Triple

T13634456
Position Surface form Disambiguated ID Type / Status
Subject Claire Marino E325810 entity
Predicate knownAs P39 FINISHED
Object Claire Marino E325810 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: Claire Marino | Statement: [Claire Marino, knownAs, Claire Marino]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Claire Marino
Context triple: [Claire Marino, knownAs, Claire Marino]
  • A. Claire Marino chosen
    Claire Marino is the wife of Hall of Fame NFL quarterback Dan Marino and is known for her long-standing involvement in charitable and community work alongside him.
  • B. Claire Lademacher
    Claire Lademacher is a German-born bioethics researcher who became a member of the Luxembourg royal family through her marriage to Prince Félix.
  • C. Claire Gregory
    Claire Gregory is the wealthy socialite who becomes the focus of a detective’s protection and affection in the film "Someone to Watch Over Me."
  • D. Claire Washburn
    Claire Washburn is a medical examiner and close friend of Lindsay Boxer in James Patterson's Women's Murder Club crime thriller series.
  • E. Claire Keesey
    Claire Keesey is a Boston bank manager who becomes romantically involved with a career criminal, unaware of his role in the heist that traumatized her, in the film "The Town."
  • 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_69d8076beddc8190a53156f5bea77f5e completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc5a490508190924ac40f1dd519d6 completed April 12, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd466ac0f08190b99dd4cdcef339c6 completed May 8, 2026, 2:11 a.m.
Created at: April 9, 2026, 9:51 p.m.