yuansui/folio huggingface dataset:
Premises:
Boves is a railway station located in France. 
The preceding station of Boves is Longueau.
The preceding station of Dommartin is Boves.
France is a European country.
Dommartin is situated on the Paris–Lille railway. 
Any two contiguous stations are on the same railway.
Boves is served by regional TER Hauts-de-France trains.
If place A is located in place B and place B is located in place C, then place A is located in place C.
If place A precedes place B and place B precedes place C, then place A precedes place C.

Premises in first-order logic:
RailwayStation(boves) ∧ In(boves, france)
Precede(longueau, boves)
Precede(boves, dommartin)
In(france, europe)
SituatedOn(dommartin, pairsLille)
∀x ∀y ∀z (SituatedOn(x, z) ∧ (Precede(x, y) ∨ Precede(y, x)) → SituatedOn(y, z))
Serve(boves, hautsDeFrance)
∀x ∀y ∀z ((In(x, y) ∧ In(y, z)) → In(x, z))
∀x ∀y ∀z ((Precede(x, y) ∧ Precede(y, z)) → Precede(x, z))

Conclusions:
Longueau is situated on the Paris–Lille railway.
Boves is not in Europe.
Longueau is served by regional TER Hauts-de-France trains.

Conclusions in first-order logic:
SituatedOn(longueau, pairsLille)
¬In(boves, europe)
Serve(longueau, hautsDeFrance)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
F
U
Premises:
Some cats are not pets.
All cats are mammals.

Premises in first-order logic:
∃x (Cat(x) ∧ ¬Pet(x))
∀x (Cat(x) → Mammal(x))

Conclusions:
Some mammals are not pets.

Conclusions in first-order logic:
∃x ∃y (Mammal(x) ∧ Mammal(y) ∧ ¬Pet(x) ∧ ¬Pet(y))

Truth value of each conclusion (T = true, F = false, U = unknown):
T
Premises:
TOra is a GUI.
GUIs are software.
Software can be free or paid.
Paid Software is not under the GNU General Public License.
TOra is under the GNU General Public License.

Premises in first-order logic:
GUI(tora)
∀x (GUI(x) → Software(x))
∀x (Software(x) → Free(x) ⊕ Paid(x))
∀x (Paid(x) ∧ Software(x) → ¬UnderGNULicense(x))
UnderGNULicense(tora)

Conclusions:
TOra is a paid software.
TOra is a free software.

Conclusions in first-order logic:
Paid(tora) ∧ Software(tora)
Free(tora) ∧ Software(tora)

Truth value of each conclusion (T = true, F = false, U = unknown):
F
T
Premises:
Pets are allowed in some managed buildings.
A deposit is required to rent an apartment in a managed building.
The security deposit can be either equal to the monthly rent at a managed building 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.
$2000 is more than $1500.
Tom will rent an apartment in a managed building if he is allowed to move in with Fluffy, and the security deposit is no more than $1500.
If a managed building allows pets, then people are allowed to move in with a pet. 

Premises in first-order logic:
∃x (ManagedBuilding(x) ∧ AllowPet(x))
∀x ∀y (ManagedBuilding(x) ∧ RentApartmentIn(y, x) → DepositRequired(y))
∀x ∀y ∀z (SecurityDeposit(x) ∧ ManagedBuilding(z) ∧ MonthlyRentAt(y, z) → (MoreThan(x, y) ⊕ Equal(x, y))
Cat(fluffy) ∧ BelongTo(fluffy, tom)
∀x (Cat(x) → Pet(x))
ManagedBuilding(oliveGarden)
MonthlyRentAt(uSD2000, oliveGarden)
MoreThan(uSD2000, uSD1500)
∀x ∀y (ManagedBuilding(x) ∧ AllowedToMoveInWith(tom, x, fluffy) ∧ SecurityDeposit(y) ∧ ¬MoreThan(y, uSD1500) → RentApartmentIn(tom, x))
∀x ∀y ∀z (ManagedBuilding(x) ∧ AllowPet(x) ∧ Pet(z) → AllowedToMoveInWith(y, x, z))

Conclusions:
Tom will rent an apartment in The Olive Garden. 
Tom is allowed to move into an apartment in The Olive Garden with Fluffy. 
The security deposit at the Olive Garden is either $2000 or more.  

