Datasets:
cestwc
/
lsnli-full-uncertain

Dataset card Data Studio Files Community
premise
stringlengths
7
402
hypothesis
stringlengths
1
295
label
int64
-1
2
probability
float64
-100
1
explanation_1
stringlengths
0
917
explanation_2
stringclasses
1 value
explanation_3
stringclasses
1 value
premise_logic
stringlengths
3
2.12k
hypothesis_logic
stringlengths
3
2.09k
A woman is walking across the street eating a banana, while a man is following with his briefcase.
the woman is a seductress
1
0.011402
The woman's actions to not imply any personal information about her. Now it is known that she is a seductress.
exists e1 e2 p1 x1 x2 x3 x5.(n_briefcase(x5) & of(x5,x2) & n_male(x2) & n_street(x2) & for(e1,e2) & exists e3 x4.(with(e3,x5) & Actor(e3,x4) & v_follow(e3) & n_man(x4)) & while(e2,p1) & Theme(e2,x3) & Actor(e2,x1) & v_eat(e2) & n_banana(x3) & across(e1,x2) & Actor(e1,x1) & v_walk(e1) & n_woman(x1))
exists p1 x1.(n_woman(x1) & exists x2.((x1 = x2) & n_seductress(x2)))
A woman is walking across the street eating a banana, while a man is following with his briefcase.
a woman sits for lunch
2
-100
One cannot walks and sits at the same time.
(-x)
(-x)
A woman is walking across the street eating a banana, while a man is following with his briefcase.
the woman is having coffee at the cafe
2
0.00004
one cannot have coffee and banana at the same time.
exists e1 e2 p1 x1 x2 x3 x5.(n_briefcase(x5) & of(x5,x2) & n_male(x2) & n_street(x2) & for(e1,e2) & exists e3 x4.(with(e3,x5) & Actor(e3,x4) & v_follow(e3) & n_man(x4)) & while(e2,p1) & Theme(e2,x3) & Actor(e2,x1) & v_eat(e2) & n_banana(x3) & across(e1,x2) & Actor(e1,x1) & v_walk(e1) & n_woman(x1))
exists e1 x1 x2 x3.(n_cafe(x3) & n_woman(x1) & at(e1,x3) & Theme(e1,x2) & Actor(e1,x1) & v_have(e1) & n_coffee(x2))
A woman is walking across the street eating a banana, while a man is following with his briefcase.
The woman is eating a banana.
0
-100
The woman is eating the banana while she walks across the street.
(-x)
(-x)
A woman is walking across the street eating a banana, while a man is following with his briefcase.
the woman is outside
0
1
You have to be outside to be walking across the street.
exists e1 e2 p1 x1 x2 x3 x5.(n_briefcase(x5) & of(x5,x2) & n_male(x2) & n_street(x2) & for(e1,e2) & exists e3 x4.(with(e3,x5) & Actor(e3,x4) & v_follow(e3) & n_man(x4)) & while(e2,p1) & Theme(e2,x3) & Actor(e2,x1) & v_eat(e2) & n_banana(x3) & across(e1,x2) & Actor(e1,x1) & v_walk(e1) & n_woman(x1))
exists s1 x1.(n_woman(x1) & Theme(s1,x1) & a_outside(s1))
A skier slides along a metal rail.
A skier is near the rail looking down.
1
-100
Sentence 1: A skier slides along a metal rail. Sentence 2: A skier is near the rail looking down.
(-x)
(-x)
A skier slides along a metal rail.
A skier is near the rail.
0
-100
The skier would have to be near the rail to slide along the metal rail.
(-x)
(-x)
A skier slides along a metal rail.
A skier is away from the rail.
2
-100
A slider can slide along or away from the rail.
(-x)
(-x)
A person on skis on a rail at night.
The woman eats a car
2
-100
A person can be man or woman.
(-x)
(-x)
A person on skis on a rail at night.
The person skiis
0
-100
If the person skiis, then a person is on skis.
(-x)
(-x)
A person on skis on a rail at night.
They are fantastic skiiers
1
-100
Sentence 1: A person on skis on a rail at night. Sentence 2: They are fantastic skiiers
(-x)
(-x)
A skier in electric green on the edge of a ramp made of metal bars.
The skier was on the edge of the ramp.
