Triple

T14763934
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth Anne Bloomer E346943 entity
Predicate alsoKnownAs P39 FINISHED
Object Elizabeth Anne Ford E375310 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: Elizabeth Anne Ford | Statement: [Elizabeth Anne Bloomer, alsoKnownAs, Elizabeth Anne Ford]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth Anne Ford
Context triple: [Elizabeth Anne Bloomer, alsoKnownAs, Elizabeth Anne Ford]
  • A. Elizabeth Anne Ford chosen
    Elizabeth Anne Ford was the First Lady of the United States from 1974 to 1977 and a prominent advocate for women's rights and addiction treatment.
  • B. Margaret Ford
    Margaret Ford is a successful psychiatrist who becomes entangled in the dangerous world of con artists in David Mamet’s psychological thriller film "House of Games."
  • C. Mary Rose Foster
    Mary Rose Foster is the fictional protagonist of the 1979 musical drama film "The Rose," loosely inspired by the life and career of rock singer Janis Joplin.
  • D. Jane Ford
    Jane Ford is an American entrepreneur and co-founder of the global beauty brand Benefit Cosmetics.
  • E. Constance Ford
    Constance Ford was an American actress best known for her work in mid-20th-century film, television, and soap operas, including a long-running role on "Another World."
  • 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_69d822e8896c819091169882f9b20486 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec7f3a1608190b1b17624003a0c7f completed April 14, 2026, 11:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee5dee8988190b80cb487c12bfc2d completed May 9, 2026, 7:44 a.m.
Created at: April 10, 2026, 1:30 a.m.