Triple

T20903496
Position Surface form Disambiguated ID Type / Status
Subject Toru Watanabe E514730 entity
Predicate associatedWith P37 FINISHED
Object Kizuki NE NERFINISHED

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: Kizuki | Statement: [Toru Watanabe, associatedWith, Kizuki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kizuki
Context triple: [Toru Watanabe, associatedWith, Kizuki]
  • A. Kizuki chosen
    Kizuki is a pivotal, emotionally fragile character in Haruki Murakami's novel "Norwegian Wood," whose death profoundly shapes the lives of those close to him.
  • B. Wajima
    Wajima is a coastal city in Ishikawa Prefecture, Japan, renowned for its traditional Wajima-nuri lacquerware and historic morning market.
  • C. Akizuki
    Akizuki was a Japanese Akizuki-class destroyer of the Imperial Japanese Navy that served in World War II before being sunk in the Battle off Cape Engaño in 1944.
  • D. Makinami
    Makinami was a Japanese destroyer of the Imperial Japanese Navy that served in World War II, notably in Pacific naval engagements.
  • E. Shimotsuki
    Shimotsuki was a Japanese destroyer of the Imperial Japanese Navy that served in World War II before being sunk in late 1944.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4f8a1108190bce3d31331290ced completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6e8fe4b808190bbc1bbde7a11f283 completed April 21, 2026, 3:03 a.m.
Created at: April 16, 2026, 12:47 p.m.