TSTP Solution File: CSR117+1 by Enigma---0.5.1
View Problem
- Process Solution
%------------------------------------------------------------------------------
% File : Enigma---0.5.1
% Problem : CSR117+1 : TPTP v8.1.0. Released v4.1.0.
% Transfm : none
% Format : tptp:raw
% Command : enigmatic-eprover.py %s %d 1
% Computer : n019.cluster.edu
% Model : x86_64 x86_64
% CPU : Intel(R) Xeon(R) CPU E5-2620 v4 2.10GHz
% Memory : 8042.1875MB
% OS : Linux 3.10.0-693.el7.x86_64
% CPULimit : 300s
% WCLimit : 600s
% DateTime : Fri Jul 15 02:49:38 EDT 2022
% Result : Theorem 7.45s 2.47s
% Output : CNFRefutation 7.45s
% Verified :
% SZS Type : Refutation
% Derivation depth : 13
% Number of leaves : 30
% Syntax : Number of clauses : 79 ( 45 unt; 0 nHn; 79 RR)
% Number of literals : 235 ( 12 equ; 167 neg)
% Maximal clause size : 18 ( 2 avg)
% Maximal term depth : 2 ( 1 avg)
% Number of predicates : 20 ( 18 usr; 1 prp; 0-5 aty)
% Number of functors : 20 ( 20 usr; 18 con; 0-1 aty)
% Number of variables : 116 ( 11 sgn)
% Comments :
%------------------------------------------------------------------------------
cnf(i_0_16,plain,
( s__Region(X1)
| ~ s__GeographicArea(X1) ),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_16) ).
cnf(i_0_17,plain,
( s__GeographicArea(X1)
| ~ s__GeopoliticalArea(X1) ),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_17) ).
cnf(i_0_28,plain,
( s__Sea(esk15_1(X1))
| ~ s__City(X1)
| ~ is_instance(X1,s__CoastalCitiesClass) ),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_28) ).
cnf(i_0_35,plain,
is_instance(s__Copenhagen,s__CoastalCitiesClass),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_35) ).
cnf(i_0_102,plain,
s__City(s__Copenhagen),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_102) ).
cnf(i_0_15,plain,
( s__Object(X1)
| ~ s__Region(X1) ),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_15) ).
cnf(i_0_22,plain,
( s__BodyOfWater(X1)
| ~ s__Sea(X1) ),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_22) ).
cnf(i_0_30,negated_conjecture,
( to_int(X1) != to_int(X2)
| ~ s__Object(X3)
| ~ s__Object(X4)
| ~ s__SymbolicString(X5)
| ~ s__SymbolicString(X6)
| ~ s__SymbolicString(X7)
| ~ s__SymbolicString(X8)
| ~ real(X9)
| ~ real(X2)
| ~ real(X10)
| ~ real(X1)
| ~ int(X11)
| ~ capital_city(X4,X3)
| ~ is_instance(X3,s__OECDMemberEconomiesClass)
| ~ look_different(X4,s__Moscow)
| ~ s__capability(s__Flooding__t,s__located__m,X4)
| ~ latlong(X4,X1,X10,X8,X7)
| ~ latlong(s__Moscow,X2,X9,X6,X5) ),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_30) ).
cnf(i_0_206,plain,
look_different(s__Copenhagen,s__Moscow),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_206) ).
cnf(i_0_19,plain,
( s__GeopoliticalArea(X1)
| ~ s__City(X1) ),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_19) ).
cnf(i_0_27,plain,
( s__orientation(X1,esk15_1(X1),s__Near)
| ~ s__City(X1)
| ~ is_instance(X1,s__CoastalCitiesClass) ),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_27) ).
cnf(i_0_21,plain,
( s__WaterArea(X1)
| ~ s__BodyOfWater(X1) ),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_21) ).
cnf(i_0_29,plain,
( s__capability(s__Flooding__t,s__located__m,X1)
| ~ s__City(X1)
| ~ s__WaterArea(X2)
| ~ s__orientation(X1,X2,s__Near) ),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_29) ).
