Triple

T12062983
Position Surface form Disambiguated ID Type / Status
Subject Almira Russell Hancock E287219 entity
Predicate familyName P18 FINISHED
Object Hancock E3527 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: Hancock | Statement: [Almira Russell Hancock, familyName, Hancock]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hancock
Context triple: [Almira Russell Hancock, familyName, Hancock]
  • A. Hancock chosen
    Hancock is a prominent surname most famously associated with John Hancock, a key figure of the American Revolution and first signer of the United States Declaration of Independence.
  • B. Hancock
    Hancock is a small rural town in western Massachusetts known for its scenic Berkshire landscapes and outdoor recreation.
  • C. Hancock
    Hancock is a small city in Michigan’s Upper Peninsula known for its Finnish-American heritage and proximity to Lake Superior.
  • D. Hancock (film)
    Hancock is a 2008 superhero action-comedy film starring Will Smith as a troubled, alcoholic superhero seeking redemption in modern-day Los Angeles.
  • E. Kingman
    Kingman is a surname most notably associated with Sir John Kingman, a prominent British mathematician and statistician.
  • 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_69d6ab4846e081908ee7bbd66a6d3459 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9043f82248190b05692aa0dc178a8 completed April 10, 2026, 2:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f654eb1881908d656009f1362ecf completed May 2, 2026, 1:04 p.m.
Created at: April 8, 2026, 9:48 p.m.