niryuu/sni-each-converted huggingface dataset:
Command: round_eq { avg { all_rows ; frequency ( per hour ) } ; 3.11 }, interpretation: the maximum time record of all rows is 2:54 . the opponent record of the row with superlative time record is clay davidson .
Output: no
Command: and { eq { max { all_rows ; time } ; 2:54 } ; eq { hop { argmax { all_rows ; time } ; opponent } ; clay davidson } }, interpretation: the maximum time record of all rows is 2:54 . the opponent record of the row with superlative time record is clay davidson .
Output: yes
Command: and { eq { max { all_rows ; time } ; 2:54 } ; eq { hop { argmax { all_rows ; time } ; opponent } ; clay davidson } }, interpretation: for the crowd records of all rows , all of them are greater than or equal to 8000 .
Output: no
Command: all_greater_eq { all_rows ; crowd ; 8000 }, interpretation: for the crowd records of all rows , all of them are greater than or equal to 8000 .
Output: yes
Command: all_greater_eq { all_rows ; crowd ; 8000 }, interpretation: for the championship records of all rows , most of them fuzzily match to nabisco dinah shore .
Output: no
Command: most_eq { all_rows ; championship ; nabisco dinah shore }, interpretation: for the championship records of all rows , most of them fuzzily match to nabisco dinah shore .
Output: yes
Command: most_eq { all_rows ; championship ; nabisco dinah shore }, interpretation: select the rows whose candidates record fuzzily matches to unopposed . there is only one such row in the table . the incumbent record of this unqiue row is tony p hall .
Output: no
Command: and { only { filter_eq { all_rows ; candidates ; unopposed } } ; eq { hop { filter_eq { all_rows ; candidates ; unopposed } ; incumbent } ; tony p hall } }, interpretation: select the rows whose candidates record fuzzily matches to unopposed . there is only one such row in the table . the incumbent record of this unqiue row is tony p hall .
Output: yes
Command: and { only { filter_eq { all_rows ; candidates ; unopposed } } ; eq { hop { filter_eq { all_rows ; candidates ; unopposed } ; incumbent } ; tony p hall } }, interpretation: select the rows whose position record fuzzily matches to guard . the minimum round record of these rows is 11 .
Output: no
Command: eq { min { filter_eq { all_rows ; position ; guard } ; round } ; 11 }, interpretation: select the rows whose position record fuzzily matches to guard . the minimum round record of these rows is 11 .
Output: yes
Command: eq { min { filter_eq { all_rows ; position ; guard } ; round } ; 11 }, interpretation: select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 1 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 1 } } ; 2 }, interpretation: select the rows whose street address record fuzzily matches to 424 church street . take the floors record of this row . select the rows whose street address record fuzzily matches to 211 union street . take the floors record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; street address ; 424 church street } ; floors } ; hop { filter_eq { all_rows ; street address ; 211 union street } ; floors } }, interpretation: select the rows whose street address record fuzzily matches to 424 church street . take the floors record of this row . select the rows whose street address record fuzzily matches to 211 union street . take the floors record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; street address ; 424 church street } ; floors } ; hop { filter_eq { all_rows ; street address ; 211 union street } ; floors } }, interpretation: the average of the crowd record of all rows is 13841 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 13841 }, interpretation: the average of the crowd record of all rows is 13841 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 13841 }, interpretation: select the rows whose earpads record fuzzily matches to circumaural bowl . there is only one such row in the table . the headphone model record of this unqiue row is gs1000i .
Output: no
Command: and { only { filter_eq { all_rows ; earpads ; circumaural bowl } } ; eq { hop { filter_eq { all_rows ; earpads ; circumaural bowl } ; headphone model } ; gs1000i } }, interpretation: select the rows whose earpads record fuzzily matches to circumaural bowl . there is only one such row in the table . the headphone model record of this unqiue row is gs1000i .
Output: yes
Command: and { only { filter_eq { all_rows ; earpads ; circumaural bowl } } ; eq { hop { filter_eq { all_rows ; earpads ; circumaural bowl } ; headphone model } ; gs1000i } }, interpretation: the sum of the top - 25 record of all rows is 48 .
Output: no
Command: round_eq { sum { all_rows ; top - 25 } ; 48 }, interpretation: the sum of the top - 25 record of all rows is 48 .
Output: yes
Command: round_eq { sum { all_rows ; top - 25 } ; 48 }, interpretation: select the row whose against record of all rows is maximum . the opposing teams record of this row is new zealand .
Output: no
Command: eq { hop { argmax { all_rows ; against } ; opposing teams } ; new zealand }, interpretation: select the row whose against record of all rows is maximum . the opposing teams record of this row is new zealand .
Output: yes
Command: eq { hop { argmax { all_rows ; against } ; opposing teams } ; new zealand }, interpretation: select the row whose home team score record of all rows is 2nd minimum . the home team record of this row is north melbourne .
