Triple

T17109805
Position Surface form Disambiguated ID Type / Status
Subject Kawasaki Heavy Industries E415195 entity
Predicate brand P1500 FINISHED
Object Kawasaki (motorcycles) NE ONNED1

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: Kawasaki (motorcycles) | Statement: [Kawasaki Heavy Industries, brand, Kawasaki (motorcycles)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kawasaki (motorcycles)
Context triple: [Kawasaki Heavy Industries, brand, Kawasaki (motorcycles)]
  • A. Kawasaki Motors chosen
    Kawasaki Motors is the motorcycle, ATV, and small engine manufacturing division of Kawasaki Heavy Industries, known worldwide for its high-performance bikes and power sports vehicles.
  • B. Kawasaki
    Kawasaki is a major industrial and residential city in Kanagawa Prefecture, Japan, located between Tokyo and Yokohama along the Tama River.
  • C. Suzuki
    Suzuki is a common Japanese surname borne by many notable individuals across sports, entertainment, and other fields.
  • D. Suzuki Motor Corporation
    Suzuki Motor Corporation is a Japanese multinational automaker best known for its compact cars, motorcycles, and all-terrain vehicles sold worldwide.
  • E. Kawasaki Daishi
    Kawasaki Daishi is a major Shingon Buddhist temple in Kawasaki, Japan, renowned as a popular site for New Year’s visits and prayers for protection from misfortune.
  • 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_69d886d090cc8190a39cb94992586905 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dc29c884819092a97d9663a1b1f7 completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0195428c6c8190a11e3f7c8f6796fe in_progress May 11, 2026, 8:37 a.m.
Created at: April 10, 2026, 5:35 a.m.