niryuu/sni-each-converted huggingface dataset:
Command: round_eq { avg { all_rows ; crowd } ; 22250 }, interpretation: select the rows whose team ( s ) record fuzzily matches to dingman brothers racing . there is only one such row in the table . the year record of this unqiue row is 1987 .
Output: no
Command: and { only { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } } ; eq { hop { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } ; year } ; 1987 } }, interpretation: select the rows whose team ( s ) record fuzzily matches to dingman brothers racing . there is only one such row in the table . the year record of this unqiue row is 1987 .
Output: yes
Command: and { only { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } } ; eq { hop { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } ; year } ; 1987 } }, interpretation: select the row whose first elected record of all rows is minimum . the incumbent record of this row is mike doyle .
Output: no
Command: eq { hop { argmin { all_rows ; first elected } ; incumbent } ; mike doyle }, interpretation: select the row whose first elected record of all rows is minimum . the incumbent record of this row is mike doyle .
Output: yes
Command: eq { hop { argmin { all_rows ; first elected } ; incumbent } ; mike doyle }, interpretation: for the nationality records of all rows , most of them fuzzily match to canada .
Output: no
Command: most_eq { all_rows ; nationality ; canada }, interpretation: for the date records of all rows , all of them fuzzily match to 10 may 1969 .
Output: no
Command: all_eq { all_rows ; date ; 10 may 1969 }, interpretation: for the date records of all rows , all of them fuzzily match to 10 may 1969 .
Output: yes
Command: all_eq { all_rows ; date ; 10 may 1969 }, interpretation: select the rows whose country of origin record fuzzily matches to united states . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; country of origin ; united states } } ; 2 }, interpretation: select the rows whose country of origin record fuzzily matches to united states . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; country of origin ; united states } } ; 2 }, interpretation: select the row whose goals against record of all rows is maximum . the team record of this row is montreal crystals .
Output: no
Command: eq { hop { argmax { all_rows ; goals against } ; team } ; montreal crystals }, interpretation: select the row whose goals against record of all rows is maximum . the team record of this row is montreal crystals .
Output: yes
Command: eq { hop { argmax { all_rows ; goals against } ; team } ; montreal crystals }, interpretation: select the rows whose president record fuzzily matches to joão havelange . take the took office record of this row . select the rows whose president record fuzzily matches to sepp blatter . take the took office record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; president ; joão havelange } ; took office } ; hop { filter_eq { all_rows ; president ; sepp blatter } ; took office } }, interpretation: select the rows whose president record fuzzily matches to joão havelange . take the took office record of this row . select the rows whose president record fuzzily matches to sepp blatter . take the took office record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; president ; joão havelange } ; took office } ; hop { filter_eq { all_rows ; president ; sepp blatter } ; took office } }, interpretation: select the rows whose 9:00 record fuzzily matches to the office . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; 9:00 ; the office } } ; 2 }, interpretation: select the rows whose 9:00 record fuzzily matches to the office . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; 9:00 ; the office } } ; 2 }, interpretation: select the rows whose enrollment record is greater than 3000 . the average of the varsity sports record of these rows is 10.4 .
Output: no
Command: round_eq { avg { filter_greater { all_rows ; enrollment ; 3000 } ; varsity sports } ; 10.4 }, interpretation: select the rows whose enrollment record is greater than 3000 . the average of the varsity sports record of these rows is 10.4 .
Output: yes
Command: round_eq { avg { filter_greater { all_rows ; enrollment ; 3000 } ; varsity sports } ; 10.4 }, interpretation: select the rows whose nominee record fuzzily matches to anthony hopkins . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; nominee ; anthony hopkins } } ; 2 }, interpretation: select the rows whose nominee record fuzzily matches to anthony hopkins . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; nominee ; anthony hopkins } } ; 2 }, interpretation: select the rows whose result record fuzzily matches to re - elected . the number of such rows is 9 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; re - elected } } ; 9 }, interpretation: select the rows whose result record fuzzily matches to re - elected . the number of such rows is 9 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; re - elected } } ; 9 }, interpretation: select the rows whose club ( city / town ) record fuzzily matches to hankook verdes united ( san ignacio ) . take the goals for / against record of this row . select the rows whose club ( city / town ) record fuzzily matches to fc belize ( belize city ) . take the goals for / against record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; club ( city / town ) ; hankook verdes united ( san ignacio ) } ; goals for / against } ; hop { filter_eq { all_rows ; club ( city / town ) ; fc belize ( belize city ) } ; goals for / against } }, interpretation: select the rows whose club ( city / town ) record fuzzily matches to hankook verdes united ( san ignacio ) . take the goals for / against record of this row . select the rows whose club ( city / town ) record fuzzily matches to fc belize ( belize city ) . take the goals for / against record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; club ( city / town ) ; hankook verdes united ( san ignacio ) } ; goals for / against } ; hop { filter_eq { all_rows ; club ( city / town ) ; fc belize ( belize city ) } ; goals for / against } }, interpretation: select the rows whose competition record fuzzily matches to friendly . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; competition ; friendly } } ; 4 }, interpretation: select the row whose year record of all rows is 3rd maximum . the version record of this row is album version .
