Triple

T15566153
Position Surface form Disambiguated ID Type / Status
Subject John Lithgow E371119 entity
Predicate spouse P13 FINISHED
Object Jean Taynton E371119 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: Jean Taynton | Statement: [John Lithgow, spouse, Jean Taynton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jean Taynton
Context triple: [John Lithgow, spouse, Jean Taynton]
  • A. Jean Taynton chosen
    Jean Taynton is an American teacher best known as the former wife of actor John Lithgow.
  • B. Mary Tourtel
    Mary Tourtel was an English illustrator and author best known for creating the beloved children's comic strip character Rupert Bear in the early 20th century.
  • C. Jean Willes
    Jean Willes was an American character actress known for her prolific work in film and television from the 1940s through the 1960s, often appearing in comedies, Westerns, and crime dramas.
  • D. Jean Fayle
    Jean Fayle is a person after whom another individual or entity named Jean was named, suggesting they were an influential or significant namesake.
  • E. Jeanette Demont
    Jeanette Demont was the first wife of American oil tycoon and art collector J. Paul Getty.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04ddd753c8190b51eaef433258081 completed April 16, 2026, 2:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff456a3b08819092f9f517bbef5577 completed May 9, 2026, 2:32 p.m.
Created at: April 10, 2026, 4:10 a.m.