Output: no
Command: eq { hop { nth_argmin { all_rows ; home team score ; 2 } ; home team } ; north melbourne }, interpretation: select the row whose home team score record of all rows is 2nd minimum . the home team record of this row is north melbourne .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; home team score ; 2 } ; home team } ; north melbourne }, interpretation: for the release date records of all rows , all of them fuzzily match to september 14 , 2010 .
Output: no
Command: all_eq { all_rows ; release date ; september 14 , 2010 }, interpretation: for the release date records of all rows , all of them fuzzily match to september 14 , 2010 .
Output: yes
Command: all_eq { all_rows ; release date ; september 14 , 2010 }, interpretation: select the rows whose player record fuzzily matches to leron mitchell . take the pick record of this row . select the rows whose player record fuzzily matches to aaron wagner . take the pick record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; player ; leron mitchell } ; pick } ; hop { filter_eq { all_rows ; player ; aaron wagner } ; pick } }, interpretation: select the rows whose player record fuzzily matches to leron mitchell . take the pick record of this row . select the rows whose player record fuzzily matches to aaron wagner . take the pick record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; player ; leron mitchell } ; pick } ; hop { filter_eq { all_rows ; player ; aaron wagner } ; pick } }, interpretation: the sum of the laps record of all rows is 1,268 .
Output: no
Command: round_eq { sum { all_rows ; laps } ; 1,268 }, interpretation: the sum of the laps record of all rows is 1,268 .
Output: yes
Command: round_eq { sum { all_rows ; laps } ; 1,268 }, interpretation: for the high assists records of all rows , most of them fuzzily match to steve nash .
Output: no
Command: most_eq { all_rows ; high assists ; steve nash }, interpretation: for the high assists records of all rows , most of them fuzzily match to steve nash .
Output: yes
Command: most_eq { all_rows ; high assists ; steve nash }, interpretation: select the rows whose entrant record fuzzily matches to jolly club spa . there is only one such row in the table . the engine record of this unqiue row is motori moderni tipo 615 - 90 1.5 v6 t .
Output: no
Command: and { only { filter_eq { all_rows ; entrant ; jolly club spa } } ; eq { hop { filter_eq { all_rows ; entrant ; jolly club spa } ; engine } ; motori moderni tipo 615 - 90 1.5 v6 t } }, interpretation: select the rows whose entrant record fuzzily matches to jolly club spa . there is only one such row in the table . the engine record of this unqiue row is motori moderni tipo 615 - 90 1.5 v6 t .
Output: yes
Command: and { only { filter_eq { all_rows ; entrant ; jolly club spa } } ; eq { hop { filter_eq { all_rows ; entrant ; jolly club spa } ; engine } ; motori moderni tipo 615 - 90 1.5 v6 t } }, interpretation: for the surface records of all rows , most of them fuzzily match to clay .
Output: no
Command: most_eq { all_rows ; surface ; clay }, interpretation: select the row whose game record of all rows is minimum . the location attendance record of this row is pepsi center 14243 .
Output: no
Command: eq { hop { argmin { all_rows ; game } ; location attendance } ; pepsi center 14243 }, interpretation: select the row whose game record of all rows is minimum . the location attendance record of this row is pepsi center 14243 .
Output: yes
Command: eq { hop { argmin { all_rows ; game } ; location attendance } ; pepsi center 14243 }, interpretation: select the row whose average record of all rows is maximum . the professional partner record of this row is janja lesar .
Output: no
Command: eq { hop { argmax { all_rows ; average } ; professional partner } ; janja lesar }, interpretation: select the row whose average record of all rows is maximum . the professional partner record of this row is janja lesar .
Output: yes
Command: eq { hop { argmax { all_rows ; average } ; professional partner } ; janja lesar }, interpretation: select the rows whose expected year of completion record is equal to 2006 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; expected year of completion ; 2006 } } ; 3 }, interpretation: select the rows whose expected year of completion record is equal to 2006 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; expected year of completion ; 2006 } } ; 3 }, interpretation: select the rows whose first elected record is equal to 1944 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; first elected ; 1944 } } ; 2 }, interpretation: select the rows whose first elected record is equal to 1944 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; first elected ; 1944 } } ; 2 }, interpretation: select the rows whose railway record fuzzily matches to pdswjr . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; railway ; pdswjr } } ; 3 }, interpretation: select the rows whose railway record fuzzily matches to pdswjr . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; railway ; pdswjr } } ; 3 }, interpretation: select the rows whose nation record is arbitrary . the number of such rows is 6 .
