# data[].attributes.body
positive form:
subject + will + verb

I will eat later.

She will eat at home.

negative form:
subject + will + not + verb

We won’t eat anything tonight.

He will not eat fish.

question form:
(question word +) will + subject + verb?

What will you eat for lunch?

Will she eat meat?

# data[].attributes.body
positive form:
subject + will + be + verb-ing

I will be eating at 8pm.

She will be eating a sandwich.

negative form:
subject + will + not + be + verb-ing

They won’t be eating.

He will not be eating anything.

question form:
(question word +) will + subject + be + verb-ing?

What will you be eating?

Will they be eating at 6pm?

# data[].attributes.body
positive form:
subject + will + have + past participle

I will have eaten lunch by 3 o’clock.

She’ll have eaten all the chocolate.

negative form:
subject + will + not + have + past participle

He won’t have eaten lunch yet. They will not have eaten their sandwiches.

question form:
(question word +) will + subject + have + past participle?

When will you have eaten?

Will she have eaten dinner by nine?

# data[].attributes.body
positive form:
subject + will + have + been + verb-ing

They will have been eating fruit.

We’ll have been eating all day.

negative form:
subject + will + not + have + been + verb-ing

I won’t have been eating fish. They will not have been eating lunch.

question form:
(question word +) will + subject + have + been + verb-ing?

What will she have been eating?

Will you have been eating chocolate?

# data[].attributes.body
positive form:
subject + past simple (verb +ed)

I ate an apple.

She ate some rice.

negative form:
subject + did + not + verb

She didn’t eat anything.

They did not eat breakfast.

question form:
(question word +) did + subject + verb?

Did you eat the chocolate?

What did they eat for lunch?

# data[].attributes.body
positive form:
subject + was / were

I was hungry.

They were in a restaurant.

negative form:
subject + was / were + not

We weren’t hungry.

She wasn’t at home.

question form:
(question word +) was / were + subject…?

Why was she hungry?

Were you hungry?

# data[].attributes.body
positive form:
subject + was / were + verb-ing

I was eating.

They were eating lunch.

negative form:
subject + was / were + not + verbing

You weren’t eating.

She wasn’t eating a sandwich.

question form:
(question word +) was / were + subject + verb-ing?

Where were you eating?

Was she eating an apple?

# data[].attributes.body
positive form:
subject + had + past participle

I had eaten.

She’d eaten lunch.

negative form:
subject + had + not + past participle

We hadn’t eaten.

He had not eaten an apple.

question form:
(question word +) had + subject + past participle

Why had you eaten?

Had they eaten fish before?

# data[].attributes.headline
extra  document spelling rules present simple and past simple

# data[].attributes.subject
extra  document spelling rules present simple and past simple

# data[].attributes.attachment_link.title
spelling rules tenses extra IUMAFIS student

# data[].attributes.body
positive form:
1st and 2nd subject  + verb

3rd: subject + verb (+ s)

I eat pizza

She eats toast.

negative form:
subject + do / does + not + verb

I don’t eat toast.

She doesn’t eat fish.

question form:
(question word +) do / does + subject + verb?

Do you eat breakfast every day

When does she eat lunch?

# data[].attributes.body
positive form:
subject + am / is / are

I am hungry.

She is in a cafe.

negative form:
subject + am / is / are + not

I’m not hungry.

She isn’t happy.

question form:
(question word +) am  / is / are + subject…?

Is he at home?

Why are you hungry?

Why are you sad?

# data[].attributes.body
positive form:
subject + am / is / are + verb + ing

She’s eating toast.

They are eating lunch now.

I am walking to my house

negative form:
subject + am / is / are + not + verb + ing

He isn’t eating.

We are not eating at home.

I am not walking to my hous

I’m not walking to my house

question form:
(question word +)  am / is / are + subject + verb-ing?
When are they eating?
Is he eating now?
am I walking to your house?

# data[].attributes.body
positive form:
subject + have / has + past participle

I’ve eaten breakfast.

