Triple

T7325113
Position Surface form Disambiguated ID Type / Status
Subject Lotte Orions E168850 entity
Predicate basedIn P40 FINISHED
Object Kawasaki E61828 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: Kawasaki | Statement: [Lotte Orions, basedIn, Kawasaki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kawasaki
Context triple: [Lotte Orions, basedIn, Kawasaki]
  • A. Kawasaki chosen
    Kawasaki is a major industrial and residential city in Kanagawa Prefecture, Japan, located between Tokyo and Yokohama along the Tama River.
  • B. Suzuki
    Suzuki is a common Japanese surname borne by many notable individuals across sports, entertainment, and other fields.
  • C. Kawasaki Ninja series
    The Kawasaki Ninja series is a renowned line of high-performance sport motorcycles known for their aggressive styling, powerful engines, and strong presence in both street riding and motorcycle racing.
  • 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. Yamaha Motor Company
    Yamaha Motor Company is a Japanese manufacturer best known for its motorcycles, marine products, and power equipment, and is one of the world’s leading powersports brands.
  • 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_69c68a54cacc81908e3b773441f19566 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f04993408190b73fb46d83a632d5 completed March 27, 2026, 9:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7ef0a1200819089fe3e18493d8bee completed March 28, 2026, 3:08 p.m.
Created at: March 27, 2026, 3:03 p.m.