_rows ; capacity ; 10000 }, interpretation: select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose party record fuzzily matches to republican . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; party ; republican } } ; 2 }, interpretation: select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose party record fuzzily matches to republican . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; party ; republican } } ; 2 }, interpretation: for the date records of all rows , all of them fuzzily match to 23 july .
Output: no
Command: all_eq { all_rows ; date ; 23 july }, interpretation: for the date records of all rows , all of them fuzzily match to 23 july .
Output: yes
Command: all_eq { all_rows ; date ; 23 july }, interpretation: the sum of the won record of all rows is 128 .
Output: no
Command: round_eq { sum { all_rows ; won } ; 128 }, interpretation: the sum of the won record of all rows is 128 .
Output: yes
Command: round_eq { sum { all_rows ; won } ; 128 }, interpretation: select the row whose 1st ship delivery date record of all rows is 2nd maximum . the yard name record of this row is pendleton shipyards corp .
Output: no
Command: eq { hop { nth_argmax { all_rows ; 1st ship delivery date ; 2 } ; yard name } ; pendleton shipyards corp }, interpretation: select the row whose 1st ship delivery date record of all rows is 2nd maximum . the yard name record of this row is pendleton shipyards corp .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; 1st ship delivery date ; 2 } ; yard name } ; pendleton shipyards corp }, interpretation: select the rows whose lost record is greater than or equal to 10 . select the row whose goals against record of these rows is maximum . the team record of this row is rossendale united . the won record of this row is 10 .
Output: no
Command: and { eq { hop { argmax { filter_greater_eq { all_rows ; lost ; 10 } ; goals against } ; team } ; rossendale united } ; eq { hop { argmax { filter_greater_eq { all_rows ; lost ; 10 } ; goals against } ; won } ; 10 } }, interpretation: select the rows whose lost record is greater than or equal to 10 . select the row whose goals against record of these rows is maximum . the team record of this row is rossendale united . the won record of this row is 10 .
Output: yes
Command: and { eq { hop { argmax { filter_greater_eq { all_rows ; lost ; 10 } ; goals against } ; team } ; rossendale united } ; eq { hop { argmax { filter_greater_eq { all_rows ; lost ; 10 } ; goals against } ; won } ; 10 } }, interpretation: select the row whose 2005 record of all rows is maximum . the tournament record of this row is year end ranking .
Output: no
Command: eq { hop { argmax { all_rows ; 2005 } ; tournament } ; year end ranking }, interpretation: select the row whose 2005 record of all rows is maximum . the tournament record of this row is year end ranking .
Output: yes
Command: eq { hop { argmax { all_rows ; 2005 } ; tournament } ; year end ranking }, interpretation: select the rows whose largest ethnic group ( 2002 ) record fuzzily matches to slovaks . there is only one such row in the table . the settlement record of this unqiue row is pivnice .
Output: no
Command: and { only { filter_eq { all_rows ; largest ethnic group ( 2002 ) ; slovaks } } ; eq { hop { filter_eq { all_rows ; largest ethnic group ( 2002 ) ; slovaks } ; settlement } ; pivnice } }, interpretation: select the rows whose largest ethnic group ( 2002 ) record fuzzily matches to slovaks . there is only one such row in the table . the settlement record of this unqiue row is pivnice .
Output: yes
Command: and { only { filter_eq { all_rows ; largest ethnic group ( 2002 ) ; slovaks } } ; eq { hop { filter_eq { all_rows ; largest ethnic group ( 2002 ) ; slovaks } ; settlement } ; pivnice } }, interpretation: select the row whose silver record of all rows is maximum . the nation record of this row is england .
Output: no
Command: eq { hop { argmax { all_rows ; silver } ; nation } ; england }, interpretation: select the row whose silver record of all rows is maximum . the nation record of this row is england .