He has eaten lunch.

negative form:
subject + have / has + not + past participle

We haven’t eaten yet.

We have not eaten yet.

She hasn’t eaten the chocolate.

She has not eaten the chocolate.

question form:
(question word +) have / has + subject + past participle?

What have you eaten today?

Has she eaten lunch?

Have we gone to her house?

# data[].attributes.body
positive form:
subject + have / has + been + verb + ing

I’ve been eating.

I have been taking pills

She’s been eating biscuits/cookies all day.

She has been eating biscuits/cookies all day

negative form:
subject + have / has + not + been + verb-ing

They haven’t been eating.

He has not been eating toast.

question form:
(question word +) have / has + subject + been + verb-ing?

What has she been eating?

Have they been eating breakfast?

# data[].attributes.body
| I |  Am not
| You |  are not
| We |  are not
| they | are not
| you | are not
| He | is not
| she | is not
| it | is not

| To be contraction  negative
| ’m not / I am not
| aren’t / are not
| aren’t / are not
| aren’t / are not
| aren’t / are not
| isn’t / is not
| isn’t / is not
| isn’t / is not

# data[].attributes.body
| Do  negative
| Do  not  / don’t
| do not / don’t
| do not / don’t
| do not / don’t
| do not / don’t
| Does not / doesn’t
| does not / doesn’t
| does not/ doesn’t

# data[].attributes.body
| Have  negative
| have not / haven’t
| have not / haven’t
| have not / haven’t
| have not / haven’t
| have not / haven’t
| has not / hasn’t
| has not / hasn’t
| has not / hasn’t

# data[].attributes.body
| pronoun | To be positive
| I | am
| You | are
| We | are
| they | are
| you | are
| He | is
| she | is
| it | is

| To be contraction positive
| ’m
| ’re
| ’re
| ’re
| ’re
| ’s
| ’s
| ’s

# data[].attributes.body
| Do positive
| do
| do
| do
| do
| do
| Does
| does
| does

# data[].attributes.body
| Have  positive
| have
| have
| have
| have
| have
| has
| has
| has

# data[].attributes.body
past simple; formula positive: this/that/these/those + was / were + adjective

# data[].attributes.headline
demonstrative adjectives positive with examples

# data[].attributes.subject
demonstrative adjectives positive with examples

# data[].attributes.body
| Positive near (singular)
| This was big
| This was small
| This was delicious
| This was great
| This was new
| This was old
| This was ugly

| Positive far (singular)
| That was big
| That was small
| That was delicious
| That was great
| That was new
| That was old
| That was ugly

| Positive near (plural)
| These were big
| These were small
| These were delicious
| These were great
| These were new
| These were old
| These were ugly

| Positive far(plural)
| Those were big
| Those were small
| Those were delicious
| Those were great
| Those were new
| Those were old
| Those were ugly

# data[].attributes.body
past simple formula negative:  this/that/these/those + was not/ were not + adjective

# data[].attributes.headline
demonstrative adjectives negative with examples

# data[].attributes.subject
demonstrative adjectives negative with examples

# data[].attributes.body
| This was not big
| This was not small
| This was not delicious
| This was not great
| This was not new
| This was not old
| This was not ugly

| negative far (singular)
| That was not big
| That was not small
| That was not delicious
| That was not great
| That was not new
| That was not old
| That was ugly

| negative near (plural)
| These were not big
| These were not small
| These were not delicious
| These were not great
| These were not new
| These were not old
| These were not ugly
| negative far(plural)
| Those are not big
| Those are not small
| Those are not delicious
| Those are not great
| Those are not new
| Those are not old
| Those are not ugly

