prajaktakini/folio huggingface dataset:
Premises:
American superheroes come from either the DC Universe or Marvel Universe.
Captain America is one of America's top-ten favorite superheroes
Captain America does not come from the DC Universe.
America's top-ten favorite superheroes speak English.
Some superheroes speak both English and Spanish.
Premises (first-order logic):
∀x (Superhero(x) ∧ American(x) → ComeFrom(x, dCUniverse) ⊕ ComeFrom(x, marvelUniverse))
American(captainAmerica) ∧ TopTenFavorite(captainAmerica) ∧ Superhero(captainAmerica)
¬ComeFrom(captainAmerica, dCUniverse)
∀x (American(x) ∧ TopTenFavorite(x) ∧ Superhero(x)  → Speak(x, english))
∃x (Superhero(x) → (Speak(x, english) ∧ Speak(x, spanish)))

Conclusion: Captain America does not speak English.
Conclusion (first-order logic): ¬Speak(captainAmerica, english)
Label: False

Conclusion: Captain America comes from the Marvel universe.
Conclusion (first-order logic): ComeFrom(captainAmerica, marvelUniverse)
Label: True

Conclusion: Captain America speaks Spanish.
Conclusion (first-order logic): Speak(captainAmerica, spanish)
Label: Uncertain
Premises:
Robert Zimmer was a philosopher born in Germany.
Robert Zimmer is an essayist.
Robert Zimmer was born in 1953.
Every essayist is a writer.
Premises (first-order logic):
BornIn(robertZimmer, germany) ∧ Philosopher(robertZimmer)
Essayist(robertZimmer)
BornIn(robertZimmer, yr1953)
∀x (Essayist(x) → Writer(x))

Conclusion: Robert Zimmer is not a writer.
Conclusion (first-order logic): ¬Writer(robertZimmer)
Label: False

Conclusion: Robert Zimmer is a biographer.
Conclusion (first-order logic): Biographer(robertZimmer)
Label: Uncertain
Premises:
All people who repay their loans on time have a high credit score.
Some people with high credit scores and high salaries are approved for mortgages.
John has a high salary.
Premises (first-order logic):
∀x (RepayOnTime(x) → Has(x, highCreditScore))
∃x ((Has(x, highCreditScore) ∧ Has(x, highSalary)) → ApprovedFor(x, mortgage))
Has(john, highSalary)

Conclusion: If John repays his loans on time, he will be approved for a mortgage.
Conclusion (first-order logic): RepayOnTime(john) → ApprovedFor(john, mortgage)
Label: Uncertain
Premises:
All students are members of the university.
All graduate students are students.
All PhD students are graduate students.
Some PhD students are Teaching Fellows.
If John is not a PhD student, then he is not a member of the university.
If John is a Teaching Fellow, then he is a PhD student or a graduate student.
Premises (first-order logic):
∀x (Student(x) → MemberOf(x, university))
∀x (GraduateStudent(x) → Student(x))
∀x (PhDStudent(x) → GraduateStudent(x))
∃x (PhDStudent(x) ∧ TeachingFellow(x))
¬PhDStudent(john) → ¬MemberOf(john, university)
TeachingFellow(john) → PhDStudent(john) ⊕ GraduateStudent(john)

Conclusion: John is a PhD student.
Conclusion (first-order logic): PhDStudent(john)
Label: Uncertain
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 (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)

Conclusion: If John goes to Europe, he will see the Eiffel Tower.
Conclusion (first-order logic): GoTo(john, europe) → See(john, eiffelTower)
Label: Uncertain

Conclusion: The Eiffel Tower is located in the capital of France.
Conclusion (first-order logic): ∃x (CapitalOf(x, france) ∧ LocatedIn(eiffelTower, x))
Label: True

Conclusion: John lives in Belgium.
Conclusion (first-order logic): LiveIn(john, belgium)
Label: Uncertain
Premises:
All sports cars are loud.
No loud cars are electric.
If a car is a Ferrari, then it is a sports car.
All cars made in Maranello are Ferraris.
The Toyota Prius is made in Maranello or is a loud car, or both.
Premises (first-order logic):
∀x (SportsCar(x) → LoudCar(x))
∀x (LoudCar(x) → ¬ElectricCar(x))
∀x (Ferrari(x) → SportsCar(x))
∀x ((Car(x) ∧ MadeIn(x, maranello)) → Ferrari(x))
(Car(toyotaPrius) ∧ MadeIn(toyotaPrius, maranello)) ∨ LoudCar(toyotaPrius)

Conclusion: Prius is an electric car.
Conclusion (first-order logic): ElectricCar(toyotaPrius)
Label: False

