Triple

T6816790
Position Surface form Disambiguated ID Type / Status
Subject William Goldenberg E156782 entity
Predicate name P16 FINISHED
Object William Goldenberg E156782 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: William Goldenberg | Statement: [William Goldenberg, name, William Goldenberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: William Goldenberg
Context triple: [William Goldenberg, name, William Goldenberg]
  • A. William Goldenberg chosen
    William Goldenberg is an American film editor known for his work on numerous acclaimed movies, including several collaborations with directors like Michael Mann and Ben Affleck.
  • B. Martin Goldstein
    Martin Goldstein, nicknamed "Buggsy," was an American mobster and hitman associated with Murder, Inc. during the 1930s and 1940s.
  • C. Melvyn Goldstein
    Melvyn Goldstein is an American anthropologist and Tibetologist renowned for his extensive research and publications on Tibetan society, history, and language.
  • D. Bernard Goldstein
    Bernard Goldstein is a notable individual whose achievements or prominence have made the surname Goldstein particularly recognized.
  • E. Harry Goldfarb
    Harry Goldfarb is a young heroin addict whose escalating dependence and shattered dreams form one of the central, tragic storylines in the film "Requiem for a Dream."
  • 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_69c688298a288190af3f285d57f76bbe completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d354177481908ab3cf5437c095e2 completed March 27, 2026, 6:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8a1f8da648190987fad6e37620cef completed March 29, 2026, 3:52 a.m.
Created at: March 27, 2026, 2:17 p.m.