# data[].attributes.body
present simple; this/that/these/those + is / are + adjective

# data[].attributes.headline
demonstrative adjectives positive with examples

# data[].attributes.subject
demonstrative adjectives positive with examples

# data[].attributes.body
| Positive near (singular)
| This is big
| This is small
| This is delicious
| This is great
| This is new
| This is old
| This is ugly

| Positive far (singular)
| That is big
| That is small
| That is delicious
| That is great
| That is new
| That is old
| That is ugly

| Positive near (plural)
| These are big
| These are small
| These are delicious
| These are great
| These are new
| These are old
| These are ugly

| Positive far(plural)
| Those are big
| Those are small
| Those are delicious
| Those are great
| Those are new
| Those are old
| Those are ugly

# data[].attributes.body
present simple; formula negative: this/that/these/those + is not/ are not + adjective

# data[].attributes.headline
demonstrative adjectives negative with examples

# data[].attributes.subject
demonstrative adjectives negative with examples

# data[].attributes.body
| negative near(singular)
| This is not big
| This is not small
| This is not delicious
| This is not great
| This is not new
| This is not old
| This is not ugly

| negative far (singular)
| That is not big
| That is not small
| That is not delicious
| That is not great
| That is not new
| That is not old
| That is ugly

| negative near (plural)
| These are not big
| These are not small
| These are not delicious
| These are not great
| These are not new
| These are not old
| These are not ugly

| negative far(plural)
| Those are not big
| Those are not small
| Those are not delicious
| Those are not great
| Those are not new
| Those are not old
| Those are not ugly

# data[].attributes.body
present simple; formula negative: this/that/these/those + is not/ are not + adjective

# data[].attributes.headline
demonstrative adjectives positive with examples

# data[].attributes.subject
demonstrative adjectives positive with examples

# data[].attributes.body
| Positive near (singular)
| This is big
| This is small
| This is delicious
| This is great
| This is new
| This is old
| This is ugly

| Positive far (singular)
| That is big
| That is small
| That is delicious
| That is great
| That is new
| That is old
| That is ugly

| Positive near (plural)
| These are big
| These are small
| These are delicious
| These are great
| These are new
| These are old
| These are ugly

| Positive far(plural)
| Those are big
| Those are small
| Those are delicious
| Those are great
| Those are new
| Those are old
| Those are ugly

# data[].attributes.body
present simple; formula question: is / are + this/that/these/those +adjective

# data[].attributes.headline
demonstrative adjectives question with examples

# data[].attributes.subject
demonstrative adjectives question with examples

# data[].attributes.body
| question near (singular)
| is This big?
| is This small?
| is This delicious?
| is This great?
| is This new?
| is This old?
| is This ugly?

| question far (singular)
| is That big?
| is That small?
| is That delicious?
| is That great?
| is That new?
| is That old?
| is That ugly?

| question near (plural)
| are These big?
| are These small?
| are These delicious?
| are These great?
| are These new?
| are These old?
| are These ugly?

| question far (plural)
| are Those big?
| are Those small?
| are Those delicious?
| are Those great?
| are Those new?
| are Those old?
| are Those ugly?

# data[].attributes.body
formula positive; subject + was / were + verb+ ing + complimentary

formula negative; subject + was / were + not + verb+ ing + complimentary

formula question; was / were + subject + verb+ ing + complimentary + ?

# data[].attributes.body
| positive
| I was working
| you were studying
| we were writing
| they were speaking
| he was playing football
| she was talking
| it was eating

| negative
| I was not working
| you were not studying
| we were not writing
| they were not speaking
| he was not playing football
| she was not talking
| it was not eating

| question
| was I working?
| were you studying?
| were we writing?
| were they speaking?
| was he playing football?
| was she talking?
| was it eating?

# data[].attributes.body
formula question; wh + am/is/are + subject + verb+ ing + complimentary + ?

| question
| Where was I working?
| What were you studying?
| Why were we writing?
| With who were they speaking?
| Where was he playing football?
| How was she talking?
| How much was it eating?

# data[].attributes.body
formula positive; subject + am/is/are + verb+ ing + complimentary

formula negative; subject + am/is/are + not + verb+ ing + complimentary

formula question; am/is/are + subject + verb+ ing + complimentary + ?