cnf(i_0_41,plain,
is_instance(s__Denmark,s__OECDMemberEconomiesClass),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_41) ).
cnf(i_0_103,plain,
capital_city(s__Copenhagen,s__Denmark),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_103) ).
cnf(i_0_18,plain,
( s__GeopoliticalArea(X1)
| ~ s__Nation(X1) ),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_18) ).
cnf(i_0_40,plain,
s__Nation(s__Denmark),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_40) ).
cnf(i_0_238,plain,
to_int('55.75695') = '55',
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_238) ).
cnf(i_0_170,plain,
real('55.75695'),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_170) ).
cnf(i_0_237,plain,
to_int('55.67631') = '55',
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_237) ).
cnf(i_0_150,plain,
real('55.67631'),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_150) ).
cnf(i_0_174,plain,
latlong(s__Moscow,'55.75695','37.614975',moscow,ru),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_174) ).
cnf(i_0_171,plain,
real('37.614975'),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_171) ).
cnf(i_0_172,plain,
s__SymbolicString(moscow),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_172) ).
cnf(i_0_173,plain,
s__SymbolicString(ru),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_173) ).
cnf(i_0_154,plain,
latlong(s__Copenhagen,'55.67631','12.569355',copenhagen,dk),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_154) ).
cnf(i_0_151,plain,
real('12.569355'),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_151) ).
cnf(i_0_152,plain,
s__SymbolicString(copenhagen),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_152) ).
cnf(i_0_153,plain,
s__SymbolicString(dk),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_153) ).
cnf(i_0_239,plain,
int('60'),
file('/export/starexec/sandbox2/tmp/enigma-theBenchmark.p-qdnosuxl/lgb.p',i_0_239) ).
cnf(c_0_270,plain,
( s__Region(X1)
| ~ s__GeographicArea(X1) ),
i_0_16 ).
cnf(c_0_271,plain,
( s__GeographicArea(X1)
| ~ s__GeopoliticalArea(X1) ),
i_0_17 ).
cnf(c_0_272,plain,
( s__Sea(esk15_1(X1))
| ~ s__City(X1)
| ~ is_instance(X1,s__CoastalCitiesClass) ),
i_0_28 ).
cnf(c_0_273,plain,
is_instance(s__Copenhagen,s__CoastalCitiesClass),
i_0_35 ).
cnf(c_0_274,plain,
s__City(s__Copenhagen),
i_0_102 ).
cnf(c_0_275,plain,
( s__Object(X1)
| ~ s__Region(X1) ),
i_0_15 ).
cnf(c_0_276,plain,
( s__Region(X1)
| ~ s__GeopoliticalArea(X1) ),
inference(spm,[status(thm)],[c_0_270,c_0_271]) ).
cnf(c_0_277,plain,
( s__BodyOfWater(X1)
| ~ s__Sea(X1) ),
i_0_22 ).
cnf(c_0_278,plain,
s__Sea(esk15_1(s__Copenhagen)),
inference(cn,[status(thm)],[inference(rw,[status(thm)],[inference(spm,[status(thm)],[c_0_272,c_0_273]),c_0_274])]) ).
cnf(c_0_279,negated_conjecture,
( to_int(X1) != to_int(X2)
| ~ s__Object(X3)
| ~ s__Object(X4)
| ~ s__SymbolicString(X5)
| ~ s__SymbolicString(X6)
| ~ s__SymbolicString(X7)
| ~ s__SymbolicString(X8)
| ~ real(X9)
| ~ real(X2)
| ~ real(X10)
| ~ real(X1)
| ~ int(X11)
| ~ capital_city(X4,X3)
| ~ is_instance(X3,s__OECDMemberEconomiesClass)
| ~ look_different(X4,s__Moscow)
| ~ s__capability(s__Flooding__t,s__located__m,X4)
| ~ latlong(X4,X1,X10,X8,X7)
| ~ latlong(s__Moscow,X2,X9,X6,X5) ),
i_0_30 ).
cnf(c_0_280,plain,
look_different(s__Copenhagen,s__Moscow),
i_0_206 ).
