Triple

T2013647
Position Surface form Disambiguated ID Type / Status
Subject Gyeonggi Province E43744 entity
Predicate capital P234 FINISHED
Object Suwon
Suwon is a major South Korean city best known for its UNESCO-listed Hwaseong Fortress and as a key cultural and economic center just south of Seoul.
 E401676 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: Suwon | Statement: [Gyeonggi Province, capital, Suwon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Suwon
Context triple: [Gyeonggi Province, capital, Suwon]
  • A. Anseong
    Anseong is a city in Gyeonggi Province, South Korea, known for its traditional culture, agricultural heritage, and annual Baudeogi Festival.
  • B. Daegu
    Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
  • C. Ulsan
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • D. Daejeon
    Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
  • E. 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.
  • 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: Suwon
Triple: [Gyeonggi Province, capital, Suwon]
Generated description
Suwon is a major South Korean city best known for its UNESCO-listed Hwaseong Fortress and as a key cultural and economic center just south of Seoul.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Suwon
Target entity description: Suwon is a major South Korean city best known for its UNESCO-listed Hwaseong Fortress and as a key cultural and economic center just south of Seoul.
  • A. Anseong
    Anseong is a city in Gyeonggi Province, South Korea, known for its traditional culture, agricultural heritage, and annual Baudeogi Festival.
  • B. Daegu
    Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
  • C. Ulsan
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • D. Daejeon
    Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
  • E. 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.
  • 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_69a88716e9f08190946313fdc949e3cf completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb8b42d508190bf2b63132bb2ad77 completed March 7, 2026, 5:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69b5334e25ec819090bdefaef44e849b completed March 14, 2026, 10:07 a.m.
NEDg Description generation batch_69b5345b408c8190a68c530edd1efb1f completed March 14, 2026, 10:11 a.m.
NED2 Entity disambiguation (via description) batch_69b5351fe4d081909c606f2ced0efbdc completed March 14, 2026, 10:14 a.m.
Created at: March 4, 2026, 7:37 p.m.