yuansui/folio huggingface dataset:
Premises:
If you go somewhere by train, you will not lose time.
If you go somewhere by car and meet a traffic jam, you will lose time.
If you lose time, you will be late for work.
Mary can get from New Haven to New York City either by train or car.
Mary is late for work.

Premises in first-order logic:
∀x (GoByTrain(x) → ¬LoseTime(x))
∀x((GoByCar(x) ∧ Meet(x, trafficJam)) → LoseTime(x))
∀x (LoseTime(x) → LateForWork(x))
FromAndTo(newHaven, newYork) ∧ (GoByTrain(mary) ⊕ GoByCar(mary))
LateForWork(mary)

Conclusions:
Mary gets from New Haven to New York City by train.
Mary gets from New Haven to New York City by car.
Mary meets a traffic jam.

Conclusions in first-order logic:
FromAndTo(newHaven, newYork) ∧ GoByTrain(mary)
FromAndTo(newHaven, newYork) ∧ GoByCar(mary)
Meet(mary, trafficJam)

Truth value of each conclusion (T = true, F = false, U = unknown):
F
T
U
Premises:
Oxford Circus is a road junction connecting Oxford Street and Regent Street.
Oxford Street and Regent Street are in London.
John Nash designed a construction on Regent Street.
John Nash designed Oxford Circus.
John Nash is a British architect.
Oxford Circus is the entrance to Oxford Circus tube station, a part of the Central line in 1900.

Premises in first-order logic:
RoadJunction(oxfordCircus) ∧ Connect(oxfordCircus, oxfordSt, regentSt)
In(oxfordSt, london) ∧ In(regentSt, london)
Designed(nash, construction) ∧ On(construction, regentSt)
Designed(nash, oxfordCircus)
Architect(nash) ∧ British(nash)
EntraceTo(oxfordCircus, tubeStation) ∧ PartOf(tubeStation, centralline) ∧ In(tubeStation, 1900)

Conclusions:
Oxford Circus is in London.
Oxford Circus is designed by a British architect.
John Nash designed the Central line in 1900.
Regent Street is not in London.

Conclusions in first-order logic:
In(oxfordCircus, london)
∃x (British(x) ∧ Architect(x) ∧ Design(x, oxfordCircus))
∀x (PartOf(x, centralLine) → Design(johnNash, x))
¬In(regentStreet, london)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
T
U
F
Premises:
Peter Parker is either a superhero or a civilian.
The Hulk is a destroyer.
The Hulk wakes up when he is angry.
If the Hulk wakes up, then he will break a bridge.
Thor is a god.
Thor will break a bridge when he is happy.
A god is not a destroyer.
Peter Parker wears a uniform when he is a superhero.
Peter Parker is not a civilian if a destroyer is breaking a bridge.
If Thor is happy, the Hulk is angry.

Premises in first-order logic:
Superhero(peterParker) ⊕ Civilian(peterParker)
Destroyer(theHulk)
Angry(theHulk) → WakesUp(theHulk)
WakesUp(theHulk) → Breaks(theHulk, bridge)
God(thor)
Happy(thor) → Breaks(thor, bridge)
∀x (God(x) → ¬Destroyer(x))
Superhero(peter) → Wears(peter, uniform)
∀x ((Destroyer(x) ∧ Breaks(x,bridge)) → ¬Civilian(peter))
Happy(thor) → Angry(theHulk)

Conclusions:
If the Hulk does not wake up, then Thor is not happy. 
If Thor is happy, then Peter Parker wears a uniform. 
If Thor is not happy, then no bridge will be broken. 

Conclusions in first-order logic:
¬WakesUp(theHulk) → ¬Happy(thor)
Happy(thor) → Wears(peterParker, uniform)
¬Happy(thor) → ¬Breaks(thor, bridge)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
T
U
Premises:
Herodicus was a Greek physician, dietician, sophist, and gymnast.
Herodicus was born in the city of Selymbria.
Selymbria is a colony of the city-state Megara.
One of the tutors of Hippocrates was Herodicus.
Massages were recommended by Herodicus.
Some of the theories of Herodicus are considered to be the foundation of sports medicine.

