Triple

T1557739
Position Surface form Disambiguated ID Type / Status
Subject Suyeong District E33247 entity
Predicate borders P224 FINISHED
Object Busanjin District E34080 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: Busanjin District | Statement: [Suyeong District, borders, Busanjin District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Busanjin District
Context triple: [Suyeong District, borders, Busanjin District]
  • A. Busanjin District chosen
    Busanjin District is a central urban district of Busan, South Korea, known as a major commercial and transportation hub of the city.
  • B. Dongnae District
    Dongnae District is a historic and central administrative district of Busan, South Korea, known for its hot springs and cultural heritage sites.
  • C. 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.
  • D. Suyeong District
    Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
  • E. Geumjeong District
    Geumjeong District is an administrative district in the northeastern part of Busan, South Korea, known for its mountainous terrain, historic fortress, and educational institutions.
  • 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_69a885ef9cf48190b0af0f5ce3d02231 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9088355048190adad5ea2bb558d13 completed March 5, 2026, 4:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae303a4f5881909746b1dae558f8b0 completed March 9, 2026, 2:28 a.m.
Created at: March 4, 2026, 7:27 p.m.