Triple

T17921779
Position Surface form Disambiguated ID Type / Status
Subject Hook Norton Parish Council E448087 entity
Predicate locatedIn P40 FINISHED
Object Hook Norton NE NERFINISHED

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: Hook Norton | Statement: [Hook Norton Parish Council, locatedIn, Hook Norton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hook Norton
Context triple: [Hook Norton Parish Council, locatedIn, Hook Norton]
  • A. Hook Norton chosen
    Hook Norton is a historic Cotswold village in Oxfordshire, England, best known for its traditional Victorian tower brewery and honey-colored stone buildings.
  • B. Tilghman
    Tilghman is a surname most notably associated with Shirley M. Tilghman, a prominent molecular biologist and former president of Princeton University.
  • C. Tilghman
    Tilghman is a masculine given name of English origin that has been borne by various notable American figures, including politicians and military officers.
  • D. Sarratt
    Sarratt is a rural village in Hertfordshire, England, known for its traditional village green, historic buildings, and scenic Chilterns countryside.
  • E. Garrett Fort
    Garrett Fort was an American screenwriter best known for his work on classic Hollywood horror and adventure films in the 1930s and 1940s.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9f6d394819082a6d69fd1e23d2f completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4a30a11748190be41361d108aee58 completed April 19, 2026, 9:40 a.m.
Created at: April 10, 2026, 10:20 a.m.