Triple

T1481588
Position Surface form Disambiguated ID Type / Status
Subject Fay E30967 entity
Predicate hasNotableBearer P458 FINISHED
Object Fay Wray E4506 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: Fay Wray | Statement: [Fay, hasNotableBearer, Fay Wray]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fay Wray
Context triple: [Fay, hasNotableBearer, Fay Wray]
  • A. Fay Wray chosen
    Fay Wray was a Canadian-American actress best known for her iconic role as the damsel Ann Darrow in the classic 1933 film "King Kong."
  • B. Clara Bow
    Clara Bow was a hugely popular American silent film actress of the 1920s, famously known as the original "It Girl" and a defining sex symbol of the Jazz Age.
  • C. Jean Harlow
    Jean Harlow was a legendary American film actress and 1930s sex symbol known for her platinum blonde image and starring roles in early Hollywood comedies and dramas.
  • D. Linda Christian
    Linda Christian was a Mexican-born Hollywood actress best known as the first on-screen "Bond girl" in the 1954 television adaptation of Casino Royale.
  • E. Veronica Lake
    Veronica Lake was a popular American film actress of the 1940s, famed for her roles in film noir and her iconic peek-a-boo hairstyle.
  • 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_69a498fe55a88190ab7f9e40ace88e49 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c67699848190852e376efe22737c completed March 1, 2026, 11:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad36ffd8808190894f2139ae12204e completed March 8, 2026, 8:44 a.m.
Created at: March 1, 2026, 8:11 p.m.