Triple

T13498988
Position Surface form Disambiguated ID Type / Status
Subject Fess Parker E320834 entity
Predicate familyName P18 FINISHED
Object Parker E44427 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: Parker | Statement: [Fess Parker, familyName, Parker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Parker
Context triple: [Fess Parker, familyName, Parker]
  • A. Parker chosen
    Parker is a common English surname borne by numerous notable individuals across fields such as politics, sports, arts, and science.
  • B. Parker
    Parker is a 2013 American crime thriller film starring Jason Statham as a professional thief who seeks revenge after being double-crossed by his crew.
  • C. Parker
    Parker is a suburban town in Colorado located along the eastern edge of the Denver metropolitan area.
  • D. Tucker
    Tucker is a surname most notably associated with Albert W. Tucker, a Canadian-American mathematician and game theorist known for his contributions to topology and the formalization of the prisoner's dilemma.
  • E. Tucker
    Tucker is a paranormal investigator character from the Insidious horror film series, known for his tech-based ghost-hunting work alongside his partner Specs.
  • 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_69d807629d6c8190998f1b9bb12d2ed0 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbaf4fab688190bdc746985b0c7338 completed April 12, 2026, 2:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75487cb3c8190ab7f3bc36755b74c completed May 3, 2026, 1:58 p.m.
Created at: April 9, 2026, 9:43 p.m.