niryuu/sni-each-converted huggingface dataset:
Command: eq { count { filter_eq { all_rows ; winning driver ; aj foyt } } ; 4 }, interpretation: for the country records of all rows , most of them fuzzily match to united arab emirates .
Output: no
Command: most_eq { all_rows ; country ; united arab emirates }, interpretation: for the country records of all rows , most of them fuzzily match to united arab emirates .
Output: yes
Command: most_eq { all_rows ; country ; united arab emirates }, interpretation: select the rows whose hometown record fuzzily matches to ca . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; hometown ; ca } } ; 2 }, interpretation: select the rows whose hometown record fuzzily matches to ca . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; hometown ; ca } } ; 2 }, interpretation: select the rows whose surface record fuzzily matches to carpet ( i ) . there is only one such row in the table . the tournament record of this unqiue row is wolfsburg , germany .
Output: no
Command: and { only { filter_eq { all_rows ; surface ; carpet ( i ) } } ; eq { hop { filter_eq { all_rows ; surface ; carpet ( i ) } ; tournament } ; wolfsburg , germany } }, interpretation: select the rows whose surface record fuzzily matches to carpet ( i ) . there is only one such row in the table . the tournament record of this unqiue row is wolfsburg , germany .
Output: yes
Command: and { only { filter_eq { all_rows ; surface ; carpet ( i ) } } ; eq { hop { filter_eq { all_rows ; surface ; carpet ( i ) } ; tournament } ; wolfsburg , germany } }, interpretation: select the row whose time ( et ) record of all rows is maximum . the week record of this row is 9 . the opponent record of this row is pittsburgh steelers .
Output: no
Command: and { eq { hop { argmax { all_rows ; time ( et ) } ; week } ; 9 } ; eq { hop { argmax { all_rows ; time ( et ) } ; opponent } ; pittsburgh steelers } }, interpretation: select the row whose time ( et ) record of all rows is maximum . the week record of this row is 9 . the opponent record of this row is pittsburgh steelers .
Output: yes
Command: and { eq { hop { argmax { all_rows ; time ( et ) } ; week } ; 9 } ; eq { hop { argmax { all_rows ; time ( et ) } ; opponent } ; pittsburgh steelers } }, interpretation: for the shot pct records of all rows , most of them are less than 80 % .
Output: no
Command: most_less { all_rows ; shot pct ; 80 % }, interpretation: for the shot pct records of all rows , most of them are less than 80 % .
Output: yes
Command: most_less { all_rows ; shot pct ; 80 % }, interpretation: the sum of the loans received , 3q record of all rows is 8550000 .
Output: no
Command: round_eq { sum { all_rows ; loans received , 3q } ; 8550000 }, interpretation: the sum of the loans received , 3q record of all rows is 8550000 .
Output: yes
Command: round_eq { sum { all_rows ; loans received , 3q } ; 8550000 }, interpretation: select the row whose stage record of all rows is 2nd minimum . the year record of this row is 2003 .
Output: no
Command: eq { hop { nth_argmin { all_rows ; stage ; 2 } ; year } ; 2003 }, interpretation: select the row whose stage record of all rows is 2nd minimum . the year record of this row is 2003 .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; stage ; 2 } ; year } ; 2003 }, interpretation: select the rows whose home team score record is greater than 10 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_less { filter_greater { all_rows ; home team score ; 10 } ; crowd ; 20000 } } ; 3 }, interpretation: select the rows whose home team score record is greater than 10 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_less { filter_greater { all_rows ; home team score ; 10 } ; crowd ; 20000 } } ; 3 }, interpretation: select the rows whose fate record fuzzily matches to damaged . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; fate ; damaged } } ; 2 }, interpretation: select the rows whose fate record fuzzily matches to damaged . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; fate ; damaged } } ; 2 }, interpretation: select the rows whose competition record fuzzily matches to league . for the venue records of these rows , most of them fuzzily match to away .
Output: no
Command: most_eq { filter_eq { all_rows ; competition ; league } ; venue ; away }, interpretation: select the rows whose competition record fuzzily matches to league . for the venue records of these rows , most of them fuzzily match to away .
Output: yes
Command: most_eq { filter_eq { all_rows ; competition ; league } ; venue ; away }, interpretation: for the prone records of all rows , most of them are less than 200 .
Output: no
Command: most_less { all_rows ; prone ; 200 }, interpretation: for the prone records of all rows , most of them are less than 200 .
Output: yes
Command: most_less { all_rows ; prone ; 200 }, interpretation: select the rows whose song title record fuzzily matches to nee maatalo . take the year record of this row . select the rows whose song title record fuzzily matches to hey po . take the year record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; song title ; nee maatalo } ; year } ; hop { filter_eq { all_rows ; song title ; hey po } ; year } }, interpretation: select the rows whose song title record fuzzily matches to nee maatalo . take the year record of this row . select the rows whose song title record fuzzily matches to hey po . take the year record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; song title ; nee maatalo } ; year } ; hop { filter_eq { all_rows ; song title ; hey po } ; year } }, interpretation: select the row whose area ( km square ) record of all rows is 3rd maximum . the name of county record of this row is pest .