# data[].attributes.body
| positive
| I Am working now
| you ARE studying at the moment
| we ARE writing
| they ARE speaking
| he IS playing football
| she IS talking now
| it IS eating

| negative
| I Am not working now
| you ARE not studying at the moment
| we ARE not writing
| they ARE not speaking
| he IS not playing football
| she IS not talking now
| it IS not eating

| question
| Am I working?
| ARE you studying at the moment?
| ARE we writing?
| ARE they speaking?
| IS he playing football?
| IS she talking now?
| IS it eating?

# data[].attributes.body
formula question; wh + am/is/are + subject + verb+ ing + complimentary + ?

| Question
| Where Am I working now?
| What ARE you studying at the moment?
| Why ARE we writing?
| With who ARE they speaking? who ARE they speaking With?
| Where IS he playing football?
| How IS she talking now?
| How much IS it eating?

# data[].attributes.headline
to be examples present simple and past simple

# data[].attributes.subject
to be examples present simple and past simple

# data[].attributes.body
present simple:
| I am tall | I am not tall / I’m not tall | Am I tall?
| You are beautifull | You are not beautifull / you aren’t beautiful | Are you beautiful?
| They are good people | They are not good people / They aren’t good people | Are they good people?
| We are workers | We are not workers / We aren’t workers | Are we workers?
| You are nurses | You are not nurses / You aren’t nurses | Are you nurses?
| He is a painter | He is not a painter / He isn’t a painter | Is he a painter?
| She is a woman | She is not a woman / She isn’t a woman | Is she a woman?
| It is a dog | It is not a dog / It isn’t a dog | Is it a dog?

past simple:
| I was tall | I was not tall/ I wasn’t tall | was i tall?
| You were beautifull | You were not beautifull / you weren’t beautifull | were you beautifull?
| They were good people | They were not good people / they weren’t good people | were they good people?
| We were workers | We were not workers / we weren’t workers | were we workers?
| You were nurses | You were not nurses / you weren’t nurses | were you nurses?
| He was a painter | He was not a painter / he wasn’t a painter | was he a painter?
| She was a woman | She was not a woman / she wasn’t a woman | was she a woman?
| It was a dog | It was not a dog / it wasn’t a dog | was it a dog?

# data[].attributes.body
| Hair | My hair is blond
| Head | Your head is small
| Face | You have a beautyfull face
| Eyebrown | She has small eyebrowns
| Eyes | She has gorgeous eyes
| Nose | A small nose
| Mouth | He has a big mouth
| Lips | She has red lips
| Teeth | Her teeth are white
| Tooth | My tooth hurts
| Neck | Long neck
| Forearm | His forearm
| Arm | Her arm is white
| Hand | He has big hands
| Fingers | She has small fingers
| Nails | Her nails are clean
| Skin | Her skin is white
| Chest | His chest is shaven
| Stomage | My stomage is empty
| Legs | Her legs are short
| Knee | His knee hurts
| Foot | They have a broken foot
| Feet | Her feet are small
| Toe | I have one broken toe
| toes | Her toes are white
| Toe nails | The toe nails are clean