Output: no
Command: eq { count { filter_all { all_rows ; nation } } ; 6 }, interpretation: select the rows whose nation record is arbitrary . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_all { all_rows ; nation } } ; 6 }, interpretation: select the rows whose bsu head coach record fuzzily matches to bus conner . take the year record of this row . select the rows whose bsu head coach record fuzzily matches to greg graham . take the year record of this row . the second record is 32 years larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; bsu head coach ; bus conner } ; year } ; hop { filter_eq { all_rows ; bsu head coach ; greg graham } ; year } } ; -32 years }, interpretation: select the rows whose bsu head coach record fuzzily matches to bus conner . take the year record of this row . select the rows whose bsu head coach record fuzzily matches to greg graham . take the year record of this row . the second record is 32 years larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; bsu head coach ; bus conner } ; year } ; hop { filter_eq { all_rows ; bsu head coach ; greg graham } ; year } } ; -32 years }, interpretation: for the high assists records of all rows , most of them fuzzily match to baron davis .
Output: no
Command: most_eq { all_rows ; high assists ; baron davis }, interpretation: for the high assists records of all rows , most of them fuzzily match to baron davis .
Output: yes
Command: most_eq { all_rows ; high assists ; baron davis }, interpretation: select the rows whose opponent in the final record fuzzily matches to christína papadáki . take the date record of this row . select the rows whose opponent in the final record fuzzily matches to julia vakulenko . 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 in the final ; christína papadáki } ; date } ; hop { filter_eq { all_rows ; opponent in the final ; julia vakulenko } ; date } }, interpretation: select the rows whose opponent in the final record fuzzily matches to christína papadáki . take the date record of this row . select the rows whose opponent in the final record fuzzily matches to julia vakulenko . 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 in the final ; christína papadáki } ; date } ; hop { filter_eq { all_rows ; opponent in the final ; julia vakulenko } ; date } }, interpretation: for the crowd records of all rows , most of them are less than 30000 .
Output: no
Command: most_less { all_rows ; crowd ; 30000 }, interpretation: for the crowd records of all rows , most of them are less than 30000 .
Output: yes
Command: most_less { all_rows ; crowd ; 30000 }, interpretation: select the rows whose meas num record is arbitrary . the number of such rows is 11 .
Output: no
Command: eq { count { filter_all { all_rows ; meas num } } ; 11 }, interpretation: select the rows whose meas num record is arbitrary . the number of such rows is 11 .
Output: yes
Command: eq { count { filter_all { all_rows ; meas num } } ; 11 }, interpretation: select the rows whose engine record fuzzily matches to ford cosworth dfr v8 . there is only one such row in the table . the year record of this unqiue row is 1989 .
Output: no
Command: and { only { filter_eq { all_rows ; engine ; ford cosworth dfr v8 } } ; eq { hop { filter_eq { all_rows ; engine ; ford cosworth dfr v8 } ; year } ; 1989 } }, interpretation: select the rows whose engine record fuzzily matches to ford cosworth dfr v8 . there is only one such row in the table . the year record of this unqiue row is 1989 .
Output: yes
Command: and { only { filter_eq { all_rows ; engine ; ford cosworth dfr v8 } } ; eq { hop { filter_eq { all_rows ; engine ; ford cosworth dfr v8 } ; year } ; 1989 } }, interpretation: select the rows whose team record fuzzily matches to south china . take the loss record of this row . select the rows whose team record fuzzily matches to xiangxue pharmaceutical . take the loss record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; team ; south china } ; loss } ; hop { filter_eq { all_rows ; team ; xiangxue pharmaceutical } ; loss } }, interpretation: select the rows whose team record fuzzily matches to south china . take the loss record of this row . select the rows whose team record fuzzily matches to xiangxue pharmaceutical . take the loss record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; team ; south china } ; loss } ; hop { filter_eq { all_rows ; team ; xiangxue pharmaceutical } ; loss } }, interpretation: select the rows whose manner of departure record fuzzily matches to resigned . there is only one such row in the table . the outgoing manager record of this unqiue row is thomas doll .
Output: no
Command: and { only { filter_eq { all_rows ; manner of departure ; resigned } } ; eq { hop { filter_eq { all_rows ; manner of departure ; resigned } ; outgoing manager } ; thomas doll } }, interpretation: select the rows whose manner of departure record fuzzily matches to resigned . there is only one such row in the table . the outgoing manager record of this unqiue row is thomas doll .
Output: yes
Command: and { only { filter_eq { all_rows ; manner of departure ; resigned } } ; eq { hop { filter_eq { all_rows ; manner of departure ; resigned } ; outgoing manager } ; thomas doll } }, interpretation: select the rows whose player record fuzzily matches to alain kashama . take the pick record of this row . select the rows whose player record fuzzily matches to amarpreet sanghera . take the pick record of this row . the first record is less than the second record . the pick record of the first row is 8 . the pick record of the second row is 9 .