1
-100
Sentence 1: A skier in electric green on the edge of a ramp made of metal bars. Sentence 2: The skier was on the edge of the ramp.
(-x)
(-x)
A skier in electric green on the edge of a ramp made of metal bars.
The brightly dressed skier slid down the race course.
0
-100
If a skier is wearing electric green, then they are brightly dressed. It is logical that a skier slid down down something. A ramp is found at a race course.
(-x)
(-x)
A skier in electric green on the edge of a ramp made of metal bars.
The jogger ran through the streets.
2
-100
A jogger and a skier are two different types of people.
(-x)
(-x)
A yellow uniformed skier is performing a trick across a railed object.
A snowboarder is riding a ski lift.
2
-100
A snowboarder and skier are two different types of people. Someone cannot be performing a trick and riding on something simultaneously.
(-x)
(-x)
A yellow uniformed skier is performing a trick across a railed object.
A skier is competing in a competition.
1
-100
Sentence 1: A yellow uniformed skier is performing a trick across a railed object. Sentence 2: A skier is competing in a competition.
(-x)
(-x)
A yellow uniformed skier is performing a trick across a railed object.
Somebody is engaging in winter sports.
0
-100
If a skier is performing a trick, then they are engaging in winter sports.
(-x)
(-x)
A blond man is drinking from a public fountain.
The man is drinking water.
0
-100
Water is what a public fountain contains.
(-x)
(-x)
A blond man is drinking from a public fountain.
The man is very thirsty.
1
-100
Sentence 1: A blond man is drinking from a public fountain. Sentence 2: The man is very thirsty.
(-x)
(-x)
A blond man is drinking from a public fountain.
The man is drinking coffee.
2
-100
A public fountain contains water, which is different than coffee.
(-x)
(-x)
Wet brown dog swims towards camera.
The dog is sleeping in his bed.
2
-100
A dog cannot be sleeping while he swims.
(-x)
(-x)
Wet brown dog swims towards camera.
A dog is playing fetch in a pond.
1
-100
Sentence 1: Wet brown dog swims towards camera. Sentence 2: A dog is playing fetch in a pond.
(-x)
(-x)
Wet brown dog swims towards camera.
A dog is in the water.
0
-100
If a dog swims, then he is in the water.
(-x)
(-x)
Closeup image of a dog swimming.
A cat reluctantly takes a bath.
2
-100
A dog and a cat are two different animals. An animal cannot be swimming while it takes a bath.
(-x)
(-x)
Closeup image of a dog swimming.
An underwater camera takes a photo of a puppy.
1
-100
Sentence 1: Closeup image of a dog swimming. Sentence 2: An underwater camera takes a photo of a puppy.
(-x)
(-x)
Closeup image of a dog swimming.
A dog swims in a body of water.
0
-100
If a dog swims, then he is swimming.
(-x)
(-x)
The furry brown dog is swimming in the ocean.
A dog is chasing a fish.
1
-100
Sentence 1: The furry brown dog is swimming in the ocean. Sentence 2: A dog is chasing a fish.
(-x)
(-x)
The furry brown dog is swimming in the ocean.
A dog is swimming.
0
-100
"Swimming" is a less detailed restatement of "swimming in the ocean."
(-x)
(-x)
The furry brown dog is swimming in the ocean.
A dog is running around the yard.
2
-100
A dog cannot be swimming and running simultaneously.
(-x)
(-x)
A big brown dog swims towards the camera.
A dog is chasing a stick.
2
-100
A dog cannot be chasing something as he swims.
(-x)
(-x)
A big brown dog swims towards the camera.
A photographer is taking pictures of a dog.
1
-100
Sentence 1: A big brown dog swims towards the camera. Sentence 2: A photographer is taking pictures of a dog.
(-x)
(-x)
A big brown dog swims towards the camera.
A dog swims towards the camera.
0
-100
"A dog" is a less detailed restatement of a "big brown dog."
(-x)
(-x)
A man wearing black with a gray hat, holding a pitchfork, directs a horse-drawn cart.
The farmer wearing a gray hat is driving a horse-drawn cart.
1
-100
Sentence 1: A man wearing black with a gray hat, holding a pitchfork, directs a horse-drawn cart. Sentence 2: The farmer wearing a gray hat is driving a horse-drawn cart.
