minimario/FOLIO huggingface dataset:
Premises:
System 7 is a UK-based electronic dance music band.
Steve Hillage and Miquette Giraudy formed System 7.
Steve Hillage and Miquette Giraudy are former members of the band Gong.
Electric dance music bands are bands.
System 7 has released several club singles.
Club singles are not singles.

Premises in first-order logic:
BasedOn(system7, uk) ∧ ElectronicDanceMusicBand(system7)
Formed(stevehillage, system7) ∧ Formed(miquettegiraudy, system7)
FormerMemberOf(stevehillage, gong) ∧ FormerMemberOf(miquettegiraudy, gong)
∀x (ElectronicDanceMusicBand(x) → Band(x))
∃x (ClubSingle(x) ∧ Released(system7, x))
∀x (ClubSingle(x) → ¬Single(x))

Conclusion: System 7 was formed by former members of Gong.
Label: True

Conclusion: System 7 has released several singles.
Label: Unknown

Conclusion: System 7 is not a band.
Label: False
Premises:
A summarization model is always faithful if it uses content from the input documents.
Extractive models are a kind of summarization models.
Extractive model can only use content from the input documents.

Premises in first-order logic:
∀x (Model(x) ∧ Summarization(x) ∧ OnlyUseInputDocument(x) → Faithful(x))
∀x (Model(x) ∧ Extractive(x) → Summarization(x))
∀x (Model(x) ∧ Extractive(x) → OnlyUseInputDocument(x))

Conclusion: Extractive models are always faithful.
Label: True

Conclusion: Extractive models are not always faithful.
Label: False
Premises:
If people practice coding questions, then they are not studying to go to medical school to become a doctor.
If people want to work in the software engineering industry, then they practice coding questions.
If people enjoy healthcare fields and want to help people with medical issues, then they are studying to go to medical school to become a doctor.
If people grew up with parents who worked as doctors, then they enjoy healthcare fields and want to help people with medical issues.
If people study hard, then they grew up with parents who worked as doctors.
If Robin neither enjoys healthcare fields and wants to help people with medical issues nor grew up with parents who worked as doctors, then Robin is either a person who studies hard or grew up with parents who worked as doctors.

Premises in first-order logic:
∀x Coding(x) → ¬StudyingToBeDoctor(x)
∀x Industry(x) → Coding(x)
∀x Healthcare(x) → StudyingToBeDoctor(x)
∀x ParentDoctors(x) → Healthcare(x)
∀x Study(x) → ParentDoctors(x)
¬(Healthcare(Robin) ∨ ParentDoctors(Robin)) → (Study(Robin) ∨ ParentDoctors(Robin))

Conclusion: Robin is a person who studies hard.
Label: Unknown

Conclusion: Robin practices coding questions and wants to work in the software engineering industry.
Label: False

Conclusion: Robin neither practices coding questions nor works to work in the software engineering industry.
Label: True
Premises:
If people go to the spa at least once a week, then they are not people who are miserly and need to save a large portion of their income.
People are either miserly and need to save a large portion of their income, or people that frivolously spend a lot of money every time they go out.
If people frivolously spend a lot of money every time they go out, then they value quality manufacturing and luxury items.
If people value quality manufacturing and luxury items, then they enjoy shopping for materialistic items in their free time.
If Jess is not miserly and needs to save a large portion of their income, then Jess does not value quality manufacturing and luxury items.
If Jess either values quality manufacturing and luxury items or, if he does not, then he is miserly and needs to save a large portion of his income, then Jess frivolously spends a lot of money every time he goes out.

Premises in first-order logic:
∀x Spa(x) → ¬Miserly(x)
∀x Miserly(x) ∨ Frivolous(x)
∀x Frivolous(x) → Luxury(x)
∀x Luxury(x) → Shopping(x)
¬Miserly(Jess) → ¬Luxury(Jess)
Luxury(Jess) ⊕ Miserly(Jess)) →  Luxury(Jess)

Conclusion: Jess frivolously spends a lot of money every time they go out.
Label: Unknown

Conclusion: Jess either enjoys shopping for materialistic items in his free time or, if he does not, then he goes to the spa at least once a week.
Label: True

Conclusion: If Jess either enjoys shopping for materialistic items in his free time or, if he does not, then he goes to the spa at least once a week, then Jess neither values quality manufacturing and luxury items nor goes to the spa at least once a week.
Label: False
Premises:
The indie pop band Phoenix has released 6 albums. 
Phoenix's album "Wolfgang Amadeus Phoenix" sold over 500,000 copies. 
A certified gold album or single is one which sold over half a million copies. 
"1901" is a single from Phoenix's album "Wolfgang Amadeus Phoenix."
Over 400,000 copies of "1901" have been sold. 

