yuansui/folio huggingface dataset:
Premises:
All inductive reasoning processes derive general principles from a body of observations.
Two major types of reasoning rules are inductive reasoning and deductive reasoning. 
All deductive reasoning processes are only based on facts and rules. 
Nothing only based on facts and rules is used for statistical generalization. 
Modus Ponens is not both used in inductive reasoning and used for statistical generalization. 
Modus Ponens is a component of a major part of reasoning rule. 

Premises in first-order logic:
∀x (InductiveReasoning(x) → DeriveFrom(generalPrinciple, observations))
∀x (MajorArgumentForm(x) → (InductiveReasoning(x) ⊕ DeductiveReasoning(x))
∀x (DeductiveReasoning(x) → (BasedOn(x, fact) ∨ BasedOn(x, rule)))
∀x ((BasedOn(x, fact) ∨ BasedOn(x, rule)) → (¬UsedFor(x, statisticalGeneralization)))
¬(InductiveReasoning(modusPonens) ∧ UsedFor(modusPonens, statisticalGeneralization))
ArgumentForm(modusPonens)

Conclusions:
Reasoning with Modus Ponens is based on facts and rules. 
Modus Ponens derives general principles from a body of observations and is used for statistical generalization. 
If Modus Ponens either derives general principles from a body of observations and is used for statistical generalization, or neither, then Modus Ponens is is neither used in inductive reasoning  nor used for statistical generalization.

Conclusions in first-order logic:
BasedOn(x, fact) ∨ BasedOn(x, rule)
DeriveFrom(generalPrinciple, observations) ∧ UsedFor(x, statisticalGeneralization)
¬(Derive(generalPrinciple, observations) ⊕ UsedFor(x, statisticalGeneralization)) → (¬InductiveReasoning(modusPonens) ∧ (¬UsedFor(modusPonens, statisticalGeneralization)))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
F
T
Premises:
Some employees in James's town who work in business analysis are good at math. 
All of the employees in James's town who work in business analysis are working for this company. 
None of the employees in James's town who work for this company are from China. 
All of the employees in James's town working in software engineering are from China. 
Leif is an employee in James's town, and he is working in software engineering.  

Premises in first-order logic:
∃x ∃y (EmployeeIn(x, jamesSTown) ∧ WorkIn(x, businessAnalysis) ∧ GoodAt(x, math) ∧ (¬(x=y)) ∧ EmployeeIn(y, jamesSTown) ∧ WorkIn(y, businessAnalysis) ∧ GoodAt(y, math))
∀x ((EmployeeIn(x, jamesSTown) ∧ WorkIn(x, businessAnalysis)) → WorkFor(x, thisCompany))
∀x ((EmployeeIn(x, jamesSTown) ∧ WorkFor(x, thisCompany)) → ¬From(x, china))
∀x (EmployeeIn(x, jamesSTown) ∧ WorkIn(x, softwareEngineering) → From(x, china))
EmployeeIn(leif, jamesSTown) ∧ WorkIn(leif, softwareEngineering)

Conclusions:
Leif is good at math. 
Leif is not both good at math and working in business analysis. 
If Leif is not both good at math and in business analysis, then he is neither working in this company nor working in software engineering.

Conclusions in first-order logic:
EmployeesInJamesSTown(leif) ∧ GoodAt(leif, math)
¬(GoodAt(leif, math) ∧ WorkIn(leif, businessAnalysis))
¬(GoodAt(leif, math) ∧ WorkIn(leif, businessAnalysis)) → (¬WorkFor(x, thisCompany) ∧ ¬WorkIn(x, softwareEngineering))

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

Conclusions:
John is a Teaching Fellow
John is not a Teaching Fellow.
John is a PhD student.

