prajaktakini/folio huggingface dataset:
Premises:
Badults is a British Sitcom series, starring members of Pappy's.
Badults piloted in July 2013 on BBC Three.
The Working title "The Secret Dude Society" was used for Badults.
Andrew Collins was the script editor for Badults
Premises (first-order logic):
∃x (MemberOf(x, pappys) ∧ BritishSitcom(badults) ∧ Series(badults) ∧ Starring(badults, x))
PilotedIn(badults, july2013) ∧ PilotedOn(badults, bbcthree)
WorkingTitle(thesecretdudesociety, badults)
ScriptEditor(andrewcollins) ∧ ScriptEditorFor(andrewcollins, badults)

Conclusion: Andrew Collins was the script editor for a series with the working title "The Secret Dude Society".
Conclusion (first-order logic): ∃x (ScriptEditorFor(andrewcollins, x) ∧ Series(x) ∧ WorkingTitle(thesecretdudesociety, x))
Label: True

Conclusion: No members of Pappy's have starred for a show piloting on BBC Two or BBC Three.
Conclusion (first-order logic): ∀x ∀y (MemberOf(x, pappys) ∧ Starring(y, x) → ¬(PilotedOn(y, bbctwo) ∨ PilotedOn(y, bbcthree)))
Label: False
Premises:
All growth companies’ stocks are volatile.
If the stock price is volatile, then it is not suitable for a retirement fund.
Some companies’ stocks are growth companies’ stocks.
All mature companies’ stocks are suitable for a retirement fund.
KO is a mature company’s stock.
Premises (first-order logic):
∀x (GrowthCompanies’Stocks(x) → PriceVolatile(x))
 ∀x (PriceVolatile(x) → ¬SuitableForRetirementRund(x)) 
 ∃x (Companies’Stocks(x) ∧ GrowthCompanies’Stocks(x))
 ∀x (MatureCompanies’Stocks(x) → SuitableForRetirementRund(x))
 MatureCompanies’Stocks(kO)

Conclusion: KO is a company stock.
Conclusion (first-order logic): Companies’Stocks(kO)
Label: Uncertain

Conclusion: KO is a company stock and a growth company’s stock.
Conclusion (first-order logic): Companies’Stocks(kO) ∧ GrowthCompanies’Stocks(kO)
Label: False

Conclusion: If KO is a growth company’s stock or if its price is volatile, then KO is neither a company’s stock nor is its price volatile.
Conclusion (first-order logic): GrowthCompanies’Stocks(kO) ∨ PriceVolatile(kO) → ¬Companies’Stocks(kO) ∨ ¬PriceVolatile(kO)
Label: True
Premises:
All vehicle registration plates in Istanbul begin with the number 34.
Plates that do not begin with the number 34 are not from Istanbul. 
Joe's vehicle registration plate is from Istanbul. 
Tom's license plate begins with the number 35. 
[BG] If a license plate begins with the number 35, then it does not begin with the number 34.
Premises (first-order logic):
∀x (Vehicleregistrationplate(x, istanbul) → Beginwith(x, number34))
∀x (¬Beginwith(x, number34) → ¬FromIstanbul(x))
Vehicleregistrationplate(joe, istanbul)
Beginwith(tom, number35)
∀x (Beginwith(x, number35) → ¬Beginwith(x, number34))

Conclusion: Joe's license plate begins with the number 34.
Conclusion (first-order logic): Beginwith(joe, number34)
Label: True