Output: no
Command: eq { hop { nth_argmax { all_rows ; area ( km square ) ; 3 } ; name of county } ; pest }, interpretation: select the row whose area ( km square ) record of all rows is 3rd maximum . the name of county record of this row is pest .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; area ( km square ) ; 3 } ; name of county } ; pest }, interpretation: select the rows whose won record is greater than 10 . among these rows , select the rows whose lost record is equal to 3 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_greater { all_rows ; won ; 10 } ; lost ; 3 } } ; 2 }, interpretation: select the rows whose won record is greater than 10 . among these rows , select the rows whose lost record is equal to 3 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_greater { all_rows ; won ; 10 } ; lost ; 3 } } ; 2 }, interpretation: select the rows whose champion record fuzzily matches to karrie webb . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; champion ; karrie webb } } ; 2 }, interpretation: select the rows whose champion record fuzzily matches to karrie webb . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; champion ; karrie webb } } ; 2 }, interpretation: select the rows whose club record fuzzily matches to bath city . take the fa cup record of this row . select the rows whose club record fuzzily matches to telford united . take the fa cup record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; club ; bath city } ; fa cup } ; hop { filter_eq { all_rows ; club ; telford united } ; fa cup } }, interpretation: select the rows whose club record fuzzily matches to bath city . take the fa cup record of this row . select the rows whose club record fuzzily matches to telford united . take the fa cup record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; club ; bath city } ; fa cup } ; hop { filter_eq { all_rows ; club ; telford united } ; fa cup } }, interpretation: for the established records of all rows , most of them are less than 2005 .
Output: no
Command: most_less { all_rows ; established ; 2005 }, interpretation: for the established records of all rows , most of them are less than 2005 .
Output: yes
Command: most_less { all_rows ; established ; 2005 }, interpretation: select the row whose est record of all rows is minimum . the local government area record of this row is outback areas community development trust .
Output: no
Command: eq { hop { argmin { all_rows ; est } ; local government area } ; outback areas community development trust }, interpretation: select the row whose est record of all rows is minimum . the local government area record of this row is outback areas community development trust .
Output: yes
Command: eq { hop { argmin { all_rows ; est } ; local government area } ; outback areas community development trust }, interpretation: the average of the score record of all rows is 70.46 .
Output: no
Command: round_eq { avg { all_rows ; score } ; 70.46 }, interpretation: the average of the score record of all rows is 70.46 .
Output: yes
Command: round_eq { avg { all_rows ; score } ; 70.46 }, interpretation: select the row whose gold record of all rows is maximum . the nation record of this row is united states ( usa ) .
Output: no
Command: eq { hop { argmax { all_rows ; gold } ; nation } ; united states ( usa ) }, interpretation: select the row whose gold record of all rows is maximum . the nation record of this row is united states ( usa ) .
Output: yes
Command: eq { hop { argmax { all_rows ; gold } ; nation } ; united states ( usa ) }, interpretation: select the rows whose opponent record fuzzily matches to minnesota vikings . take the date record of this row . select the rows whose opponent record fuzzily matches to green bay packers . 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 ; minnesota vikings } ; date } ; hop { filter_eq { all_rows ; opponent ; green bay packers } ; date } }, interpretation: select the rows whose opponent record fuzzily matches to minnesota vikings . take the date record of this row . select the rows whose opponent record fuzzily matches to green bay packers . 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 ; minnesota vikings } ; date } ; hop { filter_eq { all_rows ; opponent ; green bay packers } ; date } }, interpretation: select the rows whose rider record fuzzily matches to marino lejarreta ( 3 ) . take the final position - giro record of this row . select the rows whose rider record fuzzily matches to marino lejarreta ( 2 ) . take the final position - giro record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; rider ; marino lejarreta ( 3 ) } ; final position - giro } ; hop { filter_eq { all_rows ; rider ; marino lejarreta ( 2 ) } ; final position - giro } }, interpretation: select the rows whose rider record fuzzily matches to marino lejarreta ( 3 ) . take the final position - giro record of this row . select the rows whose rider record fuzzily matches to marino lejarreta ( 2 ) . take the final position - giro record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; rider ; marino lejarreta ( 3 ) } ; final position - giro } ; hop { filter_eq { all_rows ; rider ; marino lejarreta ( 2 ) } ; final position - giro } }, interpretation: select the rows whose author record fuzzily matches to barbara clegg and marc platt . there is only one such row in the table . the title record of this unqiue row is point of entry .