Conclusions in first-order logic:
TF(john)
¬TF(john)
PhDStudent(john)

Truth value of each conclusion (T = true, F = false, U = unknown):
F
T
U
Premises:
Grass is not food
All meadows are grass.
All edible things are food. 
All fruits are edible.
All lemons are fruit.
Grapes are not both edible and lemons.
Bananas are grasses or fruits. 

Premises in first-order logic:
∀x (Grass(x) → ¬Food(x))
∀x (Meadow(x) → Grass(x))
∀x (Edible(x) → Food(x))
∀x (Fruit(x) → Edible(x))
∀x (Lemon(x) → Fruit(x))
¬(Edible(grape) ∧ Lemon(grape))
Grass(banana) ∨ Fruit(banana)

Conclusions:
Grapes are food.
Grapes are not a food.
Bananas are both lemons and meadows. 
Bananas are not both a lemon and a meadow.

Conclusions in first-order logic:
Food(grape)
¬Food(grape)
Lemon(banana) ∧ Meadow(banana)
¬(Lemon(banana) ∧ Meadow(banana))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
U
F
T
Premises:
In Love City, everyone considers physical touch or words of affirmation to be their most important love language.
If someone in Love City considers physical touch as their most important love language, then they are good with pets.
If someone in Love City is good with pets, then they are not scared of animals.
In Love City, everyone is scared of animals, or loves animals, or both.
Adam, who is in Love City, either values physical touch as his most important love language or loves animals.

Premises in first-order logic:
∀x (In(x, loveCity) → (ConsidersMostImportantLoveLanguage(x, physicalTouch)  ∨ ConsidersMostImportantLoveLanguage(x, wordOfAffirmation))
∀x ((ConsidersMostImportantLoveLanguage(x, physicalTouch)  ∧ In(x, loveCity) → GoodWith(x, pet))
∀x ((GoodWith(x, pet) ∧ In(x, loveCity)) → ¬ScaredOf(x, animal))
∀x (In(x, loveCity) → (ScaredOf(x, animal) ∨ Loves(x, animal)))
(ConsidersMostImportantLoveLanguage(adam, physicalTouch) ⊕ Loves(adam, animal)) ∧ In(adam, loveCity)

Conclusions:
Adam is scared of animals.
Adam considers words of affirmation to be the most important love language.
Adam considers physical touch as the most important love language and considers words of affirmation as the most important love language.
Adam either values physical touch as an especially important love language or values words of affirmation as an especially important love language.
If Adam values physical touch as an especially important love language or is good with pets, then Adam values words of affirmation as an especially important love language.

Conclusions in first-order logic:
ScaredOf(adam, animal)
ConsidersMostImportantLoveLanguage(adam, wordOfAffirmation)
ConsidersMostImportantLoveLanguage(adam, physicalTouch) ∧ ConsidersMostImportantLoveLanguage(adam, wordOfAffirmation)
ConsidersMostImportantLoveLanguage(adam, physicalTouch) ⊕ ConsidersMostImportantLoveLanguage(adam, wordOfAffirmation)
(ConsidersMostImportantLoveLanguage(adam, physicalTouch, mostImportantLoveLanguage) ∨ GoodWith(x, pet)) → ConsidersMostImportantLoveLanguage(adam, wordOfAffirmation)

Truth value of each conclusion (T = true, F = false, U = unknown):
U
T
F
T
T
Premises:
If someone in Potterville yells, then they are not cool.
If someone in Potterville is angry, then they yell.
If someone in Potterville flies, then they are cool.
Every person in Potterville that knows magic flies.
All wizards in Potterville know magic.
Harry, who lives in Potterville either yells or flies. 
Potter, who lives in Potterville, is a wizard and flies.