Conclusions in first-order logic:
RentApartmentIn(tom, oliveGarden)
AllowedToMoveInWith(tom, oliveGarden, fluffy)
∀x (SecurityDeposit(x, oliveGarden) → MoreThan(x, uSD2000) ⊕ Equal(x, uSD2000))

Truth value of each conclusion (T = true, F = false, U = unknown):
F
U
T
Premises:
All cats are mammals.
Some pets are not mammals.

Premises in first-order logic:
∀x (Cat(x) → Mammal(x))
∃x (Pet(x) ∧ ¬Mammal(x))

Conclusions:
No pets are cats.

Conclusions in first-order logic:
∀x (Pet(x) → ¬Cat(x))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
Premises:
Luzon is an island in the Philippines.
In December 1999, an earthquake struck Luzon.
People died in the December 1999 earthquake in Luzon.

Premises in first-order logic:
Island(luzon) ∧ In(luzon, philippines)
∃x (Earthquake(x) ∧ StrikeInYr(x, year1999) ∧ StrikeInMo(x, december) ∧ StrikeInCity(x, luzon))
∃x (Earthquake(x) ∧ StrikeInYr(x, year1999) ∧ StrikeInMo(x, december) ∧ StrikeInCity(x, luzon) ∧ Deadly(x))

Conclusions:
Leyte is an island in the Philippines.
No one has ever died in an earthquake that struck the Philippines.
In 1999, there was at least one earthquake in the Philippines.

Conclusions in first-order logic:
Island(leyte) ∧ In(leyte, philippines)
∀x ∀y ((Earthquake(x) ∧ StrikeInCity(x, y) ∧ In(y, philippines)) → ¬Deadly(x))
∃x ∃y (Earthquake(x) ∧ StrikeInYr(x, year1999) ∧ StrikeInMo(x, december) ∧ StrikeInCity(x, y) ∧ In(y, philippines))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
F
T
Premises:
Philatelic literature is divided into the following categories: Stamp catalogs, Periodicals, Auction catalogs, Books, Bibliographies, and Background Material.
Mort is not a Stamp catalog.
Mort is not a periodical, auction catalog, bibliography, or background material.
Mort is a piece of Philatelic literature.

Premises in first-order logic:
∀x (PhilatelicLit(x) → (Stamp(x) ∨ Periodical(x) ∨ Auction(x) ∨ Book(x) ∨ Bibliography(x) ∨ Background(x)))
¬Stamp(mort)
¬(Periodical(mort) ∨ Auction(mort) ∨ Bibliography(mort) ∨ Background(mort))
PhilatelicLit(mort)

Conclusions:
Mort is background material.
Eragon is a piece of Philatelic literature.

Conclusions in first-order logic:
Background(mort)
PhilatelicLit(eragon)

Truth value of each conclusion (T = true, F = false, U = unknown):
F
U
Premises:
Vic DiCara plays guitar and bass.
The only style of music Vic DiCara plays is punk music.
Vic DiCara played in the band Inside Out.

Premises in first-order logic:
Play(vicDicara, guitar) ∧ Play(vicDicara, bass)
∀x (Music(vicDicara, x) → ¬(x=punk)))
Band(vicDicara, insideOut)

Conclusions:
Inside Out was a punk band.
A musician from Inside Out plays bass.

Conclusions in first-order logic:
Music(insideOut, punk)
∃x (Band(x, insideOut) ∧ Play(x, bass))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
T
Premises:
"The Wind is Watching" is an American film.
"The Wind is Watching" is produced in Ohio.
Some American films are dystopian.
Faraj and Cook both directed the film "The Wind is Watching."
If a film is directed by Faraj, then the film has a small budget.
If films are produced in Ohio and they have a small budget, then it is hard to get access to their copies.

Premises in first-order logic:
AmericanFilm(theWindIsWatching)
ProducedIn(theWindIsWatching, ohio)
∃x ∃y (AmericanFilm(x) ∧ Dystopian(x) ∧ AmericanFilm(y) ∧ Dystopian(y))
DirectedBy(theWindIsWatching, faraj) ∧ DirectedBy(theWindIsWatching, cook)
∀x (DirectedBy(x, faraj) → Have(x, smallBudget))
∀x ∀y(ProducedIn(x, ohio) ∧ Have(x, smallBudget) ∧ CopyOf(y, x) → HardToAccess(y))

Conclusions:
It is hard to get access to copies of the film "The Wind is Watching".
"The Wind is Watching" has a small budget.
"The Wind is Watching" is a dystopian film.