Output: no
Command: eq { hop { nth_argmax { all_rows ; year ; 3 } ; version } ; album version }, interpretation: select the row whose year record of all rows is 3rd maximum . the version record of this row is album version .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; year ; 3 } ; version } ; album version }, interpretation: select the row whose year record of all rows is 2nd minimum . the driver record of this row is tony stewart . the average speed ( mph ) record of this row is 134.891 .
Output: no
Command: and { eq { hop { nth_argmin { all_rows ; year ; 2 } ; driver } ; tony stewart } ; eq { hop { nth_argmin { all_rows ; year ; 2 } ; average speed ( mph ) } ; 134.891 } }, interpretation: select the row whose year record of all rows is 2nd minimum . the driver record of this row is tony stewart . the average speed ( mph ) record of this row is 134.891 .
Output: yes
Command: and { eq { hop { nth_argmin { all_rows ; year ; 2 } ; driver } ; tony stewart } ; eq { hop { nth_argmin { all_rows ; year ; 2 } ; average speed ( mph ) } ; 134.891 } }, interpretation: select the rows whose total record is equal to 5 . there is only one such row in the table . the country record of this unqiue row is russia .
Output: no
Command: and { only { filter_eq { all_rows ; total ; 5 } } ; eq { hop { filter_eq { all_rows ; total ; 5 } ; country } ; russia } }, interpretation: select the rows whose total record is equal to 5 . there is only one such row in the table . the country record of this unqiue row is russia .
Output: yes
Command: and { only { filter_eq { all_rows ; total ; 5 } } ; eq { hop { filter_eq { all_rows ; total ; 5 } ; country } ; russia } }, interpretation: select the rows whose total record is greater than or equal to 10 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater_eq { all_rows ; total ; 10 } } ; 3 }, interpretation: select the rows whose total record is greater than or equal to 10 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater_eq { all_rows ; total ; 10 } } ; 3 }, interpretation: select the rows whose venue record is arbitrary . the number of such rows is 6 .
Output: no
Command: eq { count { filter_all { all_rows ; venue } } ; 6 }, interpretation: for the nationality records of all rows , most of them fuzzily match to united states .
Output: no
Command: most_eq { all_rows ; nationality ; united states }, interpretation: for the nationality records of all rows , most of them fuzzily match to united states .
Output: yes
Command: most_eq { all_rows ; nationality ; united states }, interpretation: select the rows whose aircraft record fuzzily matches to boeing . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; aircraft ; boeing } } ; 3 }, interpretation: select the rows whose aircraft record fuzzily matches to boeing . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; aircraft ; boeing } } ; 3 }, interpretation: the average of the score record of all rows is 210.6 .
Output: no
Command: round_eq { avg { all_rows ; score } ; 210.6 }, interpretation: the average of the score record of all rows is 210.6 .
Output: yes
Command: round_eq { avg { all_rows ; score } ; 210.6 }, interpretation: select the row whose goals scored record of all rows is 2nd maximum . the team record of this row is san salvador fc .
Output: no
Command: eq { hop { nth_argmax { all_rows ; goals scored ; 2 } ; team } ; san salvador fc }, interpretation: select the row whose goals scored record of all rows is 2nd maximum . the team record of this row is san salvador fc .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; goals scored ; 2 } ; team } ; san salvador fc }, interpretation: select the row whose winnings record of all rows is maximum . the year record of this row is 1998 .
Output: no
Command: eq { hop { argmax { all_rows ; winnings } ; year } ; 1998 }, interpretation: select the row whose winnings record of all rows is maximum . the year record of this row is 1998 .
Output: yes
Command: eq { hop { argmax { all_rows ; winnings } ; year } ; 1998 }, interpretation: select the rows whose tournament record fuzzily matches to switzerland . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; tournament ; switzerland } } ; 3 }, interpretation: select the rows whose tournament record fuzzily matches to switzerland . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; tournament ; switzerland } } ; 3 }, interpretation: select the rows whose service record fuzzily matches to marine corps . among these rows , select the rows whose rank record fuzzily matches to private . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; service ; marine corps } ; rank ; private } } ; 5 }, interpretation: select the rows whose service record fuzzily matches to marine corps . among these rows , select the rows whose rank record fuzzily matches to private . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; service ; marine corps } ; rank ; private } } ; 5 }, interpretation: select the rows whose first elected record fuzzily matches to 196 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; first elected ; 196 } } ; 2 }, interpretation: select the rows whose first elected record fuzzily matches to 196 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; first elected ; 196 } } ; 2 }, interpretation: select the rows whose first premiere record fuzzily matches to 2009 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; first premiere ; 2009 } } ; 3 }, interpretation: select the rows whose first premiere record fuzzily matches to 2009 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; first premiere ; 2009 } } ; 3 }, interpretation: the sum of the crowd record of all rows is 123128 .
