Triple

T2014011
Position Surface form Disambiguated ID Type / Status
Subject Callaghan E43751 entity
Predicate hasNotableBearer P458 FINISHED
Object Michael Callaghan E112999 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: Michael Callaghan | Statement: [Callaghan, hasNotableBearer, Michael Callaghan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Callaghan
Context triple: [Callaghan, hasNotableBearer, Michael Callaghan]
  • A. Michael Callaghan chosen
    Michael Callaghan is one of the children of former UK Prime Minister James Callaghan.
  • B. Brian Callaghan
    Brian Callaghan is a personal name shared by multiple individuals, typically of Irish or British origin, who may be notable in various professional or public contexts.
  • C. Daniel Callaghan
    Daniel Callaghan was a U.S. Navy rear admiral and Medal of Honor recipient noted for his leadership and death in action during the Naval Battle of Guadalcanal in World War II.
  • D. Richard Callaghan
    Richard Callaghan is a prominent American figure skating coach best known for guiding Olympic champion Tara Lipinski to international success.
  • E. Michael McCusker
    Michael McCusker is an American film editor known for his work on major Hollywood productions, including the thriller "The Girl on the Train" (2016).
  • 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_69a88716e9f08190946313fdc949e3cf completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb8b42d508190bf2b63132bb2ad77 completed March 7, 2026, 5:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69afb66209bc8190aa030b147e4d34cb completed March 10, 2026, 6:12 a.m.
Created at: March 4, 2026, 7:37 p.m.