Output: no
Command: and { only { filter_eq { all_rows ; author ; barbara clegg and marc platt } } ; eq { hop { filter_eq { all_rows ; author ; barbara clegg and marc platt } ; title } ; point of entry } }, interpretation: select the rows whose author record fuzzily matches to barbara clegg and marc platt . there is only one such row in the table . the title record of this unqiue row is point of entry .
Output: yes
Command: and { only { filter_eq { all_rows ; author ; barbara clegg and marc platt } } ; eq { hop { filter_eq { all_rows ; author ; barbara clegg and marc platt } ; title } ; point of entry } }, interpretation: select the rows whose tag team record fuzzily matches to rosey and the hurricane . take the eliminated record of this row . select the rows whose tag team record fuzzily matches to jindrak and cade . take the eliminated record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; tag team ; rosey and the hurricane } ; eliminated } ; hop { filter_eq { all_rows ; tag team ; jindrak and cade } ; eliminated } }, interpretation: select the rows whose tag team record fuzzily matches to rosey and the hurricane . take the eliminated record of this row . select the rows whose tag team record fuzzily matches to jindrak and cade . take the eliminated record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; tag team ; rosey and the hurricane } ; eliminated } ; hop { filter_eq { all_rows ; tag team ; jindrak and cade } ; eliminated } }, interpretation: for the station type records of all rows , most of them fuzzily match to relay .
Output: no
Command: most_eq { all_rows ; station type ; relay }, interpretation: for the station type records of all rows , most of them fuzzily match to relay .
Output: yes
Command: most_eq { all_rows ; station type ; relay }, interpretation: for the gold records of all rows , most of them are equal to 0 .
Output: no
Command: most_eq { all_rows ; gold ; 0 }, interpretation: for the result records of all rows , all of them fuzzily match to re-elected .
Output: no
Command: all_eq { all_rows ; result ; re-elected }, interpretation: for the dominant religion ( 2002 ) records of all rows , most of them fuzzily match to orthodox christianity .
Output: no
Command: most_eq { all_rows ; dominant religion ( 2002 ) ; orthodox christianity }, interpretation: for the dominant religion ( 2002 ) records of all rows , most of them fuzzily match to orthodox christianity .
Output: yes
Command: most_eq { all_rows ; dominant religion ( 2002 ) ; orthodox christianity }, interpretation: select the rows whose weight ( lb ) record is less than 200 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_less { all_rows ; weight ( lb ) ; 200 } } ; 4 }, interpretation: select the rows whose weight ( lb ) record is less than 200 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_less { all_rows ; weight ( lb ) ; 200 } } ; 4 }, interpretation: for the motor records of all rows , all of them fuzzily match to castor 4b .
Output: no
Command: all_eq { all_rows ; motor ; castor 4b }, interpretation: for the motor records of all rows , all of them fuzzily match to castor 4b .
Output: yes
Command: all_eq { all_rows ; motor ; castor 4b }, interpretation: select the row whose laps record of all rows is 2nd minimum . the driver record of this row is graham hill .
Output: no
Command: eq { hop { nth_argmin { all_rows ; laps ; 2 } ; driver } ; graham hill }, interpretation: select the row whose laps record of all rows is 2nd minimum . the driver record of this row is graham hill .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; laps ; 2 } ; driver } ; graham hill }, interpretation: select the rows whose soap opera record fuzzily matches to as the world turns . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; soap opera ; as the world turns } } ; 3 }, interpretation: select the rows whose soap opera record fuzzily matches to as the world turns . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; soap opera ; as the world turns } } ; 3 }, interpretation: select the row whose per capita income record of all rows is 2nd minimum . the county record of this row is piscataquis .
Output: no
Command: eq { hop { nth_argmin { all_rows ; per capita income ; 2 } ; county } ; piscataquis }, interpretation: select the row whose per capita income record of all rows is 2nd minimum . the county record of this row is piscataquis .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; per capita income ; 2 } ; county } ; piscataquis }, interpretation: select the rows whose points record is greater than 90 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater { all_rows ; points ; 90 } } ; 2 }, interpretation: select the rows whose points record is greater than 90 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater { all_rows ; points ; 90 } } ; 2 }, interpretation: select the rows whose result record fuzzily matches to 3-1 . there is only one such row in the table . the date record of this unqiue row is 15 august 2012 .
Output: no
Command: and { only { filter_eq { all_rows ; result ; 3-1 } } ; eq { hop { filter_eq { all_rows ; result ; 3-1 } ; date } ; 15 august 2012 } }, interpretation: select the rows whose result record fuzzily matches to 3-1 . there is only one such row in the table . the date record of this unqiue row is 15 august 2012 .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; 3-1 } } ; eq { hop { filter_eq { all_rows ; result ; 3-1 } ; date } ; 15 august 2012 } }, interpretation: select the rows whose mascot record fuzzily matches to scorpion . there is only one such row in the table . the elementary schools record of this unqiue row is san tan ( home ) .