Conclusions in first-order logic:
HardToAccess(theWindIsWatching)
Have(theWindIsWatching, smallBudget)
Dystopian(theWindIsWatching)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
T
U
Premises:
The National Lobster Hatchery is a hatchery located in Padstow, England.
The National Lobster Hatchery is open to visitors.
A hatchery is either for profit or for conservation.
If a hatchery is for conservation, it might release animals into the wild.
The National Lobster Hatchery is not for profit.

Premises in first-order logic:
Hatchery(nationalLobsterHatchery) ∧ LocatedIn(nationalLobsterHatchery, padstowEngland)
OpenToVisitor(nationalLobsterHatchery)
∀x (Hatchery(x) → ForConservation(x) ⊕ ForProfit(x))
∃x (Hatchery(x) ∧ ForConservation(x) ∧ ReleaseAnimalToWild(x))
¬ForProfit(nationalLobsterHatchery)

Conclusions:
The National Lobster Hatchery is for conservation.
All hatcheries are open to visitors.
The National Lobster Hatchery releases animals into the wild.

Conclusions in first-order logic:
ForConservation(nationalLobsterhatchery)
∀x (Hatchery(x) → OpenToVisitors(x))
ReleaseAnimalToWild(nationalLobsterhatchery)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
U
U
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:
Medication(diethylcarbamazine) ∧ DiscoversIn(diethylcarbamazine, yr1947)
Treats(diethylcarbamazine, riverBlindness)
PreferredTreatmentFor(riverBlindness, ivermectin)
¬(Is(diethylcarbamazine, ivermectin))

Conclusions:
Diethylcarbamazine is not preferred for the treatment of river blindness.
Diethylcarbamazine was often used to treat river blindness.
Diethylcarbamazine is used in the treatment of filariasis.

Conclusions in first-order logic:
¬(PreferredTreatmentFor(riverBlindness, diethylcarbamazine))
Treats(diethylcarbamazine, riverBlindness)
Treats(diethylcarbamazine, filariasis)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
U
U
Premises:
Machine Learning algorithms can be categorized as supervised learning, unsupervised learning, and reinforcement learning.
Unsupervised learning algorithms do not require labeled data.
The state-of-the-art text summarization model is trained with machine learning algorithms.
Reinforcement learning is not used to train the state-of-the-art text summarization model.
The Machine Learning algorithm for training text summarization models requires labeled data.

Premises in first-order logic:
∀x (MachineLearningAlgorithm(x) → SupervisedLearningAlgorithm(x) ∨ UnsupervisedLearningAlgorithm(x) ∨ ReinforcementLearningAlgorithm(x))
∀x (UnsupervisedLearningAlgorithm(x) → ¬Require(x, labeledData))
∀x (TrainedWith(stateOfTheArtTextSummarizationModel, x) → MachineLearningAlgorithm(x))
∀x (ReinforcementLearningAlgorithm(x) → ¬TrainedWith(stateOfTheArtTextSummarizationModel, x))
∀x ((MachineLearningAlgorithm(x) ∧ TrainedWith(stateOfTheArtTextSummarizationModel, x)) → Require(x, labeledData))

Conclusions:
Supervised learning is used to train the state-of-the-art text summarization model. 
Unsupervised learning is used to train the state-of-the-art text summarization model. 

Conclusions in first-order logic:
∃x (SupervisedLearningAlgorithm(x) ∧ TrainedWith(stateOfTheArtTextSummarizationModel, x))
∃x (UnsupervisedLearningAlgorithm(x) ∧ TrainedWith(stateOfTheArtTextSummarizationModel, x))

Truth value of each conclusion (T = true, F = false, U = unknown):
T
F
Premises:
Jan Jelinek makes glitch and minimal techno music.
Anyone making glitch, minimal techno, or microhouse is an electronic musician.
Jan Jelinek publishes music through the Faitiche label.
Any musician releasing music through a label is a signed musician.

Premises in first-order logic:
Make(janJelinek, glitch) ∧ Make(janJelinek, minimalTechno)
∀x ((Make(x, glitch) ∨ Make(x, minimalTechno) ∨ Make(x, microHouse)) → ElectronicMusician(x))
PublishThroughLabel(janJelinek, faitiche)
∀x ((∃y (PublishThroughLabel(x, y))) → SignedMusician(x))

Conclusions:
Jan Jelinek is an electronic musician.
Jan Jelinek is not a signed musician.
Jan Jelinek is German.