# data[].attributes.body
| Other | The other red house is better
| New | This is a new white book
| Good | You’re a good person
| High | The black airplane flies high
| Old | The brown lady is old
| Great | That is a great idea!
| Big | This yellow room is big
| American | He is american
| Small | It is a small grey mouse
| Large | These beige offices are large
| National | Colombia’s flag is yellow, blue and red
| Young | The blond puppy is young
| Different | You are different
| Black | The black flat computer
| Long | is it a long trip?
| Little | The problem is quite little
| Important | You are important for me
| Political | It is a political situation
| Bad | The painter painted the house bad, it wasn’t this green
| White | Snow is white as snow white
| Real | Is this real?
| Best | You are the best!
| Right | That is right
| Social | She is very social
| Only | You are the only one for me
| Public | This place is publis
| Sure | Are you sure?
| Low | The battery is low
| Early | She wakes up early
| Able | He is able to work today
| Human | We are human beings
| Local | This a local dish
| Late | You are late!
| hard | English isn’t so hard
| Major | It is a major problem
| Better | You are better
| Economic | It is economic
| Strong | He is strong
| Possible | How is tthis possible?
| Whole | This cake is whole
| Free | Internet is free here
| Military | It is military
| True | That is true
| Federal | The situation is federal
| International | English is international
| Full | The university is full with students
| Special | You are so special for me
| Easy | English is easy
| Clear | This topic is so clear
| Recent | He is recent here
| Certain | So certain!
| Personal | This is personal
| Open | The door is open
| Red | The house is red
| Difficult | This isn’t difficult
| Available | She is available
| Likely | It is very likely
| Short | She is short
| Single | She isn’t single anymore
| Medical | It is medical
| Current | The current is current
| Wrong | It is wrong!
| Private | This is private
| Past | The  past is the past
| Foreign | He is foreign, he is from the netherlands
| Fine | I am fine
| Common | That is common
| poor | They are poor
| Natural | This juice is natural
| Significant | It is so significant
| Similar | They are similar
| Hot | The coffee is hot
| Dead | The cat is dead
| Central | Medellin is central
| Happy | She is happy
| Serious | He is serious
| Ready | It is ready to deliver
| Simple | It is simple
| Left | It is on the left

# data[].attributes.body
| Red | I have a red house
| Blue | the kitchen of the chef is blue
| Brown | The doctor’s office is brown
| Gray/grey | The office is silver
| Yellow | The flowers of the secretary are yellow
| Black | The computers are black
| White | The paper of the reports are white
| Orange | The orange of grocery worker is orange
| Green | The judg’s pen is green
| Purple | The painter of the painter is purple
| Pink | The clothes of the model are pink
| Violet | The violet is violet
| beige | The uniform of the hairdresser is beige
| silver | The computer of the designer is silver

# data[].attributes.body
| Accountant | a person that works with the money and accounts of company.
| Actor /Actress | a person that acts in a play or a movie
| Architect | a person that designs building and houses.
| Astronomer | a person who studies the stars and the universe
| Author | They write books or novels.
| Baker | They make bread and cakes and normally work in a bakery.
| Bricklayer | a person that helps to build houses using bricks.
| Bus driver | a person that drives buses.
| Butcher | a person that works with meat. They cut the meat and sell it in their shop.
| Carpenter | a person that makes things from wood including houses and furniture.
| Chef/Cook | a person that prepared food for others, often in a restaurant or café.
| Cleaner | a person that cleans/tidies an area or place (such as in an office)
| Dentist | a person that can fix problems you have with your teeth.
| Designer | a person who has the job of designing things.
| Doctor | a person you go to see when you are ill or have some type of health problem.
| Dustman/Refuse collector | a person that collects trash/rubbish from bins in the street.
| Electrician | a person that works with electric circuits.
| Engineer | a person who develops solutions to technical problems. They sometimes design, build, or maintain engines, machines, structures or public works.
| Factory worker | a person that works in a factory.
| Farmer | a person that works on a farm, usually with animals.
| Fireman/Fire fighter | a person that puts out fires.
| Fisherman | a person that catches fish
| Florist | a person that works with flowers.
| Gardener | a person that keeps gardens clean and tidy. They take care of the plants in the garden.
| Hairdresser | they cut your hair or give it a new style.
| Journalist | a person that makes new reports in writing or through television.
| Judge | a qualified person that decides cases in a law court.
| Lawyer | a person that defends people in court and gives legal advice.
| Lecturer | a person that gives lectures, usually in a university.
| Librarian | a person that works in a library.
| Lifeguard | a person that saves lives where people swim (at a beach or swimming pool).
| Mechanic | a person that repairs machines, especially car motors.
| Model | a (usually attractive) person that works in fashion, modeling clothes and   accessories.
| Newsreader | a person that reads the news, normally on television.