Premises in first-order logic:
AlbumsReleased(phoenix, l6)
Album(wolfgangamadeusphoenix) ∧ IsAlbumOf(wolfgangamadeusphoenix, phoenix) ∧ SoldOver(wolfgangamadeusphoenix, l500000)
∀x ((Album(x) ∨ Single(x)) ∧ SoldOver(x, l500000) → CertifiedGold(x))
Single(l1901) ∧ From(l1901, wolfgangamadeusphoenix) ∧ By(l1901, phoenix)
SoldOver(l1901, l400000)

Conclusion: The album "Wolfgang Amadeus Phoenix" is a certified gold album.
Label: True

Conclusion: The single "1901" is a certified gold single.
Label: Unknown
Premises:
Peter Parker is a 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 and a destroyer is not a god.
Peter Parker wears a uniform when he is a superhero.
Peter Parker is not a civilian if and only if a destroyer is breaking a bridge.
If Thor is happy, the Hulk is angry.

Premises in first-order logic:
Superhero(peter) ⊕ Civilian(peter)
Destroyer(hulk)
Angry(hulk) → Wake(hulk)
Wake(hulk) → BreakBridge(hulk)
God(thor)
Happy(thor) → BreakBridge(thor)
∀x (God(x) ↔ ¬Destroyer(x))
Superhero(peter) → WearUniform(peter)
∀x (¬Civilian(peter) ↔ (Destroyer(x) ∧ BreakBridge(x)))
Happy(thor) → Angry(hulk)

Conclusion: If the Hulk does not wake up, then Thor is not happy.
Label: True

Conclusion: If Thor is happy, then Peter Parker wears a uniform.
Label: True

Conclusion: If Thor is not happy, then no bridge will be broken.
Label: Unknown
Premises:
Diethylcarbamazine is a medication discovered in the year 1947.
Diethylcarbamazine can be used to treat river blindness.
The only preferred treatment for river blindness is ivermectin.
Diethylcarbamazine is not ivermectin.

Premises in first-order logic:
∀x (Diethylcarbamazine(x) ∧ Medication(x) ∧ DiscoveredIn(x, year1947))
∀x ∀y (Diethylcarbamazine(x) ∧ RiverBlindness(y) → Treats(x, y))
∀x ∀y (RiverBlindness(y) ∧ PreferredTreatment(x, y) → Ivermectin(x))
∀x (Diethylcarbamazine(x) → ¬Ivermectin(x))

Conclusion: Diethylcarbamazine is not preferred for the treatment of river blindness.
Label: True

Conclusion: Diethylcarbamazine was often used to treat river blindness.
Label: Unknown

Conclusion: Diethylcarbamazine is used in the treatment of filariasis.
Label: Unknown
Premises:
All univariate analyses use a generalized linear model. 
All multivoxel (pattern) analyses are implementing machine learning algorithms.
All implementing machine learning algorithms are to learn and classify multivariate data.
The Latent Dirichlet allocation model uses a generalized linear model or a univariate analysis. 
If the Latent Dirichlet allocation model does not learn and classify multivariate data, then the Latent Dirichlet allocation model uses a generalized linear model and a multivoxel (pattern) analysis.

Premises in first-order logic:
∀x (UnivariateAnalysis(x) → UsesGeneralizedLinearModel(x)) 
 ∀x (MultivoxelPatternAnalysis(x) → ImplementingMachineLearningAlgorithms(x))
 ∀x (ImplementingMachineLearningAlgorithms(x) → LearnAndClassifyMultivariateData(x)) 
 UsesGeneralizedLinearModel(latentDirichletAllocationModel) ∨ UnivariateAnalysis(latentDirichletAllocationModel)
 ¬LearnAndClassifyMultivariateData(latentDirichletAllocationModel) → UsesGeneralizedLinearModel(latentDirichletAllocationModel) ∧ MultivoxelPatternAnalysis(latentDirichletAllocationModel)

Conclusion: Latent Dirichlet allocation model is implementing machine learning algorithms.
Label: Unknown

Conclusion: Latent Dirichlet allocation model uses a generalized linear model and learns and classifies multivariate data.
Label: True

Conclusion: Latent Dirichlet allocation model either uses a generalized linear model or learns and classifies multivariate data.
Label: False
Premises:
If something requires a medical diagnosis, then lab tests or imaging is required. 
All rare diseases require a medical diagnosis.
If something is mild, then no lab tests or imaging is required. 
All blood cancers are rare diseases.
All Leukemia is blood cancer. 
If bladder cancer is either both blood cancer and Leukemia or neither blood cancer nor Leukemia, then bladder cancer is Leukemia.

