Triple

T11460236
Position Surface form Disambiguated ID Type / Status
Subject Steve Hartnett E271635 entity
Predicate hasSurname P18 FINISHED
Object Hartnett E47461 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: Hartnett | Statement: [Steve Hartnett, hasSurname, Hartnett]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hartnett
Context triple: [Steve Hartnett, hasSurname, Hartnett]
  • A. Hartnett chosen
    Hartnett is a surname most prominently associated with American actor Josh Hartnett, known for his film and television roles since the late 1990s.
  • B. Robert Hartnett
    Robert Hartnett is an individual notable enough to be specifically cited as a bearer of the surname Hartnett.
  • C. Hillegas
    Hillegas is a surname most notably associated with Michael Hillegas, the first Treasurer of the United States.
  • D. Haardt
    Haardt is a district of Neustadt an der Weinstraße in Rhineland-Palatinate, Germany, known for its scenic location along the German Wine Route and proximity to the Palatinate Forest.
  • E. Hayne
    Hayne is a surname most notably associated with Robert Y. Hayne, a prominent 19th-century American politician and orator from South Carolina.
  • 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_69d6aadff8888190a13f253f0d460874 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d822f384f08190b1150ed1389dd31a completed April 9, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5e91f1bb881909a9c36d837e4059b completed April 20, 2026, 8:51 a.m.
Created at: April 8, 2026, 9:35 p.m.