(-x)
(-x)
A man wearing black with a gray hat, holding a pitchfork, directs a horse-drawn cart.
The man with the gray hat and pitchfork is directing the cart.
0
-100
If a man is wearing a gray hat, then he is with the gray hat. If a man is holding a pitchfork, then he is with a pitchfork. "Directing the cart" is a less detailed restatement of "directs a horse-drawn cart."
(-x)
(-x)
A man wearing black with a gray hat, holding a pitchfork, directs a horse-drawn cart.
The woman in a blue dress is hitching a horse to a cart.
2
-100
A man and a woman are two separate genders. A person cannot be hitching something while he directs. A horse-drawn cart already has a horse attached, so you cannot attach a horse to a cart simultaneously.
(-x)
(-x)
A man is leading a Clydesdale up a hay road, within a Old Country.
A man is walking with his horse up a country road.
0
-100
A Clydesdale is a horse. A hay road within an Old Country would be a country road.
(-x)
(-x)
A man is leading a Clydesdale up a hay road, within a Old Country.
A woman in a gray business suit is drinking tea.
2
-100
A man and a woman are two different genders. One cannot be leading a Clydesdale while drinking tea simultaneously.
(-x)
(-x)
A man is leading a Clydesdale up a hay road, within a Old Country.
A man in a straw hat and overalls is walking with his horse up a country road.
1
-100
A man in a straw hat and overalls doesn't necessary to walking with his horse up a country road
(-x)
(-x)
A farmer fertilizing his garden with manure with a horse and wagon.
The man is fertilizering his garden.
0
-100
A man can be a farmer
(-x)
(-x)
A farmer fertilizing his garden with manure with a horse and wagon.
The man is on the city street with his horse and wagon.
2
-100
The wagon is either in a garden or on a city street.
(-x)
(-x)
A farmer fertilizing his garden with manure with a horse and wagon.
The man is in an open field with a horse and wagon.
1
-100
A farmer in his garden is not the same as being in a field.
(-x)
(-x)
A white horse is pulling a cart while a man stands and watches.
A horse is hauling goods.
1
-100
The cart does not necessarily have to be filled with goods, as it could be empty.
(-x)
(-x)
A white horse is pulling a cart while a man stands and watches.
An animal is walking outside.
0
-100
A horse is an animal, a horse must be walking when pulling a cart.
(-x)
(-x)
A white horse is pulling a cart while a man stands and watches.
A man is watching a horse race.
2
-100
a horse race is a different activity from a horse pulling a cart.
(-x)
(-x)
A small group of church-goers watch a choir practice.
A group watches a practice.
0
-100
church-goers is a group so the sentences are the same.
(-x)
(-x)
A small group of church-goers watch a choir practice.
A choir performs in front of packed crowd.
-1
-100
(x)
(x)
A small group of church-goers watch a choir practice.
The pastor and elders watch the choir to make sure they are good.
1
-100
The pastor and elders watch the choir they doesn't need to make it sure they are good
(-x)
(-x)
A dog drops a red disc on a beach.
a dog catch the ball on a beach
2
-100
If a dog drops something, he cannot catch it at the same time. A disc and a ball are two different objects.
(-x)
(-x)
A dog drops a red disc on a beach.
a dog drops a disc with a boy
1
-100
a dog doesn't need a boy to drops a disc with
(-x)
(-x)
A dog drops a red disc on a beach.
a dog drops a red disc
0
-100
On a beach is a description that can be left out without substantially changing the meaning.
(-x)
(-x)
A man and a woman are walking on a street at the top of a hill.
A married couple walks atop a hill.
1
-100
Anyone could walks a top of hill doesn't necessarily to be couple
(-x)
(-x)
A man and a woman are walking on a street at the top of a hill.
A man and woman walk on a street.
0
-100
At the top of a hill is a description that does not change the meaning significantly.
(-x)
(-x)
A man and a woman are walking on a street at the top of a hill.
Two men play catch on a hill.
2
-100
A man and a woman is not the same as two men. Walking and playing catch is different.
(-x)
(-x)
An elderly man is drinking orange juice at a cafe.
An elderly man is drinking apple juice at a bar.
2
-100
Drinking orange juice at a cafe is not the same as drinking apple juice at a bar. Two different drinks, two different locales.