Conclusion: Tom's license plate is from Istanbul.
Conclusion (first-order logic): Vehicleregistrationplate(tom, istanbul)
Label: False
Premises:
If people work in student jobs on campus, then they need to earn money to help pay for their college tuition.
If people order takeout frequently in college, then they work in student jobs on campus.
People either order takeout frequently in college or enjoy the dining hall meals and recipes.
If people enjoy the dining hall meals and recipes, then they are not picky eaters.
If people enjoy the dining hall meals and recipes, then they spend a lot of their time eating and catching up with friends in the campus dining halls.
If Mary works in student jobs on campus and needs to earn money to help pay for her college tuition, then Mary is neither picky nor needs to earn money to help pay for her college tuition.
Premises (first-order logic):
∀x (StudentJobs(x) → Tuition(x))
∀x (Takeout(x) → StudentJobs(x))
∀x (Takeout(x) ∨ DiningHall(x))
∀x (DiningHall(x) → NotPicky(x))
∀x (DiningHall(x) → Eating(x))
StudentJobs(mary) ∧ Tuition(mary) → ¬(NotPicky(mary) ∨ Tuition(mary))

Conclusion: Mary needs to earn money to help pay for her college tuition.
Conclusion (first-order logic): Tuition(mary)
Label: Uncertain

Conclusion: Mary is not a picky eater and spends a lot of her time eating and catching up with friends in the campus dining halls.
Conclusion (first-order logic): NotPicky(mary) ∧ Eating(mary)
Label: True

Conclusion: Mary either is not a picky eater or, if she is, then she spends a lot of her time eating and catching up with friends in the campus dining halls.
Conclusion (first-order logic): NotPicky(mary) ⊕ Eating(mary)
Label: False
Premises:
No bakery is spicy.
All cupcakes are bakeries.
All hotpots are spicy.
All products of Baked by Melissa are cupcakes.
If dried Thai chilies are spicy or a bakery, then dried Thai chilies are a hotpot or are spicy.
Premises (first-order logic):
∀x (Bakery(x) → ¬Spicy(x))
∀x (Cupcakes(x) → Bakery(x))
∀x (Hotpot(x) → Spicy(x))
∀x (ProductsOfBakedByMelissa(x) → Cupcakes(x))
Spicy(driedThaiChilies) ∨ Bakery(driedThaiChilies) → Hotpot(driedThaiChilies) ∨ Spicy(driedThaiChilies)

Conclusion: Dried Thai chilies are a product of Baked by Melissa.
Conclusion (first-order logic): ProductsOfBakedByMelissa(driedThaiChilies)
Label: False

Conclusion: Dried Thai chilies are not a product of Baked by Melissa.
Conclusion (first-order logic): ¬ProductsOfBakedByMelissa(driedThaiChilies)
Label: True

Conclusion: Dried Thai chilies are a hotpot.
Conclusion (first-order logic): Hotpot(driedThaiChilies)
Label: Uncertain

Conclusion: Dried Thai chilies are neither a product of Baked by Melissa nor a bakery.
Conclusion (first-order logic): ¬ProductsOfBakedByMelissa(driedThaiChilies) ∧ ¬Bakery(driedThaiChilies)
Label: True

Conclusion: Dried Thai chilies are cupcakes and a product of Baked by Melissa.
Conclusion (first-order logic): Cupcake(driedThaiChilies) ∧ ProductsOfBakedByMelissa(driedThaiChilies)
Label: False
Premises:
If the restaurant is listed in Yelp’s recommendations, then the restaurant does not receive many negative reviews.
All restaurants with a rating greater than 9 are listed in Yelp’s recommendations.
Some restaurants that do not provide take-out service receive many negative reviews.
All restaurants that are popular among local residents have ratings greater than 9.
Subway has a rating greater than 9 or is popular among local residents.
Premises (first-order logic):
∀x (YelpRecommendation(x) → ¬NegativeReview(x))
∀x (RatingGreaterThan9(x) → YelpRecommendation(x))
∃x (NoTakeOutService(x) ∧ NegativeReview(x))
∀x (PopularAmongLocalResidents(x) → RatingGreaterThan9(x))
RatingGreaterThan9(subway) ⊕ PopularAmongLocalResidents(subway)

Conclusion: If Subway provides take-out service and receives many negative reviews, then its rating is greater than 9 and it does not provide take-out service.
Conclusion (first-order logic): NoTakeOutService(subway)
Label: Uncertain

