Triple

T1978691
Position Surface form Disambiguated ID Type / Status
Subject Pyeongtaek E42974 entity
Predicate borderedBy P224 FINISHED
Object Dangjin
Dangjin is a coastal city in South Chungcheong Province, South Korea, known for its heavy industry, steel production, and port facilities on the Yellow Sea.
 E390386 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: Dangjin | Statement: [Pyeongtaek, borderedBy, Dangjin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dangjin
Context triple: [Pyeongtaek, borderedBy, Dangjin]
  • A. Anseong
    Anseong is a city in Gyeonggi Province, South Korea, known for its traditional culture, agricultural heritage, and annual Baudeogi Festival.
  • B. Wonsan
    Wonsan is a port city on North Korea’s east coast, known for its strategic military importance and role as a regional transportation and industrial hub.
  • C. Pyeongtaek
    Pyeongtaek is a South Korean city in Gyeonggi Province known for its major U.S. and UN military presence, including large bases such as Camp Humphreys.
  • D. Geumwang-eup
    Geumwang-eup is a town-level administrative division in Eumseong County, located in North Chungcheong Province, South Korea.
  • 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. 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: Dangjin
Triple: [Pyeongtaek, borderedBy, Dangjin]
Generated description
Dangjin is a coastal city in South Chungcheong Province, South Korea, known for its heavy industry, steel production, and port facilities on the Yellow Sea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dangjin
Target entity description: Dangjin is a coastal city in South Chungcheong Province, South Korea, known for its heavy industry, steel production, and port facilities on the Yellow Sea.
  • A. Anseong
    Anseong is a city in Gyeonggi Province, South Korea, known for its traditional culture, agricultural heritage, and annual Baudeogi Festival.
  • B. Wonsan
    Wonsan is a port city on North Korea’s east coast, known for its strategic military importance and role as a regional transportation and industrial hub.
  • C. Pyeongtaek
    Pyeongtaek is a South Korean city in Gyeonggi Province known for its major U.S. and UN military presence, including large bases such as Camp Humphreys.
  • D. Geumwang-eup
    Geumwang-eup is a town-level administrative division in Eumseong County, located in North Chungcheong Province, South Korea.
  • 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. chosen

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_69a8871289048190b00b0d7744b7b2b1 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb43011188190b6a41c004e9e4802 completed March 7, 2026, 5:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69b4fac8ae5c8190bfa6c12e3997374b completed March 14, 2026, 6:06 a.m.
NEDg Description generation batch_69b4fc08d65081908953482b10fa5611 completed March 14, 2026, 6:11 a.m.
NED2 Entity disambiguation (via description) batch_69b4fc7d8cf081909c4447818b5363c5 completed March 14, 2026, 6:13 a.m.
Created at: March 4, 2026, 7:36 p.m.