Premises in first-order logic:
∀x (In(x, potterville) ∧ Yell(x) → ¬Cool(x))
∀x (In(x, potterville) ∧ Angry(x) → Yell(x))
∀x (In(x, potterville) ∧ Fly(x) → Cool(x))
∀x (In(x, potterville) ∧ Know(x, magic) → Fly(x))
∀x (In(x, potterville) ∧ Wizard(x) → Know(x, magic))
In(harry, potterville) ∧ (Yell(harry) ⊕ Fly(harry))
Wizard(potter) ∧ Fly(potter)

Conclusions:
Harry is cool.
Harry is not cool.
Harry is a wizard or angry.
Harry is neither a wizard nor angry.

Conclusions in first-order logic:
Cool(harry)
¬Cool(harry)
Wizard(harry) ∨ Angry(harry) 
¬Wizard(harry) ∧ ¬Angry(harry)

Truth value of each conclusion (T = true, F = false, U = unknown):
U
U
F
T
Premises:
All disposables are designed to be used only once.
Some items used in Tom's house are eco-friendly.
Every item used in Tom's house is either disposable or reusable. 
If something is made from metal, then it is not made from plastic. 
All reusable items used in Tom's house are made from metal.
The chopsticks used in Tom's house are either made from metals and plastics, or that they are neither made from metals nor plastics.

Premises in first-order logic:
∀x (Disposable(x) → DesignedToBeOnlyUsedOnce(x))
∃x (EcoFriendly(x))
∀x (UsedIn(x, tomsHouse) → Disposable(x) ⊕ Reusable(x))
∀x (MadeFrom(x, metal) → ¬MadeFrom(x, plastic))
∀x (Reusable(x) → MadeFrom(x, metal))
¬(MadeFrom(chopsticksUsedInTomsHouse, metal) ⊕ MadeFrom(chopsticksUsedInTomsHouse, plastic))

Conclusions:
The chopsticks used in Tom's house are eco-friendly. 
The chopsticks used in Tom's house are eco-friendly or designed to be used only once.
If chopsticks used in Tom's house are made from plastic or designed to be used only once, then they are made from plastic and are eco-friendly.

Conclusions in first-order logic:
EcoFriendly(chopsticks)
EcoFriendly(chopsticks) ∨ DesignedToBeOnlyUsedOnce(chopsticks)
MadeFrom(chopsticks, plastic) ∨ DesignedBeOnlyUsedOnce(chopsticks) → MadeFrom(chopsticks, plastic) ∧ EcoFriendly(chopsticks)

Truth value of each conclusion (T = true, F = false, U = unknown):
U
T
F
Premises:
No battery-powered watch is automatic.
All digital watches are battery-powered.
Some mechanical watches are automatic.
All smart watches are digital.
Moonwatch is either a digital watch and an automatic, or it is neither.

Premises in first-order logic:
∀x (BatteryPoweredWatch(x) → ¬AutomaticWatch(x))
∀x (DigitalWatch(x) → BatteryPoweredWatch(x))
∃x (MechanicalWatch(x) ∧ AutomaticWatch(x))
∀x (SmartWatch(x) → DigitalWatch(x))
¬(DigitalWatch(moonwatch) ⊕ AutomaticWatch(moonwatch))

Conclusions:
Moonwatch is a mechanical watch.
Moonwatch is a smartwatch and a mechanical watch.
If Moonwatch is a smartwatch and a mechanical watch, then Moonwatch is not a mechanical watch.
If Moonwatch is a mechanical or battery-powered watch, then Moonwatch is not a smartwatch.

Conclusions in first-order logic:
MechanicalWatch(moonWatch)
SmartWatch(moonwatch) ∧ MechanicalWatch(moonwatch)
SmartWatch(moonwatch) ∧ MechanicalWatch(moonwatch) → ¬MechanicalWatch(moonwatch)
MechanicalWatch(moonwatch)) ∨ BatteryPoweredWatch(moonwatch) → ¬SmartWatch(moonwatch)

Truth value of each conclusion (T = true, F = false, U = unknown):
U
F
T
T
Premises:
Animals are either invertebrates or vertebrates.
All animals with backbones reproduce by male-and-female mating. 
All vertebrate animals have a backbone.
All bees do not reproduce by male-and-female mating.
All queen bees are bees.
Harry is a bee.