Output: no
Command: and { only { filter_eq { all_rows ; mascot ; scorpion } } ; eq { hop { filter_eq { all_rows ; mascot ; scorpion } ; elementary schools } ; san tan ( home ) } }, interpretation: select the rows whose mascot record fuzzily matches to scorpion . there is only one such row in the table . the elementary schools record of this unqiue row is san tan ( home ) .
Output: yes
Command: and { only { filter_eq { all_rows ; mascot ; scorpion } } ; eq { hop { filter_eq { all_rows ; mascot ; scorpion } ; elementary schools } ; san tan ( home ) } }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is saturday , april 10 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; saturday , april 10 }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is saturday , april 10 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; saturday , april 10 }, interpretation: for the country records of all rows , most of them fuzzily match to united states .
Output: no
Command: most_eq { all_rows ; country ; united states }, interpretation: select the rows whose clinical status record fuzzily matches to fda - approved . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; clinical status ; fda - approved } } ; 6 }, interpretation: select the rows whose clinical status record fuzzily matches to fda - approved . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; clinical status ; fda - approved } } ; 6 }, interpretation: select the rows whose hometown record fuzzily matches to ca . there is only one such row in the table . the player record of this unqiue row is james kaprelian .
Output: no
Command: and { only { filter_eq { all_rows ; hometown ; ca } } ; eq { hop { filter_eq { all_rows ; hometown ; ca } ; player } ; james kaprelian } }, interpretation: select the rows whose hometown record fuzzily matches to ca . there is only one such row in the table . the player record of this unqiue row is james kaprelian .
Output: yes
Command: and { only { filter_eq { all_rows ; hometown ; ca } } ; eq { hop { filter_eq { all_rows ; hometown ; ca } ; player } ; james kaprelian } }, interpretation: the average of the score record of all rows is 3.7-2 .7 .
Output: no
Command: round_eq { avg { all_rows ; score } ; 3.7-2 .7 }, interpretation: the average of the score record of all rows is 3.7-2 .7 .
Output: yes
Command: round_eq { avg { all_rows ; score } ; 3.7-2 .7 }, interpretation: select the row whose win / loss record of all rows is 4th maximum . the season record of this row is 2006 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; win / loss ; 4 } ; season } ; 2006 }, interpretation: select the row whose win / loss record of all rows is 4th maximum . the season record of this row is 2006 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; win / loss ; 4 } ; season } ; 2006 }, interpretation: select the rows whose competition record fuzzily matches to friendly . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; competition ; friendly } } ; 2 }, interpretation: select the rows whose competition record fuzzily matches to friendly . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; competition ; friendly } } ; 2 }, interpretation: the 2nd maximum points record of all rows is 48 .
Output: no
Command: eq { nth_max { all_rows ; points ; 2 } ; 48 }, interpretation: the 2nd maximum points record of all rows is 48 .
Output: yes
Command: eq { nth_max { all_rows ; points ; 2 } ; 48 }, interpretation: select the rows whose content record fuzzily matches to salute . there is only one such row in the table . the television service record of this unqiue row is erde und mensch .
Output: no
Command: and { only { filter_eq { all_rows ; content ; salute } } ; eq { hop { filter_eq { all_rows ; content ; salute } ; television service } ; erde und mensch } }, interpretation: select the rows whose content record fuzzily matches to salute . there is only one such row in the table . the television service record of this unqiue row is erde und mensch .
Output: yes
Command: and { only { filter_eq { all_rows ; content ; salute } } ; eq { hop { filter_eq { all_rows ; content ; salute } ; television service } ; erde und mensch } }, interpretation: select the rows whose average record is greater than 500 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater { all_rows ; average ; 500 } } ; 2 }, interpretation: select the rows whose average record is greater than 500 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater { all_rows ; average ; 500 } } ; 2 }, interpretation: select the rows whose cover model record fuzzily matches to two alternative covers . there is only one such row in the table . the date record of this unqiue row is 3 - 04 .
Output: no
Command: and { only { filter_eq { all_rows ; cover model ; two alternative covers } } ; eq { hop { filter_eq { all_rows ; cover model ; two alternative covers } ; date } ; 3 - 04 } }, interpretation: select the rows whose cover model record fuzzily matches to two alternative covers . there is only one such row in the table . the date record of this unqiue row is 3 - 04 .