Premises in first-order logic:
Greek(herodicus) ∧ Physician(herodicus) ∧ Dietician(herodicus) ∧ Sophist(herodicus) ∧ Gymnast(herodicus)
Born(herodicus, selymbia) ∧ City(selymbia)
Colony(selymbia, megara) ∧ CityState(megara)
Tutor(herodicus, hippocrates)
Recommend(herodicus, massages)
∃x ∃y (Theory(x) ∧ From(x, herodicus) ∧ FoundationOf(x, sportsMedicine) ∧ (¬(x=y)) ∧ Theory(y) ∧ From(y, herodicus) ∧ FoundationOf(y, sportsMedicine))

Conclusions:
Herodicus tutored Hippocrates.
Herodicus was tutored by Hippocrates.
Herodicus was born in a city-state.
Herodicus did not recommend massages.
Herodicus was born in a colony of a city-state.

Conclusions in first-order logic:
Tutor(herodicus, hippocrates)
Tutor(hippocrates, herodicus)
∃x (Born(herodicus, x) ∧ CityState(x))
¬Recommend(herodicus, massages)
∃x ∃y (Born(herodicus, x) ∧ Colony(x, y) ∧ CityState(y))

Truth value of each conclusion (T = true, F = false, U = unknown):
T
U
U
F
T
Premises:
If George likes music, he wants to compose.
If George has access to a program, he can compose.
If George can compose and wants to compose, he will compose.

Premises in first-order logic:
LikeMusic(george) → WantToCompose(george)
AccesstoProgram(george) → CanCompose(george)
(WantToCompose(george) ∧ CanCompose(george)) → WillCompose(george)

Conclusions:
If George likes music and has access to a program, he will compose.
If George will not compose, George can not compose.

Conclusions in first-order logic:
(LikeMusic(george) ∧ AccesstoProgram(george)) → WillCompose(george)
¬WantToCompose(george) → ¬WillCompose(george)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
U
Premises:
ETS develops various standardized tests primarily in the United States for K-12 and higher education. 
ETS administers international tests, including the TOEFL, TOEIC, GRE, and subject tests.
Many of the assessments ETS develops are associated with entry to the US tertiary and quaternary education institutions. 
ETS also develops K-12 statewide assessments used for accountability testing in many states.

Premises in first-order logic:
∃x ∃y (Develop(eTS, x) ∧ Develop(eTS, y) ∧ StandardizedTest(x) ∧ StandardizedTest(y) ∧ In(x, unitedState) ∧ In(y, unitedState) ∧ For(x, k12AndHigherEducation) ∧ For(y, k12AndHigherEducation))
∃x (Administer(eTS, x) ∧ InternationalTest(x) ∧ (TOEFL(x) ∨ TOEIC(x) ∨ GRE(x) ∨ SubjectTest(x)))
∃x (Develop(eTS, x) ∧ AssociatedWith(x, entryToUSEducationInstitution))
∃x (Develop(eTS, x) ∧ StateWideAssesment(x) ∧ UsedFor(x, accountabilityTesting))

Conclusions:
ETS develops assessments for K-12 statewide as well as entry to US tertiary and quaternary education institutions.
ETS doesn't administer tests internationally.
ETS administers international tests including the TOEFL, GRE and subject tests in China.

Conclusions in first-order logic:
∃x ∃y (Develop(eTS, x) ∧ StateWideAssesment(x) ∧ Develop(eTS, y) ∧ AssociatedWith(y, entryToUSEducationInstitution))
∀x (Administer(eTS, x) → ¬InternationalTest(x))
∃x (Administer(eTS, x) ∧ InChina(x) ∧ (TOEFL(x) ∨ TOEIC(x) ∨ GRE(x) ∨ SubjectTest(x)))

Truth value of each conclusion (T = true, F = false, U = unknown):
T
F
U
Premises:
A plane goes faster than a car.
A car goes faster than walking.
Riding bikes is faster than walking.

