Triple

T112023
Position Surface form Disambiguated ID Type / Status
Subject Hawker Hurricane E2268 entity
Predicate designer P184 FINISHED
Object Sydney Camm E7252 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: Sydney Camm | Statement: [Hawker Hurricane, designer, Sydney Camm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sydney Camm
Context triple: [Hawker Hurricane, designer, Sydney Camm]
  • A. Sydney Camm chosen
    Sydney Camm was a British aircraft designer best known for creating the Hawker Hurricane, one of the Royal Air Force’s key fighter planes during World War II.
  • B. Marc Seriff
    Marc Seriff is an American computer scientist and entrepreneur best known as a co-founder and early technical leader of the pioneering internet company AOL.
  • C. Max Hollein
    Max Hollein is an Austrian-born museum director and art historian best known for leading major institutions in Europe and the United States, including serving as director of New York’s Metropolitan Museum of Art.
  • D. Jamie King
    Jamie King is an American actress and former fashion model known for her roles in films like "Sin City" and the TV series "Hart of Dixie."
  • E. Lee Dixon
    Lee Dixon was an American actor and dancer best known for his work in mid-20th-century stage and film musicals.
  • 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_69a24fcdaeb48190a2d796677e4b3281 completed Feb. 28, 2026, 2:15 a.m.
NER Named-entity recognition batch_69a256ec650c8190bee2067e37065527 completed Feb. 28, 2026, 2:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2a3d459888190ab08a6afdec37d71 completed Feb. 28, 2026, 8:14 a.m.
Created at: Feb. 28, 2026, 2:20 a.m.