Conclusion: Subway provides take-out service and does not receive many negative reviews.
Conclusion (first-order logic): ¬(NoTakeOutService(subway) ∧ NegativeReview(subway))
Label: True

Conclusion: Subway does not provide take-out service.
Conclusion (first-order logic): ¬(NoTakeOutService(subway) ∧ NegativeReview(subway)) → PopularAmongLocalResidents(subway) ∧ NoTakeOutService(subway)
Label: False
Premises:
[BG] Books contain tons of knowledge.
[BG] When a person reads a book, that person gains knowledge. 
[BG] If a person gains knowledge, they become smarter.
Harry read the book “Walden” by Henry Thoreau.
Premises (first-order logic):
∀x (Book(x) → Knowledge(x))
∀x ∀y (ReadBook(x, y) → GainKnowledge(x))
∀x (GainKnowledge(x) → Smarter(x))
ReadBook(harry, walden) ∧ Book(walden)

Conclusion: Walden contains knowledge.
Conclusion (first-order logic): Knowledge(walden)
Label: True

Conclusion: Harry is smarter than before.
Conclusion (first-order logic): Smarter(harry)
Label: True

Conclusion: A person who is smarter has gained knowledge.
Conclusion (first-order logic): ∀x (Smarter(x) → GainKnowledge(x))
Label: Uncertain
Premises:
Natural language processing tasks can be categorized as either language generation task or 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 (first-order logic):
∀x (NLP(x) → NLG(x) ⊕ NLU(x))
∀x (NLP(x) ∧ OutputSequence(x) → NLG(x))
NLP(machineTranslation)
OutputSequence(machineTranslation)

Conclusion: Machine translation is a language generation task.
Conclusion (first-order logic): NLG(machineTranslation)
Label: True

Conclusion: Machine translation is a language understanding task.
Conclusion (first-order logic): NLU(machineTranslation)
Label: False
Premises:
Pets are allowed in some managed buildings.
[BG] A deposit is required to rent an apartment in a managed building.
The security deposit can be either equal to one month's rent or more.
Fluffy is Tom's cat.
Cats are pets.
The Olive Garden is a managed building.
The monthly rent at the Olive Garden is $2000.
Tom will rent an apartment in a managed building if and only if he is allowed to move in with Fluffy, and the security deposit is no more than $1500.
2000$ is more than $1500.
Premises (first-order logic):
∃x (ManagedBuilding(x) ∧ AllowPet(x))
∀x (ManagedBuilding(x) → RequireDeposit(x))
∀x (Rent2000(x) ↔ ¬DepositNoMoreThan1500(x))
∀x (Rent2000(x) ↔ DepositNoLessThan2000(x))
Cat(fluffy)
∀x (Cat(x) → Pet(x))
ManagedBuilding(oliveGarden)
Rent2000(oliveGarden)
∀x (TomRent(x) ↔ (ManagedBuilding(x) ∧ AllowPet(x) ∧ DepositNoMoreThan1500(x)))

Conclusion: Tom will rent an apartment in The Olive Garden.
Conclusion (first-order logic): TomRent(oliveGarden)
Label: False

Conclusion: Tom is allowed to move in an apartment in The Olive Garden with Fluffy.
Conclusion (first-order logic): AllowPet(oliveGarden)
Label: Uncertain

Conclusion: The security deposit at the Olive Garden is either $2000 or more.
Conclusion (first-order logic): DepositNoLessThan2000(oliveGarden)
Label: True
Premises:
If animals are loved by tourists, then they are Max's favorite animals. 
All animals from Australia are loved by tourists. 
All quokka are animals from Australia. 
All of Max's favorite animals are very fluffy.
All of Max's favorite animals love to sleep. 
If a koala is very fluffy, then the koala is not a quokka.
Premises (first-order logic):
∀x (AnimalsLovedByTourists(x) → MaxFavoriteAnimals(x))
 ∀x (AnimalsFromAustralia(x) → AnimalsLovedByTourists(x))
 ∀x (Quokka(x) → AnimalsFromAustralia(x))
 ∀x (MaxFavoriteAnimals(x) → VeryFluffy(x)) 
 ∀x (MaxFavoriteAnimals(x) → LoveToSleep(x))
 VeryFluffy(koala) → ¬Quokka(koala)