Output: yes
Command: and { only { filter_eq { all_rows ; cover model ; two alternative covers } } ; eq { hop { filter_eq { all_rows ; cover model ; two alternative covers } ; date } ; 3 - 04 } }, interpretation: select the rows whose year record fuzzily matches to 1994 . take the wins record of this row . select the rows whose year record fuzzily matches to 1996 . take the wins record of this row . the first record is 1 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; year ; 1994 } ; wins } ; hop { filter_eq { all_rows ; year ; 1996 } ; wins } } ; 1 }, interpretation: select the rows whose year record fuzzily matches to 1994 . take the wins record of this row . select the rows whose year record fuzzily matches to 1996 . take the wins record of this row . the first record is 1 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; year ; 1994 } ; wins } ; hop { filter_eq { all_rows ; year ; 1996 } ; wins } } ; 1 }, interpretation: select the rows whose manner of departure record fuzzily matches to mutual consent . there is only one such row in the table . the outgoing manager record of this unqiue row is jimmy calderwood .
Output: no
Command: and { only { filter_eq { all_rows ; manner of departure ; mutual consent } } ; eq { hop { filter_eq { all_rows ; manner of departure ; mutual consent } ; outgoing manager } ; jimmy calderwood } }, interpretation: select the rows whose manner of departure record fuzzily matches to mutual consent . there is only one such row in the table . the outgoing manager record of this unqiue row is jimmy calderwood .
Output: yes
Command: and { only { filter_eq { all_rows ; manner of departure ; mutual consent } } ; eq { hop { filter_eq { all_rows ; manner of departure ; mutual consent } ; outgoing manager } ; jimmy calderwood } }, interpretation: select the rows whose screens record is greater than 100 . there is only one such row in the table . the territory record of this unqiue row is turkey .
Output: no
Command: and { only { filter_greater { all_rows ; screens ; 100 } } ; eq { hop { filter_greater { all_rows ; screens ; 100 } ; territory } ; turkey } }, interpretation: select the rows whose screens record is greater than 100 . there is only one such row in the table . the territory record of this unqiue row is turkey .
Output: yes
Command: and { only { filter_greater { all_rows ; screens ; 100 } } ; eq { hop { filter_greater { all_rows ; screens ; 100 } ; territory } ; turkey } }, interpretation: select the rows whose date record fuzzily matches to 10 june 2009 . take the competition record of this row . select the rows whose date record fuzzily matches to 5 september 2009 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is 2010 fifa world cup qualification . the competition record of the second row is 2010 fifa world cup qualification .
Output: no
Command: and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; 2010 fifa world cup qualification } ; eq { hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } ; 2010 fifa world cup qualification } } }, interpretation: select the rows whose date record fuzzily matches to 10 june 2009 . take the competition record of this row . select the rows whose date record fuzzily matches to 5 september 2009 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is 2010 fifa world cup qualification . the competition record of the second row is 2010 fifa world cup qualification .
Output: yes
Command: and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; 2010 fifa world cup qualification } ; eq { hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } ; 2010 fifa world cup qualification } } }, interpretation: select the row whose points record of all rows is maximum . the club record of this row is wigan warriors .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; club } ; wigan warriors }, interpretation: select the row whose points record of all rows is maximum . the club record of this row is wigan warriors .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; club } ; wigan warriors }, interpretation: select the rows whose title record fuzzily matches to empire . take the order record of this row . select the rows whose title record fuzzily matches to hollywood . take the order record of this row . the first record is less than the second record . the order record of the first row is 4 . the order record of the second row is 5 .
Output: no
Command: and { less { hop { filter_eq { all_rows ; title ; empire } ; order } ; hop { filter_eq { all_rows ; title ; hollywood } ; order } } ; and { eq { hop { filter_eq { all_rows ; title ; empire } ; order } ; 4 } ; eq { hop { filter_eq { all_rows ; title ; hollywood } ; order } ; 5 } } }, interpretation: select the rows whose title record fuzzily matches to empire . take the order record of this row . select the rows whose title record fuzzily matches to hollywood . take the order record of this row . the first record is less than the second record . the order record of the first row is 4 . the order record of the second row is 5 .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; title ; empire } ; order } ; hop { filter_eq { all_rows ; title ; hollywood } ; order } } ; and { eq { hop { filter_eq { all_rows ; title ; empire } ; order } ; 4 } ; eq { hop { filter_eq { all_rows ; title ; hollywood } ; order } ; 5 } } }, interpretation: for the result records of all rows , most of them fuzzily match to safe .
Output: no
Command: most_eq { all_rows ; result ; safe }, interpretation: select the rows whose title record fuzzily matches to trust and consequence . take the original air date record of this row . select the rows whose title record fuzzily matches to father , can you hair me . take the original air date record of this row . the second record is 7 days larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; title ; trust and consequence } ; original air date } ; hop { filter_eq { all_rows ; title ; father , can you hair me } ; original air date } } ; -7 days }, interpretation: select the rows whose title record fuzzily matches to trust and consequence . take the original air date record of this row . select the rows whose title record fuzzily matches to father , can you hair me . take the original air date record of this row . the second record is 7 days larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; title ; trust and consequence } ; original air date } ; hop { filter_eq { all_rows ; title ; father , can you hair me } ; original air date } } ; -7 days }, interpretation: select the rows whose power rpm record is equal to 6800 . there is only one such row in the table . the model record of this unqiue row is sl 63 amg .