Output: no
Command: round_eq { sum { all_rows ; crowd } ; 123128 }, interpretation: the sum of the crowd record of all rows is 123128 .
Output: yes
Command: round_eq { sum { all_rows ; crowd } ; 123128 }, interpretation: select the rows whose category record fuzzily matches to best supporting actress . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; category ; best supporting actress } }, interpretation: select the rows whose category record fuzzily matches to best supporting actress . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; category ; best supporting actress } }, interpretation: select the row whose earnings record of all rows is maximum . the player record of this row is tiger woods .
Output: no
Command: eq { hop { argmax { all_rows ; earnings } ; player } ; tiger woods }, interpretation: select the row whose earnings record of all rows is maximum . the player record of this row is tiger woods .
Output: yes
Command: eq { hop { argmax { all_rows ; earnings } ; player } ; tiger woods }, interpretation: select the row whose capacity record of all rows is maximum . the club record of this row is standard liège .
Output: no
Command: eq { hop { argmax { all_rows ; capacity } ; club } ; standard liège }, interpretation: select the row whose capacity record of all rows is maximum . the club record of this row is standard liège .
Output: yes
Command: eq { hop { argmax { all_rows ; capacity } ; club } ; standard liège }, interpretation: the sum of the laps record of all rows is 3670 .
Output: no
Command: round_eq { sum { all_rows ; laps } ; 3670 }, interpretation: the sum of the laps record of all rows is 3670 .
Output: yes
Command: round_eq { sum { all_rows ; laps } ; 3670 }, interpretation: for the margin of victory records of all rows , most of them fuzzily match to 1 stroke .
Output: no
Command: most_eq { all_rows ; margin of victory ; 1 stroke }, interpretation: for the margin of victory records of all rows , most of them fuzzily match to 1 stroke .
Output: yes
Command: most_eq { all_rows ; margin of victory ; 1 stroke }, interpretation: select the row whose no record of all rows is 2nd minimum . the player record of this row is rick roberson .
Output: no
Command: eq { hop { nth_argmin { all_rows ; no ; 2 } ; player } ; rick roberson }, interpretation: select the row whose no record of all rows is 2nd minimum . the player record of this row is rick roberson .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; no ; 2 } ; player } ; rick roberson }, interpretation: select the rows whose venue record fuzzily matches to glasgow . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; venue ; glasgow } } ; 6 }, interpretation: select the rows whose venue record fuzzily matches to glasgow . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; venue ; glasgow } } ; 6 }, interpretation: select the rows whose top - 5 record is equal to 1 . there is only one such row in the table . the tournament record of this unqiue row is the open championship .
Output: no
Command: and { only { filter_eq { all_rows ; top - 5 ; 1 } } ; eq { hop { filter_eq { all_rows ; top - 5 ; 1 } ; tournament } ; the open championship } }, interpretation: select the rows whose top - 5 record is equal to 1 . there is only one such row in the table . the tournament record of this unqiue row is the open championship .
Output: yes
Command: and { only { filter_eq { all_rows ; top - 5 ; 1 } } ; eq { hop { filter_eq { all_rows ; top - 5 ; 1 } ; tournament } ; the open championship } }, interpretation: the sum of the points record of all rows is 14 .
Output: no
Command: round_eq { sum { all_rows ; points } ; 14 }, interpretation: the sum of the points record of all rows is 14 .
Output: yes
Command: round_eq { sum { all_rows ; points } ; 14 }, interpretation: select the rows whose programming record fuzzily matches to main wybe programming . take the channel record of this row . select the rows whose programming record fuzzily matches to russia today . take the channel record of this row . the second record is 0.3 larger than the first record . the channel record of the first row is 35.1 . the channel record of the second row is 35.4 .
Output: no
Command: and { eq { diff { hop { filter_eq { all_rows ; programming ; main wybe programming } ; channel } ; hop { filter_eq { all_rows ; programming ; russia today } ; channel } } ; -0.3 } ; and { eq { hop { filter_eq { all_rows ; programming ; main wybe programming } ; channel } ; 35.1 } ; eq { hop { filter_eq { all_rows ; programming ; russia today } ; channel } ; 35.4 } } }, interpretation: select the rows whose programming record fuzzily matches to main wybe programming . take the channel record of this row . select the rows whose programming record fuzzily matches to russia today . take the channel record of this row . the second record is 0.3 larger than the first record . the channel record of the first row is 35.1 . the channel record of the second row is 35.4 .
Output: yes
Command: and { eq { diff { hop { filter_eq { all_rows ; programming ; main wybe programming } ; channel } ; hop { filter_eq { all_rows ; programming ; russia today } ; channel } } ; -0.3 } ; and { eq { hop { filter_eq { all_rows ; programming ; main wybe programming } ; channel } ; 35.1 } ; eq { hop { filter_eq { all_rows ; programming ; russia today } ; channel } ; 35.4 } } }, interpretation: select the row whose swansea win record of all rows is maximum . the competition record of this row is league .
