Triple

T6553357
Position Surface form Disambiguated ID Type / Status
Subject Abeno district E152381 entity
Predicate partOf P40 FINISHED
Object Osaka city E486 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: Osaka city | Statement: [Abeno district, partOf, Osaka city]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Osaka city
Context triple: [Abeno district, partOf, Osaka city]
  • A. Osaka chosen
    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. Suita, Osaka
    Suita, Osaka is a city in northern Osaka Prefecture, Japan, known as a major suburban and educational hub that hosts the main campus of Osaka University.
  • D. Higashiōsaka
    Higashiōsaka is an industrial and residential city in Japan known for its manufacturing base and location within the Osaka metropolitan area.
  • E. Sakai, Osaka
    Sakai, Osaka is a historic port city in Japan’s Osaka Prefecture, known for its ancient burial mounds, traditional craftsmanship, and role as a major commercial center.
  • 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_69c688058d6881908c19b309cc55dbfa completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ae0847d88190b38f9d7dba0faae1 completed March 27, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7880545c4819091979008c84b3325 completed March 28, 2026, 7:49 a.m.
Created at: March 27, 2026, 1:51 p.m.