Global entities are automatically detected by the system, so you don’t have to add them manually. Global entities include things like POSTAL_CODE, where enumerating the full list would be difficult, and STREET, where predefining a format would be impossible. Global entities include:
|AIRPORT||An airport code||SEATAC
|DATE||Dates and absolute date×tamps||today
6 p.m. tomorrow
|DURATION||A time period||2 weeks
2 weeks and 3 days
half a day
|An email email@example.com|
|MONEY||Numbers with currency||$2000
|ORG||Names of institutions||Nike factory
World Health Organization
|PERSON_NAME||Names of persons||John
|PHONE||A phone number||800-555-1212
|POSITION_IN_SERIES||A number used in the context of order||15th
|POSTAL_CODE||United States postal code||10001|
In, “The meeting with Bob is weekly on Tuesdays,” PERSON_NAME = Bob, SET = weekly, DATE = Tuesdays
|STATE||United States state||NY
|STREET||United States descriptors for street names||Main St.
123 Main St. NE
123 East-West Highway Apt. 107
|TIME||Time of day||2 p.m.
Keep in mind that the detection of global entities is highly dependent on context. As a result, the system is powerful and capable of detecting the following:
- Message: My name is Paris and I live in Paris
Entities: PERSON_NAME = Paris, CITY = Paris
- Message: Hi Tuesday, can you arrive at 2pm on Tuesday?
- Entities: PERSON_NAME = Tuesday, TIME = 2pm, DATE = Tuesday
Detection of entities is trained on commercial messages, so depending on context, you might get results that you don’t expect:
- Non-commercial message: Washington cherry trees are beautiful this time of year
- Entities: CITY = Washington but
- Commercial message: Do you ship this product to Washington?
- Entities: STATE = Washington