Premises in first-order logic:
∀x ∀y (Plane(x) ∧ Car(y) → Faster(x, y))
∀x ∀y (Car(x) ∧ Walking(y) → Faster(x, y))
∀x ∀y (RidingBike(x) ∧ Walking(y) → Faster(x, y))

Conclusions:
A plane goes faster than riding a bike.
A plane goes faster than walking.

Conclusions in first-order logic:
∀x ∀y (Plane(x) ∧ RidingBike(y) → Faster(x, y))
∀x ∀y (Plane(x) ∧ Walking(y) → Faster(x, y))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
U
Premises:
Lawton Park is a neighborhood in Seattle. 
All citizens of Lawton Park use the zip code 98199. 
Tom is a citizen of Lawton Park.
Daniel uses the zip code 98199. 

Premises in first-order logic:
NeighbourhoodIn(lawtonPark, seattle)
∀x (Residentof(x, lawtonPark) → UseZipCode(x, num98199))
ResidentOf(tom, lawtonPark)
UseZipCode(daniel, num98199)

Conclusions:
Tom uses the zip code 98199. 
Tom doesn't use the zip code 98199. 
Tom is a citizen of Washington. 
Daniel is a citizen of Lawton Park. 

Conclusions in first-order logic:
UseZipCode(tom, num98199)
¬UseZipCode(tom, num98199)
ResidentOf(tom, washington)
ResidentOf(daniel, lawtonPark)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
F
U
U
Premises:
No road is dustless.
Some streets are roads.

Premises in first-order logic:
∀x (Road(x) → ¬Dustless(x))
∃x ∃y (Street(x) ∧ Street(y) ∧ Road(x) ∧ Road(y) ∧ ¬(x=y))

Conclusions:
Some streets are dustless.

Conclusions in first-order logic:
∃x ∃y (Street(x) ∧ Street(y) ∧ Dustless(x) ∧ Dustless(y) ∧ ¬(x=y))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
Premises:
Palstaves are a type of early bronze axe.
Palstaves are found in northern, western, and southwestern Europe and are cast in molds.
John Evans is an archeologist who popularized the term "palstave."
Paalstabs are not a type of axe but rather a digging shovel.

Premises in first-order logic:
EarlyBronzeAge(palstave) ∧ Axe(palstave)
FoundIn(palstave, northernEurope) ∨ FoundIn(palstave, westernEurope) ∨ FoundIn(palstave, southWesternEurope)) ∧ CastIn(palstave, molds)
Archeologist(johnEvans) ∧ Popularize(johnEvans, termPalstave)
¬Axe(paalstab) ∧ DiggingShovel(paalstab)

Conclusions:
John Evans Popularized the term paalstab.
There is an axe that is found in Western Europe.
Archeologists haven't popularized anything.

Conclusions in first-order logic:
Popularized(johnEvans, termPalstave)
∃x (Axe(x) ∧ FoundIn(x, westernEurope))
∀x ∀y (Archeologist(x) → ¬Popularize(x, y))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
U
F
Premises:
All proteins are organic compounds.
All enzymes are organic compounds.

Premises in first-order logic:
∀x (Protein(x) → OrganicCompound(x))
∀x (Enzyme(x) → OrganicCompound(x))

Conclusions:
All enzymes are proteins.

Conclusions in first-order logic:
∀x (Enzyme(x) → Protein(x))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
Premises:
A natural language processing task can be categorized as either a language generation task or a language understanding task.
If the output of a natural language processing task is a text sequence, then it is a language generation task.
Machine translation is a natural language processing task.
The output of machine translation is a text sequence.

Premises in first-order logic:
∀x (NaturalLanguageProcessingTask(x) → LanguageGenerationTask(x) ⊕ LanguageUnderstandingTask(x))
∀x (NaturalLanguageProcessingTasks(x) ∧ OutputSequence(x, text) → LanguageGenerationTask(x))
NaturalLanguageProcessingTask(machineTranslation)
OutputSequence(machineTranslation, text)

Conclusions:
Machine translation is a language generation task.
Machine translation is a language understanding task.