Output: yes
Command: eq { hop { argmax { all_rows ; silver } ; nation } ; england }, interpretation: select the rows whose home team record fuzzily matches to geelong . take the home team score record of this row . select the rows whose home team record fuzzily matches to richmond . take the home team score record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; home team ; geelong } ; home team score } ; hop { filter_eq { all_rows ; home team ; richmond } ; home team score } }, interpretation: select the rows whose home team record fuzzily matches to geelong . take the home team score record of this row . select the rows whose home team record fuzzily matches to richmond . take the home team score record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; home team ; geelong } ; home team score } ; hop { filter_eq { all_rows ; home team ; richmond } ; home team score } }, interpretation: select the rows whose date record fuzzily matches to 6 june 2010 ( round 13 ) . take the total record of this row . select the rows whose date record fuzzily matches to 5 april 2008 ( round 4 ) . 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 ; date ; 6 june 2010 ( round 13 ) } ; total } ; hop { filter_eq { all_rows ; date ; 5 april 2008 ( round 4 ) } ; total } }, interpretation: select the rows whose date record fuzzily matches to 6 june 2010 ( round 13 ) . take the total record of this row . select the rows whose date record fuzzily matches to 5 april 2008 ( round 4 ) . 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 ; date ; 6 june 2010 ( round 13 ) } ; total } ; hop { filter_eq { all_rows ; date ; 5 april 2008 ( round 4 ) } ; total } }, interpretation: select the rows whose nationality record fuzzily matches to aut . there is only one such row in the table . the name record of this unqiue row is gregor schlierenzauer .
Output: no
Command: and { only { filter_eq { all_rows ; nationality ; aut } } ; eq { hop { filter_eq { all_rows ; nationality ; aut } ; name } ; gregor schlierenzauer } }, interpretation: select the rows whose nationality record fuzzily matches to aut . there is only one such row in the table . the name record of this unqiue row is gregor schlierenzauer .
Output: yes
Command: and { only { filter_eq { all_rows ; nationality ; aut } } ; eq { hop { filter_eq { all_rows ; nationality ; aut } ; name } ; gregor schlierenzauer } }, interpretation: select the row whose points record of all rows is maximum . the team record of this row is libertad .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; team } ; libertad }, interpretation: select the row whose points record of all rows is maximum . the team record of this row is libertad .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; team } ; libertad }, interpretation: select the row whose us viewers ( in millions ) record of all rows is maximum . the title record of this row is rebel without a pause .
Output: no
Command: eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; rebel without a pause }, interpretation: select the row whose us viewers ( in millions ) record of all rows is maximum . the title record of this row is rebel without a pause .
Output: yes
Command: eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; rebel without a pause }, interpretation: select the rows whose actor required record fuzzily matches to female , older . there is only one such row in the table . the actor in original production record of this unqiue row is beth tuckley .
Output: no
Command: and { only { filter_eq { all_rows ; actor required ; female , older } } ; eq { hop { filter_eq { all_rows ; actor required ; female , older } ; actor in original production } ; beth tuckley } }, interpretation: select the rows whose actor required record fuzzily matches to female , older . there is only one such row in the table . the actor in original production record of this unqiue row is beth tuckley .
Output: yes
Command: and { only { filter_eq { all_rows ; actor required ; female , older } } ; eq { hop { filter_eq { all_rows ; actor required ; female , older } ; actor in original production } ; beth tuckley } }, interpretation: for the length records of all rows , most of them are less than 5:00 .
Output: no
Command: most_less { all_rows ; length ; 5:00 }, interpretation: for the length records of all rows , most of them are less than 5:00 .
Output: yes
Command: most_less { all_rows ; length ; 5:00 }, interpretation: select the rows whose episode record fuzzily matches to episode 4 . take the viewers ( millions ) record of this row . select the rows whose episode record fuzzily matches to episode 2 . take the viewers ( millions ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; episode ; episode 4 } ; viewers ( millions ) } ; hop { filter_eq { all_rows ; episode ; episode 2 } ; viewers ( millions ) } }, interpretation: select the rows whose episode record fuzzily matches to episode 4 . take the viewers ( millions ) record of this row . select the rows whose episode record fuzzily matches to episode 2 . take the viewers ( millions ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; episode ; episode 4 } ; viewers ( millions ) } ; hop { filter_eq { all_rows ; episode ; episode 2 } ; viewers ( millions ) } }, interpretation: the average of the attendance record of all rows is 50000-53000 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 50000-53000 }, interpretation: the average of the attendance record of all rows is 50000-53000 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 50000-53000 }, interpretation: select the rows whose week record fuzzily matches to top 24 ( 12 women ) . take the order record of this row . select the rows whose week record fuzzily matches to top 20 ( 10 women ) . take the order record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; week ; top 24 ( 12 women ) } ; order } ; hop { filter_eq { all_rows ; week ; top 20 ( 10 women ) } ; order } }, interpretation: select the rows whose week record fuzzily matches to top 24 ( 12 women ) . take the order record of this row . select the rows whose week record fuzzily matches to top 20 ( 10 women ) . take the order record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; week ; top 24 ( 12 women ) } ; order } ; hop { filter_eq { all_rows ; week ; top 20 ( 10 women ) } ; order } }, interpretation: select the rows whose series record is arbitrary . the number of such rows is 7 .
