Triple

T24451
Position Surface form Disambiguated ID Type / Status
Subject Osaka E486 entity
Predicate hasDistrict P459 FINISHED
Object Umeda E10063 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: Umeda | Statement: [Osaka, hasDistrict, Umeda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Umeda
Context triple: [Osaka, hasDistrict, Umeda]
  • A. Osaka
    Osaka is Japan's third-largest city and a major economic, cultural, and historical hub known for its vibrant street food, bustling nightlife, and role as a commercial center in the Kansai region.
  • B. Nagoya
    Nagoya is a major industrial and commercial city in central Japan, known as a manufacturing hub and the capital of Aichi Prefecture.
  • C. Shin-Osaka Station
    Shin-Osaka Station is a major railway and Shinkansen hub in Osaka that serves as a key gateway connecting the city with other regions across Japan.
  • D. Yokohama
    Yokohama is Japan’s second-largest city and a major international port located just south of Tokyo.
  • E. Osaka Station chosen
    Osaka Station is a major railway terminal and transportation hub in Osaka, Japan, serving numerous local and long-distance train lines and connecting key commercial districts.
  • 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_69a243b4ac2c8190b93c303df797b7b2 completed Feb. 28, 2026, 1:24 a.m.
NER Named-entity recognition batch_69a2481e91c88190ad0fb09cddc5f446 completed Feb. 28, 2026, 1:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2d0beb4e0819094f538cfb51c1cb6 completed Feb. 28, 2026, 11:25 a.m.
Created at: Feb. 28, 2026, 1:34 a.m.