Conclusions in first-order logic:
LanguageGenerationTask(machineTranslation)
LanguageUnderstandingTask(machineTranslation)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
F
Premises:
If someone can run far and navigate using a map, they can participate in orienteering.
Fit people can run far.
People who have a good sense of direction can use a map.
Military officers are fit.
Hailee is a military officer and has a good sense of direction.
Karl is not a military officer and can use a map.

Premises in first-order logic:
∀x ((RunFar(x) ∧ UseMap(x)) → Orienteer(x))
∀x (Fit(x) → RunFar(x))
∀x (SenseDirection(x) → UseMap(x))
∀x (MilitaryOfficer(x) → Fit(x))
MilitaryOfficer(hailee) ∧ SenseDirection(hailee)
¬MilitaryOfficer(karl) ∧ UseMap(karl)

Conclusions:
Hailee can participate in orienteering.
Karl can not participate in orienteering.

Conclusions in first-order logic:
Orienteer(hailee)
¬Orienteer(karl)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
U
Premises:
Elephantopus is a genus of perennial plants in the daisy family.
Elephantopus is widespread over much of Africa, southern Asia, Australia, and the Americas.
Several species of Elephantopus are native to the southeastern United States.
Elephantopus scaber is a traditional medicine.

Premises in first-order logic:
∀x (Elephantopus(x) → (Genus(x, perennialplants) ∧ BelongTo(x, daisyfamily)))
∃x ∃y ∃z(Elephantopus(x) ∧ In(x,africa) ∧ (¬(x=y)) ∧ Elephantopus(y) ∧ In(y, southernasia) ∧ (¬(x=z)) ∧ (¬(y=z)) ∧ Elephantopus(z) ∧ In(z, australia))
∃x ∃y (Elephantopus(x) ∧ NativeTo(x, southeasternunitedstates) ∧ (¬(x=y)) ∧ Elephantopus(y) ∧ NativeTo(y, southeasternunitedstates))
∀x (ElephantopusScaber(x) → TraditionalMedicine(x))

Conclusions:
Elephantopus is found in Australia and Southern Asia.
No Elephantopus is native to the southeastern United States.
Elephantopus is a traditional medicine.

Conclusions in first-order logic:
∃x ∃y (Elephantopus(x) ∧ In(x,africa) ∧ Elephantopus(y) ∧ In(y,africa))
∀x (Elephantopus(x) → ¬NativeTo(x, southeasternunitedstates))
∀x (Elephantopus(x) → TraditionalMedicine(x))

Truth value of each conclusion (T = true, F = false, U = unknown):
T
F
U
Premises:
A controlled substance is a drug.
There exist both harmful and beneficial controlled substances.
If a child is exposed to a controlled substance, they are in chemical endangerment.
Chemical Endangerment is harmful. 
The Controlled Substances Act was an act passed in 1971.
Some Acts prevent harmful things.

Premises in first-order logic:
∀x (ControlledSubstances(x) → Drugs(x))
∃x ∃y (ControlledSubstances(x) ∧ ControlledSubstances(y) ∧ (¬(x=y)) ∧ Beneficial(x) ∧ Harmful(y))
∀x ∀y ((Child(x) ∧ ControlledSubstances(y) ∧ ExposedTo(x, y)) → InChemicalEndangerment(x))
∀x (InChemicalEndangerment(x) → Harmful(x))
PassedIn(controlledSubstancesAct, yr1971) ∧ Act(controlledSubstancesAct)
∃x ∃y (Act(x) ∧ PreventsHarm(x) ∧ (¬(x=y)) ∧ Act(y) ∧ PreventsHarm(y))

Conclusions:
The Controlled Substances Act prevents harmful things.
Some drugs are beneficial. 
A child in chemical endangerment is in harm. 

Conclusions in first-order logic:
PreventsHarm(controlledSubstancesAct)
∃x ∃y (Drugs(x) ∧ Beneficial(x) ∧ (¬(x=y)) ∧ Drugs(y) ∧ Beneficial(y))
∀x ((Child(x) ∧ InChemicalEndangerment(x)) → Harmful(x))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
T
T
Premises:
Heptalogyy is a compound literary or narrative work that is made up of seven distinct works.
The Harry Potter series consists of 7 distinct works.
The Chronicles of Narnia consists of 7 distinct works.