Output: no
Command: eq { hop { argmax { all_rows ; swansea win } ; competition } ; league }, interpretation: select the row whose swansea win record of all rows is maximum . the competition record of this row is league .
Output: yes
Command: eq { hop { argmax { all_rows ; swansea win } ; competition } ; league }, interpretation: for the crowd records of all rows , most of them are greater than 10000 .
Output: no
Command: most_greater { all_rows ; crowd ; 10000 }, interpretation: select the rows whose game site record fuzzily matches to shea stadium . the number of such rows is 8 .
Output: no
Command: eq { count { filter_eq { all_rows ; game site ; shea stadium } } ; 8 }, interpretation: select the rows whose game site record fuzzily matches to shea stadium . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_eq { all_rows ; game site ; shea stadium } } ; 8 }, interpretation: select the row whose pick record of all rows is minimum . the pick record of this row is 5 .
Output: no
Command: eq { hop { argmin { all_rows ; pick } ; pick } ; 5 }, interpretation: select the row whose pick record of all rows is minimum . the pick record of this row is 5 .
Output: yes
Command: eq { hop { argmin { all_rows ; pick } ; pick } ; 5 }, interpretation: for the producer ( s ) records of all rows , most of them fuzzily match to mike e clark .
Output: no
Command: most_eq { all_rows ; producer ( s ) ; mike e clark }, interpretation: for the producer ( s ) records of all rows , most of them fuzzily match to mike e clark .
Output: yes
Command: most_eq { all_rows ; producer ( s ) ; mike e clark }, interpretation: select the row whose gold record of all rows is maximum . the nation record of this row is soviet union .
Output: no
Command: eq { hop { argmax { all_rows ; gold } ; nation } ; soviet union }, interpretation: select the row whose gold record of all rows is maximum . the nation record of this row is soviet union .
Output: yes
Command: eq { hop { argmax { all_rows ; gold } ; nation } ; soviet union }, interpretation: the average of the crowd record of all rows is 19900 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 19900 }, interpretation: the average of the crowd record of all rows is 19900 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 19900 }, interpretation: for the position records of all rows , most of them fuzzily match to 1st .
Output: no
Command: most_eq { all_rows ; position ; 1st }, interpretation: for the position records of all rows , most of them fuzzily match to 1st .
Output: yes
Command: most_eq { all_rows ; position ; 1st }, interpretation: select the rows whose position record fuzzily matches to fl . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; position ; fl } }, interpretation: select the rows whose position record fuzzily matches to fl . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; position ; fl } }, interpretation: select the row whose total trade record of all rows is 1st minimum . the country record of this row is iran .
Output: no
Command: eq { hop { nth_argmin { all_rows ; total trade ; 1 } ; country } ; iran }, interpretation: select the row whose total trade record of all rows is 1st minimum . the country record of this row is iran .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; total trade ; 1 } ; country } ; iran }, interpretation: select the row whose date record of all rows is 2nd minimum . the event record of this row is 2008 european poker championships .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 2 } ; event } ; 2008 european poker championships }, interpretation: select the row whose date record of all rows is 2nd minimum . the event record of this row is 2008 european poker championships .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 2 } ; event } ; 2008 european poker championships }, interpretation: select the rows whose visitor record fuzzily matches to dallas . take the date record of this row . select the rows whose visitor record fuzzily matches to montreal . 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 ; dallas } ; date } ; hop { filter_eq { all_rows ; visitor ; montreal } ; date } }, interpretation: select the rows whose visitor record fuzzily matches to dallas . take the date record of this row . select the rows whose visitor record fuzzily matches to montreal . 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 ; dallas } ; date } ; hop { filter_eq { all_rows ; visitor ; montreal } ; date } }, interpretation: select the rows whose nationality record fuzzily matches to alatri . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; nationality ; alatri } }, interpretation: select the rows whose nationality record fuzzily matches to alatri . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; nationality ; alatri } }, interpretation: for the date records of all rows , most of them fuzzily match to may .
Output: no
Command: most_eq { all_rows ; date ; may }, interpretation: select the rows whose frequency record fuzzily matches to 91.1 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; frequency ; 91.1 } } ; 2 }, interpretation: select the rows whose frequency record fuzzily matches to 91.1 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; frequency ; 91.1 } } ; 2 }, interpretation: select the rows whose record label record fuzzily matches to for us . there is only one such row in the table . the single record of this unqiue row is moped girls .
Output: no
Command: and { only { filter_eq { all_rows ; record label ; for us } } ; eq { hop { filter_eq { all_rows ; record label ; for us } ; single } ; moped girls } }, interpretation: select the rows whose record label record fuzzily matches to for us . there is only one such row in the table . the single record of this unqiue row is moped girls .
Output: yes
Command: and { only { filter_eq { all_rows ; record label ; for us } } ; eq { hop { filter_eq { all_rows ; record label ; for us } ; single } ; moped girls } }, interpretation: select the rows whose team record fuzzily matches to ducati . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; team ; ducati } } ; 3 }, interpretation: select the rows whose team record fuzzily matches to ducati . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; team ; ducati } } ; 3 }, interpretation: select the row whose votes record of all rows is 1st minimum . the party record of this row is country party .