Premises in first-order logic:
∀x (RequiresAMedicalDiagnosis(x) → LabTestsOrImagingRequired(x)) 
∀x (RareDiseases(x) → RequiresAMedicalDiagnosis(x))
∀x (Mild(x) → ¬LabTestsOrImagingRequired(x)) 
∀x (BloodCancer(x) → RareDiseases(x))
∀x (Leukemia(x) → BloodCancer(x))
¬(BloodCancer(bladderCancer) ⊕ Leukemia(bladderCancer)) → Leukemia(bladderCancer)

Conclusion: Bladder cancer is mild.
Label: False

Conclusion: Bladder cancer is Leukemia.
Label: Unknown

Conclusion: Bladder cancer is either a rare disease or mild.
Label: True
Premises:
No precious metals aren't scarce.
All non-rare earth metals are not scarce.
Either non-rare earth metals or rare earth metals. 
All rare earth metals can be used for batteries.
All rare earth metals are essential for electric vehicles.
Lithium is neither a non-rare earth metal nor essential for electric vehicles.

Premises in first-order logic:
∀x (PreciousMetals(x) → ¬NotScarce(x))
 ∀x (Non-rareEarthMetals(x) → NotScarce(x)) 
 ∀x (Non-rareEarthMetals(x) ∨ RareEarthMetals(x)) 
 ∀x (RareEarthMetals(x) → BeUsedForBatteries(x)) 
 ∀x (RareEarthMetals(x) → EssentialForElectricVehicles(x))
 ¬Non-rareEarthMetals(lithium) ⊕ ¬EssentialForElectricVehicles(lithium)

Conclusion: Lithium is a rare earth metal.
Label: Unknown

Conclusion: Lithium is a precious metal and is used for batteries.
Label: False

Conclusion: If Lithium is not essential for electric vehicles or a precious metal, then Lithium is not a non-rare earth metal or usable for batteries.
Label: True
Premises:
If people own at least one pet, then they do not have tidy houses.
If people grew up with childhood pets, then they own at least one pet.
If people hire a maid or cleaning service, then they have tidy houses.
If people live in the suburbs, then they have tidy houses.
Jack either does not hire a maid or cleaning service or, if he does, then he does not own at least one pet.

Premises in first-order logic:
∀x (OwnPet(x) → ¬TidyHouse(x))
∀x (ChildhoodPet(x) → OwnPet(x))
∀x (HireCleaning(x) → TidyHouse(x))
∀x (Suburbs(x) → ChildhoodPet(x))
¬(HireCleaning(jack) ⊕ OwnPet(jack))

Conclusion: Jack lives in the suburbs.
Label: False

Conclusion: Jack does not live in the suburbs.
Label: True

Conclusion: Jack has a tidy house.
Label: Unknown

Conclusion: Jack neither lives in the suburbs nor owns at least one pet.
Label: True

Conclusion: Jack grew up with childhood pets and lives in the suburbs.
Label: False
Premises:
A bottle is either standing upright or toppled over. 
The bottle is not upright.

Premises in first-order logic:
Upright(bottle) ⊕ ToppledOver(bottle)
¬Upright(bottle)

Conclusion: The bottle is toppled over.
Label: True
Premises:
If people eat cereal and milk every day for breakfast, then they have very busy morning schedules every day.
All young children below the age of ten eat cereal and milk every day for breakfast.
People are either young children below the age of ten or have a lot of commitments they voluntarily signed up for.
If people have a lot of commitments they voluntarily signed up for, then they are hardworking individuals with high ambitions and goals for the future.
If people are hardworking individuals with high ambitions and goals for the future, then they are not good at staying organized and motivated.
John either is both a hardworking individual with high ambitions and goals for the future and has busy morning schedules every day, or he is neither a hardworking individual with high ambitions and goals for the future nor does he have a busy morning schedule every day.

