Triple

T11279576
Position Surface form Disambiguated ID Type / Status
Subject Anne E267026 entity
Predicate hasVariant P455 FINISHED
Object Anya unclear NED1 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: Anya | Statement: [Anne, hasVariant, Anya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Anya
Context triple: [Anne, hasVariant, Anya]
  • A. Anya
    Anya is a person known primarily through her relationship to someone named Hannah, likely as a friend or family member.
  • B. Anya
    Anya is the given name of actress Anya Taylor-Joy, known for her roles in films like "The Witch" and the series "The Queen's Gambit."
  • C. Anya
    Anya is the spirited, amnesiac young woman in the animated film "Anastasia" who embarks on a journey to discover whether she is the lost Russian Grand Duchess.
  • D. Anya Major
    Anya Major is a British athlete and actress best known for playing the hammer-throwing heroine in Apple’s iconic 1984 Macintosh television commercial.
  • E. Natalya
    Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide. chosen

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_69d6aac8c2f48190ad0596f1f89f0470 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e969b3448190940e2bd499d2d7de completed April 9, 2026, 6:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5258cc5208190be268ac6a82c9419 completed April 19, 2026, 6:57 p.m.
Created at: April 8, 2026, 9:31 p.m.