Conclusion: Koalas love to sleep.
Conclusion (first-order logic): LoveToSleep(koala)
Label: Uncertain

Conclusion: Koalas love to sleep and are quokka.
Conclusion (first-order logic): LoveToSleep(koala) ∧ Quokka(koala)
Label: False

Conclusion: If a koala is a quokka, then the koala either loves to sleep or is very fluffy.
Conclusion (first-order logic): Quokka(koala) → LoveToSleep(koala) ⊕ VeryFluffy(koala)
Label: True
Premises:
A man is either kind or evil.
No ugly person is handsome.
All evil people are ugly.
All gentlemen are handsome.
All CEOs are gentlemen.
Harry is a gentleman.
Premises (first-order logic):
∀x (Kind(x) ⊕ Evil(x))
∀x (Ugly(x) → ¬Handsome(x))
∀x (Evil(x) → Ugly(x))
∀x (Gentleman(x) → Handsome(x))
∀x (CEO(x) → Gentleman(x))
Gentleman(harry)

Conclusion: Harry is a CEO.
Conclusion (first-order logic): CEO(harry)
Label: Uncertain

Conclusion: Harry is kind or a CEO.
Conclusion (first-order logic): Kind(harry) ∨ CEO(harry)
Label: True

Conclusion: If Harry is either both evil and ugly, or neither evil nor ugly, then Harry is neither kind nor a CEO.
Conclusion (first-order logic): (Evil(harry) ∧ Ugly(harry)) ⊕ (¬Evil(harry) ∧ ¬Ugly(harry)) → ¬Kind(harry) ∧ ¬CEO(harry)
Label: False
Premises:
If people are twins or triplets, then they spend a lot of time hanging out with and playing with their siblings.
If people have siblings who are the same age, then they are twins or triplets.
If people complain often about annoying siblings, then they have siblings who are the same age.
If people do not live at home regularly, then they do not develop strong bonds with their family members.
If people spend a lot of time hanging out with and playing with their siblings, then they often did not live at home regularly.
Luke is either both a twin or triplet and develops strong bonds with his family members, or he neither is a twin or triplet nor develops strong bonds with his family members.   
Premises (first-order logic):
∀x (TwinTriplet(x) → Playing(x))
∀x (SameAge(x) → TwinTriplet(x))
∀x (Complain(x) → SameAge(x))
∀x (NotHome(x) → ¬StrongBonds(x))
∀x (Playing(x) → NotHome(x))
(TwinTriplet(luke) ∧ StrongBonds(luke)) ⊕ (¬TwinTriplet(luke) ∧ ¬StrongBonds(luke))

Conclusion: Luke spends a lot of time hanging out and playing with his siblings.
Conclusion (first-order logic): Playing(luke)
Label: Uncertain

Conclusion: Luke complains often about annoying siblings.
Conclusion (first-order logic): Complain(luke)
Label: False

Conclusion: Luke is neither a twin nor triplet nor complains often about annoying siblings.
Conclusion (first-order logic): ¬(TwinTriplet(luke) ∨ Complain(luke))
Label: True
Premises:
It costs US $205 to take the GRE test.
ETS provides financial aid to those GRE applicants who prove economic hardship.
Economic hardship refers to difficulty caused by having too little money or too few resources.
Tom lives in a single-parent family.
His dad has been out of work for more than a year.
Premises (first-order logic):
Cost205(gre)
∀x (Cost205(x) → CostBelow300(x))
∀x (Hardship(x) → FinancialAid(x))
∀x (SingleParent(x) ∨ FewResources(x) → Hardship(x))
SingleParent(tom)
FewResources(tom)

Conclusion: Tom can apply for financial aid from ETS to take the GRE test.
Conclusion (first-order logic): FinancialAid(tom)
Label: True