Premises in first-order logic:
∀x (Cereal(x) → BusyMorning(x))
∀x (YoungChildren(x) → BusyMorning(x))
∀x (YoungChildren(x) ⊕ Committments(x))
∀x (Committments(x) → Hardworking(x))
∀x (Hardworking(x) → ¬Organized(x))
(Hardworking(john) ∧ BusyMorning(john)) ⊕ (¬Hardworking(john) ∧ ¬BusyMorning(john))

Conclusion: John is good at staying organized and motivated.
Label: False

Conclusion: John eats cereal and milk every day for breakfast.
Label: Unknown

Conclusion: John is either a hardworking individual with high ambitions and goals for the future or is good at staying organized and motivated.
Label: True
Premises:
Ableton has an office in Germany.
Ableton has an office in the USA.
USA and Germany are different countries.
Any company that has offices in different countries is a multinational company.
Ableton makes music software.

Premises in first-order logic:
OfficeIn(ableton, germany)
OfficeIn(ableton, usa)
¬SameCountry(germany, usa)
∀x ∀y ∀z (OfficeIn(x, y) ∧ OfficeIn(x, z) ∧ ¬SameCountry(y, z) → IsMultinationalCompany(x))
MakesMusicSoftware(ableton)

Conclusion: Ableton is a multinational company.
Label: True

Conclusion: Ableton makes AI software.
Label: Unknown

Conclusion: Ableton does not have an office in Germany.
Label: False
Premises:
Every swimmer can go underwater.
Everyone that eats fish they catch themselves is a swimmer.
Every penguin eats fish they catch themselves.
All nonflying birds in Antarctica are penguins.
Fido is a penguin or a swimmer.

Premises in first-order logic:
∀x (Swimmer(x) → GoesUnderwater(x))
∀x (EatsFishCaughtThemselves(x) → Swimmer(x))
∀x (Penguin(x) → EatsFishCaughtThemselves(x))
∀x (NonFlyingBirdAntartica(x) → Penguin(x))
Penguin(fido) ∨ Swimmer(fido)

Conclusion: Fido eats fish they catch themselves.
Label: Unknown

Conclusion: Fido is neither a non-flying bird nor can Fido go underwater.
Label: False

Conclusion: If Fido can either go underwater or is a swimmer, then Fido is a nonflying bird in Antartica.
Label: True
Premises:
All professors are teachers.
All researchers are professors.
No teachers are students.
All people who study are students.
Leon is not studying or a researcher.
If Leon is not studying, then Leon is a teacher.

Premises in first-order logic:
∀x (Professor(x) → Teacher(x))
∀x (Researcher(x) → Professor(x))
∀x (Teacher(x) → ¬Student(x))
∀x (Study(x) → Student(x))
¬Study(leon) ⊕ Researcher(leon)
¬Study(leon) → Teacher(leon)

Conclusion: Leon is a professor.
Label: Unknown

Conclusion: Leon is neither a student nor a researcher.
Label: True

Conclusion: If Leon is not a researcher, then Leon is a student.
Label: False
Premises:
A cutman is responsible for preventing and treating physical damage to a fighter.
Cutmen appear in matches such as a boxing, kickboxing or a mixed martial arts bout. 
Cutmen typically handle swelling, nosebleeds and lacerations. 
Jack is a cutman.

Premises in first-order logic:
∀x (Cutman(x) → Prevent(x, physicalDamage) ∧ Treat(x, physicalDamage))
∀x (Cutman(x) → Appear(x, boxing) ∨ Appear(x, kickboxing) ∨ Appear(x, mixedMartialBout))
∀x (Cutman(x) → Handle(x, swelling) ∧ Handle(x, nosebleeds) ∧ Handle(x, lacerations))
Cutman(jack)

Conclusion: No cutmen appear in boxing matches.
Label: Unknown

Conclusion: If someone is not a cutman, then they cannot handle nosebleeds.
Label: Unknown

Conclusion: Jack can treat physical damage.
Label: True
Premises:
The Mona Lisa is one of the world's best-known artworks.
The Mona Lisa is a portrait painted by Leonardo da Vinci.
Leonardo da Vinci was a scientist and painter.
Painting genres are history, portrait, animal, landscape, and still life.

