Triple

T3812669
Position Surface form Disambiguated ID Type / Status
Subject King Kong franchise E93170 entity
Predicate hasCharacter P2308 FINISHED
Object Ann Darrow E308967 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: Ann Darrow | Statement: [King Kong franchise, hasCharacter, Ann Darrow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ann Darrow
Context triple: [King Kong franchise, hasCharacter, Ann Darrow]
  • A. Ann Darrow chosen
    Ann Darrow is the fictional leading lady and damsel-in-distress from the King Kong franchise, best known as the woman whom the giant ape becomes infatuated with.
  • B. Florence Cameron
    Florence Cameron is the daughter of former UK Prime Minister David Cameron and businesswoman Samantha Cameron.
  • C. Kate Reed
    Kate Reed is the sharp, idealistic former lawyer turned mediator at the center of the legal dramedy series "Fairly Legal."
  • D. Rose Allerton
    Rose Allerton was a member of the early 17th-century Allerton family associated with the Pilgrim settlers of Plymouth Colony.
  • E. Helen Flint
    Helen Flint is a television and film producer known for her work as an executive producer on high-profile drama series.
  • 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_69aed96a60088190ab1df8390fffc935 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aee8db8a288190afd1e3b9dcf02e97 completed March 9, 2026, 3:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4fb3a69908190ba8e7ac37c8ca0f8 completed March 14, 2026, 6:07 a.m.
Created at: March 9, 2026, 3:16 p.m.