Triple

T6686598
Position Surface form Disambiguated ID Type / Status
Subject Daegu University E152112 entity
Predicate locatedNear P294 FINISHED
Object Daegu, South Korea E511913 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: Daegu, South Korea | Statement: [Daegu University, locatedNear, Daegu, South Korea]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daegu, South Korea
Context triple: [Daegu University, locatedNear, Daegu, South Korea]
  • A. Daegu, South Korea chosen
    Daegu, South Korea is a major city in the southeastern part of the country known for its role as an industrial, cultural, and educational center.
  • B. Suwon, South Korea
    Suwon, South Korea is a major city just south of Seoul known for its high-tech industry and the UNESCO-listed Hwaseong Fortress.
  • C. Osan, South Korea
    Osan is a city in Gyeonggi Province, South Korea, known for its proximity to Osan Air Base and its role as a transportation and commercial hub south of Seoul.
  • D. Jinju, South Korea
    Jinju, South Korea is a historic city in South Gyeongsang Province known for its riverside fortress, role in the Imjin War, and annual lantern festival.
  • E. Gunsan, South Korea
    Gunsan, South Korea is a coastal industrial city in North Jeolla Province known for its port, manufacturing facilities, and role as a regional transportation hub.
  • 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_69c687f9977c819097e7f5ada4fe522e completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b14cd6748190aad4badd5f253478 completed March 27, 2026, 4:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c9bcbe7f488190aee034a51e8a281b completed March 29, 2026, 11:58 p.m.
Created at: March 27, 2026, 2:04 p.m.