Premises in first-order logic:
Painting(monaLisa) ∧ WorldsBestKnown(monaLisa)
PaintedBy(monaLisa, leonardodaVinci) ∧ Portrait(monaLisa)
Scientist(leonardodaVinci) ∧ Painter(leonardodaVinci)
∀x (Painting(x) → (History(x) ∨ Portrait(x) ∨ Animal(x) ∨ Landscape(x) ∨ StillLife(x)))

Conclusion: One of the world's best-known artworks is painted by a scientist.
Label: True

Conclusion: Leonardo da Vinci has artworks in the landscape genre.
Label: Unknown

Conclusion: No world's best-known artworks are portraits.
Label: False
Premises:
No professional tennis umpires are professional tennis players.
If you are a World Tour player, then you are a professional tennis player.
All Grand Slam champions are World Tour players.
All Grand Slam umpires are professional tennis umpires.
Nadal is a World Tour player or a Grand Slam champion

Premises in first-order logic:
∀x (ProfessionalTennisUmpire(x) → ¬ProfessionalTennisPlayer(x))
∀x (WorldTourPlayer(x) → ProfessionalTennisPlayer(x))
∀x (GrandSlamChampion(x) → WorldTourPlayer(x))
∀x (GrandSlamUmpire(x) → ProfessionalTennisUmpire(x))
WorldTourPlayer(nadal) ∨ GrandSlamChampion(nadal)

Conclusion: Nadal is a Grand Slam umpire.
Label: False

Conclusion: Nadal is not a Grand Slam umpire.
Label: True

Conclusion: Nadal is a Grand Slam champion.
Label: Unknown

Conclusion: Nadal is neither a Grand Slam umpire nor a professional tennis umpire.
Label: True

Conclusion: If Nadal is a professional tennis umpire, then Nadal is a Grand Slam Umpire.
Label: True

Conclusion: If Nadal is a Grand Slam umpire or a professional tennis player, then Nadal is a Grand Slam umpire.
Label: False
Premises:
Buisnesses are either sanctioned or unsanctioned.
Sanctioned buisnesses are limited.
Unsanctioned buisnesses are free.
The Crude Oil Data Exchange is a buisness that isn't free.

Premises in first-order logic:
∀x (Buisness(x) → Sanctioned(x) ⊕ ¬Sanctioned(x))
∀x (Buisness(x) ∧ Sanctioned(x) → Limited(x))
∀x (Buisness(x) ∧ ¬Sanctioned(x) → Free(x))
Buisness(crudeoildataexchange) ∧ ¬Free(crudeoildataexchange)

Conclusion: Crude Oil Data Exchange is sanctioned.
Label: True

Conclusion: Crude Oil Data Exchange is unsanctioned.
Label: False

Conclusion: Crude Oil Data Exchange is limited.
Label: True
Premises:
When something is depressing, it is sad.
V is depressing. 

Premises in first-order logic:
∀x (Depressing(x) → Sad(x))
∀x (V(x) → Depressing(x))

Conclusion: V is invigorating
Label: Unknown
Premises:
Palstaves are a type of early bronze axe.
Commonly found in northern, western and south-western Europe, palstaves are cast in moulds.
John Evans is an archeologist who popularized the term "palstave".
A paalstab is not an axe, but rather a digging shovel.

Premises in first-order logic:
∀x (Palstave(x) → From(x, earlybronzeage) ∧ Axe(x))
∀x ∀y (Palstave(x) ∧ Mould(y) → FoundIn(x, northerneurope) ∧ FoundIn(x, westerneurope) ∧ FoundIn(x, south-westerneurope) ∧ CastIn(x, y))
Archeologist(johnevans) ∧ Popularized(johnevans, termpalstave)
∀x (Paalstab(x) → ¬Axe(x) ∧ DiggingShovel(x))

Conclusion: John Evans Popularized the term paalstab.
Label: Unknown

Conclusion: There is an axe that is commonly found in Western Europe.
Label: Unknown

Conclusion: Archeologists haven't popularized anything.
Label: False
Premises:
Koei Tecmo is a Japanese video game and anime holding company.
Holding companies hold several companies.
Tecmo was disbanded in Japan, while Koei survived, but was renamed.
Video game holding companies are holding companies.

Premises in first-order logic:
Japanese(koeitecmo) ∧ VideoGameHoldingCompany(koeitecmo) ∧ AnimeHoldingCompany(koeitecmo)
∀x ∃y (HoldingCompany(x) → Company(y) ∧ Holds(x, y))
Disbanded(tecmo) ∧ DisbandedIn(tecmo, japan) ∧ Survived(koei) ∧ Renamed(koei)
∀x (VideoGameHoldingCompany(x) → HoldingCompany(x))

Conclusion: Koei Tecmo holds another company.
Label: True

Conclusion: Tecmo holds another company.
Label: Unknown

Conclusion: Koei Tecmo holds anime.
Label: Unknown
Premises:
The PlayStation EyeToy is a camera accessory for the Playstation 2. 
The PlayStation Eye is a camera accessory for the Playstation 3.
The Playstation Camera is a camera accessory for the Playstation 4 and the Playstation 5.
Camera accessory for the system is compatible with that system
Only the Playstation Camera is compatible with more than one system.