Output: no
Command: and { less { hop { filter_eq { all_rows ; player ; alain kashama } ; pick } ; hop { filter_eq { all_rows ; player ; amarpreet sanghera } ; pick } } ; and { eq { hop { filter_eq { all_rows ; player ; alain kashama } ; pick } ; 8 } ; eq { hop { filter_eq { all_rows ; player ; amarpreet sanghera } ; pick } ; 9 } } }, interpretation: select the rows whose player record fuzzily matches to alain kashama . take the pick record of this row . select the rows whose player record fuzzily matches to amarpreet sanghera . take the pick record of this row . the first record is less than the second record . the pick record of the first row is 8 . the pick record of the second row is 9 .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; player ; alain kashama } ; pick } ; hop { filter_eq { all_rows ; player ; amarpreet sanghera } ; pick } } ; and { eq { hop { filter_eq { all_rows ; player ; alain kashama } ; pick } ; 8 } ; eq { hop { filter_eq { all_rows ; player ; amarpreet sanghera } ; pick } ; 9 } } }, interpretation: the average of the losses record of all rows is 13 .
Output: no
Command: round_eq { avg { all_rows ; losses } ; 13 }, interpretation: the average of the losses record of all rows is 13 .
Output: yes
Command: round_eq { avg { all_rows ; losses } ; 13 }, interpretation: select the rows whose state record fuzzily matches to illinois . select the row whose enrollment record of these rows is maximum . the founded record of this row is 1887 .
Output: no
Command: eq { hop { argmax { filter_eq { all_rows ; state ; illinois } ; enrollment } ; founded } ; 1887 }, interpretation: select the rows whose state record fuzzily matches to illinois . select the row whose enrollment record of these rows is maximum . the founded record of this row is 1887 .
Output: yes
Command: eq { hop { argmax { filter_eq { all_rows ; state ; illinois } ; enrollment } ; founded } ; 1887 }, interpretation: select the rows whose position record fuzzily matches to guard . among these rows , select the rows whose height in ft record fuzzily matches to 6 - 2 . there is only one such row in the table . the player record of this unqiue row is garland , winston winston garland .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; position ; guard } ; height in ft ; 6 - 2 } } ; eq { hop { filter_eq { filter_eq { all_rows ; position ; guard } ; height in ft ; 6 - 2 } ; player } ; garland , winston winston garland } }, interpretation: select the rows whose position record fuzzily matches to guard . among these rows , select the rows whose height in ft record fuzzily matches to 6 - 2 . there is only one such row in the table . the player record of this unqiue row is garland , winston winston garland .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; position ; guard } ; height in ft ; 6 - 2 } } ; eq { hop { filter_eq { filter_eq { all_rows ; position ; guard } ; height in ft ; 6 - 2 } ; player } ; garland , winston winston garland } }, interpretation: the average of the crowd record of all rows is 17724 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 17724 }, interpretation: the average of the crowd record of all rows is 17724 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 17724 }, interpretation: the average of the points record of all rows is 62 .
Output: no
Command: round_eq { avg { all_rows ; points } ; 62 }, interpretation: the average of the points record of all rows is 62 .
Output: yes
Command: round_eq { avg { all_rows ; points } ; 62 }, interpretation: select the rows whose district record fuzzily matches to hisar . the average of the number of electorates ( 2009 ) record of these rows is 132744 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; district ; hisar } ; number of electorates ( 2009 ) } ; 132744 }, interpretation: select the rows whose district record fuzzily matches to hisar . the average of the number of electorates ( 2009 ) record of these rows is 132744 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; district ; hisar } ; number of electorates ( 2009 ) } ; 132744 }, interpretation: select the rows whose institution record fuzzily matches to university of dayton . take the founded record of this row . select the rows whose institution record fuzzily matches to university of memphis . take the founded record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; institution ; university of dayton } ; founded } ; hop { filter_eq { all_rows ; institution ; university of memphis } ; founded } }, interpretation: select the rows whose institution record fuzzily matches to university of dayton . take the founded record of this row . select the rows whose institution record fuzzily matches to university of memphis . take the founded record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; institution ; university of dayton } ; founded } ; hop { filter_eq { all_rows ; institution ; university of memphis } ; founded } }, interpretation: select the rows whose player record fuzzily matches to ( a ) . there is only one such row in the table . the player record of this unqiue row is jim simons ( a ) .
Output: no
Command: and { only { filter_eq { all_rows ; player ; ( a ) } } ; eq { hop { filter_eq { all_rows ; player ; ( a ) } ; player } ; jim simons ( a ) } }, interpretation: select the rows whose player record fuzzily matches to ( a ) . there is only one such row in the table . the player record of this unqiue row is jim simons ( a ) .
Output: yes
Command: and { only { filter_eq { all_rows ; player ; ( a ) } } ; eq { hop { filter_eq { all_rows ; player ; ( a ) } ; player } ; jim simons ( a ) } }, interpretation: select the row whose rank record of all rows is maximum . the name record of this row is environmental sustainability index .
Output: no
Command: eq { hop { argmax { all_rows ; rank } ; name } ; environmental sustainability index }, interpretation: select the row whose rank record of all rows is maximum . the name record of this row is environmental sustainability index .
