Triple

T20102908
Position Surface form Disambiguated ID Type / Status
Subject Wolfgang Hahn Prize E496590 entity
Predicate notableRecipient P108 FINISHED
Object Jac Leirner 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: Jac Leirner | Statement: [Wolfgang Hahn Prize, notableRecipient, Jac Leirner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jac Leirner
Context triple: [Wolfgang Hahn Prize, notableRecipient, Jac Leirner]
  • A. Jac Leirner chosen
    Jac Leirner is a Brazilian contemporary artist known for her conceptual works that repurpose everyday objects to explore themes of value, accumulation, and consumer culture.
  • B. Catherine Feller
    Catherine Feller is a British actress best known for her roles in 1960s film and television, including appearances in classic Hammer horror productions.
  • C. Lisa Eilbacher
    Lisa Eilbacher is an American actress best known for her roles in 1980s films and television series, including prominent appearances in action and drama movies.
  • D. Catherine Kellner
    Catherine Kellner is an American actress known for supporting roles in films such as Pearl Harbor and various independent movies and television series.
  • E. Bettina Gilois
    Bettina Gilois was a German-American screenwriter and author known for her work on inspirational, fact-based films such as "McFarland, USA" and "Glory Road."
  • 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_69da626eee3881909f3454986d4a6511 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6667170a4819085d07a4188ded541 completed April 20, 2026, 5:46 p.m.
Created at: April 11, 2026, 11:27 p.m.