Triple

T9001950
Position Surface form Disambiguated ID Type / Status
Subject Oreshura E215056 entity
Predicate endingThemePerformer P42862 FINISHED
Object Yukari Tamura E823060 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: Yukari Tamura | Statement: [Oreshura, endingThemePerformer, Yukari Tamura]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yukari Tamura
Context triple: [Oreshura, endingThemePerformer, Yukari Tamura]
  • A. Yukari Tamura chosen
    Yukari Tamura is a popular Japanese voice actress and singer known for her work in anime and J-pop, often performing theme songs for various series.
  • B. Yukari Sugi
    Yukari Sugi is a Japanese actress and the former wife of acclaimed actor Ken Watanabe.
  • C. Rieko Kodama
    Rieko Kodama was a pioneering Japanese video game designer and producer at Sega, best known for her influential work on classic role-playing games and for being one of the first prominent women in the game industry.
  • D. Shioli Kutsuna
    Shioli Kutsuna is a Japanese-Australian actress known for roles in international films and series, including major Hollywood productions and high-profile video game projects.
  • E. Reika Kirishima
    Reika Kirishima is a Japanese actress known for her role in the film adaptation of Haruki Murakami’s novel "Norwegian Wood" (2010).
  • 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_69ca83a12d648190b1e4fe11e8a31890 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6956a6e08190bd3853a7c1c130eb completed April 1, 2026, 12:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1e3d6191c8190adb41feec1bfa76e completed April 5, 2026, 4:23 a.m.
Created at: March 30, 2026, 7:05 p.m.