Triple

T22703195
Position Surface form Disambiguated ID Type / Status
Subject Pyeongchang County E561376 entity
Predicate locatedNear P294 FINISHED
Object Jeongseon 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: Jeongseon County | Statement: [Pyeongchang County, locatedNear, Jeongseon County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jeongseon County
Context triple: [Pyeongchang County, locatedNear, Jeongseon County]
  • A. Jeongseon County chosen
    Jeongseon County is a mountainous rural county in eastern South Korea known for its traditional culture, scenic landscapes, and coal-mining history.
  • B. Seongju County
    Seongju County is a rural administrative region in southeastern South Korea known for its melon farming and traditional cultural heritage.
  • C. Hoengseong County
    Hoengseong County is a rural administrative region in northeastern South Korea known for its mountainous landscapes and premium Korean beef (Hoengseong hanwoo).
  • D. Yeongwol County
    Yeongwol County is a rural county in Gangwon Province, South Korea, known for its scenic river valleys, historical sites, and cultural heritage.
  • E. Dalseong County
    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.
  • 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_69e2454e615481909c177440be559d2c completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f178cbf5788190bc8cd1bc71a861e5 completed April 29, 2026, 3:19 a.m.
Created at: April 17, 2026, 3:16 p.m.