Triple

T1307836
Position Surface form Disambiguated ID Type / Status
Subject Daegu E27919 entity
Predicate partOf P40 FINISHED
Object Yeongnam region E29096 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: Yeongnam region | Statement: [Daegu, partOf, Yeongnam region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yeongnam region
Context triple: [Daegu, partOf, Yeongnam region]
  • A. Yeongnam region chosen
    The Yeongnam region is a major southeastern area of South Korea encompassing key cities such as Busan and Daegu, known for its dense population, industry, and distinct cultural identity.
  • B. Gyeonggi Province
    Gyeonggi Province is a populous region in northwestern South Korea that surrounds Seoul and serves as a key political, economic, and military hub of the country.
  • C. Busanjin District
    Busanjin District is a central urban district of Busan, South Korea, known as a major commercial and transportation hub of the city.
  • D. Yeongdo District
    Yeongdo District is a coastal district of Busan, South Korea, known for its island setting, shipbuilding industry, and scenic views of the city and harbor.
  • E. Jung District
    Jung District is a central administrative and commercial district of Busan, South Korea, known for its historic markets, port-side location, and dense urban landscape.
  • 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_69a496d7d83481908f83085854e51328 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c13806b48190a0db33f8e5d53734 completed March 1, 2026, 10:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbaec736881909645919764d73f5f completed March 7, 2026, 11:55 p.m.
Created at: March 1, 2026, 7:51 p.m.