Output: no
Command: eq { count { filter_all { all_rows ; series } } ; 7 }, interpretation: select the rows whose series record is arbitrary . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_all { all_rows ; series } } ; 7 }, interpretation: the sum of the total number of students record of all rows is 123950 .
Output: no
Command: round_eq { sum { all_rows ; total number of students } ; 123950 }, interpretation: the sum of the total number of students record of all rows is 123950 .
Output: yes
Command: round_eq { sum { all_rows ; total number of students } ; 123950 }, interpretation: select the rows whose year completed record is less than 2000 . among these rows , select the rows whose floors ( stories ) record is equal to 12 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_less { all_rows ; year completed ; 2000 } ; floors ( stories ) ; 12 } } ; 2 }, interpretation: select the rows whose year completed record is less than 2000 . among these rows , select the rows whose floors ( stories ) record is equal to 12 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_less { all_rows ; year completed ; 2000 } ; floors ( stories ) ; 12 } } ; 2 }, interpretation: select the rows whose label record fuzzily matches to epic . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; label ; epic } } ; 6 }, interpretation: select the rows whose label record fuzzily matches to epic . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; label ; epic } } ; 6 }, interpretation: for the crowd records of all rows , most of them are greater than 15000 .
Output: no
Command: most_greater { all_rows ; crowd ; 15000 }, interpretation: for the crowd records of all rows , most of them are greater than 15000 .
Output: yes
Command: most_greater { all_rows ; crowd ; 15000 }, interpretation: select the rows whose country record fuzzily matches to madagascar . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; country ; madagascar } } ; 3 }, interpretation: select the rows whose country record fuzzily matches to madagascar . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; country ; madagascar } } ; 3 }, interpretation: select the rows whose date record fuzzily matches to 2004 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; date ; 2004 } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to 2004 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; date ; 2004 } } ; 2 }, interpretation: select the rows whose call letters record fuzzily matches to kfro . take the frequency ( khz ) record of this row . select the rows whose call letters record fuzzily matches to keel . take the frequency ( khz ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; call letters ; kfro } ; frequency ( khz ) } ; hop { filter_eq { all_rows ; call letters ; keel } ; frequency ( khz ) } }, interpretation: select the rows whose call letters record fuzzily matches to kfro . take the frequency ( khz ) record of this row . select the rows whose call letters record fuzzily matches to keel . take the frequency ( khz ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; call letters ; kfro } ; frequency ( khz ) } ; hop { filter_eq { all_rows ; call letters ; keel } ; frequency ( khz ) } }, interpretation: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is mcg .
Output: no
Command: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; mcg }, interpretation: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is mcg .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; mcg }, interpretation: select the rows whose region 2 record fuzzily matches to 2004 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; region 2 ; 2004 } } ; 3 }, interpretation: select the rows whose region 2 record fuzzily matches to 2004 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; region 2 ; 2004 } } ; 3 }, interpretation: for the preliminary average records of all rows , most of them are less than 9.0 .
Output: no
Command: most_less { all_rows ; preliminary average ; 9.0 }, interpretation: for the preliminary average records of all rows , most of them are less than 9.0 .
Output: yes
Command: most_less { all_rows ; preliminary average ; 9.0 }, interpretation: the average of the time record of all rows is 5.603 .
Output: no
Command: round_eq { avg { all_rows ; time } ; 5.603 }, interpretation: the average of the time record of all rows is 5.603 .
Output: yes
Command: round_eq { avg { all_rows ; time } ; 5.603 }, interpretation: select the rows whose high rebounds record fuzzily matches to udonis haslem . among these rows , select the rows whose high assists record fuzzily matches to dwyane wade . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; high rebounds ; udonis haslem } ; high assists ; dwyane wade } } ; 3 }, interpretation: select the rows whose high rebounds record fuzzily matches to udonis haslem . among these rows , select the rows whose high assists record fuzzily matches to dwyane wade . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; high rebounds ; udonis haslem } ; high assists ; dwyane wade } } ; 3 }, interpretation: for the date records of all rows , all of them fuzzily match to 25 august 1979 .