Output: no
Command: and { only { filter_eq { all_rows ; power rpm ; 6800 } } ; eq { hop { filter_eq { all_rows ; power rpm ; 6800 } ; model } ; sl 63 amg } }, interpretation: select the rows whose power rpm record is equal to 6800 . there is only one such row in the table . the model record of this unqiue row is sl 63 amg .
Output: yes
Command: and { only { filter_eq { all_rows ; power rpm ; 6800 } } ; eq { hop { filter_eq { all_rows ; power rpm ; 6800 } ; model } ; sl 63 amg } }, interpretation: select the rows whose top - 25 record is equal to 0 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; top - 25 ; 0 } } ; 2 }, interpretation: select the rows whose top - 25 record is equal to 0 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; top - 25 ; 0 } } ; 2 }, interpretation: for the chassis records of all rows , most of them fuzzily match to talbot-lago t26c da .
Output: no
Command: most_eq { all_rows ; chassis ; talbot-lago t26c da }, interpretation: for the chassis records of all rows , most of them fuzzily match to talbot-lago t26c da .
Output: yes
Command: most_eq { all_rows ; chassis ; talbot-lago t26c da }, interpretation: select the row whose ratings record of all rows is 1st maximum . the broadcast date record of this row is 14 august 2012 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; ratings ; 1 } ; broadcast date } ; 14 august 2012 }, interpretation: select the row whose ratings record of all rows is 1st maximum . the broadcast date record of this row is 14 august 2012 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; ratings ; 1 } ; broadcast date } ; 14 august 2012 }, interpretation: select the row whose us viewers ( millions ) record of all rows is maximum . the original air date record of this row is november 7 , 2010 .
Output: no
Command: eq { hop { argmax { all_rows ; us viewers ( millions ) } ; original air date } ; november 7 , 2010 }, interpretation: select the row whose us viewers ( millions ) record of all rows is maximum . the original air date record of this row is november 7 , 2010 .
Output: yes
Command: eq { hop { argmax { all_rows ; us viewers ( millions ) } ; original air date } ; november 7 , 2010 }, interpretation: select the rows whose 1st leg record fuzzily matches to 0-0 . there is only one such row in the table . the team 1 record of this unqiue row is são paulo . the team 2 record of this unqiue row is nacional .
Output: no
Command: and { only { filter_eq { all_rows ; 1st leg ; 0-0 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 1 } ; são paulo } ; eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 } ; nacional } } }, interpretation: select the rows whose 1st leg record fuzzily matches to 0-0 . there is only one such row in the table . the team 1 record of this unqiue row is são paulo . the team 2 record of this unqiue row is nacional .
Output: yes
Command: and { only { filter_eq { all_rows ; 1st leg ; 0-0 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 1 } ; são paulo } ; eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 } ; nacional } } }, interpretation: for the result records of all rows , all of them fuzzily match to re-elected .
Output: no
Command: all_eq { all_rows ; result ; re-elected }, interpretation: select the row whose population record of all rows is maximum . the official name record of this row is moncton .
Output: no
Command: eq { hop { argmax { all_rows ; population } ; official name } ; moncton }, interpretation: select the row whose population record of all rows is maximum . the official name record of this row is moncton .
Output: yes
Command: eq { hop { argmax { all_rows ; population } ; official name } ; moncton }, interpretation: for the date records of all rows , all of them fuzzily match to 10 july 1954 .
Output: no
Command: all_eq { all_rows ; date ; 10 july 1954 }, interpretation: for the date records of all rows , all of them fuzzily match to 10 july 1954 .
Output: yes
Command: all_eq { all_rows ; date ; 10 july 1954 }, interpretation: the average of the races record of all rows is 12 .
Output: no
Command: round_eq { avg { all_rows ; races } ; 12 }, interpretation: the average of the races record of all rows is 12 .
Output: yes
Command: round_eq { avg { all_rows ; races } ; 12 }, interpretation: the sum of the points record of all rows is 96 .
Output: no
Command: round_eq { sum { all_rows ; points } ; 96 }, interpretation: the sum of the points record of all rows is 96 .
Output: yes
Command: round_eq { sum { all_rows ; points } ; 96 }, interpretation: select the row whose appearances record of all rows is maximum . the name record of this row is philippe jeannol .
Output: no
Command: eq { hop { argmax { all_rows ; appearances } ; name } ; philippe jeannol }, interpretation: select the row whose appearances record of all rows is maximum . the name record of this row is philippe jeannol .