cnf(c_0_281,plain,
( s__Object(X1)
| ~ s__GeopoliticalArea(X1) ),
inference(spm,[status(thm)],[c_0_275,c_0_276]) ).
cnf(c_0_282,plain,
( s__GeopoliticalArea(X1)
| ~ s__City(X1) ),
i_0_19 ).
cnf(c_0_283,plain,
( s__orientation(X1,esk15_1(X1),s__Near)
| ~ s__City(X1)
| ~ is_instance(X1,s__CoastalCitiesClass) ),
i_0_27 ).
cnf(c_0_284,plain,
( s__WaterArea(X1)
| ~ s__BodyOfWater(X1) ),
i_0_21 ).
cnf(c_0_285,plain,
s__BodyOfWater(esk15_1(s__Copenhagen)),
inference(spm,[status(thm)],[c_0_277,c_0_278]) ).
cnf(c_0_286,negated_conjecture,
( to_int(X1) != to_int(X2)
| ~ latlong(s__Moscow,X2,X3,X4,X5)
| ~ latlong(s__Copenhagen,X1,X6,X7,X8)
| ~ s__capability(s__Flooding__t,s__located__m,s__Copenhagen)
| ~ is_instance(X9,s__OECDMemberEconomiesClass)
| ~ capital_city(s__Copenhagen,X9)
| ~ int(X10)
| ~ real(X6)
| ~ real(X3)
| ~ real(X2)
| ~ real(X1)
| ~ s__SymbolicString(X7)
| ~ s__SymbolicString(X8)
| ~ s__SymbolicString(X4)
| ~ s__SymbolicString(X5)
| ~ s__Object(s__Copenhagen)
| ~ s__Object(X9) ),
inference(spm,[status(thm)],[c_0_279,c_0_280]) ).
cnf(c_0_287,plain,
( s__Object(X1)
| ~ s__City(X1) ),
inference(spm,[status(thm)],[c_0_281,c_0_282]) ).
cnf(c_0_288,plain,
( s__capability(s__Flooding__t,s__located__m,X1)
| ~ s__City(X1)
| ~ s__WaterArea(X2)
| ~ s__orientation(X1,X2,s__Near) ),
i_0_29 ).
cnf(c_0_289,plain,
s__orientation(s__Copenhagen,esk15_1(s__Copenhagen),s__Near),
inference(cn,[status(thm)],[inference(rw,[status(thm)],[inference(spm,[status(thm)],[c_0_283,c_0_273]),c_0_274])]) ).
cnf(c_0_290,plain,
s__WaterArea(esk15_1(s__Copenhagen)),
inference(spm,[status(thm)],[c_0_284,c_0_285]) ).
cnf(c_0_291,negated_conjecture,
( to_int(X1) != to_int(X2)
| ~ latlong(s__Moscow,X2,X3,X4,X5)
| ~ latlong(s__Copenhagen,X1,X6,X7,X8)
| ~ s__capability(s__Flooding__t,s__located__m,s__Copenhagen)
| ~ is_instance(X9,s__OECDMemberEconomiesClass)
| ~ capital_city(s__Copenhagen,X9)
| ~ int(X10)
| ~ real(X6)
| ~ real(X3)
| ~ real(X2)
| ~ real(X1)
| ~ s__SymbolicString(X7)
| ~ s__SymbolicString(X8)
| ~ s__SymbolicString(X4)
| ~ s__SymbolicString(X5)
| ~ s__Object(X9) ),
inference(cn,[status(thm)],[inference(rw,[status(thm)],[inference(spm,[status(thm)],[c_0_286,c_0_287]),c_0_274])]) ).
cnf(c_0_292,plain,
s__capability(s__Flooding__t,s__located__m,s__Copenhagen),
inference(cn,[status(thm)],[inference(rw,[status(thm)],[inference(rw,[status(thm)],[inference(spm,[status(thm)],[c_0_288,c_0_289]),c_0_290]),c_0_274])]) ).