Output: yes
Command: eq { hop { argmax { all_rows ; rank } ; name } ; environmental sustainability index }, interpretation: select the rows whose name record fuzzily matches to margravine johanna of baden - baden . take the became duchess record of this row . select the rows whose name record fuzzily matches to victoria of saxe - coburg and gotha . take the became duchess record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; margravine johanna of baden - baden } ; became duchess } ; hop { filter_eq { all_rows ; name ; victoria of saxe - coburg and gotha } ; became duchess } }, interpretation: select the rows whose name record fuzzily matches to margravine johanna of baden - baden . take the became duchess record of this row . select the rows whose name record fuzzily matches to victoria of saxe - coburg and gotha . take the became duchess record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; margravine johanna of baden - baden } ; became duchess } ; hop { filter_eq { all_rows ; name ; victoria of saxe - coburg and gotha } ; became duchess } }, interpretation: select the rows whose athlete record fuzzily matches to federico muller . take the round of 16 record of this row . select the rows whose athlete record fuzzily matches to felipe saucedo . take the round of 16 record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; athlete ; federico muller } ; round of 16 } ; hop { filter_eq { all_rows ; athlete ; felipe saucedo } ; round of 16 } }, interpretation: select the rows whose athlete record fuzzily matches to federico muller . take the round of 16 record of this row . select the rows whose athlete record fuzzily matches to felipe saucedo . take the round of 16 record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; athlete ; federico muller } ; round of 16 } ; hop { filter_eq { all_rows ; athlete ; felipe saucedo } ; round of 16 } }, interpretation: select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 66 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 66 }, interpretation: select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 66 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 66 }, 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 team 1 record fuzzily matches to energy invest rustavi . take the agg record of this row . select the rows whose team 1 record fuzzily matches to spartak pleven . 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 1 ; energy invest rustavi } ; agg } ; hop { filter_eq { all_rows ; team 1 ; spartak pleven } ; agg } }, interpretation: select the rows whose team 1 record fuzzily matches to energy invest rustavi . take the agg record of this row . select the rows whose team 1 record fuzzily matches to spartak pleven . 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 1 ; energy invest rustavi } ; agg } ; hop { filter_eq { all_rows ; team 1 ; spartak pleven } ; agg } }, 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: the average of the age record of all rows is 30 .
Output: no
Command: round_eq { avg { all_rows ; age } ; 30 }, interpretation: the average of the age record of all rows is 30 .
Output: yes
Command: round_eq { avg { all_rows ; age } ; 30 }, interpretation: select the row whose population record of all rows is 1st maximum . the official name record of this row is chipman .
Output: no
Command: eq { hop { nth_argmax { all_rows ; population ; 1 } ; official name } ; chipman }, interpretation: select the row whose population record of all rows is 1st maximum . the official name record of this row is chipman .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; population ; 1 } ; official name } ; chipman }, interpretation: select the row whose commissioned record of all rows is maximum . the name record of this row is chivalrous .
Output: no
Command: eq { hop { argmax { all_rows ; commissioned } ; name } ; chivalrous }, interpretation: select the row whose commissioned record of all rows is maximum . the name record of this row is chivalrous .
Output: yes
Command: eq { hop { argmax { all_rows ; commissioned } ; name } ; chivalrous }, interpretation: select the rows whose final position / round record fuzzily matches to runner - up . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; final position / round ; runner - up } } ; 4 }, interpretation: select the rows whose final position / round record fuzzily matches to runner - up . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; final position / round ; runner - up } } ; 4 }, interpretation: for the series records of all rows , most of them fuzzily match to formula 3 championship .
Output: no
Command: most_eq { all_rows ; series ; formula 3 championship }, interpretation: for the series records of all rows , most of them fuzzily match to formula 3 championship .
Output: yes
Command: most_eq { all_rows ; series ; formula 3 championship }, interpretation: select the rows whose erp / power w record is greater than 5000 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { all_rows ; erp / power w ; 5000 } } ; 3 }, interpretation: select the rows whose erp / power w record is greater than 5000 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { all_rows ; erp / power w ; 5000 } } ; 3 }, interpretation: select the rows whose rank record fuzzily matches to 1 . take the bronze record of this row . select the rows whose rank record fuzzily matches to 4 . take the bronze record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; rank ; 1 } ; bronze } ; hop { filter_eq { all_rows ; rank ; 4 } ; bronze } }, interpretation: select the rows whose rank record fuzzily matches to 1 . take the bronze record of this row . select the rows whose rank record fuzzily matches to 4 . take the bronze record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; rank ; 1 } ; bronze } ; hop { filter_eq { all_rows ; rank ; 4 } ; bronze } }, interpretation: select the row whose enrollment record of all rows is maximum . the institution record of this row is rochester institute of technology .
