Triple

T8889138
Position Surface form Disambiguated ID Type / Status
Subject Garrett Fort E211619 entity
Predicate name P16 FINISHED
Object Garrett Fort E211619 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: Garrett Fort | Statement: [Garrett Fort, name, Garrett Fort]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Garrett Fort
Context triple: [Garrett Fort, name, Garrett Fort]
  • A. Garrett Fort chosen
    Garrett Fort was an American screenwriter best known for his work on classic Hollywood horror and adventure films in the 1930s and 1940s.
  • B. Garrett Camp
    Garrett Camp is a Canadian entrepreneur and investor best known as the co-founder of Uber and the founder of the discovery platform StumbleUpon.
  • C. Hook Norton
    Hook Norton is a historic Cotswold village in Oxfordshire, England, best known for its traditional Victorian tower brewery and honey-colored stone buildings.
  • D. Point Bennett
    Point Bennett is a remote, wildlife-rich headland on the western tip of San Miguel Island in California’s Channel Islands, known for its large colonies of seals and sea lions.
  • E. Clyfford
    Clyfford Still was a pioneering American abstract expressionist painter known for his monumental, jagged color-field canvases that profoundly influenced postwar modern art.
  • 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_69ca83907954819096d52a245b635841 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc618f94108190b03248832bfe68a9 completed April 1, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfabe955e08190a8d61cfaa2d52731 completed April 3, 2026, noon
Created at: March 30, 2026, 6:53 p.m.