Triple

T5286594
Position Surface form Disambiguated ID Type / Status
Subject 1776 (musical) E119633 entity
Predicate bookBy P2353 FINISHED
Object Peter Stone E399159 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: Peter Stone | Statement: [1776 (musical), bookBy, Peter Stone]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peter Stone
Context triple: [1776 (musical), bookBy, Peter Stone]
  • A. Peter Stone chosen
    Peter Stone was an American screenwriter and playwright best known for crafting witty, sophisticated scripts for films such as "Charade" and the musical "1776."
  • B. Peter Stone
    Peter Stone is an American computer scientist known for his influential work in artificial intelligence and robotics, particularly in multiagent systems and robot soccer.
  • C. John Stonehouse
    John Stonehouse was a British Labour politician and former cabinet minister best known for faking his own death in 1974 in an attempt to escape financial and legal troubles.
  • D. Harry Stone
    Harry Stone was a key music industry figure best known for helping establish the Country Music Association, which played a major role in promoting and organizing country music.
  • E. Stephen Stanton
    Stephen Stanton is an American voice actor known for his work in animation, film, and video games, including roles in various Star Wars projects and other popular franchises.
  • 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_69bd446de5648190b313a90bd96730d2 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd84d9a0788190a4a85a9cab07903f completed March 20, 2026, 5:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf10dc64f4819091fcbc39c0e3034b completed March 21, 2026, 9:42 p.m.
Created at: March 20, 2026, 1:52 p.m.