Triple

T10345615
Position Surface form Disambiguated ID Type / Status
Subject Lillita E243736 entity
Predicate alsoKnownAs P39 FINISHED
Object Lita Grey E46655 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Lita Grey | Statement: [Lillita, alsoKnownAs, Lita Grey]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lita Grey
Context triple: [Lillita, alsoKnownAs, Lita Grey]
  • A. Lita Grey chosen
    Lita Grey was an American actress best known for her early silent-film work and her highly publicized, scandalous marriage to Charlie Chaplin as a teenager.
  • B. Lita
    Lita is a WWE Hall of Famer and pioneering women's professional wrestler known for her high-flying style and influential role in the evolution of women's wrestling.
  • C. Lita Baron
    Lita Baron was a Spanish-born American actress and nightclub singer active in Hollywood films and television during the mid-20th century.
  • D. Leona Vicario
    Leona Vicario was a prominent Mexican independence heroine, journalist, and supporter of the insurgent cause against Spanish rule in the early 19th century.
  • E. Laura Grey
    Laura Grey is an American comedian, writer, and actress known for her work on satirical television and sketch comedy, as well as her collaborations with fellow comedian Jordan Klepper.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d381b22b8c8190aaed476be5f872a9 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e923e3d08190971073ce41ff860f completed April 7, 2026, 11:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69d79513ea3081908fee1a404f950be5 completed April 9, 2026, 12:01 p.m.
Created at: April 6, 2026, 11:56 a.m.