(-x)
(-x)
An elderly man is drinking orange juice at a cafe.
An older gentleman is enjoying his orange juice at a new cafe.
1
-100
An older gentleman could enjoy anything at new cafe it doesn't has to be orange juice
(-x)
(-x)
An elderly man is drinking orange juice at a cafe.
An old man is enjoying a beverage at a cafe.
0
-100
Elderly is the same as old and orange juice is a beverage.
(-x)
(-x)
A couple holding hands walks down a street.
Two men hold hands while walking down the street.
1
-100
Two men holds hands , walking down the street is unnecessary
(-x)
(-x)
A couple holding hands walks down a street.
There are people sitting on the side of the road.
2
-100
The couple can either be walking down a street or sitting at side of road, not both simultaneously.
(-x)
(-x)
A couple holding hands walks down a street.
People are holding hands and walking.
0
-100
A couple are people.
(-x)
(-x)
A couple walk through a white brick town.
People are walking outdoors.
0
-100
A white brick town is outdoors.
(-x)
(-x)
A couple walk through a white brick town.
A man and woman walk in a brown brick city.
2
-100
The town or city can not be described as being built of white brick, then as being built of brown brick. It can be one color or the other, or a mix of bricks.
(-x)
(-x)
A couple walk through a white brick town.
A man and woman walk through a big city.
1
-100
A man and woman walk , a big city is unnecessary
(-x)
(-x)
Children going home from school.
The children are at the library.
2
-100
If the children are going home and they cannot still be in library.
(-x)
(-x)
Children going home from school.
The school children head home.
0
-100
The children headed home from school are also school children.
(-x)
(-x)
Children going home from school.
The children are walking in the afternooon.
1
-100
It cannot be inferred that the children are walking or that it is afternoon.
(-x)
(-x)
People listening to a choir in a Catholic church.
People are listening to a metal band.
2
-100
Music from a church choir is very different from metal band music.
(-x)
(-x)
People listening to a choir in a Catholic church.
Choir singing in mass.
0
-100
People listening to a choir, who is singing in mass of a Catholic church.
(-x)
(-x)
People listening to a choir in a Catholic church.
People are in mass for a first communion
1
-100
People are in mass they doesn't need to be in communion
(-x)
(-x)
Bicyclists waiting at an intersection.
A bicyclist is sitting down having lunch at the mall.
2
0.000003
THE BICYCLIST IS EITHER SITTING DOWN HAVING LUNCH OR WAITING AT AN INTERSECTION.
exists e1 x1 x2 x3.(a_topic(x1) & at(e1,x3) & n_intersection(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_bicyclist(x1))
exists e1 e2 s1 x1 x2 x3.(n_mall(x3) & for(e1,e2) & at(e2,x3) & Theme(e2,x2) & Actor(e2,x1) & v_have(e2) & n_lunch(x2) & a_down(s1) & Manner(e1,s1) & Actor(e1,x1) & v_sit(e1) & n_bicyclist(x1))
Bicyclists waiting at an intersection.
The bicyclists are outside.
0
-100
An intersection is outside.
(-x)
(-x)
Bicyclists waiting at an intersection.
Bicyclists waiting for a car to pass.
1
0.5099
You can not infer they are waiting for a car.
exists e1 x1 x2 x3.(a_topic(x1) & at(e1,x3) & n_intersection(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_bicyclist(x1))
exists e1 p1 x1 x2 x3.(a_topic(x1) & for(e1,x3) & Topic(x3,p1) & exists e2.(Actor(e2,x3) & v_pass(e2)) & n_car(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_bicyclist(x1))
Bicyclists waiting at an intersection.
A person on a bike is waiting while the light is green.
1
0.001633
A person is waiting while the light is green they doesn't need to be on bike
exists e1 x1 x2 x3.(a_topic(x1) & at(e1,x3) & n_intersection(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_bicyclist(x1))
exists e1 p1 x1 x2 x3.(n_light(x3) & exists s1.(Theme(s1,x3) & a_green(s1)) & while(e1,p1) & Actor(e1,x1) & v_wait(e1) & on(x1,x2) & n_bike(x2) & n_person(x1))
Bicyclists waiting at an intersection.
The bicycles are on a road.
