Triple

T15916361
Position Surface form Disambiguated ID Type / Status
Subject Fast Retailing E385979 entity
Predicate legalForm P64 FINISHED
Object Kabushiki gaisha E381838 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: Kabushiki gaisha | Statement: [Fast Retailing, legalForm, Kabushiki gaisha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kabushiki gaisha
Context triple: [Fast Retailing, legalForm, Kabushiki gaisha]
  • A. Kabushiki gaisha chosen
    Kabushiki gaisha is a common Japanese joint-stock company structure similar to a corporation, used by many major businesses in Japan.
  • B. Keio Corporation
    Keio Corporation is a major Japanese private railway and transportation company operating rail lines and related services in the Tokyo metropolitan area.
  • C. Koizumi Sangyo Corporation
    Koizumi Sangyo Corporation is a Japanese company known for manufacturing and supplying lighting fixtures, electrical equipment, and related industrial products.
  • D. Nippon Kobo
    Nippon Kobo was a Japanese design and architecture firm active in the mid-20th century, known for its collaborations with prominent modernist designers such as Charlotte Perriand.
  • E. Taisei Corporation
    Taisei Corporation is a major Japanese construction and civil engineering company known for leading large-scale infrastructure and landmark building projects in Japan and abroad.
  • 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_69d86da686e4819097cbf3b1fc2d881d completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e15662e2c481909e3582be01f05d08 completed April 16, 2026, 9:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb05b15e8819083b67afa52f46283 completed May 9, 2026, 10:08 p.m.
Created at: April 10, 2026, 4:52 a.m.