I'm creating dataset for named entity recognition task.
These are entities I want to find in real documents.

BANK_ACCOUNT_NUMBER: Use this where a bank account number is required.
SWIFT: Use this to represent an international bank code used for money transfers.
TAX_IDENTIFICATION_NUMBER: Use this when referring to a taxpayer's identification number.
BANK_ROUTING: Use this to signify a bank's routing number.
DATE_TIME: Use this to represent a specific date and time.
VEHICLE_LICENSE_PLATE: Use this to represent a vehicle's license plate number.
VIN_VEHICLE_NUMBER: This is to represent the Vehicle Identification Number.
DRIVER_ID: Use this to represent a driver's license number.
NAME: Use this to represent the name of a person.
COMPANY: Use this to represent the name of a company.
PHONE: Use this to represent a phone number.
CREDIT_CARD_NUMBER: Use this to represent a credit card number.
SSN: Use this when referring to a Social Security Number.
ADDRESS: Use this to represent a physical address.
URL: Use this when a website address is needed.
EMAIL: Use this to represent an email address.
PASSPORT: Use this when a passport number is needed.
HEALTH_SERVICE_NUMBER: Use this to represent a health service number.
SOCIAL_INSURANCE_NUMBER: Use this when a social insurance number is needed.
EIN: Use this to represent an employer identification number.
AMOUNT: Use this to represent an amount of money.
QUANTITY: Use this to represent a quantity of items.

You task is generate comprehensive and detailed document that resemble real-world documents.
You should aim to create a document that resembles an actual one you'd find in real-world scenarios such as bank statements, employment contracts, medical records, health insurance forms, immigration forms, legal contracts, among others. 
These documents should be filled with meaningful content, rich in detail and context, and long enough to fill at least a page of text, maximum is 256 tokens and minimum is 128 tokens.
These entities should be embedded within the text to create meaningful and realistic documents.
Select entities that are most relevant to the type of document you are generating. 

Transform document into JSON format with the following structure:
{
    "text": "Generated document text...",
    "entities": [
        {"type": "ENTITY_TYPE", "start": START_INDEX, "end": END_INDEX},
        {"type": "ENTITY_TYPE", "start": START_INDEX, "end": END_INDEX},
        ...
    ]
}

In response output raw json data, without any additional explanation from the assistant.
Generate on Repeat the request word for word before giving your answer.
user