0
0.995864
An intersection is on a road.
exists e1 x1 x2 x3.(a_topic(x1) & at(e1,x3) & n_intersection(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_bicyclist(x1))
exists p1 x1.(n_bicycle(x1) & exists x2.(on(x1,x2) & n_road(x2)))
Bicyclists waiting at an intersection.
A person on a bike is near a street.
0
-100
An intersection is part of a street.
(-x)
(-x)
Bicyclists waiting at an intersection.
Bikers stop and wait for traffic at the intersection.
0
-100
Waiting implies that they stopped.
(-x)
(-x)
Bicyclists waiting at an intersection.
The bicyclists ride through the mall on their bikes.
2
-100
Cyclists cannot be immobile waiting and in motion riding simultaneously. There are no intersections at a mall.
(-x)
(-x)
Bicyclists waiting at an intersection.
Bicyclists waiting their turn to cross.
0
-100
People cross at an intersection.
(-x)
(-x)
Bicyclists waiting at an intersection.
Bicyclists riding along a freeway.
2
-100
Cyclists cannot be immobile waiting and in motion riding simultaneously. There are no intersections on a freeway.
(-x)
(-x)
Bicyclists waiting at an intersection.
The bicyclists are dead.
2
-100
To be described as waiting is to be assumed as being alive to be able to wait. Dead bicyclists do not wait.
(-x)
(-x)
Bicyclists waiting at an intersection.
The bicyclists are riding to the book store.
1
0.000133
The bicyclists are could be riding anywhere it doesn't has to be book store
exists e1 x1 x2 x3.(a_topic(x1) & at(e1,x3) & n_intersection(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_bicyclist(x1))
exists e1 x1 x2 x3.(n_store(x2) & of(x2,x3) & n_book(x3) & n_bicyclist(x1) & to(e1,x2) & Actor(e1,x1) & v_ride(e1))
Bicyclists waiting at an intersection.
The bicyclists are in a race.
1
0.00068
No all bicyclists are in a race.
exists e1 x1 x2 x3.(a_topic(x1) & at(e1,x3) & n_intersection(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_bicyclist(x1))
exists p1 x1.(n_bicyclist(x1) & exists x2.(in(x1,x2) & n_race(x2)))
Bicyclists waiting at an intersection.
The bicyclists are at home.
2
-100
There is no intersection in a home. The cyclist cannot be two places at once.
(-x)
(-x)
Bicyclists waiting at an intersection.
A group of bicyclists remain on the sidewalk at the intersection.
1
0.851325
Just because a bicyclists is waiting at an intersection doesn't imply that they are on a sidewalk.
exists e1 x1 x2 x3.(a_topic(x1) & at(e1,x3) & n_intersection(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_bicyclist(x1))
exists e1 x1 x2 x3 x4.(n_intersection(x4) & n_sidewalk(x3) & on(e1,x3) & at(x3,x4) & Actor(e1,x1) & v_remain(e1) & of(x1,x2) & n_bicyclist(x2) & n_group(x1))
People waiting at a light on bikes.
A bunch of friends are on a boat
2
0.000016
There are no stoplights on a boat.
exists e1 x1 x2 x3 x4.(a_topic(x1) & at(e1,x3) & on(x3,x4) & n_bike(x4) & n_light(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_people(x1))
exists p1 x1 x2.(exists x3.(on(x1,x3) & n_boat(x3)) & of(x1,x2) & n_friend(x2) & n_bunch(x1))
People waiting at a light on bikes.
People with bikes.
0
-100
With bikes is a restatement of on bikes.
(-x)
(-x)
People waiting at a light on bikes.
There are people on bikes at a light
1
0.897411
Not all people are waiting on bikes at a light .
exists e1 x1 x2 x3 x4.(a_topic(x1) & at(e1,x3) & on(x3,x4) & n_bike(x4) & n_light(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_people(x1))
exists p1 x1 x2 x3 x4.((x1 = x2) & on(x2,x3) & at(x3,x4) & n_light(x4) & n_bike(x3) & n_people(x2))
People waiting at a light on bikes.
The people are inside waiting.
2
-100
People are not bikes.
(-x)
(-x)
People waiting at a light on bikes.
There are some people outside.
0
-100
A light is outside.
(-x)
(-x)
People waiting at a light on bikes.
People are on their bikes.