Conclusion: It cost below US $300 to take the GRE test.
Conclusion (first-order logic): CostBelow300(gre)
Label: True

Conclusion: There is no financial aid available for the GRE test.
Conclusion (first-order logic): ¬(∃x (FinancialAid(x)))
Label: False
Premises:
All fir trees are evergreens.
Some objects of worship are fir trees.
Premises (first-order logic):
∀x (FirTree(x) → Evergreen(x))
∃x (ObjectOfWorship(x) ∧ FirTree(x))

Conclusion: Some evergreens are not objects of worship.
Conclusion (first-order logic): ∃x (Evergreen(x) ∧ ¬ObjectOfWorship(x))
Label: Uncertain
Premises:
The Picuris Mountains are a mountain range in New Mexico or Texas.
Juan de Onate visited the Picuris Mountains.
The Harding Pegmatite Mine, located in the Picuris Mountains, was donated.
There are no mountain ranges in texas that have mines which have been donated.
Premises (first-order logic):
MountainRange(picurismountains) ∧ In(picurismountains, newmexico) ∨ In(picurismountains, texas)
Visited(juandeonate, picurismountains)
In(hardingpegmatitemine, picurismountains) ∧ Mine(hardingpegmatitemine) ∧ Donated(hardingpegmatitemine)
∀x ∀y (Mine(x) ∧ Donated(x) ∧ In(x, y) ∧ MountainRange(y) → ¬In(y, texas))

Conclusion: Juan de Onate visited a mountain range in Texas.
Conclusion (first-order logic): ∃x (Visited(juandeonate, x) ∧ MountainRange(x) ∧ In(x, texas))
Label: Uncertain
Premises:
If a man is taller than another man, the taller one can block the other's shooting.
Michael is a man who is taller than everyone else in his class.
If person x is taller than person y, and person y is taller than person z, than x is taller than z
Peter is a man who is taller than Michael.
Michael can block any shooting from a person as long as the person does not jump when shooting.
Michael cannot block Windy's shooting.
Every shooter who can jump when shooting is a great shooter.
Premises (first-order logic):
∀x ∀y (IsMan(x) ∧ IsMan(y) ∧ Taller(x, y) → CanBlock(x, y))
∀x (IsMan(x) ∧ SameClass(x, michael) ∧ ¬Be(x, michael) → Taller(michael, x))
∀x ∀y ∀z ((Taller(x, y) ∧ Taller(y, z)) → Taller(x, z))
IsMan(peter) ∧ Taller(peter, michael)
∀x (¬JumpWhenShooting(x) → CanBlock(michael, x))
¬CanBlock(michael, windy)
∀x (JumpWhenShooting(x) → GreatShooter(x))

Conclusion: Peter is shorter than a man in Michael's class.
Conclusion (first-order logic): ∃x (IsMan(x) ∧ SameClass(x, michael) ∧ ¬Be(x, michael) ∧ ¬Taller(peter, x))
Label: False

Conclusion: Windy is a great shooter.
Conclusion (first-order logic): GreatShooter(windy)
Label: True

Conclusion: Peter can block Windy's shooting.
Conclusion (first-order logic): CanBlock(peter, windy)
Label: Uncertain
Premises:
Barutin Cove is named after the Bulgarian settlement of Barutin.
Barutin Cove is on the southwest coast of Snow Island.
The South Shetland Islands include Snow Island, Greenwich Island, and Deception Island.
The South Shetland Islands are considered part of Antarctica.
If A is located in B and B is located in C, then A is located in C.
Premises (first-order logic):
Cove(barutin) ∧ NameAfter(barutin, settlement) ∧ Locate(settlement, bulgaria)
Locate(barutin, snow)
Locate(snow, shetland) ∧ Locate(greenwich, shetland) ∧ Locate(deception, shetland)
Locate(shetland, antarctica)
∀x ∀y ∀z ((Locate(x, y) ∧ Locate(y, z)) → Locate(x, z))