Premises in first-order logic:
∀x (SevenDistinctWorks(x) → Heptalogy(x))
SevenDistinctWorks(harryPotter)
SevenDistinctWorks(chroniclesOfNarnia)

Conclusions:
The Harry Potter series of books is Heptalogy. 
The Chronicles of Narnia series of books is not Heptalogy.
The Lord of the Rings is Heptalogy. 

Conclusions in first-order logic:
Heptalogy(harryPotter)
¬Heptalogy(chroniclesOfNarnia)
Heptalogy(lordOfRings)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
F
U
Premises:
All pets are animals.
Pets can be either a dog or a cat.
If a person has a pet, they care for that pet. 
Dogs and cats can be naughty. 
Pets who are naughty are not liked as much. 
Charlie has a naughty pet dog named Leo. 

Premises in first-order logic:
∀x (Pet(x) → Animal(x))
∀x (Pet(x) → (Dog(x) ⊕ Cat(x)))
∀x ∀y ((Pet(y) ∧ OwnedBy(x,y)) → Cares(x, y))
∃x ∃y (Cat(x) ∧ Naughty(x) ∧ (¬(x=y)) ∧ Dog(y) ∧ Naughty(y))
∀x ∀y ((Pet(x) ∧ Naughty(x) ∧ OwnedBy(x,y)) → ¬Liked(x, y))
OwnedBy(leo, charlie) ∧ Pet(leo) ∧ Dog(leo) ∧ Naughty(leo)

Conclusions:
Leo is an animal. 
Charlie does not like Leo and does not care for Leo.
Dogs are not always naughty.

Conclusions in first-order logic:
Animal(leo)
¬Liked(leo, charlie) ∧ ¬Cares(charlie, leo)
∀x (Dog(x) → ¬Naughty(x))

Truth value of each conclusion (T = true, F = false, U = unknown):
T
F
F
Premises:
A werewolf is a human that can turn into a wolf.
A werewolf has been scratched or bitten by another werewolf.
If someone has been scratched or bitten by some entity, they have been attacked by that entity.

Premises in first-order logic:
∀x (Human(x) ∧ CanTurnInto(x, wolf) → Werewolf(x))
∀x ∃y (Werewolf(x) → (BittenBy(x, y) ∨ ScratchedBy(x, y)) ∧ Werewolf(y))
∀x ∃y (BittenBy(x, y) ∨ ScratchedBy(x, y)) → AttackedBy(x,y)

Conclusions:
All humans are werewolves.
A werewolf is a wolf.
A werewolf has scratched someone before.

Conclusions in first-order logic:
∀x (Human(x) → Werewolf(x))
∀x (Werewolf(x) → Wolf(x))
∀x ∃y (Werewolf(x) → ScratchedBy(y, x))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
U
U
Premises:
Donald Ervin Knuth is an American computer scientist, mathematician, and Professor Emeritus at Stanford University.
Knuth has been called the "father of the analysis of algorithms."

Premises in first-order logic:
American(donaldErvinKnuth) ∧ ComputerScientist(donaldErvinKnuth) ∧ Mathematician(donaldErvinKnuth) ∧ ProfessorEmeritusAt(donaldErvinKnuth, stanford)
Called(donaldErvinKnuth, fatherOfTheAnalysisOfAlgorithms)

Conclusions:
An American scientist has been called the "father of the analysis of algorithms".
A mathematician has been called the "father of the analysis of algorithms".
Donald Knuth is a well-known figure in the field of artificial intelligence.

Conclusions in first-order logic:
∃x (American(x) ∧ ComputerScientist(x) ∧ Called(x, fatherOfTheAnalysisOfAlgorithms))
∃x (Mathematician(x) ∧ Called(x, fatherOfTheAnalysisOfAlgorithms))
WellKnownFigureIn(donaldErvinKnuth, artificialIntelligence)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
T
U
Premises:
Mr. and Mrs. Smith make a travel plan: they want to go to a city in California or Florida where neither of them has ever been.
The cities in California that they are interested in are San Francisco, Los Angeles, and San Diego.
Cities in Florida that they are interested in are Orlando and Miami.
Mr. Smith has been to two cities in California.
Mrs. Smith has been to one city in Florida.

