Triple

T5575198
Position Surface form Disambiguated ID Type / Status
Subject Jongno-gu E146300 entity
Predicate romanization P2508 FINISHED
Object Chongno-gu
Chongno-gu is a central district in Seoul, South Korea, known as the historic and cultural heart of the city, home to major palaces, government institutions, and traditional markets.
 E575125 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: Chongno-gu | Statement: [Jongno-gu, romanization, Chongno-gu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Chongno-gu
Context triple: [Jongno-gu, romanization, Chongno-gu]
  • A. Yongsan-gu
    Yongsan-gu is a central district of Seoul, South Korea, known for its diverse neighborhoods, major transportation hubs, and significant commercial and cultural centers.
  • B. Jung-gu
    Jung-gu is a central district of the metropolitan city of Daejeon in South Korea, known for its mix of commercial, residential, and administrative areas.
  • C. Jung-gu
    Jung-gu is a central administrative district of the metropolitan city of Ulsan in South Korea.
  • D. Dong-gu
    Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
  • E. Dong-gu
    Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
  • 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: Chongno-gu
Triple: [Jongno-gu, romanization, Chongno-gu]
Generated description
Chongno-gu is a central district in Seoul, South Korea, known as the historic and cultural heart of the city, home to major palaces, government institutions, and traditional markets.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Chongno-gu
Target entity description: Chongno-gu is a central district in Seoul, South Korea, known as the historic and cultural heart of the city, home to major palaces, government institutions, and traditional markets.
  • A. Yongsan-gu
    Yongsan-gu is a central district of Seoul, South Korea, known for its diverse neighborhoods, major transportation hubs, and significant commercial and cultural centers.
  • B. Jung-gu
    Jung-gu is a central administrative district of the metropolitan city of Ulsan in South Korea.
  • C. Jung-gu
    Jung-gu is a central district of the metropolitan city of Daejeon in South Korea, known for its mix of commercial, residential, and administrative areas.
  • D. Dong-gu
    Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
  • E. Dong-gu
    Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
  • 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_69c008ffed108190a084602227af6157 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c02067e8d8819090a006cb266da5fe completed March 22, 2026, 5:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69c16e69e9188190a4dd94c34657a74f completed March 23, 2026, 4:46 p.m.
NEDg Description generation batch_69c1e248fa748190b15a92135e67d420 completed March 24, 2026, 1 a.m.
NED2 Entity disambiguation (via description) batch_69c1e3323f788190a8cc4c870fef1d2b completed March 24, 2026, 1:04 a.m.
Created at: March 22, 2026, 3:37 p.m.