Conclusion: The Toyota Prius is not an electric car.
Conclusion (first-order logic): ¬ElectricCar(toyotaPrius)
Label: True

Conclusion: The Toyota Prius is a equipped with a Ferrari V12 engine.
Conclusion (first-order logic): MadeIn(toyotaPrius, maranello)
Label: Uncertain

Conclusion: If The Toyota Prius is a Ferrari or a loud car, then The Toyota Prius is an electric car.
Conclusion (first-order logic): Ferrari(toyotaPrius) ∨ LoudCar(toyotaPrius) → ElectricCar(toyotaPrius)
Label: False
Premises:
If something is a plant, then it is not a cute animal. Simeng: All plants are not cute animals.
All flowers are plants.
Every kitten is a cute animal.
If something is grown in a garden, then it is a flower.
Piper is a kitten or a cute animal.
Premises (first-order logic):
∀x (Plant(x) → ¬CuteAnimal(x))
∀x (Flower(x) → Plant(x))
∀x (Kitten(x) → CuteAnimal(x))
∀x (GrownIn(x, garden) → Flower(x))
Kitten(piper) ∨ CuteAnimal(piper)

Conclusion: Piper was grown in a garden.
Conclusion (first-order logic): GrownIn(piper, garden)
Label: False

Conclusion: Piper was not grown in a garden.
Conclusion (first-order logic): ¬GrownIn(piper, garden)
Label: True

Conclusion: Piper is a kitten.
Conclusion (first-order logic): Kitten(piper)
Label: Uncertain
Premises:
Michael O'Donnell is a British physician, journalist, author, and broadcaster.
One of the word-setters of My Word! was Michael O'Donnell.
The magazine World Medicine was edited by Michael O'Donnell.
Michael O'Donnell was born in Yorkshire as the son of a general practitioner.
Premises (first-order logic):
British(michael) ∧ Physician(michael) ∧ Journalist(michael) ∧ Author(michael) ∧ Broadcaster(michael)
WordSetter(michael)
Magazine(worldMedicine) ∧ EditedBy(worldMedicine, michael)
BornIn(michael, yorkshire) ∧ ∃x(SonOf(michael, x) ∧ GeneralPractitioner(x))

Conclusion: The son of a general practitioner was a word-setter of My Word!.
Conclusion (first-order logic): ∃x ∃y (SonOf(x, y) ∧ GeneralPractitioner(y) ∧ WordSetter(x))
Label: True

Conclusion: There are no British authors.
Conclusion (first-order logic): ∀x (British(x) → ¬Author(x))
Label: False

Conclusion: There are no journalists that were born in Yorkshire.
Conclusion (first-order logic): ∀x (Journalist(x) → ¬BornIn(x, yorkshire))
Label: False

Conclusion: There is a son of a general practitioner that is not an author.
Conclusion (first-order logic): ∃x ∃y (Son(x, y) ∧ GeneralPractitioner(y) ∧ ¬Author(x))
Label: Uncertain
Premises:
No homework is fun.
Some reading is homework.
Premises (first-order logic):
∀x (Homework(x) → ¬Fun(x))
∃x (Reading(x) ∧ Homework(x))

Conclusion: Some reading is fun.
Conclusion (first-order logic): ∃x (Reading(x) ∧ Fun(x))
Label: Uncertain
Premises:
The handbrake of a car is either up or down.
The handbrake is down when a car is parked.
Premises (first-order logic):
∀x ∀y (HandbrakeOf(x, y) ∧ Car(y) → Up(x) ⊕ Down(x))
∀x ∀y (HandbrakeOf(x, y) ∧ Parked(y) ∧ Car(y) → Down(x))

Conclusion: The handbrake is up when some cars are parked.
Conclusion (first-order logic): ∃x ∃y (HandbrakeOf(x, y) ∧ Parked(y) ∧ Car(y) ∧ Up(x))
Label: False
Premises:
All people in this midwest town who own horse ranches regularly ride horses for pleasure and sport.
All people in this midwest town with a lot of disposable income have a horse ranch.
If people in this midwest town compete in horse dressage shows, then they have a lot of disposable income.
If people in this midwest town compete in horse dressage shows, then they have invested in high-quality equestrian gear and equipment.
If people in this midwest town regularly ride horses for pleasure and sport, then they do not live in cramped residential buildings.
Manny is in this midwest town, and she either has a horse ranch and lives in a cramped residential building, or she does neither.
Premises (first-order logic):
∀x (InThisMidwestTown(x) ∧ Have(x, horseRanch) → RegularlyRideHorseForPleasure(x))
∀x (InThisMidwestTown(x) ∧ Have(x, disposableIncome) → Have(x, horseRank))
∀x (InThisMidwestTown(x) ∧ CompeteIn(x, horseDressageShow) → Have(x, disposableIncome))
∀x (InThisMidwestTown(x) ∧ CompeteIn(x, horseDressageShow) → InvestedIn(x, equestrianGearAndEquipment))
∀x (InThisMidwestTown(x) ∧ RegularlyRideHorseForPleasure(x) → ¬LiveIn(x, crampedBuilding))
InThisMidwestTown(manny) ∧ ¬(Have(manny, horseRanch) ⊕ LiveIn(manny, crampedBuilding))

