Triple

T8235727
Position Surface form Disambiguated ID Type / Status
Subject Busan Central Bus Terminal E192399 entity
Predicate connectsTo P845 FINISHED
Object Jinju E510642 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: Jinju | Statement: [Busan Central Bus Terminal, connectsTo, Jinju]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jinju
Context triple: [Busan Central Bus Terminal, connectsTo, 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 (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_69ca82dc8f148190a2c75a98501a7b91 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb782a5e18819096235679f5a644a8 completed March 31, 2026, 7:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde74c4e6c8190a426139f71689a3e completed April 2, 2026, 3:49 a.m.
Created at: March 30, 2026, 5:46 p.m.