Output: no
Command: eq { hop { argmax { all_rows ; enrollment } ; institution } ; rochester institute of technology }, interpretation: select the row whose enrollment record of all rows is maximum . the institution record of this row is rochester institute of technology .
Output: yes
Command: eq { hop { argmax { all_rows ; enrollment } ; institution } ; rochester institute of technology }, interpretation: select the rows whose builder record fuzzily matches to rh & dr . the maximum year built record of these rows is c1949 .
Output: no
Command: eq { max { filter_eq { all_rows ; builder ; rh & dr } ; year built } ; c1949 }, interpretation: select the rows whose builder record fuzzily matches to rh & dr . the maximum year built record of these rows is c1949 .
Output: yes
Command: eq { max { filter_eq { all_rows ; builder ; rh & dr } ; year built } ; c1949 }, interpretation: select the rows whose yards record is greater than 1400 . there is only one such row in the table . the player record of this unqiue row is derrick lewis .
Output: no
Command: and { only { filter_greater { all_rows ; yards ; 1400 } } ; eq { hop { filter_greater { all_rows ; yards ; 1400 } ; player } ; derrick lewis } }, interpretation: select the rows whose yards record is greater than 1400 . there is only one such row in the table . the player record of this unqiue row is derrick lewis .
Output: yes
Command: and { only { filter_greater { all_rows ; yards ; 1400 } } ; eq { hop { filter_greater { all_rows ; yards ; 1400 } ; player } ; derrick lewis } }, interpretation: select the rows whose method record fuzzily matches to draw . there is only one such row in the table . the opponent record of this unqiue row is yoko takahashi .
Output: no
Command: and { only { filter_eq { all_rows ; method ; draw } } ; eq { hop { filter_eq { all_rows ; method ; draw } ; opponent } ; yoko takahashi } }, interpretation: select the rows whose method record fuzzily matches to draw . there is only one such row in the table . the opponent record of this unqiue row is yoko takahashi .
Output: yes
Command: and { only { filter_eq { all_rows ; method ; draw } } ; eq { hop { filter_eq { all_rows ; method ; draw } ; opponent } ; yoko takahashi } }, interpretation: for the outcome records of all rows , most of them fuzzily match to runner-up .
Output: no
Command: most_eq { all_rows ; outcome ; runner-up }, interpretation: for the outcome records of all rows , most of them fuzzily match to runner-up .
Output: yes
Command: most_eq { all_rows ; outcome ; runner-up }, interpretation: select the rows whose visitor record fuzzily matches to suns . take the date record of this row . select the rows whose visitor record fuzzily matches to timberwolves . 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 ; visitor ; suns } ; date } ; hop { filter_eq { all_rows ; visitor ; timberwolves } ; date } }, interpretation: select the rows whose visitor record fuzzily matches to suns . take the date record of this row . select the rows whose visitor record fuzzily matches to timberwolves . 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 ; visitor ; suns } ; date } ; hop { filter_eq { all_rows ; visitor ; timberwolves } ; date } }, interpretation: select the rows whose election record fuzzily matches to 1988 . take the of total votes record of this row . select the rows whose election record fuzzily matches to 1993 . take the of total votes record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; election ; 1988 } ; of total votes } ; hop { filter_eq { all_rows ; election ; 1993 } ; of total votes } }, interpretation: select the rows whose election record fuzzily matches to 1988 . take the of total votes record of this row . select the rows whose election record fuzzily matches to 1993 . take the of total votes record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; election ; 1988 } ; of total votes } ; hop { filter_eq { all_rows ; election ; 1993 } ; of total votes } }, interpretation: the average of the points record of all rows is 0 .
Output: no
Command: round_eq { avg { all_rows ; points } ; 0 }, interpretation: the average of the points record of all rows is 0 .
Output: yes
Command: round_eq { avg { all_rows ; points } ; 0 }, interpretation: for the fate records of all rows , most of them fuzzily match to sunk .
Output: no
Command: most_eq { all_rows ; fate ; sunk }, interpretation: for the fate records of all rows , most of them fuzzily match to sunk .
Output: yes
Command: most_eq { all_rows ; fate ; sunk }, interpretation: select the rows whose losses record is equal to 13 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; losses ; 13 } } ; 4 }, interpretation: select the rows whose losses record is equal to 13 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; losses ; 13 } } ; 4 }, interpretation: select the rows whose team record fuzzily matches to rhein fire . the average of the capacity record of these rows is 56308 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; team ; rhein fire } ; capacity } ; 56308 }, interpretation: select the rows whose team record fuzzily matches to rhein fire . the average of the capacity record of these rows is 56308 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; team ; rhein fire } ; capacity } ; 56308 }, interpretation: select the rows whose location attendance record fuzzily matches to oracle arena . there is only one such row in the table . the date record of this unqiue row is november 18 .
