Triple

T14959139
Position Surface form Disambiguated ID Type / Status
Subject Lauren Hutton E373013 entity
Predicate notableWork P4 FINISHED
Object The Gambler E660520 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: The Gambler | Statement: [Lauren Hutton, notableWork, The Gambler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Gambler
Context triple: [Lauren Hutton, notableWork, The Gambler]
  • A. The Gambler
    The Gambler is a classic country song and album by Kenny Rogers that tells the story of life lessons learned from a seasoned card player, becoming one of his signature hits.
  • B. The Gambler
    The Gambler is a short novel by Fyodor Dostoevsky that explores obsession, addiction, and psychological turmoil through the story of a tutor ensnared by roulette and destructive love.
  • C. The Gambler chosen
    The Gambler is a 1974 American drama film starring James Caan as a literature professor whose secret gambling addiction spirals out of control.
  • D. The Gamblers
    The Gamblers were a 1960s American surf rock band known for their instrumental track "Moon Dawg!" which became an early surf music classic.
  • E. Casino
    Casino is a town in northern New South Wales, Australia, known as a regional service centre and gateway to the surrounding agricultural and beef-producing areas.
  • 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_69d85cca979481908747d2a81eba1cea completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded6cd85bc81909040b7ff78f62554 completed April 15, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe7ea1d78c81909b877fda05ef9231 completed May 9, 2026, 12:24 a.m.
Created at: April 10, 2026, 2:40 a.m.