Triple

T20601807
Position Surface form Disambiguated ID Type / Status
Subject Nam River E506198 entity
Predicate hasCulturalSignificanceIn P1579 FINISHED
Object Jinju 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: Jinju | Statement: [Nam River, hasCulturalSignificanceIn, Jinju]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jinju
Context triple: [Nam River, hasCulturalSignificanceIn, Jinju]
  • A. Seogwipo
    Seogwipo is a coastal city on South Korea’s Jeju Island known for its waterfalls, volcanic landscapes, and popular tourist attractions.
  • B. Jinju-si chosen
    Jinju-si is a city in South Gyeongsang Province, South Korea, known for its historic Jinju Fortress and the annual Namgang Yudeung (Lantern) Festival.
  • C. Gyeongseong
    Gyeongseong was the Japanese colonial-era name for Seoul, which served as the administrative and political center of Korea under Japanese rule.
  • D. Hongseong
    Hongseong is a town in South Korea that serves as the administrative capital of South Chungcheong Province.
  • E. Neryungri
    Neryungri is a major coal-mining and industrial city in southeastern Siberia, Russia, known as one of the key urban centers of the Sakha Republic (Yakutia).
  • 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_69e0b4ba6ae88190af871e1f9522c704 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6aa20f5c881909265ce7d96efc487 completed April 20, 2026, 10:35 p.m.
Created at: April 16, 2026, 11:41 a.m.