Triple

T1691091
Position Surface form Disambiguated ID Type / Status
Subject Gijang County E36549 entity
Predicate hasCapital P204 FINISHED
Object Gijang-eup
Gijang-eup is the main urban and administrative center of Gijang County in Busan, South Korea.
 E36549 NE FINISHED

How this triple was built (4 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: Gijang-eup | Statement: [Gijang County, hasCapital, Gijang-eup]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gijang-eup
Context triple: [Gijang County, hasCapital, Gijang-eup]
  • A. Gijang County
    Gijang County is a coastal administrative region in northeastern Busan, South Korea, known for its scenic shoreline, seafood, and growing residential and tourist areas.
  • B. Seo-gu
    Seo-gu is a district of the metropolitan city of Daejeon in South Korea, known for its residential areas, commercial centers, and educational institutions.
  • C. Gapcheon
    Gapcheon is a major river flowing through the city of Daejeon in South Korea, serving as a central natural and recreational landmark.
  • D. Yeongdo District
    Yeongdo District is a coastal district of Busan, South Korea, known for its island setting, shipbuilding industry, and scenic views of the city and harbor.
  • E. Daedeok-gu
    Daedeok-gu is a district in the city of Daejeon, South Korea, known for encompassing parts of the country’s major research and science complex.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Gijang-eup
Triple: [Gijang County, hasCapital, Gijang-eup]
Generated description
Gijang-eup is the main urban and administrative center of Gijang County in Busan, South Korea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gijang-eup
Target entity description: Gijang-eup is the main urban and administrative center of Gijang County in Busan, South Korea.
  • A. Gijang County chosen
    Gijang County is a coastal administrative region in northeastern Busan, South Korea, known for its scenic shoreline, seafood, and growing residential and tourist areas.
  • B. Seo-gu
    Seo-gu is a district of the metropolitan city of Daejeon in South Korea, known for its residential areas, commercial centers, and educational institutions.
  • C. Gapcheon
    Gapcheon is a major river flowing through the city of Daejeon in South Korea, serving as a central natural and recreational landmark.
  • D. Yeongdo District
    Yeongdo District is a coastal district of Busan, South Korea, known for its island setting, shipbuilding industry, and scenic views of the city and harbor.
  • E. Daedeok-gu
    Daedeok-gu is a district in the city of Daejeon, South Korea, known for encompassing parts of the country’s major research and science complex.
  • F. None of above.

Provenance (5 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_69a886151508819084fa7f1ce6e05577 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa6298fa748190acabb9f1d42bd3f5 completed March 6, 2026, 5:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69adfb96d12481908f8d7d5c9f1f103f completed March 8, 2026, 10:43 p.m.
NEDg Description generation batch_69adfc3d37e4819082673b84eb5a19f2 completed March 8, 2026, 10:46 p.m.
NED2 Entity disambiguation (via description) batch_69adfd7ef5588190ab85f3981466d1e0 completed March 8, 2026, 10:51 p.m.
Created at: March 4, 2026, 7:29 p.m.