Conclusion: Manny regularly rides horses for pleasure and sport.
Conclusion (first-order logic): RegularlyRideHorsesForPleasure(manny)
Label: Uncertain
Premises:
A roundel is a rounded artillery fortification.
A roundel is not higher than adjacent walls.
Cannons can be deployed on artillery fortifications.
Roundels are the oldest artillery fortifications.
Battery towers are artillery fortifications.
Premises (first-order logic):
∀x (Roundel(x) → (Rounded(x) ∧ ArtilleryFortification(x)))
∀x ∀y ((Roundel(x) ∧ AdjacentWalls(x,y)) → ¬Higher(x, y))
∀x (ArtilleryFortification(x) → DeployCannons(x))
∀x ∀y ((Roundel(x) ∧ ArtilleryFortification(y)) → Older(x, y))
∀x (BatteryTower(x) → ArtilleryFortification(x))

Conclusion: Cannons can be deployed on battery towers.
Conclusion (first-order logic): ∀x (BatteryTower(x) → DeployCannons(x))
Label: True

Conclusion: Roundels are older than battery towers.
Conclusion (first-order logic): ∀x ∀y ((Roundel(x) ∧ BatteryTower(y)) → Older(x, y))
Label: True

Conclusion: Battery towers are higher than adjacent walls.
Conclusion (first-order logic): ∀x ∀y ((BatteryTower(x) ∧ AdjacentWall(x,y)) → Higher(x, y))
Label: Uncertain

Conclusion: Cannons can be deployed on roundels.
Conclusion (first-order logic): ∀x (Roundel(x) → DeployCannons(x))
Label: True
Premises:
Tissues are soft.
Some papers are tissues.
Premises (first-order logic):
∀x (Tissue(x) → Soft(x))
∃x ∃y (Paper(x) ∧ Paper(x) ∧ Tissue(x) ∧ Tissue(y) ∧ ¬(x=y))

Conclusion: Some papers are hard.
Conclusion (first-order logic): ∃x ∃y (Paper(x) ∧ Paper(y) ∧ Hard(x) ∧ Hard(y) ∧ ¬(x=y))
Label: Uncertain
Premises:
All volunteers receive intangible benefits for their work.
Volunteers work regularly or on an as-needed basis.
Some volunteers are trained.
Volunteers work in groups or individually.
Environmental volunteers contribute toward environmental management or conservation.
Participating in natural disaster response is an example of volunteers working in groups on an as-needed basis.
Premises (first-order logic):
∀x (Volunteer(x) → Receive(x, intangibleBenefit))
∀x (Volunteer(x) → WorkRegularly(x) ⊕ WorkAsNeeded(x))
∃x (Volunteer(x) → Trained(x))
∀x (Volunteer(x) → (WorkInGroup(x) ∨ WorkIndividually(x)))
∀x (Volunteer(x) ∧ Environmental(x) → (ContributeTo(x, environmentalManagement) ∨ ContributeTo(x, environmentalConservation)))
∃x (Volunteer(x) ∧ ContributeTo(x, naturalDisasterResponse) → WorkInGroup(x) ∧ WorkAsNeeded(x))

Conclusion: Volunteers who participate in natural disaster response receive intangible benefits for their work.
Conclusion (first-order logic): ∀x (Volunteer(x) ∧ ContributeTo(x, naturalDisasterResponse) → Receive(x, intangibleBenefit))
Label: True

Conclusion: Environmental volunteers work in groups.
Conclusion (first-order logic): ∀x (Volunteer(x) ∧ Environmental(x) → WorkInGroup(x))
Label: Uncertain