Conclusion: Barutin is named after all islands in Antarctica.
Conclusion (first-order logic): ∀x (Locate(x, antarctica) → NameAfter(barutin, x))
Label: Uncertain

Conclusion: There is at least one cove in Antarctica named after a place in Bulgaria.
Conclusion (first-order logic): ∃x ∃y (Cove(x) ∧ NameAfter(x, y) ∧ Locate(y, bulgaria))
Label: True

Conclusion: Barutin is not located in Antarctica.
Conclusion (first-order logic): ¬Locate(barutin, antarctica)
Label: False
Premises:
Some affection is love.
Some love is positive.
Premises (first-order logic):
∃x (Affection(x) ∧ Love(x))
∃x (Love(x) ∧ Positive(x))

Conclusion: Some affection is positive.
Conclusion (first-order logic): ∃x (Affection(x) ∧ Positive(x))
Label: Uncertain
Premises:
If something can transport multiple passengers, then they are not empty.
All airlines can transport multiple passengers.
All planes are empty.
All Boeing 707s are planes.
If Greyhound is not an airline, then there are no Greyhound planes.
Premises (first-order logic):
∀x (TransportMultiplePassengers(x) → ¬Empty(x))
∀x (Airline(x) → TransportMultiplePassengers(x))
∀x (Plane(x) → Empty(x))
∀x (Boeing707(x) → Plane(x))
¬Airline(greyhound) → ¬(Airline(greyhound) ∨ Plane(greyhound))

Conclusion: Greyhound is an airline.
Conclusion (first-order logic): Airline(greyhound)
Label: Uncertain

Conclusion: A Greyhound is a Boeing 707.
Conclusion (first-order logic): Boeing707(greyhound)
Label: False

Conclusion: A Greyhound is not a Boeing 707.
Conclusion (first-order logic): ¬Boeing707(greyhound)
Label: True
Premises:
All mammals are animals.
All monkeys are mammals. 
An animal is either a monkey or a bird.
All birds fly.
All animals breathe.
If something can fly, then it has wings.
If Rock is neither a fly nor a bird, then Rock neither flies nor breathes.
Premises (first-order logic):
∀x  (Mammal (x)→  Animal (x))
∀x  (Monkey (x)→  Mammal (x))
∀x ( Monkey (x) ∨  Bird (x))
∀x  (Bird (x)→  Fly (x))
∀x  (Animal (x)→  Breathe (x))
∀x  (Fly (x)→  HaveWings (x))
¬ Fly (rock) ∧ ¬ Bird (rock) → ¬ Fly (rock) ∧ ¬ Breathe (rock)

Conclusion: Rock is a monkey.
Conclusion (first-order logic): Monkey(rock)
Label: Uncertain

Conclusion: Rock has wings.
Conclusion (first-order logic): HaveWings(rock)
Label: True

Conclusion: Rock does not have wings.
Conclusion (first-order logic): ¬ HaveWings(rock)
Label: False
Premises:
All people who have a wedding are people who have at least one child.
All people who travel to Paris for their honeymoon are people who have a wedding.
All weddings that occur in July belong to people who travel to Paris for their honeymoon.
Some weddings in July are well-attended.
If John has at least one child, then John does not travel to Paris for his honeymoon and does not have a wedding.
If John has a wedding that is well-attended, then John has a wedding in July or, if not, then John travels to Paris for their honeymoon. 
Premises (first-order logic):
∀x (Wedding(x) → Child(x))
∀x (Paris(x) → Wedding(x))
∀x (July(x) → Paris(x))
∃x (July(x) ∧ WellAttended(x))
Child(john) → ¬(Paris(john) ∧ Wedding(john))
WellAttended(john) → July(john) ⊕ Paris(john)

Conclusion: John has a wedding that is well-attended.
Conclusion (first-order logic): WellAttended(john)
Label: False

Conclusion: John does not have a wedding that is well-attended.
Conclusion (first-order logic): ¬WellAttended(john)
Label: True

Conclusion: John has at least one child.
Conclusion (first-order logic): Child(john)
Label: Uncertain

