Triple

T6508556
Position Surface form Disambiguated ID Type / Status
Subject Mayor of Daejeon E150070 entity
Predicate nativeLabel P657 FINISHED
Object 대전시장
대전시장은 대한민국 대전광역시의 행정과 시정을 총괄하며 시민을 대표하는 지방자치단체의 최고 책임자이다.
 E601918 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: 대전시장 | Statement: [Mayor of Daejeon, nativeLabel, 대전시장]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 대전시장
Context triple: [Mayor of Daejeon, nativeLabel, 대전시장]
  • A. Daegu Gwangyeoksi
    Daegu Gwangyeoksi is a major metropolitan city in southeastern South Korea known for its role as an economic, cultural, and transportation hub of the region.
  • B. Gijang Market
    Gijang Market is a traditional coastal seafood market in Gijang County, South Korea, known for its fresh marine products and local specialties.
  • C. Incheon Gwangyeoksi
    Incheon Gwangyeoksi is a major metropolitan city in northwestern South Korea, known for its international airport, large seaport, and role as a key gateway to Seoul and the wider region.
  • D. Busan Metropolitan City Hall
    Busan Metropolitan City Hall is the main administrative headquarters of the Busan metropolitan government in South Korea.
  • E. Ulsan-gwangyeoksi
    Ulsan-gwangyeoksi is a metropolitan city in southeastern South Korea known as a major industrial and shipping center, particularly for automobile and shipbuilding industries.
  • 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: 대전시장
Triple: [Mayor of Daejeon, nativeLabel, 대전시장]
Generated description
대전시장은 대한민국 대전광역시의 행정과 시정을 총괄하며 시민을 대표하는 지방자치단체의 최고 책임자이다.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 대전시장
Target entity description: 대전시장은 대한민국 대전광역시의 행정과 시정을 총괄하며 시민을 대표하는 지방자치단체의 최고 책임자이다.
  • A. Daegu Gwangyeoksi
    Daegu Gwangyeoksi is a major metropolitan city in southeastern South Korea known for its role as an economic, cultural, and transportation hub of the region.
  • B. Gijang Market
    Gijang Market is a traditional coastal seafood market in Gijang County, South Korea, known for its fresh marine products and local specialties.
  • C. Incheon Gwangyeoksi
    Incheon Gwangyeoksi is a major metropolitan city in northwestern South Korea, known for its international airport, large seaport, and role as a key gateway to Seoul and the wider region.
  • D. Busan Metropolitan City Hall
    Busan Metropolitan City Hall is the main administrative headquarters of the Busan metropolitan government in South Korea.
  • E. Ulsan-gwangyeoksi
    Ulsan-gwangyeoksi is a metropolitan city in southeastern South Korea known as a major industrial and shipping center, particularly for automobile and shipbuilding industries.
  • 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_69c687ef291081909d437f035eef1cda completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c699693d94819088e8adff364e834a completed March 27, 2026, 2:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cb519b8081908db92ab57ad6e871 completed March 27, 2026, 6:24 p.m.
NEDg Description generation batch_69c6cd049fac81908c955caa0ccac5ba completed March 27, 2026, 6:31 p.m.
NED2 Entity disambiguation (via description) batch_69c6ce00096c8190a3015bcd392e0ce4 completed March 27, 2026, 6:35 p.m.
Created at: March 27, 2026, 1:43 p.m.