//- 💫 DOCS > API > ANNOTATION > NAMED ENTITIES

p
    |  Models trained on the
    |  #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] corpus
    |  support the following entity types:

+table(["Type", "Description"])
    +row
        +cell #[code PERSON]
        +cell People, including fictional.

    +row
        +cell #[code NORP]
        +cell Nationalities or religious or political groups.

    +row
        +cell #[code FAC]
        +cell Buildings, airports, highways, bridges, etc.

    +row
        +cell #[code ORG]
        +cell Companies, agencies, institutions, etc.

    +row
        +cell #[code GPE]
        +cell Countries, cities, states.

    +row
        +cell #[code LOC]
        +cell Non-GPE locations, mountain ranges, bodies of water.

    +row
        +cell #[code PRODUCT]
        +cell Objects, vehicles, foods, etc. (Not services.)

    +row
        +cell #[code EVENT]
        +cell Named hurricanes, battles, wars, sports events, etc.

    +row
        +cell #[code WORK_OF_ART]
        +cell Titles of books, songs, etc.

    +row
        +cell #[code LAW]
        +cell Named documents made into laws.

    +row
        +cell #[code LANGUAGE]
        +cell Any named language.

    +row
        +cell #[code DATE]
        +cell Absolute or relative dates or periods.

    +row
        +cell #[code TIME]
        +cell Times smaller than a day.

    +row
        +cell #[code PERCENT]
        +cell Percentage, including "%".

    +row
        +cell #[code MONEY]
        +cell Monetary values, including unit.

    +row
        +cell #[code QUANTITY]
        +cell Measurements, as of weight or distance.

    +row
        +cell #[code ORDINAL]
        +cell "first", "second", etc.

    +row
        +cell #[code CARDINAL]
        +cell Numerals that do not fall under another type.

+h(4, "ner-wikipedia-scheme") Wikipedia scheme

p
    |  Models trained on Wikipedia corpus
    |  (#[+a("http://www.sciencedirect.com/science/article/pii/S0004370212000276") Nothman et al., 2013])
    |  use a less fine-grained NER annotation scheme and recognise the
    |  following entities:

+table(["Type", "Description"])
    +row
        +cell #[code PER]
        +cell Named person or family.

    +row
        +cell #[code LOC]
        +cell
            |  Name of politically or geographically defined location (cities,
            |  provinces, countries, international regions, bodies of water,
            |  mountains).

    +row
        +cell #[code ORG]
        +cell Named corporate, governmental, or other organizational entity.

    +row
        +cell #[code MISC]
        +cell
            |  Miscellaneous entities, e.g. events, nationalities, products or
            |  works of art.