Output: no
Command: eq { hop { nth_argmin { all_rows ; votes ; 1 } ; party } ; country party }, interpretation: select the row whose votes record of all rows is 1st minimum . the party record of this row is country party .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; votes ; 1 } ; party } ; country party }, interpretation: the sum of the points record of all rows is 16 .
Output: no
Command: round_eq { sum { all_rows ; points } ; 16 }, interpretation: the sum of the points record of all rows is 16 .
Output: yes
Command: round_eq { sum { all_rows ; points } ; 16 }, interpretation: select the rows whose enrollment record is greater than 350 . for the ihsaa football class records of these rows , most of them fuzzily match to a .
Output: no
Command: most_eq { filter_greater { all_rows ; enrollment ; 350 } ; ihsaa football class ; a }, interpretation: select the rows whose enrollment record is greater than 350 . for the ihsaa football class records of these rows , most of them fuzzily match to a .
Output: yes
Command: most_eq { filter_greater { all_rows ; enrollment ; 350 } ; ihsaa football class ; a }, interpretation: the sum of the crowd record of all rows is 105500 .
Output: no
Command: round_eq { sum { all_rows ; crowd } ; 105500 }, interpretation: the sum of the crowd record of all rows is 105500 .
Output: yes
Command: round_eq { sum { all_rows ; crowd } ; 105500 }, interpretation: select the rows whose tv station record fuzzily matches to fuji tv . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; tv station ; fuji tv } } ; 5 }, interpretation: select the rows whose tv station record fuzzily matches to fuji tv . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; tv station ; fuji tv } } ; 5 }, interpretation: select the rows whose club record fuzzily matches to swansea uplands rfc . take the tries for record of this row . select the rows whose club record fuzzily matches to trebanos rfc . take the tries for record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; club ; swansea uplands rfc } ; tries for } ; hop { filter_eq { all_rows ; club ; trebanos rfc } ; tries for } }, interpretation: select the rows whose club record fuzzily matches to swansea uplands rfc . take the tries for record of this row . select the rows whose club record fuzzily matches to trebanos rfc . take the tries for record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; club ; swansea uplands rfc } ; tries for } ; hop { filter_eq { all_rows ; club ; trebanos rfc } ; tries for } }, interpretation: select the rows whose party record fuzzily matches to republican . the average of the candidates record of these rows is 50.12 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; party ; republican } ; candidates } ; 50.12 }, interpretation: select the rows whose party record fuzzily matches to republican . the average of the candidates record of these rows is 50.12 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; party ; republican } ; candidates } ; 50.12 }, interpretation: select the row whose laps record of all rows is minimum . the co - drivers record of this row is gianmaria bruni pierre kaffer .
Output: no
Command: eq { hop { argmin { all_rows ; laps } ; co - drivers } ; gianmaria bruni pierre kaffer }, interpretation: select the row whose laps record of all rows is minimum . the co - drivers record of this row is gianmaria bruni pierre kaffer .
Output: yes
Command: eq { hop { argmin { all_rows ; laps } ; co - drivers } ; gianmaria bruni pierre kaffer }, interpretation: select the rows whose circuit record is arbitrary . the number of such rows is 5 .
Output: no
Command: eq { count { filter_all { all_rows ; circuit } } ; 5 }, interpretation: select the rows whose circuit record is arbitrary . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_all { all_rows ; circuit } } ; 5 }, interpretation: select the row whose date record of all rows is minimum . the circuit record of this row is calder park raceway .
Output: no
Command: eq { hop { argmin { all_rows ; date } ; circuit } ; calder park raceway }, interpretation: select the row whose date record of all rows is minimum . the circuit record of this row is calder park raceway .
Output: yes
Command: eq { hop { argmin { all_rows ; date } ; circuit } ; calder park raceway }, interpretation: select the rows whose floors record is equal to 1 . there is only one such row in the table . the name record of this unqiue row is saint michael 's church .
Output: no
Command: and { only { filter_eq { all_rows ; floors ; 1 } } ; eq { hop { filter_eq { all_rows ; floors ; 1 } ; name } ; saint michael 's church } }, interpretation: select the rows whose floors record is equal to 1 . there is only one such row in the table . the name record of this unqiue row is saint michael 's church .
Output: yes
Command: and { only { filter_eq { all_rows ; floors ; 1 } } ; eq { hop { filter_eq { all_rows ; floors ; 1 } ; name } ; saint michael 's church } }, interpretation: the 2nd minimum date record of all rows is december 21 , 1986 .
Output: no
Command: eq { nth_min { all_rows ; date ; 2 } ; december 21 , 1986 }, interpretation: the 2nd minimum date record of all rows is december 21 , 1986 .