Premises in first-order logic:
∀x (WantToGoTo(mr.AndMrs.Smith, x) ∧ City(x) → (California(x) ∨ Florida(x)) ∧ NeverGo(x))
City(sanFrancisco) ∧ California(sanFrancisco) ∧ WantToGoTo(mr.AndMrs.Smith, sanFrancisco) ∧ City(losAngeles) ∧ California(losAngeles) ∧ WantToGoTo(mr.AndMrs.Smith, losAngeles) ∧ City(sanDiego) ∧ California(sanDiego) ∧ WantToGoTo(mr.AndMrs.Smith, sanDiego)
City(orlando) ∧ Florida(orlando) ∧ WantToGo(mr.AndMrs.Smith, orlando) ∧ City(miami) ∧ Florida(miami) ∧ WantToGo(mr.AndMrs.Smith, miami)
∃x ∃y ∀z (¬(x=z) ∧ ¬(y=z) ∧ ¬(x=y) ∧ City(x) ∧ City(y) ∧ City(z) ∧ California(x) ∧ California(y) ∧ California(z) → Visit(mr.smith, x) ∧ Visit(mr.smith, y) ∧ ¬Visit(mr.smith, z))
∃x ∀y (¬(x=y) ∧ City(x) ∧ City(y) ∧ Florida(x) ∧ Florida(y) → Visit(mrs.smith, x) ∧ ¬Visit(mrs.smith, y))

Conclusions:
Mr. Smith has been to San Francisco.
They have at leat one candidate city in Florida to visit.
They have at least two candidate cities in California to visit.

Conclusions in first-order logic:
∃x (City(x) ∧ Visit(mr.smith, sanFrancisco))
∃x (WantToGoTo(x) ∧ City(x) ∧ Florida(x))
∃x ∃y (¬(x=y) ∧ City(x) ∧ City(y) ∧ WantToGoTo(mr.AndMrs.Smith, x) ∧ California(x) ∧ WantToGoTo(mr.AndMrs.Smith, y) ∧ California(y))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
U
U
Premises:
Jason Kramer is an American music supervisor.
Some American radio personalities are also music supervisors. 
Anyone who hosts a show on a public radio station is a radio personality.
Joe Rogan is a radio personality.
Jason Kramer hosted a show on a public radio station.

Premises in first-order logic:
MusicSupervisor(jasonKramer) ∧ American(jasonKramer)
∃x ∃y (American(x) ∧ MusicSupervisor(x) ∧ RadioPersonality(x) ∧ (¬(x=y)) ∧ American(y) ∧ MusicSupervisor(y) ∧ RadioPersonality(y))
∀x ∀y((HostShowOn(x, y) ∧ PublicRadioStation(x)) → RadioPersonality(x))
RadioPersonality(joeRogan)
∃x(HostShowOn(jasonKramer, x) ∧ PublicRadioStation(x))

Conclusions:
Joe Rogan is American. 
Jason Kramer is a music supervisor. 
Jason Kramer is a radio personality.

Conclusions in first-order logic:
American(joeRogan)
MusicSupervisor(jasonKramer)
RadioPersonality(jasonKramer)

Truth value of each conclusion (T = true, F = false, U = unknown):
U
T
T
Premises:
Belgium, France, and Germany are European countries.
Paris is the capital of France.
The Eiffel Tower is one of the main tourist attractions located in Paris.
Some people who live in Belgium speak French.
If John goes to Europe, he will see some tourist attractions.
John speaks French.

