Triple

T8525619
Position Surface form Disambiguated ID Type / Status
Subject 북구 E201807 entity
Predicate usedAsDistrictNameIn P50586 FINISHED
Object 수원시
수원시는 경기도 중남부에 위치한 광역시급 기초자치단체로, 행정·산업·교육의 중심지이자 수원 화성으로 유명한 도시이다.
E738075 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: 수원시 | Statement: [북구, usedAsDistrictNameIn, 수원시]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 수원시
Context triple: [북구, usedAsDistrictNameIn, 수원시]
  • A. 오산시
    오산시 is a city in Gyeonggi Province, South Korea, known as a suburban industrial and residential area located south of Seoul.
  • B. 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.
  • C. Yongin
    Yongin is a rapidly growing city in the Seoul Capital Area of South Korea, known for attractions like Everland Resort and the Korean Folk Village.
  • D. Ansan
    Ansan is a coastal industrial city in South Korea known for its manufacturing base, multicultural population, and proximity to Seoul.
  • E. Dongducheon
    Dongducheon is a city in northern South Korea known for its proximity to the Demilitarized Zone and the presence of U.S. military bases.
  • 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: 수원시
Triple: [북구, usedAsDistrictNameIn, 수원시]
Generated description
수원시는 경기도 중남부에 위치한 광역시급 기초자치단체로, 행정·산업·교육의 중심지이자 수원 화성으로 유명한 도시이다.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: 수원시
Target entity description: 수원시는 경기도 중남부에 위치한 광역시급 기초자치단체로, 행정·산업·교육의 중심지이자 수원 화성으로 유명한 도시이다.
  • A. 오산시
    오산시 is a city in Gyeonggi Province, South Korea, known as a suburban industrial and residential area located south of Seoul.
  • B. 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.
  • C. Yongin
    Yongin is a rapidly growing city in the Seoul Capital Area of South Korea, known for attractions like Everland Resort and the Korean Folk Village.
  • D. Ansan
    Ansan is a coastal industrial city in South Korea known for its manufacturing base, multicultural population, and proximity to Seoul.
  • E. Dongducheon
    Dongducheon is a city in northern South Korea known for its proximity to the Demilitarized Zone and the presence of U.S. military bases.
  • 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_69ca83228b24819085d22e7dc99f5d94 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc4578d9c8819096b3853d01c3ec11 completed March 31, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce4e975d7c8190a7a1fc25c1d67a6f completed April 2, 2026, 11:10 a.m.
NEDg Description generation batch_69ce4ffcf7488190b94cae18be14e8ff completed April 2, 2026, 11:16 a.m.
NED2 Entity disambiguation (via description) batch_69ce507766448190830dd3efc8a79a74 completed April 2, 2026, 11:18 a.m.
Created at: March 30, 2026, 6:16 p.m.