Triple

T11223599
Position Surface form Disambiguated ID Type / Status
Subject Aoyama area E265636 entity
Predicate locatedNear P294 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: [Aoyama area, locatedNear, Shibuya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shibuya
Context triple: [Aoyama area, locatedNear, 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_69d6aac59460819089b9848b27f57848 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8ec8fb08190b27144ab65f85957 completed April 9, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69e6847ad9fc819085b0d6c886488c3b completed April 20, 2026, 7:54 p.m.
Created at: April 8, 2026, 9:30 p.m.