Output: no
Command: all_eq { all_rows ; date ; 25 august 1979 }, interpretation: for the date records of all rows , all of them fuzzily match to 25 august 1979 .
Output: yes
Command: all_eq { all_rows ; date ; 25 august 1979 }, interpretation: select the rows whose points record is equal to 0 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; points ; 0 } } ; 3 }, interpretation: select the rows whose points record is equal to 0 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; points ; 0 } } ; 3 }, interpretation: select the rows whose team 2 record fuzzily matches to barcelona . take the agg record of this row . select the rows whose team 2 record fuzzily matches to manchester united . take the agg record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; team 2 ; barcelona } ; agg } ; hop { filter_eq { all_rows ; team 2 ; manchester united } ; agg } }, interpretation: select the rows whose team 2 record fuzzily matches to barcelona . take the agg record of this row . select the rows whose team 2 record fuzzily matches to manchester united . take the agg record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; team 2 ; barcelona } ; agg } ; hop { filter_eq { all_rows ; team 2 ; manchester united } ; agg } }, interpretation: select the rows whose points record is greater than 10 . among these rows , select the rows whose drawn record is equal to 2 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_greater { all_rows ; points ; 10 } ; drawn ; 2 } } ; 2 }, interpretation: select the rows whose points record is greater than 10 . among these rows , select the rows whose drawn record is equal to 2 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_greater { all_rows ; points ; 10 } ; drawn ; 2 } } ; 2 }, interpretation: select the row whose away record of all rows is maximum . the season record of this row is 2001 - 02 .
Output: no
Command: eq { hop { argmax { all_rows ; away } ; season } ; 2001 - 02 }, interpretation: select the row whose away record of all rows is maximum . the season record of this row is 2001 - 02 .
Output: yes
Command: eq { hop { argmax { all_rows ; away } ; season } ; 2001 - 02 }, interpretation: select the rows whose laps record is less than 7 . there is only one such row in the table . the year record of this unqiue row is 1980 .
Output: no
Command: and { only { filter_less { all_rows ; laps ; 7 } } ; eq { hop { filter_less { all_rows ; laps ; 7 } ; year } ; 1980 } }, interpretation: select the rows whose laps record is less than 7 . there is only one such row in the table . the year record of this unqiue row is 1980 .
Output: yes
Command: and { only { filter_less { all_rows ; laps ; 7 } } ; eq { hop { filter_less { all_rows ; laps ; 7 } ; year } ; 1980 } }, interpretation: for the lyrics records of all rows , most of them fuzzily match to english .
Output: no
Command: most_eq { all_rows ; lyrics ; english }, interpretation: for the lyrics records of all rows , most of them fuzzily match to english .
Output: yes
Command: most_eq { all_rows ; lyrics ; english }, interpretation: select the rows whose english title record fuzzily matches to the saviour of the soul . take the hk viewers record of this row . select the rows whose english title record fuzzily matches to men in pain . take the hk viewers record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; english title ; the saviour of the soul } ; hk viewers } ; hop { filter_eq { all_rows ; english title ; men in pain } ; hk viewers } }, interpretation: select the rows whose english title record fuzzily matches to the saviour of the soul . take the hk viewers record of this row . select the rows whose english title record fuzzily matches to men in pain . take the hk viewers record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; english title ; the saviour of the soul } ; hk viewers } ; hop { filter_eq { all_rows ; english title ; men in pain } ; hk viewers } }, interpretation: select the rows whose attendance record fuzzily matches to gund arena . the sum of the attendance record of these rows is 123371 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; attendance ; gund arena } ; attendance } ; 123371 }, interpretation: select the rows whose attendance record fuzzily matches to gund arena . the sum of the attendance record of these rows is 123371 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; attendance ; gund arena } ; attendance } ; 123371 }, interpretation: the average of the points record of all rows is 2 .
Output: no
Command: round_eq { avg { all_rows ; points } ; 2 }, interpretation: the average of the points record of all rows is 2 .
Output: yes
Command: round_eq { avg { all_rows ; points } ; 2 }, interpretation: the average of the blank ends record of all rows is 7.64 .
