Triple

T22142212
Position Surface form Disambiguated ID Type / Status
Subject Shiro Nakamura E547188 entity
Predicate employer P7 FINISHED
Object Isuzu Motors 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: Isuzu Motors | Statement: [Shiro Nakamura, employer, Isuzu Motors]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isuzu Motors
Context triple: [Shiro Nakamura, employer, Isuzu Motors]
  • A. Isuzu chosen
    Isuzu is a Japanese automotive manufacturer best known for producing commercial vehicles, pickup trucks, and diesel engines for global markets.
  • B. Isuzu-gawa
    Isuzu-gawa is a river in Ise, Mie Prefecture, Japan, best known for flowing through the sacred grounds of the Ise Grand Shrine.
  • C. Isuzu Yamada
    Isuzu Yamada was a renowned Japanese actress celebrated for her powerful performances in classic films, particularly in collaboration with director Akira Kurosawa.
  • D. Daihatsu Motor Co., Ltd.
    Daihatsu Motor Co., Ltd. is a Japanese automobile manufacturer best known for producing compact cars and kei vehicles, and operates as a subsidiary within the Toyota group.
  • E. Mitsubishi Motors
    Mitsubishi Motors is a Japanese automotive manufacturer known for producing a wide range of passenger cars, SUVs, and light commercial vehicles and for its involvement in global automotive alliances.
  • 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_69e11e3a95d88190a3bd80d9471976c3 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f129bf78108190b50d937917258693 completed April 28, 2026, 9:42 p.m.
Created at: April 16, 2026, 8:32 p.m.