Premises in first-order logic:
∀x (Animal(x) → Invertebrate(x) ⊕ Vertebrate(x))
∀x (Animal(x) ∧ With(x, backbone) → ReproduceBy(x, male-and-femaleMating))
∀x (Animal(x) ∧ Vertebrate(x) → With(x, backbone))
∀x (Bee(x) → ¬ReproduceBy(x, male-and-femaleMating))
∀x (QueenBee(x) → Bee(x))
Bee(harry)

Conclusions:
Harry is a queen bee.
Harry is an invertebrate or a queen bee.
If Harry is either both a vertebrate and an animal with a backbone, or neither a vertebrate nor an animal with a backbone, then Harry is neither an invertebrate nor a queen bee.

Conclusions in first-order logic:
QueenBee(harry)
Invertebrate(harry) ∨ QueenBee(harry)
¬(Invertebrate(harry) ⊕ With(harry, backbone)) → ¬Invertebrate(harry) ∧ ¬QueenBee(harry)

Truth value of each conclusion (T = true, F = false, U = unknown):
U
T
F
Premises:
If a restaurant is listed in Yelp’s recommendations, then the restaurant has not received many negative reviews.
All restaurants with a rating greater than four 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 four.
The Hamden Plaza Subway store has a rating greater than four, or it is popular among local residents.

Premises in first-order logic:
∀x ∃y (ListedIn(x, yelpRecommendation) → NegativeReview(y) ∧ ¬Receive(x, y))
∀x ∀y (HaveRating(x, y) ∧ GreaterThan(y, 4) → ListedIn(x, yelpRecommendation))
∃x ∃y (¬Provide(x, takeOutService) → NegativeReview(y) ∧ Receive(x, y))
∀x ∀y (PopularAmong(x, localResidents) → HaveRating(x, y) ∧ GreaterThan(y, 4))
∀x (HaveRating(hamdenPlazaSubway, x) ∧ GreaterThan(x, 4) ⊕ PopularAmong(hamdenPlazaSubway, localResidents))

Conclusions:
If the Hamden Plaza Subway store provides take-out service and receives many negative reviews, then its rating is greater than 4 and it does not provide take-out service.
The Hamden Plaza Subway store provides take-out service or does not receive many negative reviews.
The Hamden Plaza store does not provide take-out service.

Conclusions in first-order logic:
∃x ((TakeOutService(hamdenPlazaSubway) ∧ NegativeReview(x) ∧ Receive(hamdenPlazaSubway, x) → PopularAmong(hamdenPlazaSubway, localResidents) ∧ ¬Provide(hamdenPlazaSubway, takeOutService))
∃x (Provide(hamdenPlazaSubway, takeOutService) ∨ (NegativeReview(x) ∧ ¬Receive(hamdenPlazaSubway, x)))
¬Provide(hamdenPlazaSubway, takeOutService)

Truth value of each conclusion (T = true, F = false, U = unknown):
F
T
U
Premises:
If some diseases require a medical diagnosis, then lab tests or imaging is required. 
All rare diseases require a medical diagnosis.
If a disease is mild, then no lab tests or imaging is required. 
All blood cancers are rare diseases.
All types of leukemia are diseases and blood cancers. 
Bladder cancer is a disease and is blood cancer or Leukemia.

Premises in first-order logic:
∀x (Disease(x) ∧ Require(x, medicalDiagnosis) → RequiredFor(labTest, x) ∨ RequiredFor(imaging, x)) 
∀x (RareDisease(x) → Require(x, medicalDiagnosis))
∀x (Disease(x) ∧ Mild(x) → ¬(RequiredFor(labTest, x) ∨ RequiredFor(imaging, x))) 
∀x (BloodCancer(x) → RareDiseases(x))
∀x (Disease(x) ∧ Leukemia(x) → BloodCancer(x))
Disease(bladderCancer) ∧ (BloodCancer(bladderCancer) ∨ Leukemia(bladderCancer))

Conclusions:
Bladder cancer is a mild disease. 
Bladder cancer is Leukemia. 
Bladder cancer is either a rare disease or a mild disease.

