Triple

T2013657
Position Surface form Disambiguated ID Type / Status
Subject Gyeonggi Province E43744 entity
Predicate hasCity P316 FINISHED
Object Seongnam
Seongnam is a major satellite city of Seoul in South Korea, known for its planned urban development and role as a residential and commercial hub.
 E408723 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: Seongnam | Statement: [Gyeonggi Province, hasCity, Seongnam]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Seongnam
Context triple: [Gyeonggi Province, hasCity, Seongnam]
  • A. 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.
  • B. Ulsan
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • C. Daejeon
    Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
  • D. Daegu
    Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
  • 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: Seongnam
Triple: [Gyeonggi Province, hasCity, Seongnam]
Generated description
Seongnam is a major satellite city of Seoul in South Korea, known for its planned urban development and role as a residential and commercial hub.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Seongnam
Target entity description: Seongnam is a major satellite city of Seoul in South Korea, known for its planned urban development and role as a residential and commercial hub.
  • A. 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.
  • B. Ulsan
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • C. Daejeon
    Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
  • D. Daegu
    Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
  • 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_69b555f76b2c819084b3be0189ac453f completed March 14, 2026, 12:35 p.m.
NEDg Description generation batch_69b55681b8fc8190a9b2b8895f7a7120 completed March 14, 2026, 12:37 p.m.
NED2 Entity disambiguation (via description) batch_69b55706a2708190aeb41591b91f0fba completed March 14, 2026, 12:39 p.m.
Created at: March 4, 2026, 7:37 p.m.