Triple

T20796154
Position Surface form Disambiguated ID Type / Status
Subject Daegu E511913 entity
Predicate hasDistrict P459 FINISHED
Object Dalseong County NE NERFINISHED

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: Dalseong County | Statement: [Daegu, hasDistrict, Dalseong County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dalseong County
Context triple: [Daegu, hasDistrict, Dalseong County]
  • A. Dalseong County chosen
    Dalseong County is a largely rural administrative district on the outskirts of Daegu in South Korea, known for its natural scenery, agricultural areas, and growing suburban developments.
  • B. Bonghwa County
    Bonghwa County is a rural administrative region in northeastern South Korea known for its mountainous landscapes, forests, and traditional cultural heritage.
  • C. Hongseong County
    Hongseong County is a county in South Chungcheong Province, South Korea, known as the provincial capital and an administrative and cultural center of the region.
  • D. Seongju County
    Seongju County is a rural administrative region in southeastern South Korea known for its melon farming and traditional cultural heritage.
  • E. Yeoncheon County
    Yeoncheon County is a rural county in Gyeonggi Province, South Korea, known for its location near the Demilitarized Zone (DMZ) and its historical military significance.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4cb83948190bd57bec21d78ed53 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2ad6f0481909e0bab7119f10f9c completed April 21, 2026, 12:19 a.m.
Created at: April 16, 2026, 12:39 p.m.