Triple

T21980603
Position Surface form Disambiguated ID Type / Status
Subject When Character Was King E542828 entity
Predicate author P4 FINISHED
Object Peggy Noonan NE NERFINISHED

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: Peggy Noonan | Statement: [When Character Was King, author, Peggy Noonan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peggy Noonan
Context triple: [When Character Was King, author, Peggy Noonan]
  • A. Peggy Noonan chosen
    Peggy Noonan is an American political columnist, author, and former speechwriter best known for her work with President Ronald Reagan and her influential commentary in The Wall Street Journal.
  • B. Mary Matalin
    Mary Matalin is an American political consultant and commentator known for her work with Republican campaigns and frequent television appearances.
  • C. Margaret Hoover
    Margaret Hoover is an American political commentator, author, and television host best known for hosting PBS’s "Firing Line" and for her work as a Republican strategist.
  • D. Cokie Roberts
    Cokie Roberts was an American journalist and author best known as a longtime political commentator for NPR and ABC News.
  • E. Maureen Dowd
    Maureen Dowd is an American columnist and author best known for her sharp, often satirical political commentary in The New York Times.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c48136b081908831fa907cc02e18 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f1248cf0388190b557d065beb662b5 completed April 28, 2026, 9:20 p.m.
Created at: April 16, 2026, 8:04 p.m.