Triple

T4733122
Position Surface form Disambiguated ID Type / Status
Subject JR Namba Station E105057 entity
Predicate hasNearbyAttraction P2064 FINISHED
Object Shinsaibashi E19641 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: Shinsaibashi | Statement: [JR Namba Station, hasNearbyAttraction, Shinsaibashi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shinsaibashi
Context triple: [JR Namba Station, hasNearbyAttraction, Shinsaibashi]
  • A. Shinsaibashi chosen
    Shinsaibashi is a major shopping and entertainment district in central Osaka, Japan, known for its covered arcade, fashion boutiques, and vibrant nightlife.
  • B. Motomachi
    Motomachi is a historic commercial and shopping district in Kobe, Japan, known for its fashionable boutiques, cafes, and proximity to the city’s Chinatown and waterfront.
  • C. Motomachi
    Motomachi is a stylish shopping and entertainment district in Yokohama known for its fashionable boutiques, cafes, and Western-influenced atmosphere.
  • D. Dotonbori
    Dotonbori is a famous entertainment and nightlife district in Osaka, Japan, known for its neon billboards, street food, and canal-side atmosphere.
  • E. Umeda district
    Umeda district is a major commercial and transportation hub in Osaka, Japan, known for its skyscrapers, shopping complexes, and extensive train and subway connections.
  • 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_69bd43ee52048190b81a4f066534ffb3 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6466354481908595f5bb56025cdb completed March 20, 2026, 3:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69be5c8a20a88190a668251abbc1c7c8 completed March 21, 2026, 8:53 a.m.
Created at: March 20, 2026, 1:19 p.m.