Conclusion: To be a volunteer, you must be trained.
Conclusion (first-order logic): ∀x (Volunteer(x) → Trained(x))
Label: Uncertain
Premises:
There are no buildings in New Haven higher than 400 meters.
All buildings managed by Yale Housing are in New Haven.
All Manhattan skyscrapers are higher than 400 meters.
All buildings owned by Bloomberg are in Manhattan.
All buildings with the Bloomberg logo are buildings owned by Bloomberg.
Tower A is neither a building in New Haven nor a skyscraper in Manhattan.
Tower B is a skyscraper building in Manhattan with a Bloomberg logo.
Premises (first-order logic):
∀x ((Buildings(x) ∧ In(x, newHaven)) → ¬HigherThan(x, num400))
∀x ((Buildings(x) ∧ ManagedBy(x, yaleHousing)) → In(x, newHaven))
∀x ((Buildings(x) ∧ Skyscraper(x) ∧ In(x, manhattan)) → HigherThan(x, num400))
∀x ((Buildings(x) ∧ OwnedBy(x, bloomberg)) → Skyscraper(x) ∧ In(x, manhattan))
∀x ((Buildings(x) ∧ HasLogo(x, bloomberg)) → OwnedBy(x, bloomberg))
Buildings(towerA) ∧ (¬InNewHaven(towerA)) ∧ (¬ManhattanSkyscraper(towerA))
Buildings(towerB) ∧ HasLogo(towerB, bloomberg) ∧ Skyscraper(towerB) ∧ In(towerB, manhattan)

Conclusion: Tower A is higher than 400 meters.
Conclusion (first-order logic): HigherThan(towerA, num400)
Label: Uncertain

Conclusion: Tower A is not higher than 400 meters.
Conclusion (first-order logic): ¬HigherThan(towerA, num400)
Label: Uncertain
Premises:
No fish are birds.
An osprey is a bird.
A carp is a fish.
All goldfish are carp.
If Bubbles is either an osprey or a goldfish, then Bubbles is not also a fish.
Premises (first-order logic):
∀x (Fish(x) → ¬Bird(x))
∀x (Osprey(x) → Bird(x))
∀x (Carp(x) → Fish(x))
∀x (Goldfish(x) → Carp(x))
Osprey(bubbles) ⊕ Goldfish(bubbles) → ¬Fish(bubbles)

Conclusion: Bubbles is an Osprey.
Conclusion (first-order logic): Osprey(bubbles)
Label: Uncertain

Conclusion: Bubbles is a goldfish.
Conclusion (first-order logic): Goldfish(bubbles)
Label: False

Conclusion: Bubbles is not a goldfish.
Conclusion (first-order logic): ¬Goldfish(bubbles)
Label: True
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 (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))

Conclusion: Mr. Smith has been to San Francisco.
Conclusion (first-order logic): ∃x (City(x) ∧ Visit(mr.smith, sanFrancisco))
Label: Uncertain

Conclusion: They have at least two candidate cities in California to visit.
Conclusion (first-order logic): ∃x ∃y (¬(x=y) ∧ City(x) ∧ City(y) ∧ WantToGoTo(mr.AndMrs.Smith, x) ∧ California(x) ∧ WantToGoTo(mr.AndMrs.Smith, y) ∧ California(y))
Label: Uncertain
Premises:
Everything in Size Town is big or small.
All big things in Size Town are heavy.
All small things in Size Town are light.
All heavy things in Size Town are still.
All light things in Size Town are unstable.
All unstable things in Size Town are changing.
All unstable things in Size Town are unpredictable.
The bird is in Size Town and it is not both heavy and still.
Premises (first-order logic):
∀x (In(x, sizeTown) → (Big(x) ∨ Small(x)))
∀x (Big(x) ∧ In(x, sizeTown) → Heavy(x))
∀x (Small(x) ∧ In(x, sizeTown) → Light(x))
∀x (Heavy(x) ∧ In(x, sizeTown) → Still(x))
∀x (Light(x) ∧ In(x, sizeTown) → Unstable(x))
∀x (Unstable(x) ∧ In(x, sizeTown) → Changing(x))
∀x (Unstable(x) ∧ In(x, sizeTown) → Unpredictable(x))
In(bird, sizeTown) ∧ ¬(Heavy(bird) ∧ Still(bird))

Conclusion: The bird is still.
Conclusion (first-order logic): Still(bird)
Label: Uncertain

Conclusion: The bird is not still.
Conclusion (first-order logic): ¬Still(bird)
Label: Uncertain

Conclusion: The bird is unpredictable and changing.
Conclusion (first-order logic): Unpredictable(bird) ∧ Changing(bird)
Label: True

Conclusion: The bird is unpredictable or changing.
Conclusion (first-order logic): Unpredictable(bird) ∨ Changing(bird)
Label: True

Conclusion: The bird is either unpredictable or changing.
Conclusion (first-order logic): Unpredictable(bird) ⊕ Changing(bird)
Label: False