Conclusions in first-order logic:
Mild(bladderCancer) 
Leukemia(bladderCancer)
RareDisease(bladderCancer) ⊕ Mild(bladderCancer)

Truth value of each conclusion (T = true, F = false, U = unknown):
F
U
T
Premises:
If someone has never left the United States, then they have not been to Europe.
If someone has traveled to the Iberian Peninsula, then they have been to Europe.
Everyone who has visited Barcelona has traveled to the Iberian Peninsula.
Everyone who has taken a selfie at the Basilica Familia Sagrada has visited Madrid.
If Kate has either taken a selfie at the Basilica Familia Sagrada or has never left the United States, then Kate has not either visited Barcelona or never left the United States.

Premises in first-order logic:
∀x (¬Left(x, uSA) → ¬BeenTo(x, europe))
∀x (HaveTraveledTo(x, iberianPeninsula) → BeenTo(x, europe))
∀x (HaveVisited(x, barcelona) → HaveTraveledTo(x, iberianPeninsula))
∀x (TakenSelfieAt(x, basilicaSagradaFamilia) → HaveVisited(x, barcelona))
TakenSelfieAt(kate, basilicaSagradaFamilia) ⊕ NeverLeft(kate, uSA) → ¬(HaveVisited(kate, barcelona) ⊕ NeverLeft(kate, uSA))

Conclusions:
Kate has traveled to Spain.
Kate has taken a selfie at the Basilica Familia Sagrada.
Kate has not taken a selfie at the Basilica Familia Sagrada.
Kate has never left the United States.
Kate has left the United States before.
Kate either has taken a selfie at the Basilica Familia Sagrada  or has never left the United States.
If Kate has not both taken a selfie at the Basilica Familia Sagrada and been to Europe, then Kate has left the United States before.
If Kate has not both taken a selfie at the Basilica Familia Sagrada and been to Europe, then Kate has taken a selfie at the Basilica Familia Sagrada.

Conclusions in first-order logic:
HaveTraveledTo(kate, iberianPeninsula)
TakenSelfieAt(kate, basilicaSagradaFamilia)
¬TakenSelfieAt(kate, basilicaSagradaFamilia)
NeverLeft(kate, uSA)
¬NeverLeft(kate, uSA)
TakenSelfieAt(kate, basilicaSagradaFamilia) ⊕ NeverLeft(kate, uSA)
¬(TakenSelfieAt(kate, basilicaSagradaFamilia) ∧ BeenTo(kate, europe)) → ¬NeverLeft(kate, uSA)
¬(TakenSelfieAt(kate, basilicaSagradaFamilia) ∧ BeenTo(kate, europe)) → TakenSelfieAt(kate, basilicaSagradaFamilia)

Truth value of each conclusion (T = true, F = false, U = unknown):
U
F
T
F
T
F
T
F
Premises:
If a project is selected for "Hall of Fame" of the course, then it is not led by a team with two members.
All work that is unfinished in the course is led by a team of two members.
All work that is not presented in the final presentation is unfinished in the course. 
All work that does not receive a final grade in the course is not presented in the final presentation.
The project called “Sustainable Energy Sources” does not receive a final grade in the course or it is not selected for the Hall of Fame in the course.