cnf(c_0_293,negated_conjecture,
( to_int(X1) != to_int(X2)
| ~ latlong(s__Moscow,X2,X3,X4,X5)
| ~ latlong(s__Copenhagen,X1,X6,X7,X8)
| ~ is_instance(X9,s__OECDMemberEconomiesClass)
| ~ capital_city(s__Copenhagen,X9)
| ~ int(X10)
| ~ real(X6)
| ~ real(X3)
| ~ real(X2)
| ~ real(X1)
| ~ s__SymbolicString(X7)
| ~ s__SymbolicString(X8)
| ~ s__SymbolicString(X4)
| ~ s__SymbolicString(X5)
| ~ s__Object(X9) ),
inference(cn,[status(thm)],[inference(rw,[status(thm)],[c_0_291,c_0_292])]) ).
cnf(c_0_294,plain,
is_instance(s__Denmark,s__OECDMemberEconomiesClass),
i_0_41 ).
cnf(c_0_295,plain,
capital_city(s__Copenhagen,s__Denmark),
i_0_103 ).
cnf(c_0_296,plain,
( s__GeopoliticalArea(X1)
| ~ s__Nation(X1) ),
i_0_18 ).
cnf(c_0_297,plain,
( to_int(X1) != to_int(X2)
| ~ latlong(s__Moscow,X2,X3,X4,X5)
| ~ latlong(s__Copenhagen,X1,X6,X7,X8)
| ~ int(X9)
| ~ real(X6)
| ~ real(X3)
| ~ real(X2)
| ~ real(X1)
| ~ s__SymbolicString(X7)
| ~ s__SymbolicString(X8)
| ~ s__SymbolicString(X4)
| ~ s__SymbolicString(X5)
| ~ s__Object(s__Denmark) ),
inference(cn,[status(thm)],[inference(rw,[status(thm)],[inference(spm,[status(thm)],[c_0_293,c_0_294]),c_0_295])]) ).
cnf(c_0_298,plain,
( s__Object(X1)
| ~ s__Nation(X1) ),
inference(spm,[status(thm)],[c_0_281,c_0_296]) ).
cnf(c_0_299,plain,
s__Nation(s__Denmark),
i_0_40 ).
cnf(c_0_300,plain,
( to_int(X1) != to_int(X2)
| ~ latlong(s__Moscow,X2,X3,X4,X5)
| ~ latlong(s__Copenhagen,X1,X6,X7,X8)
| ~ int(X9)
| ~ real(X6)
| ~ real(X3)
| ~ real(X2)
| ~ real(X1)
| ~ s__SymbolicString(X7)
| ~ s__SymbolicString(X8)
| ~ s__SymbolicString(X4)
| ~ s__SymbolicString(X5) ),
inference(cn,[status(thm)],[inference(rw,[status(thm)],[inference(spm,[status(thm)],[c_0_297,c_0_298]),c_0_299])]) ).
cnf(c_0_301,plain,
to_int('55.75695') = '55',
i_0_238 ).
cnf(c_0_302,plain,
real('55.75695'),
i_0_170 ).
cnf(c_0_303,plain,
( to_int(X1) != '55'
| ~ latlong(s__Moscow,'55.75695',X2,X3,X4)
| ~ latlong(s__Copenhagen,X1,X5,X6,X7)
| ~ int(X8)
| ~ real(X5)
| ~ real(X2)
| ~ real(X1)
| ~ s__SymbolicString(X6)
| ~ s__SymbolicString(X7)
| ~ s__SymbolicString(X3)
| ~ s__SymbolicString(X4) ),
inference(cn,[status(thm)],[inference(rw,[status(thm)],[inference(spm,[status(thm)],[c_0_300,c_0_301]),c_0_302])]) ).
cnf(c_0_304,plain,
to_int('55.67631') = '55',
i_0_237 ).
cnf(c_0_305,plain,
real('55.67631'),
i_0_150 ).
cnf(c_0_306,plain,
( ~ latlong(s__Moscow,'55.75695',X1,X2,X3)
| ~ latlong(s__Copenhagen,'55.67631',X4,X5,X6)
| ~ int(X7)
| ~ real(X4)
| ~ real(X1)
| ~ s__SymbolicString(X5)
| ~ s__SymbolicString(X6)
| ~ s__SymbolicString(X2)
| ~ s__SymbolicString(X3) ),
inference(cn,[status(thm)],[inference(rw,[status(thm)],[inference(spm,[status(thm)],[c_0_303,c_0_304]),c_0_305])]) ).
