Triple

T13206486
Position Surface form Disambiguated ID Type / Status
Subject Peter Garnsey E314379 entity
Predicate name P16 FINISHED
Object Peter Garnsey E314379 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 Garnsey | Statement: [Peter Garnsey, name, Peter Garnsey]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peter Garnsey
Context triple: [Peter Garnsey, name, Peter Garnsey]
  • A. Peter Garnsey chosen
    Peter Garnsey is a prominent historian of the ancient world, particularly known for his influential scholarship on the social, economic, and legal history of the Roman Empire.
  • B. Michael Harnett
    Michael Harnett is the birth name of Michael Hartnett, a prominent Irish poet known for his lyrical work in both English and Irish.
  • C. Peter Horbury
    Peter Horbury was a prominent British automotive designer and executive known for leading design at major car manufacturers including Volvo, Ford, Geely, and later Lotus.
  • D. Geoffrey Carroll
    Geoffrey Carroll is the sinister artist and bigamist at the center of the 1947 film noir thriller "The Two Mrs. Carrolls."
  • E. Peter Horton
    Peter Horton is an American actor and director best known for his role on the television series "thirtysomething" and for directing and producing numerous TV shows and films.
  • 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_69d806aee7308190b70a237ba2a6e3e1 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c9b0cf08190a1d71cc94139539d completed April 10, 2026, 11:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7941e0560819080eee43a9ed0e1bb completed May 3, 2026, 6:29 p.m.
Created at: April 9, 2026, 9:17 p.m.