Output: yes
Command: eq { nth_min { all_rows ; date ; 2 } ; december 21 , 1986 }, interpretation: select the row whose frequency mhz record of all rows is 3rd maximum . the erp kw record of this row is 3 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; frequency mhz ; 3 } ; erp kw } ; 3 }, interpretation: select the row whose frequency mhz record of all rows is 3rd maximum . the erp kw record of this row is 3 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; frequency mhz ; 3 } ; erp kw } ; 3 }, interpretation: select the rows whose runner - up record fuzzily matches to dynamo moscow . take the season record of this row . select the rows whose runner - up record fuzzily matches to lokomotiv moscow . take the season record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; runner - up ; dynamo moscow } ; season } ; hop { filter_eq { all_rows ; runner - up ; lokomotiv moscow } ; season } }, interpretation: select the rows whose runner - up record fuzzily matches to dynamo moscow . take the season record of this row . select the rows whose runner - up record fuzzily matches to lokomotiv moscow . take the season record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; runner - up ; dynamo moscow } ; season } ; hop { filter_eq { all_rows ; runner - up ; lokomotiv moscow } ; season } }, interpretation: select the row whose distance record of all rows is minimum . the city 1 record of this row is são paulo . the city 2 record of this row is rio de janeiro .
Output: no
Command: and { eq { hop { argmin { all_rows ; distance } ; city 1 } ; são paulo } ; eq { hop { argmin { all_rows ; distance } ; city 2 } ; rio de janeiro } }, interpretation: select the row whose distance record of all rows is minimum . the city 1 record of this row is são paulo . the city 2 record of this row is rio de janeiro .
Output: yes
Command: and { eq { hop { argmin { all_rows ; distance } ; city 1 } ; são paulo } ; eq { hop { argmin { all_rows ; distance } ; city 2 } ; rio de janeiro } }, interpretation: for the result records of all rows , most of them fuzzily match to l .
Output: no
Command: most_eq { all_rows ; result ; l }, interpretation: select the rows whose goals for record is greater than or equal to 50 . the number of such rows is 7 .
Output: no
Command: eq { count { filter_greater_eq { all_rows ; goals for ; 50 } } ; 7 }, interpretation: select the rows whose goals for record is greater than or equal to 50 . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_greater_eq { all_rows ; goals for ; 50 } } ; 7 }, interpretation: for the goals records of all rows , all of them are equal to 0 .
Output: no
Command: all_eq { all_rows ; goals ; 0 }, interpretation: for the goals records of all rows , all of them are equal to 0 .
Output: yes
Command: all_eq { all_rows ; goals ; 0 }, interpretation: select the rows whose capacity record is less than 10000 . for the 201112 season records of these rows , most of them fuzzily match to divisione b .
Output: no
Command: most_eq { filter_less { all_rows ; capacity ; 10000 } ; 201112 season ; divisione b }, interpretation: select the rows whose capacity record is less than 10000 . for the 201112 season records of these rows , most of them fuzzily match to divisione b .
Output: yes
Command: most_eq { filter_less { all_rows ; capacity ; 10000 } ; 201112 season ; divisione b }, interpretation: for the tv records of all rows , most of them fuzzily match to nbc .
Output: no
Command: most_eq { all_rows ; tv ; nbc }, interpretation: for the tv records of all rows , most of them fuzzily match to nbc .
Output: yes
Command: most_eq { all_rows ; tv ; nbc }, interpretation: the 16th minimum date record of all rows is december 20 , 1981 .
Output: no
Command: eq { nth_min { all_rows ; date ; 16 } ; december 20 , 1981 }, interpretation: the 16th minimum date record of all rows is december 20 , 1981 .
Output: yes
Command: eq { nth_min { all_rows ; date ; 16 } ; december 20 , 1981 }, interpretation: select the row whose time record of all rows is 2nd maximum . the wrestler record of this row is chris masters .
Output: no
Command: eq { hop { nth_argmax { all_rows ; time ; 2 } ; wrestler } ; chris masters }, interpretation: select the row whose time record of all rows is 2nd maximum . the wrestler record of this row is chris masters .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; time ; 2 } ; wrestler } ; chris masters }, interpretation: select the rows whose club record fuzzily matches to glais rfc . take the lost record of this row . select the rows whose club record fuzzily matches to tycroes rfc . take the lost record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; club ; glais rfc } ; lost } ; hop { filter_eq { all_rows ; club ; tycroes rfc } ; lost } }, interpretation: select the rows whose club record fuzzily matches to glais rfc . take the lost record of this row . select the rows whose club record fuzzily matches to tycroes rfc . take the lost record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; club ; glais rfc } ; lost } ; hop { filter_eq { all_rows ; club ; tycroes rfc } ; lost } }, interpretation: select the row whose tonnage record of all rows is 2nd maximum . the name record of this row is ocean rover .
Output: no
Command: eq { hop { nth_argmax { all_rows ; tonnage ; 2 } ; name } ; ocean rover }, interpretation: select the row whose tonnage record of all rows is 2nd maximum . the name record of this row is ocean rover .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; tonnage ; 2 } ; name } ; ocean rover }, interpretation: select the rows whose type record fuzzily matches to nursery . there is only one such row in the table . the name record of this unqiue row is heath lane .
