Triple

T13103893
Position Surface form Disambiguated ID Type / Status
Subject Hunter, New York E310791 entity
Predicate isNamedAfter P63 FINISHED
Object John Hunter E441662 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: John Hunter | Statement: [Hunter, New York, isNamedAfter, John Hunter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Hunter
Context triple: [Hunter, New York, isNamedAfter, John Hunter]
  • A. John Hunter chosen
    John Hunter was a prominent landowner and early settler after whom the Town of Hunter in New York was named.
  • B. John Hunter
    John Hunter was an influential 18th-century Scottish surgeon and anatomist, often regarded as a founder of modern scientific surgery.
  • C. John Gillon
    John Gillon is the central protagonist of the film "Diggstown," a cunning ex-con and boxing hustler who masterminds an elaborate scheme around a small-town boxing challenge.
  • D. Philip Christison
    Philip Christison was a British Army general who held senior commands in World War II, particularly in Southeast Asia.
  • E. Charles Jackson
    Charles Jackson was an American novelist best known for his 1944 novel "The Lost Weekend," a groundbreaking portrayal of alcoholism.
  • 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_69d806a872d08190a329806f8ff30df4 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98153255c8190b6ab64ac0c4716f8 completed April 10, 2026, 11:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e277b89c8190a0d895eb46836525 completed May 3, 2026, 5:51 a.m.
Created at: April 9, 2026, 9:04 p.m.