Triple

T1058144
Position Surface form Disambiguated ID Type / Status
Subject Kyoto Prefecture E22843 entity
Predicate hasDistrict P459 FINISHED
Object Yoza District E128387 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: Yoza District | Statement: [Kyoto Prefecture, hasDistrict, Yoza District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yoza District
Context triple: [Kyoto Prefecture, hasDistrict, Yoza District]
  • A. Yosa District chosen
    Yosa District is a rural administrative district in northern Kyoto Prefecture, Japan, known for its coastal landscapes along the Sea of Japan and traditional fishing and farming communities.
  • B. Saha District
    Saha District is an administrative district (gu) in the southwestern part of Busan, South Korea, known for its coastal areas and residential neighborhoods.
  • C. Kuse District
    Kuse District is a rural administrative district located in Kyoto Prefecture, Japan, known for its small towns and agricultural landscapes.
  • D. Buk District
    Buk District is an administrative district (gu) in the northern part of Busan, South Korea, known for its residential neighborhoods and local commercial centers.
  • E. Nam District
    Nam District is an administrative district (gu) of the metropolitan city of Busan in South Korea, known for its coastal location and urban residential areas.
  • 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_69a493dada0481909c43649f9843ea91 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b8dc9e8c819099fbb192bcf80615 completed March 1, 2026, 10:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac599c08f481908b720e2cc7c4a5ef completed March 7, 2026, 5 p.m.
Created at: March 1, 2026, 7:42 p.m.