Output: no
Command: and { only { filter_eq { all_rows ; type ; nursery } } ; eq { hop { filter_eq { all_rows ; type ; nursery } ; name } ; heath lane } }, interpretation: select the rows whose type record fuzzily matches to nursery . there is only one such row in the table . the name record of this unqiue row is heath lane .
Output: yes
Command: and { only { filter_eq { all_rows ; type ; nursery } } ; eq { hop { filter_eq { all_rows ; type ; nursery } ; name } ; heath lane } }, interpretation: select the row whose floors record of all rows is 2nd maximum . the name record of this row is aul tower .
Output: no
Command: eq { hop { nth_argmax { all_rows ; floors ; 2 } ; name } ; aul tower }, interpretation: select the row whose floors record of all rows is 2nd maximum . the name record of this row is aul tower .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; floors ; 2 } ; name } ; aul tower }, interpretation: select the row whose size record of all rows is maximum . the school record of this row is mitchell .
Output: no
Command: eq { hop { argmax { all_rows ; size } ; school } ; mitchell }, interpretation: select the row whose size record of all rows is maximum . the school record of this row is mitchell .
Output: yes
Command: eq { hop { argmax { all_rows ; size } ; school } ; mitchell }, interpretation: select the rows whose opponent record fuzzily matches to arizona . among these rows , select the rows whose result record fuzzily matches to ot . there is only one such row in the table .
Output: no
Command: only { filter_eq { filter_eq { all_rows ; opponent ; arizona } ; result ; ot } }, interpretation: select the rows whose opponent record fuzzily matches to arizona . among these rows , select the rows whose result record fuzzily matches to ot . there is only one such row in the table .
Output: yes
Command: only { filter_eq { filter_eq { all_rows ; opponent ; arizona } ; result ; ot } }, interpretation: select the rows whose pole position record fuzzily matches to ayrton senna . the number of such rows is 8 .
Output: no
Command: eq { count { filter_eq { all_rows ; pole position ; ayrton senna } } ; 8 }, interpretation: select the rows whose pole position record fuzzily matches to ayrton senna . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_eq { all_rows ; pole position ; ayrton senna } } ; 8 }, interpretation: select the rows whose months in malayalam era record fuzzily matches to chingam . take the gregorian calendar record of this row . select the rows whose months in malayalam era record fuzzily matches to tulam . take the gregorian calendar record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; months in malayalam era ; chingam } ; gregorian calendar } ; hop { filter_eq { all_rows ; months in malayalam era ; tulam } ; gregorian calendar } }, interpretation: select the rows whose months in malayalam era record fuzzily matches to chingam . take the gregorian calendar record of this row . select the rows whose months in malayalam era record fuzzily matches to tulam . take the gregorian calendar record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; months in malayalam era ; chingam } ; gregorian calendar } ; hop { filter_eq { all_rows ; months in malayalam era ; tulam } ; gregorian calendar } }, interpretation: for the us viewers ( millions ) records of all rows , most of them are greater than or equal to 15 .
Output: no
Command: most_greater_eq { all_rows ; us viewers ( millions ) ; 15 }, interpretation: for the us viewers ( millions ) records of all rows , most of them are greater than or equal to 15 .
Output: yes
Command: most_greater_eq { all_rows ; us viewers ( millions ) ; 15 }, interpretation: select the row whose crowd record of all rows is 1st maximum . the away team record of this row is st kilda .
Output: no
Command: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; away team } ; st kilda }, interpretation: select the row whose crowd record of all rows is 1st maximum . the away team record of this row is st kilda .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; away team } ; st kilda }, interpretation: select the rows whose home team record fuzzily matches to collingwood . take the crowd record of this row . select the rows whose home team record fuzzily matches to carlton . 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 south melbourne . the away team record of the second row is geelong .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; home team ; collingwood } ; crowd } ; hop { filter_eq { all_rows ; home team ; carlton } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; home team ; collingwood } ; away team } ; south melbourne } ; eq { hop { filter_eq { all_rows ; home team ; carlton } ; away team } ; geelong } } }, interpretation: select the rows whose home team record fuzzily matches to collingwood . take the crowd record of this row . select the rows whose home team record fuzzily matches to carlton . 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 south melbourne . the away team record of the second row is geelong .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; home team ; collingwood } ; crowd } ; hop { filter_eq { all_rows ; home team ; carlton } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; home team ; collingwood } ; away team } ; south melbourne } ; eq { hop { filter_eq { all_rows ; home team ; carlton } ; away team } ; geelong } } }, interpretation: the average of the deaths record of all rows is 13.4 .
Output: no
Command: round_eq { avg { all_rows ; deaths } ; 13.4 }, interpretation: the average of the deaths record of all rows is 13.4 .