Conclusions in first-order logic:
ElectronicMusician(janJelinek)
¬SignedMusician(janJelinek)
German(janJelinek)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
F
U
Premises:
The Golden State Warriors are a team from San Francisco.
The Golden State Warriors won the NBA finals.
All teams attending the NBA finals have won many games.
Boston Celtics are a team that lost the NBA finals.
If a team wins the NBA finals, then they will have more income.
If a team wins or loses at the NBA finals, then they are attending the finals.

Premises in first-order logic:
Team(goldenStateWarriors) ∧ From(goldenStateWarriors, sanFrancisco)
Won(goldenStateWarriors, nbaFinals)
∀x ((Team(x) ∧ Attending(x, nbaFinals)) → WonManyGames(x))
Team(bostonCeltics) ∧ Lost(bostonCeltics, nbaFinals)
∀x ((Team(x) ∧ Won(x, nbaFinals)) → MoreIncome(x))
∀x ((Won(x, nbaFinals) ∨ Lost(x, nbaFinals)) → Attending(x, nbaFinals))

Conclusions:
The Boston Celtics are from San Francisco.
The Boston Celtics have more than 30 years of experience.
The Golden State Warriors will have more income from gate receipts.

Conclusions in first-order logic:
From(bostonCeltics, sanFrancisco)
HasMoreThanThirtyYearsOfHistory(bostonCeltics)
MoreIncome(goldenStateWarriors)

Truth value of each conclusion (T = true, F = false, U = unknown):
U
T
T
Premises:
William Dickinson was a British politician who sat in the House of Commons
William Dickinson attended Westminster school for high school and then the University of Edinburgh.
The University of Edinburgh is a university located in the United Kingdom.
William Dickinson supported the Portland Whigs.
People who supported the Portland Whigs did not get a seat in the Parliament.

Premises in first-order logic:
British(williamDickinson) ∧ Politician(williamDickinson) ∧ SatIn(williamDickinson, houseOfCommons)
Attended(williamDickinson, westminsterSchool) ∧ Highschool(westminsterSchool) ∧ Attended(williamDickinson, universityOfEdinburgh)
University(universityOfEdinburgh) ∧ LocatedIn(universityOfEdinburgh, unitedKingdom)
Supported(williamDickinson, portlandWhigs)
∀x (Supported(x, portlandWhigs) → ¬SatIn(x, parliament))

Conclusions:
William Dickinson did not get a seat in Parliament.
William Dickinson went to schools located in the United Kingdom for both high school and university.
William Dickinson attended university in the United Kingdom.
William Dickinson sat in the House of Commons.

Conclusions in first-order logic:
SatIn(williamDickinson, parliament)
∃x ∃y (Attended(williamDickinson, x) ∧ Highschool(x) ∧ LocatedIn(x, unitedKingdom) ∧ Attended(williamDickinson, y) ∧ University(y) ∧ LocatedIn(y, unitedKingdom))
∃x (Attended(williamDickinson, x) ∧ University(x) ∧ LocatedIn(x, unitedKingdom))
SatIn(williamDickinson, houseOfCommons)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
U
T
T
Premises:
Bulbophyllum attenuatum is in the genus Bulbophyllum.
All Bulbophyllum are orchids.

Premises in first-order logic:
GenusBulbophyllum(bulbophyllumAttenuatum)
∀x (GenusBulbophyllum(x) → Orchid(x))

Conclusions:
Bulbophyllum attenuatum is not an orchid.

Conclusions in first-order logic:
¬Orchid(bulbophyllumAttenuatum)

Truth value of each conclusion (T = true, F = false, U = unknown):
F
Premises:
Cancer biology is finding genetic alterations that confer a selective advantage to cancer cells. 
Cancer researchers have frequently ranked the importance of substitutions to cancer growth by the P value.
P values are thresholds for belief, not metrics of effect. 

Premises in first-order logic:
Finding(cancerBiology, geneticAlteration) ∧ Confer(geneticAlteration, selectiveAdvantage, toCancerCell)
∃x ∃y (CancerResearcher(x) ∧ Ranked(x, importanceOfSubstitutionsToCancerGrowth) ∧ PValue(y) ∧ RankedBy(importanceOfSubstitutionsToCancerGrowth, y))
∀x (PValue(x) → ThresholdForBelief(x) ∧ ¬MetricOfEffect(x))

Conclusions:
Cancer researchers tend to use the cancer effect size to determine the relative importance of the genetic alterations that confer selective advantage to cancer cells.  
P value represents the selection intensity for somatic variants in cancer cell lineages. 
Cancer effect size is preferred by cancer researchers. 
P values don't represent metrics of effect.