Conclusion: John has neither a wedding that is well-attended nor does he travel to Paris for his honeymoon.
Conclusion (first-order logic): ¬(WellAttended(john) ∨ July(john))
Label: True
Premises:
[BG] Some mammals have teeth.
Platypus have no teeth.
Platypus are mammals. 
Humans have teeth.
Premises (first-order logic):
∃x (Mammal(x) ∧ Have(x, teeth))
¬Have(platypus, teeth)
Mammal(platypus)
Have(humans, teeth)

Conclusion: Platypus are mammals with no teeth.
Conclusion (first-order logic): Mammal(platypus) ∧ ¬Have(platypus, teeth)
Label: True

Conclusion: Platypus are reptiles.
Conclusion (first-order logic): Reptile(platypus)
Label: Uncertain

Conclusion: Humans are mammals.
Conclusion (first-order logic): Mammal(humans)
Label: Uncertain
Premises:
Plungers suck.
Vacuums suck.
Vampires suck.
Space is a vacuum.
A duster is a household appliance that doesn't suck.
Premises (first-order logic):
∀x (Plunger(x) → Suck(x))
∀x (Vacuum(x) → Suck(x))
∀x (Vampire(x) → Suck(x))
Vacuum(space)
HouseHoldApp(duster) ∧ ¬ Suck(duster)

Conclusion: Space sucks.
Conclusion (first-order logic): Suck(space)
Label: True

Conclusion: Space is a vampire.
Conclusion (first-order logic): Vampire(space)
Label: Uncertain

Conclusion: If something is a household appliance, it sucks.
Conclusion (first-order logic): ∀x (HouseHoldApp(x) → Suck(x))
Label: False
Premises:
If a cartoon character is funny, then it is popular.
If a cartoon character is ugly, then it is not popular.
If a cartoon character is loved by children, then it is funny.
If a cartoon character is from The Simpsons, then it is loved by children.
If a cartoon character is yellow, then it is from The Simpsons.
Ben is either from The Simpsons or funny.
Premises (first-order logic):
∀x (Funny(x) → Popular(x))
∀x (Ugly(x) → ¬Popular(x))
∀x (Love(children, x) → Funny(x))
∀x (Simpsons(x) → Love(children, x))
∀x (Yellow(x) → Simpsons(x))
Simpsons(ben) ⊕ Funny(ben)

Conclusion: Ben is loved by children.
Conclusion (first-order logic): Love(children, ben)
Label: Uncertain

Conclusion: Ben is ugly or yellow.
Conclusion (first-order logic): Ugly(ben) ∨ Yellow(ben)
Label: False

Conclusion: Ben is either yellow and ugly or neither yellow nor ugly.
Conclusion (first-order logic): (Yellow(ben) ∧ Ugly(ben)) ⊕ (¬Yellow(ben) ∧ ¬Ugly(ben))
Label: True
Premises:
Breeding back is a form of artificial selection by the deliberate selective breeding of domestic animals.
Heck cattle were bred-back in the 1920s to resemble the aurochs.
[BG] Heck cattle are animals.
[BG] Aurochs are animals.
Some animals to be bred-back resemble animals that are extinct.
Premises (first-order logic):
∀x ∀y (BreedingBack(x, y) → ArtificiallySelect(x))
∃x ∃y (HeckCattle(x) ∧ Aurochs(y) ∧ BreedingBack(x, y))
∀x (HeckCattle(x) → Animal(x))
∀x (Aurochs(x) → Animal(x))
∃x ∃y (Animal(x) ∧ Animal(y) ∧ BreedingBack(x, y) ∧ Extinct(y))

Conclusion: Some Heck cattle are artificially selected.
Conclusion (first-order logic): ∃x (HeckCattle(x) ∧ ArtificiallySelect(x))
Label: True

Conclusion: Aurochs are extinct.
Conclusion (first-order logic): ∀x (Aurochs(x) → Extinct(x))
Label: Uncertain