0
-100
On their bikes is a restatement of on bikes.
(-x)
(-x)
People waiting at a light on bikes.
The people are riding their bikes to the gym.
1
0.003866
Not all people waiting at lights are riding their bikes to get to the gym.
exists e1 x1 x2 x3 x4.(a_topic(x1) & at(e1,x3) & on(x3,x4) & n_bike(x4) & n_light(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_people(x1))
exists e1 x1 x2 x3.(n_gym(x3) & n_bike(x2) & of(x2,x1) & n_thing(x1) & n_people(x1) & to(e1,x3) & Theme(e1,x2) & Actor(e1,x1) & v_ride(e1))
People waiting at a light on bikes.
People biking through the city.
1
0.705015
Not a lights are only in the city.
exists e1 x1 x2 x3 x4.(a_topic(x1) & at(e1,x3) & on(x3,x4) & n_bike(x4) & n_light(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_people(x1))
exists e1 x1 x2 x3.(n_city(x3) & a_topic(x1) & through(e1,x3) & Actor(e1,x2) & v_bike(e1) & (x1 = x2) & n_people(x1))
People waiting at a light on bikes.
The people are on bikes.
0
0.996675
Are on bikes is a restatement of on bikes.
exists e1 x1 x2 x3 x4.(a_topic(x1) & at(e1,x3) & on(x3,x4) & n_bike(x4) & n_light(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_people(x1))
exists p1 x1.(n_people(x1) & exists x2.(on(x1,x2) & n_bike(x2)))
People waiting at a light on bikes.
There is traffic next to the bikes.
1
0.683288
No all bikes waiting at lights have traffic next to them.
exists e1 x1 x2 x3 x4.(a_topic(x1) & at(e1,x3) & on(x3,x4) & n_bike(x4) & n_light(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_people(x1))
exists p1 x1 x4.(n_bike(x4) & exists x2 x3.((x1 = x2) & to(x3,x4) & next(x2,x3) & n_thing(x3) & n_traffic(x2)))
People waiting at a light on bikes.
The people are running a red light.
2
-100
Waiting implies that one is in an area. Running implies leaving an area.
(-x)
(-x)
People waiting at a light on bikes.
People in a car race.
2
-100
Bikes cannot be included in a car race.
(-x)
(-x)
People waiting at a light on bikes.
Men and women outside on a street corner.
0
-100
Men and women are people.
(-x)
(-x)
People waiting at a light on bikes.
A ladybug has landed on a beautiful pink rose.
2
-100
A ladybug cannot ride a bike.
(-x)
(-x)
People waiting at a light on bikes.
Men and women on their motorcycles, wearing helmets and protective jackets.
1
0.110858
Not all bikes are considered motorcycles. Not all people using bikes wear helmets and protective jackets.
exists e1 x1 x2 x3 x4.(a_topic(x1) & at(e1,x3) & on(x3,x4) & n_bike(x4) & n_light(x3) & Actor(e1,x2) & v_wait(e1) & (x1 = x2) & n_people(x1))
exists e1 s1 s2 x1 x2 x3 x4 x5 x6 x7.(a_topic(s1) & subset_of(x2,s1) & on(x2,x3) & n_jacket(x7) & a_protective(s2) & Theme(s2,x7) & n_helmet(x6) & subset_of(x7,x5) & subset_of(x6,x5) & Actor(e1,x5) & v_wear(e1) & n_motorcycle(x4) & subset_of(x5,x3) & subset_of(x4,x3) & of(x3,x1) & n_thing(x1) & n_woman(x2) & subset_of(x1,s1) & n_man(x1))
People on bicycles waiting at an intersection.
The people are on skateboards.
2
0
The people are either on skateboards or bicycles.
exists e1 x1 x2 x3 x4.(a_topic(x1) & on(x1,x2) & at(e1,x4) & n_intersection(x4) & Actor(e1,x3) & v_wait(e1) & (x2 = x3) & n_bicycle(x2) & n_people(x1))
exists p1 x1.(n_people(x1) & exists x2.(on(x1,x2) & n_skateboard(x2)))
People on bicycles waiting at an intersection.
People are riding their bicycles.
0
-100
PEOPLE ARE WAITING AT AN INTERSECTION,WHILE RIDING THEIR BICYCLES.
(-x)
(-x)