Premises in first-order logic:
Accessory(pseyetoy, ps2)
Accessory(pseye, ps3)
Accessory(pscamera, ps4) ∧ Accessory(pscamera, ps5)
∀x ∀y (Accessory(x, y) → Compatible(x, y))
¬Compatible(pseye, ps2) ∧ ¬Compatible(pseye, ps4)∧¬Compatible(pseye, ps5)∧¬Compatible(pseyetoy, ps3)∧¬Compatible(pseyetoy, ps4)∧¬Compatible(pseyetoy, ps5)

Conclusion: The Playstation Eye can be used with the Playstation 2 and the Playstation 3.
Label: False

Conclusion: The Playstation EyeToy is compatible with the Playstation 2.
Label: True

Conclusion: The Playstation Camera can be used for all Playstation consoles.
Label: Unknown
Premises:
Adam Buska is a European football player.
If a European plays football, they play what Americans call soccer.

Premises in first-order logic:
FootballPlayer(adambuska) ∧ European(adambuska)
∀x ((FootballPlayer(x) ∧ European(x)) → PlaysAmericanSoccer(x))

Conclusion: Adam Buska plays what Americans call soccer.
Label: True
Premises:
If the game has sold more than 1 million copies, then it is on the Best Seller list. 
All Games developed by Paradox Development Studio sold more than 1 million copies. 
Some games published before 2010 support the Linux OS system. 
All games on the Best Seller list are popular among young people. 
FIFA22 neither supports the Linux OS system nor is it popular among young people.

Premises in first-order logic:
∀x (SoldMillion(x) → BestSeller(x))
∀x (DevelopedByParadox(x) → SoldMillion(x))
∃x ((PublishedBefore2010(x) ∧ SupportLinuxSystem(x)))
∀x (BestSeller(x) → Popular(x))
¬(SupportLinuxSystem(fifa21) ∨ Popular(fifa21))

Conclusion: FIFA21 is published before 2010
Label: Unknown

Conclusion: FIFA21 is published before 2010 and developed by Paradox Development Studio
Label: False

Conclusion: if FIFA21 is either a game published before 2010 or developed by Paradox Development Studio, then it is either in the Best Seller list and sold more than 1 million copies; or not in the Best Seller list and hasn't sold more than 1 million copies.
Label: True
Premises:
China is one of BRICS and its economy is emerging.
India is one of BRICS and its economy is emerging.
All people from China speak Chinese.
All people from India speak Hindi or English.
There is an Indian.

Premises in first-order logic:
∀x (China(x) → BRICS(x) ∧ EmergingEconomy(x))
∀x (India(x) → BRICS(x) ∧ EmergingEconomy(x))
∀x ∀y (From(x, y) ∧ China(y) → Speak(x, chinese))
∀x ∀y (From(x, y) ∧ India(y) → Speak(x, hindi) ∨ Speak(x, english))
∃x ∃y (From(x, y) ∧ India(y))

Conclusion: All people from BRICS neither speak Hindi nor speak English.
Label: False

Conclusion: India's economy is not emerging.
Label: False

Conclusion: There is a person from BRICS speaking Hindi.
Label: Unknown
Premises:
Daveed Diggs is an actor and film producer.
Daveed Diggs played two roles in the musical Hamilton.
One of the actors from Hamilton won the best actor award.
The actor playing Thomas Jefferson won the best actor award.
Daveed Diggs played Thomas Jefferson.
Musicals are not films.

Premises in first-order logic:
Actor(daveedDiggs) ∧ FilmProducer(daveedDiggs)
PlayedTwoRoles(daveedDiggs) ∧ PlayedIn(daveedDiggs, hamilton) ∧ Musical(hamilton)
∃x (Actor(x) ∧ PlayedIn(x, hamilton) ∧ WonBestActorAward(x))
∀x (Actor(x) ∧ Played(x, thomasJefferson) → WonBestActorAward(x))
Played(daveedDiggs, thomasJefferson)
∀x (Musical(x) → ¬Film(x))

Conclusion: Hamilton is a film.
Label: False

Conclusion: Daveed Diggs won the best actor award.
Label: True

Conclusion: Hamilton won two awards.
Label: Unknown