Triple

T9209052
Position Surface form Disambiguated ID Type / Status
Subject Myeongnyun Station E221064 entity
Predicate locatedInAdministrativeTerritory P40 FINISHED
Object Dongnae District E34836 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: Dongnae District | Statement: [Myeongnyun Station, locatedInAdministrativeTerritory, Dongnae District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dongnae District
Context triple: [Myeongnyun Station, locatedInAdministrativeTerritory, Dongnae District]
  • A. Dongnae District chosen
    Dongnae District is a historic and central administrative district of Busan, South Korea, known for its hot springs and cultural heritage sites.
  • 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. Busanjin District
    Busanjin District is a central urban district of Busan, South Korea, known as a major commercial and transportation hub of the city.
  • D. Cijin District
    Cijin District is a coastal district of Kaohsiung, Taiwan, known for its historic port, seafood markets, and popular seaside attractions.
  • E. Suyeong District
    Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
  • 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_69ca83e9d0e081908bdb71097201a06c completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccd9b3c8c081909a688ce699928fc0 completed April 1, 2026, 8:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2817858008190b150417f9ef59b48 completed April 5, 2026, 3:36 p.m.
Created at: March 30, 2026, 7:26 p.m.