Output: no
Command: round_eq { avg { all_rows ; blank ends } ; 7.64 }, interpretation: the average of the blank ends record of all rows is 7.64 .
Output: yes
Command: round_eq { avg { all_rows ; blank ends } ; 7.64 }, interpretation: select the rows whose weapon record fuzzily matches to pistol . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; weapon ; pistol } } ; 2 }, interpretation: select the rows whose weapon record fuzzily matches to pistol . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; weapon ; pistol } } ; 2 }, interpretation: select the rows whose of seats won record is less than 10 . there is only one such row in the table . the election record of this unqiue row is 1993 .
Output: no
Command: and { only { filter_less { all_rows ; of seats won ; 10 } } ; eq { hop { filter_less { all_rows ; of seats won ; 10 } ; election } ; 1993 } }, interpretation: select the rows whose of seats won record is less than 10 . there is only one such row in the table . the election record of this unqiue row is 1993 .
Output: yes
Command: and { only { filter_less { all_rows ; of seats won ; 10 } } ; eq { hop { filter_less { all_rows ; of seats won ; 10 } ; election } ; 1993 } }, interpretation: select the rows whose total record is greater than 13 . the average of the fighting spirit record of these rows is 5 .
Output: no
Command: round_eq { avg { filter_greater { all_rows ; total ; 13 } ; fighting spirit } ; 5 }, interpretation: select the rows whose total record is greater than 13 . the average of the fighting spirit record of these rows is 5 .
Output: yes
Command: round_eq { avg { filter_greater { all_rows ; total ; 13 } ; fighting spirit } ; 5 }, interpretation: select the row whose year record of all rows is 2nd minimum . the formula record of this row is grand prix .
Output: no
Command: eq { hop { nth_argmin { all_rows ; year ; 2 } ; formula } ; grand prix }, interpretation: select the row whose year record of all rows is 2nd minimum . the formula record of this row is grand prix .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; year ; 2 } ; formula } ; grand prix }, interpretation: select the rows whose venue record fuzzily matches to candlestick park . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; venue ; candlestick park } } ; 3 }, interpretation: select the rows whose venue record fuzzily matches to candlestick park . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; venue ; candlestick park } } ; 3 }, interpretation: select the rows whose player record fuzzily matches to tom laidlaw . take the round record of this row . select the rows whose player record fuzzily matches to chris mclaughlin . take the round record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; player ; tom laidlaw } ; round } ; hop { filter_eq { all_rows ; player ; chris mclaughlin } ; round } }, interpretation: select the rows whose player record fuzzily matches to tom laidlaw . take the round record of this row . select the rows whose player record fuzzily matches to chris mclaughlin . take the round record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; player ; tom laidlaw } ; round } ; hop { filter_eq { all_rows ; player ; chris mclaughlin } ; round } }, interpretation: the sum of the losses record of all rows is 212 .
Output: no
Command: round_eq { sum { all_rows ; losses } ; 212 }, interpretation: the sum of the losses record of all rows is 212 .
Output: yes
Command: round_eq { sum { all_rows ; losses } ; 212 }, interpretation: select the rows whose country record fuzzily matches to scotland . there is only one such row in the table . the player record of this unqiue row is sandy lyle .
Output: no
Command: and { only { filter_eq { all_rows ; country ; scotland } } ; eq { hop { filter_eq { all_rows ; country ; scotland } ; player } ; sandy lyle } }, interpretation: select the rows whose country record fuzzily matches to scotland . there is only one such row in the table . the player record of this unqiue row is sandy lyle .
