Triple

T19558798
Position Surface form Disambiguated ID Type / Status
Subject John de Havilland E489386 entity
Predicate employer P7 FINISHED
Object de Havilland 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: de Havilland | Statement: [John de Havilland, employer, de Havilland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: de Havilland
Context triple: [John de Havilland, employer, de Havilland]
  • A. de Havilland
    De Havilland is the distinguished Anglo-French family name shared by Hollywood actresses Joan Fontaine and her sister Olivia de Havilland.
  • B. de Havilland Aircraft Company chosen
    De Havilland Aircraft Company was a major British aviation manufacturer renowned for designing innovative military and civilian aircraft, including iconic World War II planes.
  • C. De Havilland Canada
    De Havilland Canada is a Canadian aircraft manufacturer best known for its rugged short takeoff and landing (STOL) regional and utility aircraft used worldwide.
  • D. Avro Canada
    Avro Canada was a Canadian aircraft manufacturing company best known for advanced military and experimental aircraft projects such as the CF-100 Canuck and the Avro Arrow.
  • E. Canadair
    Canadair was a Canadian aircraft manufacturer best known for producing specialized amphibious firefighting and utility aircraft before becoming part of Bombardier Aerospace.
  • 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e63f723d5081909553a4363b579a6b completed April 20, 2026, 3 p.m.
Created at: April 10, 2026, 1:42 p.m.