Triple

T383147
Position Surface form Disambiguated ID Type / Status
Subject Catherine E8723 entity
Predicate hasVariant P455 FINISHED
Object Katharine E60675 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: Katharine | Statement: [Catherine, hasVariant, Katharine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Katharine
Context triple: [Catherine, hasVariant, Katharine]
  • A. Kathleen
    Kathleen is a feminine given name of Irish origin, derived from the name Catherine and widely used in English-speaking countries.
  • B. Margaret
    Margaret is a feminine given name of Greek origin, traditionally associated with the meaning "pearl" and widely used in English-speaking countries.
  • C. Katherine Hudson
    Katherine Hudson was the wife of English explorer Henry Hudson, known primarily through historical records of his voyages and family.
  • D. Elizabeth Erving
    Elizabeth Erving was the wife of American statesman and Massachusetts governor James Bowdoin, connecting her to a prominent colonial New England political family.
  • E. Katharine Towne chosen
    Katharine Towne is an American actress known for her roles in films such as "She's All That," "Mulholland Drive," and "What Lies Beneath."
  • 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_69a2e7f47dd08190a4e294ccbbe46cd4 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ec40ff8c81909306eb2dfe1512af completed Feb. 28, 2026, 1:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69a47d2668348190a654c9e0c7bf10a1 completed March 1, 2026, 5:53 p.m.
Created at: Feb. 28, 2026, 1:08 p.m.