Triple

T7206797
Position Surface form Disambiguated ID Type / Status
Subject Incheon Gwangyeoksi E148684 entity
Predicate hasDistrict P459 FINISHED
Object Namdong District E158074 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: Namdong District | Statement: [Incheon Gwangyeoksi, hasDistrict, Namdong District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Namdong District
Context triple: [Incheon Gwangyeoksi, hasDistrict, Namdong District]
  • A. Namdong District chosen
    Namdong District is a major administrative and commercial hub of Incheon, South Korea, known for housing the city hall and various industrial and residential areas.
  • B. Bupyeong District
    Bupyeong District is a populous urban district of Incheon, South Korea, known as a major residential, commercial, and transportation hub in the metropolitan area.
  • C. Suyeong District
    Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
  • D. Taesong District
    Taesong District is an administrative district of Pyongyang, North Korea, known for hosting major educational and cultural institutions.
  • E. Gwangsan District
    Gwangsan District is one of the administrative districts of Gwangju, South Korea, known for its mix of urban development and transportation hubs including Gwangju Songjeong station.
  • 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_69c687e8cf188190b5f3ecffd681f04e completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6e969c5fc819096bc03bfba12d0cf completed March 27, 2026, 8:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69c97ca3d5e0819097e904184202b75f completed March 29, 2026, 7:25 p.m.
Created at: March 27, 2026, 2:52 p.m.