Output: yes
Command: eq { hop { argmax { all_rows ; appearances } ; name } ; philippe jeannol }, interpretation: select the rows whose date record fuzzily matches to november 22 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 29 . take the attendance record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; date ; november 22 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 29 } ; attendance } }, interpretation: select the rows whose date record fuzzily matches to november 22 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 29 . take the attendance record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; date ; november 22 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 29 } ; attendance } }, interpretation: select the rows whose name record fuzzily matches to darwin robinson . take the round record of this row . select the rows whose name record fuzzily matches to joe miller . take the round record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; darwin robinson } ; round } ; hop { filter_eq { all_rows ; name ; joe miller } ; round } }, interpretation: select the rows whose name record fuzzily matches to darwin robinson . take the round record of this row . select the rows whose name record fuzzily matches to joe miller . take the round record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; darwin robinson } ; round } ; hop { filter_eq { all_rows ; name ; joe miller } ; round } }, interpretation: the average of the earnings record of all rows is 11647073 .
Output: no
Command: round_eq { avg { all_rows ; earnings } ; 11647073 }, interpretation: the average of the earnings record of all rows is 11647073 .
Output: yes
Command: round_eq { avg { all_rows ; earnings } ; 11647073 }, interpretation: select the row whose date record of all rows is 13th minimum . the race record of this row is italian grand prix .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 13 } ; race } ; italian grand prix }, interpretation: select the row whose date record of all rows is 13th minimum . the race record of this row is italian grand prix .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 13 } ; race } ; italian grand prix }, interpretation: select the rows whose year born record is equal to 1979 . there is only one such row in the table . the player record of this unqiue row is konstantinos tsartsaris .
Output: no
Command: and { only { filter_eq { all_rows ; year born ; 1979 } } ; eq { hop { filter_eq { all_rows ; year born ; 1979 } ; player } ; konstantinos tsartsaris } }, interpretation: select the rows whose year born record is equal to 1979 . there is only one such row in the table . the player record of this unqiue row is konstantinos tsartsaris .
Output: yes
Command: and { only { filter_eq { all_rows ; year born ; 1979 } } ; eq { hop { filter_eq { all_rows ; year born ; 1979 } ; player } ; konstantinos tsartsaris } }, interpretation: select the row whose goals record of all rows is 2nd maximum . the nationality record of this row is ireland .
Output: no
Command: eq { hop { nth_argmax { all_rows ; goals ; 2 } ; nationality } ; ireland }, interpretation: select the row whose goals record of all rows is 2nd maximum . the nationality record of this row is ireland .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; goals ; 2 } ; nationality } ; ireland }, interpretation: for the highest rank records of all rows , all of them fuzzily match to maegashira .
Output: no
Command: all_eq { all_rows ; highest rank ; maegashira }, interpretation: for the highest rank records of all rows , all of them fuzzily match to maegashira .
Output: yes
Command: all_eq { all_rows ; highest rank ; maegashira }, interpretation: for the location records of all rows , most of them fuzzily match to south dakota .
Output: no
Command: most_eq { all_rows ; location ; south dakota }, interpretation: for the location records of all rows , most of them fuzzily match to south dakota .
Output: yes
Command: most_eq { all_rows ; location ; south dakota }, interpretation: select the rows whose model number record fuzzily matches to core i7 - 2649 m . take the frequency record of this row . select the rows whose model number record fuzzily matches to core i7 - 2629 m . take the frequency record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; model number ; core i7 - 2649 m } ; frequency } ; hop { filter_eq { all_rows ; model number ; core i7 - 2629 m } ; frequency } }, interpretation: select the rows whose model number record fuzzily matches to core i7 - 2649 m . take the frequency record of this row . select the rows whose model number record fuzzily matches to core i7 - 2629 m . take the frequency record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; model number ; core i7 - 2649 m } ; frequency } ; hop { filter_eq { all_rows ; model number ; core i7 - 2629 m } ; frequency } }, interpretation: for the authority records of all rows , all of them fuzzily match to state .
Output: no
Command: all_eq { all_rows ; authority ; state }, interpretation: for the authority records of all rows , all of them fuzzily match to state .
Output: yes
Command: all_eq { all_rows ; authority ; state }, interpretation: for the date records of all rows , all of them fuzzily match to 9 august 1952 .
Output: no
Command: all_eq { all_rows ; date ; 9 august 1952 }, interpretation: for the date records of all rows , all of them fuzzily match to 9 august 1952 .
Output: yes
Command: all_eq { all_rows ; date ; 9 august 1952 }, interpretation: select the rows whose crowd record is less than 10,000 . there is only one such row in the table . the venue record of this unqiue row is corio oval .
