Triple

T6566508
Position Surface form Disambiguated ID Type / Status
Subject KAIST E153919 entity
Predicate locatedIn P40 FINISHED
Object Yuseong-gu E174979 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: Yuseong-gu | Statement: [KAIST, locatedIn, Yuseong-gu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yuseong-gu
Context triple: [KAIST, locatedIn, Yuseong-gu]
  • A. Yuseong-gu chosen
    Yuseong-gu is a district in the city of Daejeon, South Korea, known for its hot springs, research institutes, and high-tech industry clusters.
  • B. Suseong District
    Suseong District is an affluent residential and commercial area in southeastern Daegu, South Korea, known for its high-quality schools, parks, and cultural amenities.
  • C. Gwacheon
    Gwacheon is a small city in South Korea known for hosting major government offices, cultural institutions, and the Seoul Grand Park complex.
  • D. Ganghwa County
    Ganghwa County is a rural island county in northwestern South Korea known for its historical sites, fortresses, and strategic location near the border with North Korea.
  • E. Dong-gu
    Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
  • 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_69c6880cb35881909b763eb0125236b9 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ae5381e88190b44dc4440efdd8ae completed March 27, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6f7867e688190aad8cc2b396a64eb completed March 27, 2026, 9:32 p.m.
Created at: March 27, 2026, 1:52 p.m.