The correct options are Named entity recognition and Entity linking.

Named entity recognition extracts and classifies entities found in free text such as people, organizations, locations and dates and it returns the entity mentions and their types which is why it is one of the supported recognition kinds.

Entity linking goes further by connecting those extracted entities to entries in a knowledge base or external identifiers so that mentions can be disambiguated and mapped to canonical records and that is why it is the other supported recognition kind.

Entity normalization is not correct because normalization usually means converting values to a standard format and that is not the same as recognizing or linking entities in the extracted text.

Entity resolution is not correct because resolution refers to reconciling and merging records that refer to the same real world entity across datasets and that is a separate data integration process rather than a text extraction recognition type.

Entity relationships is not correct because extracting relationships between entities is a form of relation extraction and that is different from the two recognition outputs the question asks about.

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