Triple

T10855970
Position Surface form Disambiguated ID Type / Status
Subject Sylvia Miles E256269 entity
Predicate notableWork P4 FINISHED
Object Heat E710228 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: Heat | Statement: [Sylvia Miles, notableWork, Heat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Heat
Context triple: [Sylvia Miles, notableWork, Heat]
  • A. Heat
    Heat is a 1995 crime thriller film directed by Michael Mann, renowned for its intense heist sequences and the iconic pairing of Al Pacino and Robert De Niro.
  • B. Heat
    Heat is a chapter or section within the novel "Like Water for Chocolate" that focuses on themes of passion, desire, and emotional intensity.
  • C. Heat
    Heat is OpenStack’s orchestration service that automates the deployment and management of cloud infrastructure using template-based definitions.
  • D. Heat
    Heat is a 1963 Soviet drama film directed by Larisa Shepitko, marking her acclaimed feature-length directorial debut.
  • E. Heat chosen
    Heat is a 1972 American underground film directed by Paul Morrissey and produced by Andy Warhol, known for its satirical take on Hollywood and its place within the Warhol Factory film movement.
  • 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_69d6aa83d1448190a66d93c32394d21f completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7513695ac8190b5812e977a422c37 completed April 9, 2026, 7:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69deb197808c8190b1c80aeb2144a909 completed April 14, 2026, 9:28 p.m.
Created at: April 8, 2026, 9:20 p.m.