Output: no
Command: and { only { filter_eq { all_rows ; location attendance ; oracle arena } } ; eq { hop { filter_eq { all_rows ; location attendance ; oracle arena } ; date } ; november 18 } }, interpretation: select the rows whose location attendance record fuzzily matches to oracle arena . there is only one such row in the table . the date record of this unqiue row is november 18 .
Output: yes
Command: and { only { filter_eq { all_rows ; location attendance ; oracle arena } } ; eq { hop { filter_eq { all_rows ; location attendance ; oracle arena } ; date } ; november 18 } }, interpretation: select the rows whose winner record fuzzily matches to edd kimber . take the premiere record of this row . select the rows whose winner record fuzzily matches to joanne wheatley . take the premiere record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; winner ; edd kimber } ; premiere } ; hop { filter_eq { all_rows ; winner ; joanne wheatley } ; premiere } }, interpretation: select the rows whose winner record fuzzily matches to edd kimber . take the premiere record of this row . select the rows whose winner record fuzzily matches to joanne wheatley . take the premiere record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; winner ; edd kimber } ; premiere } ; hop { filter_eq { all_rows ; winner ; joanne wheatley } ; premiere } }, interpretation: select the row whose winter olympics record of all rows is 2nd maximum . the winner record of this row is thomas wassberg .
Output: no
Command: eq { hop { nth_argmax { all_rows ; winter olympics ; 2 } ; winner } ; thomas wassberg }, interpretation: select the row whose winter olympics record of all rows is 2nd maximum . the winner record of this row is thomas wassberg .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; winter olympics ; 2 } ; winner } ; thomas wassberg }, interpretation: select the rows whose season record fuzzily matches to 1978 - 79 . take the goals for record of this row . select the rows whose season record fuzzily matches to 1975 - 76 . take the goals for record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; season ; 1978 - 79 } ; goals for } ; hop { filter_eq { all_rows ; season ; 1975 - 76 } ; goals for } }, interpretation: select the rows whose season record fuzzily matches to 1978 - 79 . take the goals for record of this row . select the rows whose season record fuzzily matches to 1975 - 76 . take the goals for record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; season ; 1978 - 79 } ; goals for } ; hop { filter_eq { all_rows ; season ; 1975 - 76 } ; goals for } }, interpretation: select the row whose pick record of all rows is 3rd minimum . the player record of this row is dale hackbart .
Output: no
Command: eq { hop { nth_argmin { all_rows ; pick ; 3 } ; player } ; dale hackbart }, interpretation: select the row whose pick record of all rows is 3rd minimum . the player record of this row is dale hackbart .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; pick ; 3 } ; player } ; dale hackbart }, interpretation: select the row whose founding date record of all rows is 2nd minimum . the organization record of this row is delta epsilon sigma iota .
Output: no
Command: eq { hop { nth_argmin { all_rows ; founding date ; 2 } ; organization } ; delta epsilon sigma iota }, interpretation: select the row whose founding date record of all rows is 2nd minimum . the organization record of this row is delta epsilon sigma iota .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; founding date ; 2 } ; organization } ; delta epsilon sigma iota }, interpretation: the average of the 2008 record of all rows is 8133819 .
Output: no
Command: round_eq { avg { all_rows ; 2008 } ; 8133819 }, interpretation: the average of the 2008 record of all rows is 8133819 .
Output: yes
Command: round_eq { avg { all_rows ; 2008 } ; 8133819 }, interpretation: for the enrollment ( 2013 / 14 ) records of all rows , most of them are greater than 400 .
Output: no
Command: most_greater { all_rows ; enrollment ( 2013 / 14 ) ; 400 }, interpretation: for the enrollment ( 2013 / 14 ) records of all rows , most of them are greater than 400 .
Output: yes
Command: most_greater { all_rows ; enrollment ( 2013 / 14 ) ; 400 }, interpretation: the average of the length record of all rows is 3:43 .
Output: no
Command: round_eq { avg { all_rows ; length } ; 3:43 }, interpretation: the average of the length record of all rows is 3:43 .
Output: yes
Command: round_eq { avg { all_rows ; length } ; 3:43 }, interpretation: the sum of the enrollment record of all rows is 44185 .
Output: no
Command: round_eq { sum { all_rows ; enrollment } ; 44185 }, interpretation: the sum of the enrollment record of all rows is 44185 .
Output: yes
Command: round_eq { sum { all_rows ; enrollment } ; 44185 }, interpretation: select the rows whose outcome record fuzzily matches to winner . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; outcome ; winner } } ; 3 }, interpretation: select the rows whose outcome record fuzzily matches to winner . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; outcome ; winner } } ; 3 }, interpretation: select the rows whose method record fuzzily matches to ko ( punch ) . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; method ; ko ( punch ) } }, interpretation: select the rows whose method record fuzzily matches to ko ( punch ) . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; method ; ko ( punch ) } }, interpretation: select the rows whose nation record fuzzily matches to kenya . among these rows , select the rows whose time record is less than 30:34 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_less { filter_eq { all_rows ; nation ; kenya } ; time ; 30:34 } } ; 2 }, interpretation: select the rows whose nation record fuzzily matches to kenya . among these rows , select the rows whose time record is less than 30:34 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_less { filter_eq { all_rows ; nation ; kenya } ; time ; 30:34 } } ; 2 }, interpretation: select the row whose quantity made record of all rows is maximum . the class record of this row is 2 .
