Triple

T7355111
Position Surface form Disambiguated ID Type / Status
Subject Passenger 57 E169602 entity
Predicate starring P1507 FINISHED
Object Elizabeth Hurley E214704 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: Elizabeth Hurley | Statement: [Passenger 57, starring, Elizabeth Hurley]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth Hurley
Context triple: [Passenger 57, starring, Elizabeth Hurley]
  • A. Elizabeth Hurley chosen
    Elizabeth Hurley is an English actress, model, and businesswoman best known for her roles in films like "Austin Powers: International Man of Mystery" and for her work as a fashion icon.
  • B. Helen Bamber
    Helen Bamber was a British psychotherapist and human rights activist renowned for her pioneering work with survivors of torture and extreme human cruelty.
  • C. Trudie Styler
    Trudie Styler is an English actress, film producer, and environmental activist, known for her work in independent cinema and philanthropy.
  • D. Natasha Richardson
    Natasha Richardson was a British actress known for her work in film, television, and theatre, and as a member of the Redgrave acting family.
  • E. Alison Steadman
    Alison Steadman is an acclaimed English actress known for her versatile work in television, film, and theatre, including notable roles in Mike Leigh collaborations and classic literary adaptations.
  • 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_69c68a59f2288190877ca15c19b1e822 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f10e71fc81909307ca39a61142d3 completed March 27, 2026, 9:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7faa25960819084ecb6dbf9369ba5 completed March 28, 2026, 3:58 p.m.
Created at: March 27, 2026, 3:05 p.m.