Triple

T1644711
Position Surface form Disambiguated ID Type / Status
Subject Gangseo District E35554 entity
Predicate borders P224 FINISHED
Object Gimhae E304192 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: Gimhae | Statement: [Gangseo District, borders, Gimhae]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gimhae
Context triple: [Gangseo District, borders, Gimhae]
  • A. Gimhae chosen
    Gimhae is a city in South Gyeongsang Province, South Korea, known for its historical significance as the birthplace of the ancient Gaya confederacy and its proximity to the metropolitan city of Busan.
  • B. Gyeongju
    Gyeongju is a historic city in South Korea famed for its rich cultural heritage and numerous archaeological sites from the ancient Silla Kingdom.
  • C. Incheon Gwangyeoksi
    Incheon Gwangyeoksi is a major metropolitan city in northwestern South Korea, known for its international airport, large seaport, and role as a key gateway to Seoul and the wider region.
  • D. Gwangju
    Gwangju is a major metropolitan city in southwestern South Korea known for its rich cultural heritage and pivotal role in the country’s pro-democracy movement.
  • E. Ulsan
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • 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_69a88604618c81908b41f6429c431eb6 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa622e9b08819094960b2329c6e7e6 completed March 6, 2026, 5:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69b030e23ea081909737f5fd2d3c8dfb completed March 10, 2026, 2:55 p.m.
Created at: March 4, 2026, 7:28 p.m.