Triple

T14190276
Position Surface form Disambiguated ID Type / Status
Subject Nora Eddington E351693 entity
Predicate givenName P17 FINISHED
Object Nora E49090 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: Nora | Statement: [Nora Eddington, givenName, Nora]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nora
Context triple: [Nora Eddington, givenName, Nora]
  • A. Nora chosen
    Nora is a feminine given name of Latin origin, often used independently or as a diminutive of names like Honora, Eleanor, or Leonora.
  • B. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • C. Nina
    Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
  • D. Nina
    Nina is a central character in the British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
  • E. Nora Montgomery
    Nora Montgomery is a tragic, ghostly character from the television series "American Horror Story: Murder House," known for her role as a grief-stricken 1920s socialite and wife of mad surgeon Charles Montgomery.
  • 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_69d827894ac0819097803e57f3227b23 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de61df628c8190ba3f557e2128dce5 completed April 14, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd194543f081909cb11cf0881afa90 completed May 7, 2026, 10:59 p.m.
Created at: April 10, 2026, 1:03 a.m.