Premises in first-order logic:
∀x (SelectedForInCourse(x, hallOfFame) → ¬(∃y ∃z (y!=z ∧ Member(y) ∧ TeamMember(z) ∧ LedBy(x, y) ∧ LedBy(x, z))))
∀x (UnfinishedInCourse(x) → ∃y ∃z (y!=z ∧ TeamMember(y) ∧ TeamMember(z) ∧ LedBy(x, y) ∧ LedBy(x, z)))
∀x (NotPresentedIn(x, finalPresentation) → UnfinishedInCourse(x))
∀x (NoFinalGradeInCourse(x) → NotPresentedIn(x, finalPresentation))
HaveInCourse(sustainibleEnergySources, finalGrade) ∨ ¬SelectedForInCourse(sustainibleEnergySources, hallOfFame)

Conclusions:
The project named “Sustainible Energy Sources" is selected for the Hall of Fame in the course. 
The project named “Sustainible Energy Sources" is not selected for the Hall of Fame in the course. 
The project named “Sustainible Energy Sources" is unfinished in the course.

Conclusions in first-order logic:
SelectedForIncourse(sustainibleEnergySources, hallOfFame)
SelectedForInCourse(sustainibleEnergySources, hallOfFame)
UnfinishedInCourse(sustainibleEnergySources)

Truth value of each conclusion (T = true, F = false, U = unknown):
F
T
U
Premises:
No one who attended ACL in the AI lab is an author of the AI lab's paper. 
All graduate students in the AI lab are authors of the AI lab's paper. 
All computer science students in the AI Lab doing research in NLP are graduate students. 
All PhD students in the AI Lab who took the applied linguistics course are computer science students doing research in NLP. 
Paul is in the AI lab. 
If Paul is either a computer science student doing research in NLP or if Paul attended ACL, then Paul is not an author of the AI lab's paper.

Premises in first-order logic:
∀x (In(x, aILab) ∧ Attended(x, aCL) → ¬AuthorOf(x, aILabPaper))
∀x (In(x, aILab) ∧ GraduateStudent(x) → Author(x, aILabPaper))
∀x (In(x, aILab) ∧ Doing(x, nLPResearch) → GraduateStudent(x))
∀x (In(x, aILab) ∧ PhDStudent(x) ∧ Took(x, appliedLinguisticsCourse) → Doing(x, nLPResearch))
In(paul, aILab)
Doing(paul, nLPResearch) ⊕ AttendedACL(paul) → ¬AuthorOf(paul, aILabPaper)

Conclusions:
Paul is a member of the AI lab. 
Paul is a PhD student who took the applied linguistics course. 
Paul is not a PhD student who took the applied linguistics course

Conclusions in first-order logic:
GraduateStudent(paul)
PhDStudent(paul) ∧ Took(paul, appliedLinguisticsCourse)
¬(PhDStudent(paul) ∧ Took(paul, appliedLinguisticsCourse))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
F
T
Premises:
Any convicted criminal that is innocent is not truly guilty.
All convicted criminals who did not commit a crime are truly innocent.
All convicted criminals are truly guilty or found guilty.
If a convicted criminal is found guilty, then they are sentenced to a punishment.
If a convicted criminal is found guilty, then they can argue against their punishment.
Garry is a convicted criminal who not found guilty or is sentenced to punishment.

Premises in first-order logic:
∀x (ConvictedCriminal(x) ∧ Innocent(x) → ¬TrulyGuilty(x))
∀x (ConvictedCriminal(x) ∧ ¬CommitCrime(x) → Innocent(x))
∀x (ConvictedCriminal(x) ∧ (TrulyGuilty(x) ∨ FoundGuilty(x)))
∀x (ConvictedCriminal(x) ∧ FoundGuilty(x) → SentencedToPunishment(x))
∀x (ConvictedCriminal(x) ∧ FoundGuilty(x) → CanArgueAgainst(x, punishment))
ConvictedCriminal(garry) ∧ (¬(FoundGuilty(garry) ∨ SentencedToPunishment(garry)))

Conclusions:
Garry is sentenced to a punishment.
Garry did not commit a crime and can argue against his punishment.
Garry is not both innocent and someone who did not commit a crime.