# data[].attributes.body
| Auxiliary verb to be for:

| i | Am / ‘m
| you | Are / ‘re
| we | Are / ‘re
| they | Are / ‘re
| he | Is / ‘s
| she | Is / ‘s

# data[].attributes.body
| My name is ..............
| I’m / i am from ...........
| I live in ...........
| I’m ..... years old
| I have _______ hair
| I have (colour) eyes
| I am (1.80) tall
| I live with ………..

# data[].attributes.body
| What’s / what is your name?
| Where are you from?
| Where do you live?
| How old are you?
| What do you look like?
| Which colour are your eyes?
| How tall are you?
| With who do you live?

# data[].attributes.body
| Brenda | Hello, how are you?
| Menno | I am fine and you?
| Brenda | I’m fine thank you
| Menno | What is your name?
| Brenda | My name is Brenda / I am Brenda and yours?
| Menno | My name’s menno
| Brenda | That’s a nice name
| Menno | Thank you very much, yours is nicer
| Brenda | Thank you!
| Menno | Where are you from? Where do you live?
| Brenda | I am colombian, i live in medellin and you?
| Menno | I’m dutch, i live bello
| Brenda | That’s nice
| Menno | How old are you?
| Brenda | I am / i’m 31 years old and you?
| Menno | I am / i’m 34 years old
| Brenda | What do you look like?
| Menno | I have short blond hair and I have blue eyes and you?
| Brenda | I have long brown hair and black eyes
| Menno | How tall are you?
| Brenda | I am 1.60 tall
| Menno | With who do you live?
| Brenda | I live alone, I’m single and you?
| Menno | That sounds good, I live alone also and single!
| Menno | Do you want to drink or eat something in these days?
| Brenda | Sure, I would love to
| Menno | Great, in these days we will see when you can
| Brenda | That sounds good, we talk later on

# data[].attributes.headline
The verb to be: qualifies state or place (can’t change, because eyes , height etc of a person we can’t change)

# data[].attributes.subject
The verb to be: qualifies state or place (can’t change, because eyes , height etc of a person we can’t change)

# data[].attributes.body
| pronoun
| i
| You(s)
| We
| They
| You(pl)
| he
| she
| it

| positive
| Am / ‘m
| Are / ‘re
| Are / ‘re
| Are / ‘re
| Are / ‘re
| Is / ‘s
| Is / ‘s
| Is / ‘s

| negative
| Am not / I’m not
| Are not / aren’t
| Are not / aren’t
| Are not / aren’t
| Are not / aren’t
| Is not / isn’t
| Is not / isn’t
| Is not / isn’t

| Question
| Am i….?
| Are you….?
| Are we….?
| Are they..?
| Are you..?
| Is he…?
| Is she…?
| Is it….?

# data[].attributes.headline
The verb to have qualifies what somebody has (doesn’t change or can change)

# data[].attributes.subject
The verb to have qualifies what somebody has (doesn’t change or can change)

# data[].attributes.body
| pronoun
| i
| You(s)
| We
| They
| You(pl)
| he
| she
| it

| positive
| have
| have
| have
| have
| have
| has
| has
| has

| negative
| Have not / haven’t
| Have not / haven’t
| Have not / haven’t
| Have not / haven’t
| Have not / haven’t
| Has not / hasn’t
| Has not / hasn’t
| Has not / hasn’t

# data[].attributes.body
Hi, how are you?
Hello, how are you?
Hey, whatsup?
How’s life?
How’s everything / how is everything

Bye-bye
Bye-bye, see you later
See ya later

conversation:

# data[].attributes.headline
present simple explication pdf version 1

# data[].attributes.subject
present simple explication pdf version 1

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present simple explication pdf version 2

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present simple explication pdf version 2

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Learn and Speak English Present Tense Grammar Vocabulary Rap Song with Fluency MC

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The Third Person Singular Present Simple Song

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| Third person (singular)
| His - her name is ..............
| he / she is from ...........
| He - she lives in ...........
| He / she is ..... years old
| He - she has short (colour)  / long (colour) hair
| He - she has (colour) eyes
| He - she is (1.80 – one metre and 80 centimetres) tall
| He - she lives with ………..