Output: no
Command: eq { hop { argmax { all_rows ; quantity made } ; class } ; 2 }, interpretation: select the row whose quantity made record of all rows is maximum . the class record of this row is 2 .
Output: yes
Command: eq { hop { argmax { all_rows ; quantity made } ; class } ; 2 }, interpretation: select the row whose pick record of all rows is 1st minimum . the nationality record of this row is canada .
Output: no
Command: eq { hop { nth_argmin { all_rows ; pick ; 1 } ; nationality } ; canada }, interpretation: select the row whose pick record of all rows is 1st minimum . the nationality record of this row is canada .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; pick ; 1 } ; nationality } ; canada }, interpretation: select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to w .
Output: no
Command: most_eq { filter_eq { all_rows ; date ; october } ; result ; w }, interpretation: select the row whose sets lost record of all rows is 2nd maximum . the team record of this row is far eastern university .
Output: no
Command: eq { hop { nth_argmax { all_rows ; sets lost ; 2 } ; team } ; far eastern university }, interpretation: select the row whose sets lost record of all rows is 2nd maximum . the team record of this row is far eastern university .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; sets lost ; 2 } ; team } ; far eastern university }, interpretation: select the rows whose location attendance record fuzzily matches to staples center . for the high assists records of these rows , all of them fuzzily match to baron davis .
Output: no
Command: all_eq { filter_eq { all_rows ; location attendance ; staples center } ; high assists ; baron davis }, interpretation: select the rows whose location attendance record fuzzily matches to staples center . for the high assists records of these rows , all of them fuzzily match to baron davis .
Output: yes
Command: all_eq { filter_eq { all_rows ; location attendance ; staples center } ; high assists ; baron davis }, interpretation: select the row whose tries for record of all rows is maximum . the club record of this row is bridgend ravens .
Output: no
Command: eq { hop { argmax { all_rows ; tries for } ; club } ; bridgend ravens }, interpretation: select the row whose tries for record of all rows is maximum . the club record of this row is bridgend ravens .
Output: yes
Command: eq { hop { argmax { all_rows ; tries for } ; club } ; bridgend ravens }, interpretation: select the rows whose status record fuzzily matches to re - elected . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; status ; re - elected } } ; 3 }, interpretation: select the rows whose status record fuzzily matches to re - elected . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; status ; re - elected } } ; 3 }, interpretation: select the rows whose name record fuzzily matches to santa maria da feira . take the area ( km square ) record of this row . select the rows whose name record fuzzily matches to vale de cambra . take the area ( km square ) record of this row . the first record is greater than the second record . the area ( km square ) record of the first row is 215.1 . the area ( km square ) record of the second row is 146.5 .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; name ; santa maria da feira } ; area ( km square ) } ; hop { filter_eq { all_rows ; name ; vale de cambra } ; area ( km square ) } } ; and { eq { hop { filter_eq { all_rows ; name ; santa maria da feira } ; area ( km square ) } ; 215.1 } ; eq { hop { filter_eq { all_rows ; name ; vale de cambra } ; area ( km square ) } ; 146.5 } } }, interpretation: select the rows whose name record fuzzily matches to santa maria da feira . take the area ( km square ) record of this row . select the rows whose name record fuzzily matches to vale de cambra . take the area ( km square ) record of this row . the first record is greater than the second record . the area ( km square ) record of the first row is 215.1 . the area ( km square ) record of the second row is 146.5 .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; name ; santa maria da feira } ; area ( km square ) } ; hop { filter_eq { all_rows ; name ; vale de cambra } ; area ( km square ) } } ; and { eq { hop { filter_eq { all_rows ; name ; santa maria da feira } ; area ( km square ) } ; 215.1 } ; eq { hop { filter_eq { all_rows ; name ; vale de cambra } ; area ( km square ) } ; 146.5 } } }, interpretation: select the rows whose date record fuzzily matches to january 2013 . the sum of the result record of these rows is 2 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; date ; january 2013 } ; result } ; 2 }, interpretation: select the rows whose date record fuzzily matches to january 2013 . the sum of the result record of these rows is 2 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; date ; january 2013 } ; result } ; 2 }, interpretation: select the rows whose home team record fuzzily matches to fitzroy . take the crowd record of this row . select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . the first record is greater than the second record . the away team record of the first row is footscray . the away team record of the second row is st kilda .
Output: no