Triple

T10466663
Position Surface form Disambiguated ID Type / Status
Subject Alphabetical Order E246813 entity
Predicate hasCharacter P2308 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: [Alphabetical Order, hasCharacter, Nora]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nora
Context triple: [Alphabetical Order, hasCharacter, 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 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.
  • C. 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.
  • D. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • 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_69d381c16c248190a2fe5b471e584e9c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5092d6d408190b6bda4d7ced4601e completed April 7, 2026, 1:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69d8dc5c26a88190aab4a590c20191a3 completed April 10, 2026, 11:17 a.m.
Created at: April 6, 2026, 12:19 p.m.