# data[].attributes.body
| In numbers | In letters
| 1 | One
| 2 | Two
| 3 | Three
| 4 | Four
| 5 | Five
| 6 | Six
| 7 | Seven
| 8 | Eight
| 9 | Nine
| 10 | Ten
| 11 | Eleven
| 12 | Twelf / twelve
| 13 | Thirteen
| 14 | Fourteen
| 15 | Fifteen
| 16 | Sixteen
| 17 | Seventeen
| 18 | Eighteen
| 19 | Nineteen
| 20 | Twenty
| 21 | Twenty one
| 22 | Twenty two
| 23 | Twenty three
| 24 | Twenty four
| 25 | Twenty five
| 26 | Twenty six
| 27 | Twenty seven
| 28 | Twenty eight
| 29 | Twenty nine
| 30 | thirty
| 40 | Forty
| 50 | Fifty
| 60 | Sixty
| 70 | Seventy
| 80 | Eighty
| 90 | Ninety
| 100 | One hundred
| 1000 | One thousand

# data[].attributes.body
| Apartment(am)
| Flat(br)
| House
| Room
| Balcony
| Bathroom
| Bedroom
| Dinningroom
| Living room(am)
| Sitting room(br)
| Kitchen
| Garage
| Basement(am)
| Cellar(br)
| attic
| Study room
| toilet

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What’s / what is his - her name?
Where is he – she from?
Where does he - she live?
How old is he - she?
What does he - she look like?
Which colour are his - her eyes?
How tall is he - she?
With who does he - she live?

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Hello, good morning / good afternoon / good evening / good night
Hello, how are you?
Hello, how are you sir ……./ madam ……./ ms …../  mr ……..
| How is everything going?

Have a good day / afternoon / evening / night
See you soon again

conversation:
| german | Good morning director
| director | Good morning German
| german | How are you sir?
| director | I am fine thank you and you?
| german | I am fine sir, this evening I will present the report about menno’s virtual projects
| director | Let me know when you come
| german | I will let you know sir, have a nice day
| director | The same german

# data[].attributes.body
| be | was, were | been
| buy | bought | bought
| catch | caught | caught
| choose | chose | chosen
| come | came | come
| do | did | done
| draw | drew | drawn
| drink | Drank / drunk | Drunk/drank
| drive | drove | driven
| eat | ate | eaten
| feel | felt | felt
| find | found | found
| fly | flew | flown
| get | got | Got / gotten
| give | gave | given
| go | went | gone
| hear | heard | heard
| know | knew | known
| learn | Learned / learnt | Learned / learnt
| leave | left | left
| make | made | made
| meet | met | met
| pay | paid | paid
| put | put | put
| read | read | read
| ride | Rode / rid | Ridden / rid
| run | ran | run
| say | said | said
| see | saw | seen
| send | sent | sent
| show | showed | shown
| sing | sang | sung
| sit | sat | sat
| sleep | slept | slept
| speak | spoke | spoken
| swim | swam | swum
| take | took | taken
| teach | taught | taught
| tell | told | told
| think | thought | thought
| understand | understood | understood
| wake | woke | woken
| wear | wore | worn
| write | wrote | written