Premises in first-order logic:
EuropeanCountry(belgium) ∧ EuropeanCountry(france) ∧ EuropeanCountry(germany)
CapitalOf(paris, france)
TouristAttraction(eiffelTower) ∧ LocatedIn(eiffelTower, paris)
∃x (LiveIn(x, belgium) → Speak(x, french))
∃x (GoTo(john, europe) → (See(john, x) ∧ TouristAttraction(x)))
Speak(john, french)

Conclusions:
If John goes to Europe, he will see the Eiffel Tower.
The Eiffel Tower is located in the capital of France.
John lives in Belgium.

Conclusions in first-order logic:
GoTo(john, europe) → See(john, eiffelTower)
∃x (CapitalOf(x, france) ∧ LocatedIn(eiffelTower, x))
LiveIn(john, belgium)

Truth value of each conclusion (T = true, F = false, U = unknown):
U
T
U
Premises:
Roversi is an Italian surname.
Alba Roversi uses Roversi as a surname.
Paolo Roversi uses Roversi as a surname.
Roberto Roversi uses Roversi as a surname.
Paolo Roversi is a photographer.
A photographer is a professional or an amateur.

Premises in first-order logic:
ItalianName(roversi) ∧ Surname(roversi)
UseAsSurname(albaRoversi, roversi)
UseAsSurname(paoloRoversi, roversi)
UseAsSurname(robertoRoversi, roversi)
Photographer(paoloRoversi)
∀x (Photographer(x) → Professional(x) ⊕ Amateur(x))

Conclusions:

Alba Roversi uses an Italian surname.
There are no photographers using an Italian surname.
Paolo is an amateur photographer.

Conclusions in first-order logic:
∃x (ItalianName(x) ∧ Surname(x) ∧ UseAsSurname(albaRoversi, x))
¬(∃x ∃y (Photographer(x) ∧ ItalianName(y) ∧ Surname(y) ∧ UseAsSurname(x, y)))
Amateur(paoloRoversi) ∧ Photographer(paoloRoversi)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
F
U
Premises:
Symphony No. 9 is a music piece.
Composers write music pieces.
Beethoven wrote Symphony No. 9.
Vienna Music Society premiered Symphony No. 9.
Vienna Music Society is an orchestra.
Beethoven leads the Vienna Music Society.
Orchestras are led by conductors.

Premises in first-order logic:
MusicPiece(symphony9)
∀x (MusicPiece(x) → ∃y (Composer(y) ∧ Write(y, x)))
Writtenby(symphony9, beethoven)
Premiered(viennaMusicSociety, symphony9)
Orchestra(viennaMusicSociety)
Lead(beethoven, viennaMusicSociety)
∀x (Orchestra(x) → (∃y Conductor(y) ∧ Lead(y, x)))

Conclusions:
Beethoven is a composer.
Some orchestras premiered music pieces.
Beethoven is not a conductor.

Conclusions in first-order logic:
Composer(beethoven)
∃x ∃y (Orchestra(x) ∧ MusicPiece(y) ∧ Premiered(x, y))
¬Conductor(beethoven)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
T
F
Premises:
It is not true that some giant language models do not have good performance. 
All language models with good performance are used by some researchers.
If a language model is used by some researchers, it is popular. 
If BERT is a giant language model, then GPT-3 is also a giant language model. 
BERT is a giant language model. 

Premises in first-order logic:
¬(∃x (LanguageModel(x) ∧ Giant(x) ∧ ¬GoodPerformance(x)))
∀x ∃y ∃z (LanguageModel(x) ∧ GoodPerformance(x) → ¬(x=y) ∧ Researcher(y) ∧ UsedBy(x, y) ∧ Researcher(z) ∧ UsedBy(x, z))
∀x ∃y ∃z (LanguageModel(x) ∧ ¬(x=y) ∧ Researcher(y) ∧ UsedBy(x, y) ∧ Researcher(z) ∧ UsedBy(x, z) → Popular(x))
(LanguageModel(bert) ∧ Giant(bert)) → (LanguageModel(gpt-3) ∧ Giant(gpt-3)).
LanguageModel(bert) ∧ Giant(bert)

Conclusions:
GPT-3 is popular.

Conclusions in first-order logic:
Popular(gpt-3)

Truth value of each conclusion (T = true, F = false, U = unknown):
T