Triple

T14702726
Position Surface form Disambiguated ID Type / Status
Subject Jim Dear E345346 entity
Predicate spouse P13 FINISHED
Object Darling E345347 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: Darling | Statement: [Jim Dear, spouse, Darling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Darling
Context triple: [Jim Dear, spouse, Darling]
  • A. Darling
    Darling is a 1965 British drama film directed by John Schlesinger, known for its incisive portrayal of a young woman's rise in London's high society and for winning multiple Academy Awards.
  • B. Darling
    Darling is a residential suburb in Melbourne, Victoria, known for its local train station on the Glen Waverley railway line and its proximity to the city.
  • C. Darling
    Darling is a character played by Eiza González in the action film "Baby Driver," known as a stylish and dangerous bank robber and the girlfriend of fellow criminal Buddy.
  • D. Darling
    Darling is a surname most prominently associated with Ron Darling, a former Major League Baseball pitcher and current television baseball analyst.
  • E. Darling chosen
    Darling is the kind, affectionate human owner of Lady in Disney's animated film "Lady and the Tramp."
  • 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_69d822e4a8c08190a155df736bb7bc13 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb6071e5c8190bb5509c859135c2d completed April 14, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdfb8221a4819098937018f24a0b44 completed May 8, 2026, 3:04 p.m.
Created at: April 10, 2026, 1:28 a.m.