Output: yes
Command: and { only { filter_eq { all_rows ; country ; scotland } } ; eq { hop { filter_eq { all_rows ; country ; scotland } ; player } ; sandy lyle } }, interpretation: select the rows whose player record fuzzily matches to patrick collins . take the pick record of this row . select the rows whose player record fuzzily matches to sterling sharpe . take the pick record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; patrick collins } ; pick } ; hop { filter_eq { all_rows ; player ; sterling sharpe } ; pick } }, interpretation: select the rows whose player record fuzzily matches to patrick collins . take the pick record of this row . select the rows whose player record fuzzily matches to sterling sharpe . take the pick record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; patrick collins } ; pick } ; hop { filter_eq { all_rows ; player ; sterling sharpe } ; pick } }, interpretation: select the rows whose defensive record fuzzily matches to matt disher . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; defensive ; matt disher } } ; 3 }, interpretation: select the rows whose defensive record fuzzily matches to matt disher . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; defensive ; matt disher } } ; 3 }, interpretation: select the rows whose club record fuzzily matches to travnik . take the number of seasons in premier league a record of this row . select the rows whose club record fuzzily matches to zvijezda . take the number of seasons in premier league a record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; club ; travnik } ; number of seasons in premier league a } ; hop { filter_eq { all_rows ; club ; zvijezda } ; number of seasons in premier league a } }, interpretation: select the rows whose club record fuzzily matches to travnik . take the number of seasons in premier league a record of this row . select the rows whose club record fuzzily matches to zvijezda . take the number of seasons in premier league a record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; club ; travnik } ; number of seasons in premier league a } ; hop { filter_eq { all_rows ; club ; zvijezda } ; number of seasons in premier league a } }, interpretation: select the rows whose fog ( days / year ) record is greater than 30 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_greater { all_rows ; fog ( days / year ) ; 30 } } ; 4 }, interpretation: select the rows whose fog ( days / year ) record is greater than 30 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_greater { all_rows ; fog ( days / year ) ; 30 } } ; 4 }, interpretation: select the row whose candidates record of all rows is maximum . the incumbent record of this row is charles r jonas .
Output: no
Command: eq { hop { argmax { all_rows ; candidates } ; incumbent } ; charles r jonas }, interpretation: select the row whose candidates record of all rows is maximum . the incumbent record of this row is charles r jonas .
Output: yes
Command: eq { hop { argmax { all_rows ; candidates } ; incumbent } ; charles r jonas }, interpretation: select the rows whose office record fuzzily matches to state assemblyman . there is only one such row in the table . the elected record of this unqiue row is 1974 .
Output: no
Command: and { only { filter_eq { all_rows ; office ; state assemblyman } } ; eq { hop { filter_eq { all_rows ; office ; state assemblyman } ; elected } ; 1974 } }, interpretation: select the rows whose office record fuzzily matches to state assemblyman . there is only one such row in the table . the elected record of this unqiue row is 1974 .
Output: yes
Command: and { only { filter_eq { all_rows ; office ; state assemblyman } } ; eq { hop { filter_eq { all_rows ; office ; state assemblyman } ; elected } ; 1974 } }, interpretation: for the date records of all rows , most of them fuzzily match to 02 .
Output: no
Command: most_eq { all_rows ; date ; 02 }, interpretation: for the date records of all rows , most of them fuzzily match to 02 .
Output: yes
Command: most_eq { all_rows ; date ; 02 }, interpretation: select the row whose high points record of all rows is maximum . the date record of this row is june 20 .
Output: no
Command: eq { hop { argmax { all_rows ; high points } ; date } ; june 20 }, interpretation: select the row whose high points record of all rows is maximum . the date record of this row is june 20 .
Output: yes
Command: eq { hop { argmax { all_rows ; high points } ; date } ; june 20 }, interpretation: select the row whose density ( pop per km2 ) record of all rows is 2nd maximum . the regional county municipality ( rcm ) record of this row is la haute - yamaska .
Output: no
Command: eq { hop { nth_argmax { all_rows ; density ( pop per km2 ) ; 2 } ; regional county municipality ( rcm ) } ; la haute - yamaska }, interpretation: select the row whose density ( pop per km2 ) record of all rows is 2nd maximum . the regional county municipality ( rcm ) record of this row is la haute - yamaska .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; density ( pop per km2 ) ; 2 } ; regional county municipality ( rcm ) } ; la haute - yamaska }, interpretation: select the row whose orbital period record of all rows is minimum . the planet record of this row is alpha centauri bb .
Output: no
Command: eq { hop { argmin { all_rows ; orbital period } ; planet } ; alpha centauri bb }, interpretation: select the row whose orbital period record of all rows is minimum . the planet record of this row is alpha centauri bb .
