Triple

T6696993
Position Surface form Disambiguated ID Type / Status
Subject Jung District E152773 entity
Predicate romanization P2508 FINISHED
Object Jung-gu
Jung-gu is a central urban district name used in several major South Korean cities, typically encompassing key commercial, administrative, and cultural areas.
 E687873 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: Jung-gu | Statement: [Jung District, romanization, Jung-gu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jung-gu
Context triple: [Jung District, romanization, Jung-gu]
  • A. Jung-gu
    Jung-gu is a central urban district of Daegu, South Korea, known for its dense commercial areas, historic sites, and administrative importance.
  • 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. 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.
  • E. Dong-gu
    Dong-gu is an administrative district in the city of Daegu, South Korea, known for its mix of urban neighborhoods and surrounding natural landscapes.
  • 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: Jung-gu
Triple: [Jung District, romanization, Jung-gu]
Generated description
Jung-gu is a central urban district name used in several major South Korean cities, typically encompassing key commercial, administrative, and cultural areas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jung-gu
Target entity description: Jung-gu is a central urban district name used in several major South Korean cities, typically encompassing key commercial, administrative, and cultural areas.
  • A. 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.
  • 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 urban district of Daegu, South Korea, known for its dense commercial areas, historic sites, and administrative importance.
  • D. 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.
  • E. Dong-gu
    Dong-gu is an administrative district in the city of Daegu, South Korea, known for its mix of urban neighborhoods and surrounding natural landscapes.
  • 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_69c6880687b08190805278b504d1c92c completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d0a4da9881908d79c410b4cff868 completed March 27, 2026, 6:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8c7aa218081908cba76a4fdaa9f10 completed March 29, 2026, 6:33 a.m.
NEDg Description generation batch_69c8c87bca908190a44e839824c23e95 completed March 29, 2026, 6:36 a.m.
NED2 Entity disambiguation (via description) batch_69c8c8e5e9f081908288b38ea25e81d2 completed March 29, 2026, 6:38 a.m.
Created at: March 27, 2026, 2:05 p.m.