Output: yes
Command: round_eq { avg { all_rows ; deaths } ; 13.4 }, interpretation: select the rows whose race record fuzzily matches to super g . there is only one such row in the table . the season record of this unqiue row is 2009 .
Output: no
Command: and { only { filter_eq { all_rows ; race ; super g } } ; eq { hop { filter_eq { all_rows ; race ; super g } ; season } ; 2009 } }, interpretation: select the rows whose race record fuzzily matches to super g . there is only one such row in the table . the season record of this unqiue row is 2009 .
Output: yes
Command: and { only { filter_eq { all_rows ; race ; super g } } ; eq { hop { filter_eq { all_rows ; race ; super g } ; season } ; 2009 } }, interpretation: select the rows whose top 5 record is not equal to 0 . the number of such rows is 10 .
Output: no
Command: eq { count { filter_not_eq { all_rows ; top 5 ; 0 } } ; 10 }, interpretation: select the rows whose top 5 record is not equal to 0 . the number of such rows is 10 .
Output: yes
Command: eq { count { filter_not_eq { all_rows ; top 5 ; 0 } } ; 10 }, interpretation: select the rows whose city of license record fuzzily matches to san antonio . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; city of license ; san antonio } } ; 4 }, interpretation: select the rows whose city of license record fuzzily matches to san antonio . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; city of license ; san antonio } } ; 4 }, interpretation: select the rows whose withdrawn record is equal to 1951 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; withdrawn ; 1951 } } ; 5 }, interpretation: select the rows whose withdrawn record is equal to 1951 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; withdrawn ; 1951 } } ; 5 }, interpretation: select the rows whose voivodeship after 1569 record fuzzily matches to samogitian eldership . take the year established record of this row . select the rows whose voivodeship after 1569 record fuzzily matches to trakai voivodeship . take the year established record of this row . the second record is 2 years larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; voivodeship after 1569 ; samogitian eldership } ; year established } ; hop { filter_eq { all_rows ; voivodeship after 1569 ; trakai voivodeship } ; year established } } ; -2 years }, interpretation: select the rows whose voivodeship after 1569 record fuzzily matches to samogitian eldership . take the year established record of this row . select the rows whose voivodeship after 1569 record fuzzily matches to trakai voivodeship . take the year established record of this row . the second record is 2 years larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; voivodeship after 1569 ; samogitian eldership } ; year established } ; hop { filter_eq { all_rows ; voivodeship after 1569 ; trakai voivodeship } ; year established } } ; -2 years }, interpretation: for the nationality records of all rows , most of them fuzzily match to canada .
Output: no
Command: most_eq { all_rows ; nationality ; canada }, interpretation: select the rows whose mascot record fuzzily matches to tigers . there is only one such row in the table . the school record of this unqiue row is yorktown .
Output: no
Command: and { only { filter_eq { all_rows ; mascot ; tigers } } ; eq { hop { filter_eq { all_rows ; mascot ; tigers } ; school } ; yorktown } }, interpretation: select the rows whose mascot record fuzzily matches to tigers . there is only one such row in the table . the school record of this unqiue row is yorktown .
Output: yes
Command: and { only { filter_eq { all_rows ; mascot ; tigers } } ; eq { hop { filter_eq { all_rows ; mascot ; tigers } ; school } ; yorktown } }, interpretation: for the high points records of all rows , most of them fuzzily match to paul westphal .
Output: no
Command: most_eq { all_rows ; high points ; paul westphal }, interpretation: for the high points records of all rows , most of them fuzzily match to paul westphal .
Output: yes
Command: most_eq { all_rows ; high points ; paul westphal }, interpretation: for the yvert records of all rows , most of them are equal to 0 .
Output: no
Command: most_eq { all_rows ; yvert ; 0 }, interpretation: for the yvert records of all rows , most of them are equal to 0 .
Output: yes
Command: most_eq { all_rows ; yvert ; 0 }, interpretation: for the date records of all rows , most of them fuzzily match to june .
Output: no
Command: most_eq { all_rows ; date ; june }, interpretation: for the date records of all rows , most of them fuzzily match to june .
Output: yes
Command: most_eq { all_rows ; date ; june }, interpretation: select the rows whose certification record fuzzily matches to 2x platinum . there is only one such row in the table . the album record of this unqiue row is my love : essential collection .
Output: no
Command: and { only { filter_eq { all_rows ; certification ; 2x platinum } } ; eq { hop { filter_eq { all_rows ; certification ; 2x platinum } ; album } ; my love : essential collection } }, interpretation: select the rows whose certification record fuzzily matches to 2x platinum . there is only one such row in the table . the album record of this unqiue row is my love : essential collection .
Output: yes
Command: and { only { filter_eq { all_rows ; certification ; 2x platinum } } ; eq { hop { filter_eq { all_rows ; certification ; 2x platinum } ; album } ; my love : essential collection } }, interpretation: select the rows whose date record fuzzily matches to february . there is only one such row in the table . the event record of this unqiue row is indianapolis boat , sport & travel show .
Output: no