Output: yes
Command: eq { hop { argmin { all_rows ; orbital period } ; planet } ; alpha centauri bb }, interpretation: select the rows whose title record fuzzily matches to chicken run . take the year record of this row . select the rows whose title record fuzzily matches to creature comforts . take the year record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; title ; chicken run } ; year } ; hop { filter_eq { all_rows ; title ; creature comforts } ; year } }, interpretation: select the rows whose title record fuzzily matches to chicken run . take the year record of this row . select the rows whose title record fuzzily matches to creature comforts . take the year record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; title ; chicken run } ; year } ; hop { filter_eq { all_rows ; title ; creature comforts } ; year } }, interpretation: select the row whose entries record of all rows is maximum . the driver record of this row is rubens barrichello .
Output: no
Command: eq { hop { argmax { all_rows ; entries } ; driver } ; rubens barrichello }, interpretation: select the row whose entries record of all rows is maximum . the driver record of this row is rubens barrichello .
Output: yes
Command: eq { hop { argmax { all_rows ; entries } ; driver } ; rubens barrichello }, interpretation: select the rows whose nationality record fuzzily matches to canada . for the position records of these rows , most of them fuzzily match to defence .
Output: no
Command: most_eq { filter_eq { all_rows ; nationality ; canada } ; position ; defence }, interpretation: select the rows whose nationality record fuzzily matches to canada . for the position records of these rows , most of them fuzzily match to defence .
Output: yes
Command: most_eq { filter_eq { all_rows ; nationality ; canada } ; position ; defence }, interpretation: select the rows whose successor record fuzzily matches to vacant . there is only one such row in the table . the vacator record of this unqiue row is ross bass ( d ) .
Output: no
Command: and { only { filter_eq { all_rows ; successor ; vacant } } ; eq { hop { filter_eq { all_rows ; successor ; vacant } ; vacator } ; ross bass ( d ) } }, interpretation: select the rows whose successor record fuzzily matches to vacant . there is only one such row in the table . the vacator record of this unqiue row is ross bass ( d ) .
Output: yes
Command: and { only { filter_eq { all_rows ; successor ; vacant } } ; eq { hop { filter_eq { all_rows ; successor ; vacant } ; vacator } ; ross bass ( d ) } }, interpretation: select the rows whose composer record fuzzily matches to alexander lunyov . there is only one such row in the table . the song record of this unqiue row is never let you go 2 .
Output: no
Command: and { only { filter_eq { all_rows ; composer ; alexander lunyov } } ; eq { hop { filter_eq { all_rows ; composer ; alexander lunyov } ; song } ; never let you go 2 } }, interpretation: select the rows whose composer record fuzzily matches to alexander lunyov . there is only one such row in the table . the song record of this unqiue row is never let you go 2 .
Output: yes
Command: and { only { filter_eq { all_rows ; composer ; alexander lunyov } } ; eq { hop { filter_eq { all_rows ; composer ; alexander lunyov } ; song } ; never let you go 2 } }, interpretation: select the rows whose country record fuzzily matches to australia . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; country ; australia } } ; 3 }, interpretation: select the rows whose country record fuzzily matches to australia . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; country ; australia } } ; 3 }, interpretation: for the director records of all rows , all of them fuzzily match to robert alvarez .
Output: no
Command: all_eq { all_rows ; director ; robert alvarez }, interpretation: for the director records of all rows , all of them fuzzily match to robert alvarez .
Output: yes
Command: all_eq { all_rows ; director ; robert alvarez }, interpretation: select the row whose total tackles record of all rows is maximum . the team record of this row is baltimore .
Output: no
Command: eq { hop { argmax { all_rows ; total tackles } ; team } ; baltimore }, interpretation: select the row whose total tackles record of all rows is maximum . the team record of this row is baltimore .
Output: yes
Command: eq { hop { argmax { all_rows ; total tackles } ; team } ; baltimore }, interpretation: select the row whose episodes record of all rows is minimum . the character record of this row is aiden burn csi detective .
Output: no
Command: eq { hop { argmin { all_rows ; episodes } ; character } ; aiden burn csi detective }, interpretation: select the row whose episodes record of all rows is minimum . the character record of this row is aiden burn csi detective .
Output: yes
Command: eq { hop { argmin { all_rows ; episodes } ; character } ; aiden burn csi detective }, interpretation: select the row whose frequency record of all rows is 1st maximum . the model number record of this row is athlon x2 5050e .
Output: no
Command: eq { hop { nth_argmax { all_rows ; frequency ; 1 } ; model number } ; athlon x2 5050e }, interpretation: select the row whose frequency record of all rows is 1st maximum . the model number record of this row is athlon x2 5050e .
Output: yes