Triple

T7835296
Position Surface form Disambiguated ID Type / Status
Subject Ikspiari E181675 entity
Predicate operator P179 FINISHED
Object The Oriental Land Company E181674 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: The Oriental Land Company | Statement: [Ikspiari, operator, The Oriental Land Company]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Oriental Land Company
Context triple: [Ikspiari, operator, The Oriental Land Company]
  • A. The Oriental Land Company chosen
    The Oriental Land Company is a Japanese leisure and tourism corporation best known for operating and licensing the Tokyo Disney Resort.
  • B. Tokyu Land Corporation
    Tokyu Land Corporation is a major Japanese real estate developer and property management company within the Tokyu Group conglomerate.
  • C. Oriental Limited
    Oriental Limited was a premier named passenger train that provided luxury long-distance service on the Great Northern Railway in the early 20th century.
  • D. Keikyu Corporation
    Keikyu Corporation is a major private railway operator in the Greater Tokyo area, best known for its commuter and airport rail services connecting central Tokyo with Haneda Airport and the Miura Peninsula.
  • E. Seibu Group
    Seibu Group is a major Japanese conglomerate with core businesses in railways, hotels, real estate, and leisure services.
  • 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_69ca8284a25c8190a1a20afad30da792 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb064b872081908e269f4fe1b85436 completed March 30, 2026, 11:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69cc9340c99c819085294de7466f40eb completed April 1, 2026, 3:38 a.m.
Created at: March 30, 2026, 4:46 p.m.