Triple

T1310761
Position Surface form Disambiguated ID Type / Status
Subject Tokyo Prefecture E27983 entity
Predicate hasMajorBusinessDistrict P459 FINISHED
Object Shibuya E208724 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: Shibuya | Statement: [Tokyo Prefecture, hasMajorBusinessDistrict, Shibuya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shibuya
Context triple: [Tokyo Prefecture, hasMajorBusinessDistrict, Shibuya]
  • A. Shibuya chosen
    Shibuya is a major commercial and entertainment district in Tokyo, Japan, famous for its bustling streets, youth culture, and iconic landmarks.
  • B. Shinjuku
    Shinjuku is a major commercial and entertainment district in western Tokyo, known for its busy railway station, skyscrapers, shopping, nightlife, and the Tokyo Metropolitan Government Building.
  • C. Akasaka
    Akasaka is a central Tokyo district known for its business centers, upscale hotels, and vibrant nightlife.
  • D. Harajuku
    Harajuku is a vibrant Tokyo district famous for its youth culture, eclectic street fashion, and trendy shopping and entertainment spots.
  • E. Ikebukuro
    Ikebukuro is a major commercial and entertainment district in Tokyo known for its large train station, shopping complexes, and vibrant youth culture.
  • 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_69a496d7d83481908f83085854e51328 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c1560f888190bdd9107b08395e0b completed March 1, 2026, 10:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0fc0a32ec819089d3e4f7d7b86c89 completed March 11, 2026, 5:22 a.m.
Created at: March 1, 2026, 7:51 p.m.