Conclusions in first-order logic:
SentencedToPunishment(garry)
¬CommitCrime(garry) ∧ CanArgueAgainst(garry, punishment) 
¬(Innocent(garry) ∧ ¬CommitCrime(garry))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
F
T
Premises:
All philosophers reason. 
Some sophists reason. 
All who can reason can distinguish truth from falsehood.
Nobody who can distinguish truth from falsehood is morally perfect.  
The theistic God is morally perfect.

Premises in first-order logic:
∀x (Philosopher(x) → Reason(x))
∃x (Sophist(x) ∧ Reason(x))
∀x (Reason(x) → CanDistinguishFrom(x, truth, falsehood))
∀x (CanDistinguishFrom(x, truth, falsehood) → ¬MorallyPerfect(x))
MorallyPerfect(theisticGod)

Conclusions:
The theistic God is a sophist.
The theistic God is a sophist and a philosopher.
if the theistic God is a philosopher, then he is not a sophist.

Conclusions in first-order logic:
Sophist(theisticGod)
Sophist(theisticGod) ∧ Philosopher(theisticGod)
Philosopher(theisticGod) → ¬Sophist(theisticGod)

Truth value of each conclusion (T = true, F = false, U = unknown):
U
F
T
Premises:
All tigers are cats.
No cats are dogs.
All Bengal tigers are tigers.
All huskies are dogs.
Fido is either a Bengal tiger or a cat.

Premises in first-order logic:
∀x (Tiger(x) → Cat(x))
∀x (Cat(x) → ¬Dog(x))
∀x (BengalTiger(x) → Tiger(x))
∀x (Husky(x) → Dog(x))
BengalTiger(fido) ⊕ Cat(fido)

Conclusions:
Fido is a husky animal.
Fido is not a husky.
Fido is a Bengal tiger.
Fido is neither a dog nor a husky.

Conclusions in first-order logic:
Husky(fido)
¬Husky(fido)
BengalTiger(fido)
¬Dog(fido) ∧ ¬Husky(fido)

Truth value of each conclusion (T = true, F = false, U = unknown):
F
T
U
T
Premises:
Everyone who is entitled to national social insurance coverage can have their medical bills partially covered. 
All PRC nationals are entitled to national social insurance coverage.
Everyone in the Franco-China diplomatic conference is either a PRC national or a French national, but not both. 
All French nationals are citizens of the European Union. 
All Spanish nationals are citizens of the European Union. 
No North Korean nationals are citizens of the European Union. 
Mei is at the Franco-China diplomatic conference. 
Either Mei is a North Korean and can have medical bills partially covered, or neither is true.

Premises in first-order logic:
∀x (EntitledTo(x, nationalSocialInsuranceCoverage) → CanHavePartiallyCovered(x, medicalBills))
∀x (PRCNational(x) → EntitledTo(x, nationalSocialInsuranceCoverage))
∀x (In(x, franco-ChinaDiplomaticConference) → PRCNational(x) ⊕ FrenchNational(x))
∀x (FrenchNational(x) → CitizenOf(x, europeanUnion))
∀x (SpanishNational(x) → CitizenOf(x, europeanUnion))
∀x (NorthKoreanNational(x) → ¬CitizenOf(x, europeanUnion))
In(mei, franco-ChinaDiplomaticConference)
¬(NorthKoreanNational(mei) ⊕ CanHavePartiallyCovered(mei, medicalBills))

Conclusions:
Mei is a PRC national.
Mei is not a PRC national.
If Mei is either a North Korean or a Spanish national, then Mei is either both a French national and a citizen of the European Union, or neither a French national nor a citizen of the European Union.

Conclusions in first-order logic:
PRCNational(mei)
¬PRCNational(mei)
NorthKoreanNational(mei) ⊕ SpanishNational(mei) → ¬(FrenchNational(mei) ⊕ European(mei))

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