Conclusions in first-order logic:
∃x ∃y (CancerResearcher(x) ∧ Use(x, cancerEffectSize) ∧ UsedToDetermine(cancerEffectSize, relativeImportanceOfGeneteticAlterations))
SelectionIntensitySomaticVariants(pValue)
Preferred(cancerResearchers, cancerEffectSize)
∀x (PValue(x) → ¬MetricsOfEffect(x))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
U
U
T
Premises:
James Cocks was a British lawyer.
James Cocks was a Whig politician who sat in the House of Commons.
A British is a European.
Any lawyer is familiar with laws.
Some Whigs speak French.

Premises in first-order logic:
British(james) ∧ Lawyer(james)
Whig(james) ∧ Politician(james) ∧ SatInHouseOfCommons(james)
∀x (British(x) → European(x))
∀x (Lawyer(x) → FamiliarWithLaws(x))
∃x ∃y (Whig(x) ∧ SpeakFrench(x)) ∧ (¬(x=y)) ∧ (Whig(y) ∧ SpeakFrench(y))

Conclusions:
No lawyer ever sat in the House of Commons.
Some European was familiar with laws.
James Cocks speaks French.

Conclusions in first-order logic:
∀x (Lawyer(x) → ¬SatInHouseOfCommons(x))
∃x (European(x) ∧ FamiliarWithLaws(x))
SpeakFrench(james)

Truth value of each conclusion (T = true, F = false, U = unknown):
F
T
U
Premises:
Common utilities include water, electricity, gas, heating, sewer, trash, and recycling.
Many apartment rents cover the cost of water and electricity.
Susan lives in an apartment where the rent covers all utilities.
The rent of the apartment where Ava lives does not cover any utility expenses.
Noah lives in an apartment where the rent does not cover heating.

Premises in first-order logic:
CommonUtilities(water) ∧ CommonUtilities(electricity) ∧ CommonUtilities(gas) ∧ CommonUtilities(heating)
∃x (Apartment(x) ∧ Cover(x, water) ∧ Cover(x, electricity))
∀x (Apartment(x) ∧ LiveIn(susan, x) ∧ Cover(x, water) ∧ Cover(x, electricity) ∧ Cover(x, gas) ∧ Cover(x, heating))
∀x (Apartment(x) ∧ LiveIn(ava, x) ∧ ¬Cover(x, water) ∧ ¬Cover(x, electricity) ∧ ¬Cover(x, gas) ∧ ¬Cover(x, heating))
∀x (Apartment(x) ∧ LiveIn(noah, x) ∧ ¬Cover(x, heating))

Conclusions:
Noah needs to pay the water bill.
Noah and Ava both need to pay the heating bill.
Susan does not need to pay the water bill.

Conclusions in first-order logic:
∀x (Apartment(x) ∧ LiveIn(noah, x) ∧ ¬Cover(x, water))
¬Cover(noah, heating) ∧ ¬Cover(ava, heating)
∀x (Apartment(x) ∧ LiveIn(susan, x) ∧ Cover(x, water))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
T
T
Premises:
Each building is tall. 
Everything tall has height.

Premises in first-order logic:
∀x (Building(x) → Tall(x))
∀x (Tall(x) → Height(x))

Conclusions:
All buildings are magnificent.

Conclusions in first-order logic:
∀x (Building(x) → Magnificent(x))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
Premises:
Chairs are furniture.
All furniture objects.

Premises in first-order logic:
∀x (Chair(x) → Furniture(x))
∀x (Furniture(x) → Object(x))

Conclusions:
Some chairs are not objects.

Conclusions in first-order logic:
∃x ∃y (Chair(x) ∧ Chair(y) ∧ ¬Object(x) ∧ ¬Object(y) ∧ ¬(x=y))

Truth value of each conclusion (T = true, F = false, U = unknown):
F
Premises:
If a customer subscribes to AMC A-List, then he/she can watch 3 movies every week without any additional fees. 
Some customers go to cinemas every week. 
Customers who prefer TV series will not watch TV series in cinemas.
James watches TV series in cinemas. 
James subscribes to AMC A-List.
Peter prefers TV series.