Output: no
Command: and { only { filter_less { all_rows ; crowd ; 10,000 } } ; eq { hop { filter_less { all_rows ; crowd ; 10,000 } ; venue } ; corio oval } }, interpretation: select the rows whose crowd record is less than 10,000 . there is only one such row in the table . the venue record of this unqiue row is corio oval .
Output: yes
Command: and { only { filter_less { all_rows ; crowd ; 10,000 } } ; eq { hop { filter_less { all_rows ; crowd ; 10,000 } ; venue } ; corio oval } }, interpretation: select the rows whose club record fuzzily matches to pro recco . there is only one such row in the table . the name v t e record of this unqiue row is predrag jokić .
Output: no
Command: and { only { filter_eq { all_rows ; club ; pro recco } } ; eq { hop { filter_eq { all_rows ; club ; pro recco } ; name v t e } ; predrag jokić } }, interpretation: select the rows whose club record fuzzily matches to pro recco . there is only one such row in the table . the name v t e record of this unqiue row is predrag jokić .
Output: yes
Command: and { only { filter_eq { all_rows ; club ; pro recco } } ; eq { hop { filter_eq { all_rows ; club ; pro recco } ; name v t e } ; predrag jokić } }, interpretation: select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60000 . there is only one such row in the table .
Output: no
Command: only { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60000 } }, interpretation: select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60000 . there is only one such row in the table .
Output: yes
Command: only { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60000 } }, interpretation: select the rows whose player record fuzzily matches to jarron collins . take the years for jazz record of this row . select the rows whose player record fuzzily matches to dell curry . take the years for jazz record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; jarron collins } ; years for jazz } ; hop { filter_eq { all_rows ; player ; dell curry } ; years for jazz } }, interpretation: select the rows whose player record fuzzily matches to jarron collins . take the years for jazz record of this row . select the rows whose player record fuzzily matches to dell curry . take the years for jazz record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; jarron collins } ; years for jazz } ; hop { filter_eq { all_rows ; player ; dell curry } ; years for jazz } }, interpretation: select the rows whose coach record fuzzily matches to brian noble . select the row whose main article record of these rows is 4th minimum . the lost record of this row is 7 .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; coach ; brian noble } ; main article ; 4 } ; lost } ; 7 }, interpretation: select the rows whose coach record fuzzily matches to brian noble . select the row whose main article record of these rows is 4th minimum . the lost record of this row is 7 .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; coach ; brian noble } ; main article ; 4 } ; lost } ; 7 }, interpretation: select the row whose result record of all rows is maximum . the opponent record of this row is new england patriots .
Output: no
Command: eq { hop { argmax { all_rows ; result } ; opponent } ; new england patriots }, interpretation: select the row whose result record of all rows is maximum . the opponent record of this row is new england patriots .
Output: yes
Command: eq { hop { argmax { all_rows ; result } ; opponent } ; new england patriots }, interpretation: for the date records of all rows , most of them fuzzily match to january .
Output: no
Command: most_eq { all_rows ; date ; january }, interpretation: for the date records of all rows , most of them fuzzily match to january .
Output: yes
Command: most_eq { all_rows ; date ; january }, interpretation: select the rows whose event record fuzzily matches to medley . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; event ; medley } }, interpretation: select the rows whose event record fuzzily matches to medley . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; event ; medley } }, interpretation: select the row whose result f - a record of all rows is maximum . the opponents record of this row is dumbarton .
Output: no
Command: eq { hop { argmax { all_rows ; result f - a } ; opponents } ; dumbarton }, interpretation: select the row whose result f - a record of all rows is maximum . the opponents record of this row is dumbarton .
Output: yes
Command: eq { hop { argmax { all_rows ; result f - a } ; opponents } ; dumbarton }, interpretation: select the rows whose player record fuzzily matches to mark fidler . take the goals record of this row . select the rows whose player record fuzzily matches to david sacco . take the goals record of this row . the first record is 3 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; player ; mark fidler } ; goals } ; hop { filter_eq { all_rows ; player ; david sacco } ; goals } } ; 3 }, interpretation: select the rows whose player record fuzzily matches to mark fidler . take the goals record of this row . select the rows whose player record fuzzily matches to david sacco . take the goals record of this row . the first record is 3 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; player ; mark fidler } ; goals } ; hop { filter_eq { all_rows ; player ; david sacco } ; goals } } ; 3 }, interpretation: select the rows whose high points record fuzzily matches to gerald wallace . there is only one such row in the table . the date record of this unqiue row is february 8 .
Output: no
Command: and { only { filter_eq { all_rows ; high points ; gerald wallace } } ; eq { hop { filter_eq { all_rows ; high points ; gerald wallace } ; date } ; february 8 } }, interpretation: select the rows whose high points record fuzzily matches to gerald wallace . there is only one such row in the table . the date record of this unqiue row is february 8 .
Output: yes