cnf(c_0_307,plain,
latlong(s__Moscow,'55.75695','37.614975',moscow,ru),
i_0_174 ).
cnf(c_0_308,plain,
real('37.614975'),
i_0_171 ).
cnf(c_0_309,plain,
s__SymbolicString(moscow),
i_0_172 ).
cnf(c_0_310,plain,
s__SymbolicString(ru),
i_0_173 ).
cnf(c_0_311,plain,
( ~ latlong(s__Copenhagen,'55.67631',X1,X2,X3)
| ~ int(X4)
| ~ real(X1)
| ~ s__SymbolicString(X2)
| ~ s__SymbolicString(X3) ),
inference(cn,[status(thm)],[inference(rw,[status(thm)],[inference(rw,[status(thm)],[inference(rw,[status(thm)],[inference(spm,[status(thm)],[c_0_306,c_0_307]),c_0_308]),c_0_309]),c_0_310])]) ).
cnf(c_0_312,plain,
latlong(s__Copenhagen,'55.67631','12.569355',copenhagen,dk),
i_0_154 ).
cnf(c_0_313,plain,
real('12.569355'),
i_0_151 ).
cnf(c_0_314,plain,
s__SymbolicString(copenhagen),
i_0_152 ).
cnf(c_0_315,plain,
s__SymbolicString(dk),
i_0_153 ).
cnf(c_0_316,plain,
int('60'),
i_0_239 ).
cnf(c_0_317,plain,
~ int(X1),
inference(cn,[status(thm)],[inference(rw,[status(thm)],[inference(rw,[status(thm)],[inference(rw,[status(thm)],[inference(spm,[status(thm)],[c_0_311,c_0_312]),c_0_313]),c_0_314]),c_0_315])]) ).
cnf(c_0_318,plain,
$false,
inference(sr,[status(thm)],[c_0_316,c_0_317]),
[proof] ).
%------------------------------------------------------------------------------
%----ORIGINAL SYSTEM OUTPUT
% 0.07/0.12 % Problem : CSR117+1 : TPTP v8.1.0. Released v4.1.0.
% 0.07/0.13 % Command : enigmatic-eprover.py %s %d 1
% 0.13/0.34 % Computer : n019.cluster.edu
% 0.13/0.34 % Model : x86_64 x86_64
% 0.13/0.34 % CPU : Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
% 0.13/0.34 % Memory : 8042.1875MB
% 0.13/0.34 % OS : Linux 3.10.0-693.el7.x86_64
% 0.13/0.34 % CPULimit : 300
% 0.13/0.34 % WCLimit : 600
% 0.13/0.34 % DateTime : Sat Jun 11 18:16:41 EDT 2022
% 0.13/0.35 % CPUTime :
% 0.19/0.46 # ENIGMATIC: Selected complete mode:
% 7.45/2.47 # ENIGMATIC: Solved by autoschedule-lgb:
% 7.45/2.47 # No SInE strategy applied
% 7.45/2.47 # Trying AutoSched0 for 150 seconds
% 7.45/2.47 # AutoSched0-Mode selected heuristic H_____011_C18_F1_PI_SE_SP_S2S
% 7.45/2.47 # and selection function SelectNewComplexAHP.
% 7.45/2.47 #
% 7.45/2.47 # Preprocessing time : 0.016 s
% 7.45/2.47
% 7.45/2.47 # Proof found!
% 7.45/2.47 # SZS status Theorem
% 7.45/2.47 # SZS output start CNFRefutation
% See solution above
% 7.45/2.47 # Training examples: 0 positive, 0 negative
% 7.45/2.47
% 7.45/2.47 # -------------------------------------------------
% 7.45/2.47 # User time : 0.029 s
% 7.45/2.47 # System time : 0.008 s
% 7.45/2.47 # Total time : 0.037 s
% 7.45/2.47 # Maximum resident set size: 7124 pages
% 7.45/2.47
%------------------------------------------------------------------------------