Triple

T12499060
Position Surface form Disambiguated ID Type / Status
Subject Kevin Conway E298768 entity
Predicate name P16 FINISHED
Object Kevin Conway E298768 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: Kevin Conway | Statement: [Kevin Conway, name, Kevin Conway]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kevin Conway
Context triple: [Kevin Conway, name, Kevin Conway]
  • A. Kevin Conway chosen
    Kevin Conway was an American character actor known for his intense performances in films like "Gettysburg," "The Quick and the Dead," and "Thirteen Days," as well as numerous stage and television roles.
  • B. Kevin O'Connor
    Kevin O'Connor is an American entrepreneur best known as the co-founder and former CEO of the online advertising company DoubleClick.
  • C. Bill O'Herlihy
    Bill O'Herlihy was a prominent Irish sports broadcaster and television presenter best known for his long career covering major football tournaments for RTÉ.
  • D. Russ Conway
    Russ Conway was an American character actor known for his numerous supporting roles in mid-20th-century film and television.
  • E. Russ Conway
    Russ Conway was a popular British pianist and entertainer known for his cheerful boogie-woogie style and a string of chart-topping hits in the late 1950s and early 1960s.
  • 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_69d6ada4cd388190ae3bbf83ff87057a completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94dfa98348190b9ac164ecdada6fe completed April 10, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6f5b5c138819098326891f86eab48 completed May 3, 2026, 7:13 a.m.
Created at: April 8, 2026, 9:57 p.m.