# data[].attributes.body
| In numbers | In letters
| 1st | first
| 2nd | second
| 3rd | Third
| 4th | Fourth
| 5th | Fifith
| 6th | Sixth
| 7th | Seventh
| 8th | Eight
| 9th | Ninth
| 10th | Tenth
| 11th | Eleventh
| 12th | Twelfth
| 13th | Thirteenth
| 14th | Fourteenth
| 15th | Fifteenth
| 16th | Sixteenth
| 17th  | Seventeenth
| 18th | Eighteenth
| 19th | Nineteenth
| 20th  | Twentiest
| 21st  | Twenty-second
| 22nd  | Twenty-third
| 23rd  | Twenty-fourth
| 24th | Twenty-fourth
| 25th | Twenty-fifth
| 26th | Twenty-sixth
| 27th  | Twenty-seventh
| 28th  | Twenty-eight
| 29th  | Twenty-ninth
| 30th | thirtieth
| 40th | Fortieth
| 50th | Fiftieth
| 60th | Sixtieth
| 70th | Seventieth
| 80th | Eightieth
| 90th | Ninetieth
| 100th | Hundredth
| 1000th  | thousandth

# data[].attributes.body
She has an apartment in medellin
He has a flat in bello
I have a big house
I am in my room
She is on the balcony
He is in the bathroom, he takes a shower
It sleeps in its bedroom
We eat in the dinningroom
They watch football in the living room
They watch football in the sitting room
You prepare the dinner in the kitchen
He parks the car in the garage
The basement is full of things
The cellar is full of things
The attic is dusty
The study room you can use to study
She is in the toilet

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Menno Koster, elteachermono@gmail.com

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positive form:
subject + had + been + verb-ing

I had been eating chocolate all day.

She’d been eating breakfast.

negative form:
subject + had + not + been + verbing

I hadn’t been eating.

She hadn’t been eating an apple.

question form:
(question word +) had + subject + been + verb-ing?

Why had he been eating?

Had they been eating enough fruit?

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Dialogue 1: knowing each other (third person)

# data[].attributes.subject
Dialogue 1: knowing each other (third person)

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Jane Hello, how are you?
Menno I am fine and you?
Jane I’m fine thank you
Menno What is her name?
Jane her name is Brenda/ she is Brenda
Menno she’s beautifull
Jane Yes, she is
Menno Where does she live?
Jane She lives in medellin
Menno Where is she from?
Jane she is colombian
Menno How tall is she?
Jane She’s 1.60 tall
Menno with who does she live?
Jane She lives alone and is single
Menno That sounds good!

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past simple formula question; was / were + this/that/these/those + adjective

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demonstrative adjectives question with examples

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demonstrative adjectives question with examples

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| question near (singular)
| was This big?
| was This small?
| was This delicious?
| was This great?
| was This new?
| was This old?
| was This ugly?

| question far (singular)
| is That big?
| is That small?
| is That delicious?
| is That great?
| is That new?
| is That old?
| is That ugly?

| question near (plural)
| are These big?
| are These small?
| are These delicious?
| are These great?
| are These new?
| are These old?
| are These ugly?

| question far (plural)
| are Those big?
| are Those small?
| are Those delicious?
| are Those great?
| are Those new?
| are Those old?
| are Those ugly?

# data[].attributes.body
present simple:
| pronoun | positive
| i | Am / ‘m
| You(s) | Are / ‘re
| We | Are / ‘re
| They | Are / ‘re
| You(pl) | Are / ‘re
| he | Is / ‘s
| she | Is / ‘s
| it | Is / ‘s

| question
| Am i….?
| Are you….?
| Are we….?
| Are they..?
| Are you..?
| Is he…?
| Is she…?
| Is it….?

past simple:
| pronoun | positive
| i | was
| You(s) | were
| We | were
| They | were
| You(pl) | were
| he | was
| she | was
| it | was

| negative
| was not / wasn’t
| were not / weren’t
| were not / weren’t
| were not / weren’t
| were not / weren’t
| was not / wasn’t
| was not / wasn’t
| was not / wasn’t

| question
| was i….?
| were you….?
| were we….?
| were they..?
| were you..?
| was he…?
| was she…?
| was it….?

# data[].attributes.headline
extra document complete list of verb(1000)

# data[].attributes.subject
extra document complete list of verb(1000)