Premises in first-order logic:
∀x (SubscribedTo(x, aMCAList) → EligibleForThreeFreeMovies(x))
∃x (CinemaEveryWeek(x))
∀x (Prefer(x, tVSeries) → ¬WatchTVIn(x, cinemas))
WatchTVIn(james, cinemas)
SubscribedTo(james, aMCAList)
Prefer(peter, tVSeries)

Conclusions:
James cannot watch 3 movies every week without any additional fees.
James goes to cinemas every week.
Peter will not watch TV series in cinemas.

Conclusions in first-order logic:
¬EligibleForThreeFreeMovies(james)
CinemaEveryWeek(james)
¬WatchTVIn(peter, cinemas)

Truth value of each conclusion (T = true, F = false, U = unknown):
F
U
T
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:
BasedIn(system7, uk) ∧ ElectronicDanceMusicBand(system7)
Form(stevehillage, system7) ∧ Form(miquettegiraudy, system7)
FormerMemberOf(stevehillage, gong) ∧ FormerMemberOf(miquettegiraudy, gong)
∀x (ElectronicDanceMusicBand(x) → Band(x))
∃x (ClubSingle(x) ∧ Release(system7, x))
∀x (ClubSingle(x) → ¬Single(x))

Conclusions:
System 7 was formed by former members of Gong.
System 7 has released several singles.
System 7 is not a band.

Conclusions in first-order logic:
∃x (Form(x, system7) ∧ FormerMemberOf(x, gong))
∃x (Single(x) ∧ Release(system7, x))
¬Band(system7)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
U
F
Premises:
Catullus 4 is a poem written by the ancient Roman writer Catullus.
Catullus 4 is a story about the retirement of a well-traveled ship.
There is a strong analogy of human aging in the poem Catullus 4.
Catullus 4 is written in an unusual iambic trimeter to convey a sense of speed over the waves.

Premises in first-order logic:
Poem(catullus4) ∧ WrittenBy(catullus4, catullus) ∧ AncientRomanWriter(catullus)
Story(catullus4) ∧ About(catullus4, retirementOfAWellTraveledShip)
Poem(catullus4) ∧ StrongAgingAnalogy(catullus4)
Poem(catullus4) ∧ WrittenIn(catullus4, iambicTrimeter) ∧ Convey(catullus4, aSenseOfSpeedOverTheWaves)

Conclusions:
There is a poem written by an ancient Roman writer with a strong analogy of human aging.
There is a poem written by an ancient Roman writer in iambic trimeter.
Callus 4 is written in an unusual iambic trimeter to convey a strong analogy of human aging.

Conclusions in first-order logic:
∃x ∃y (Poem(x) ∧ WrittenBy(x, y) ∧ AncietRomanWriter(y) ∧ StrongAgingAnalogy(x))
∃x ∃y (Poem(x) ∧ WrittenBy(x, y) ∧ AncientRomanWriter(y) ∧ WrittenIn(x, iambicTrimeter))
Poem(catullus4) ∧ WrittenIn(catullus4, iambicTrimeter) ∧ StrongAgingAnalogy(catullus4)

Truth value of each conclusion (T = true, F = false, U = unknown):
T
U
T
Premises:
Some affection is love.
Some love is positive.

Premises in first-order logic:
∃x (Affection(x) ∧ Love(x))
∃x (Love(x) ∧ Positive(x))

Conclusions:
Some affection is positive.

Conclusions in first-order logic:
∃x (Affection(x) ∧ Positive(x))

Truth value of each conclusion (T = true, F = false, U = unknown):
U
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 that have been donated.

Premises in first-order logic:
MountainRange(thePicurisMountains) ∧ (In(thePicurisMountains, newMexico) ⊕ In(thePicurisMountains, texas))
Visited(juanDeOnate, thePicurisMountains)
In(hardingPegmatiteMine, thePicurisMountains) ∧ Mine(hardingPegmatiteMine) ∧ Donated(hardingPegmatiteMine)
∀x ∀y (Mine(x) ∧ Donated(x) ∧ In(x, y) ∧ MountainRange(y) → ¬In(y, texas))

Conclusions:
Juan de Onate visited a mountain range in New Mexico.
Juan de Onate visited a mountain range in Texas.
The Harding Pegmatite Mine is not located in a mountain range in New Mexico.

Conclusions in first-order logic:
∃x (Visited(juanDeOnate, x) ∧ MountainRange(x) ∧ In(x, newMexico))
∃x (Visited(juanDeOnate, x) ∧ MountainRange(x) ∧ In(x, texas))
∀x (In(hardingPegmatiteMine, x) → ¬(MountainRange(x) ∧ In(x, newMexico)))

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