Triple

T20892493
Position Surface form Disambiguated ID Type / Status
Subject Hanseong E514441 entity
Predicate romanizationVariant P5923 FINISHED
Object Hanseong-bu
Hanseong-bu is a historical Korean administrative district name referring to the capital area of Seoul during the late Joseon and early modern periods.
 E1521096 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: Hanseong-bu | Statement: [Hanseong, romanizationVariant, Hanseong-bu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hanseong-bu
Context triple: [Hanseong, romanizationVariant, Hanseong-bu]
  • A. Cheongnyang-eup
    Cheongnyang-eup is a town-level administrative division located within Ulju County in Ulsan, South Korea.
  • B. Daedeok-gu
    Daedeok-gu is a district in the city of Daejeon, South Korea, known for encompassing parts of the country’s major research and science complex.
  • C. Kangseo-gu
    Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
  • D. Sŏdaemun-gu
    Sŏdaemun-gu is a central district of Seoul, South Korea, known for its historic sites, universities, and vibrant urban neighborhoods.
  • E. Uijeongbu
    Uijeongbu is a city in South Korea known as a suburban hub north of Seoul, featuring residential districts, commercial centers, and a history of hosting 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: Hanseong-bu
Triple: [Hanseong, romanizationVariant, Hanseong-bu]
Generated description
Hanseong-bu is a historical Korean administrative district name referring to the capital area of Seoul during the late Joseon and early modern periods.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hanseong-bu
Target entity description: Hanseong-bu is a historical Korean administrative district name referring to the capital area of Seoul during the late Joseon and early modern periods.
  • A. Cheongnyang-eup
    Cheongnyang-eup is a town-level administrative division located within Ulju County in Ulsan, South Korea.
  • B. Daedeok-gu
    Daedeok-gu is a district in the city of Daejeon, South Korea, known for encompassing parts of the country’s major research and science complex.
  • C. Kangseo-gu
    Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
  • D. Sŏdaemun-gu
    Sŏdaemun-gu is a central district of Seoul, South Korea, known for its historic sites, universities, and vibrant urban neighborhoods.
  • E. Uijeongbu
    Uijeongbu is a city in South Korea known as a suburban hub north of Seoul, featuring residential districts, commercial centers, and a history of hosting 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_69e0b4f7ebe48190952a85547a0f31a1 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6d05f0bec8190a296db546bd34114 completed April 21, 2026, 1:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0a96dfbd5481908eaaa45696e5b09e completed May 18, 2026, 4:34 a.m.
NEDg Description generation batch_6a0a980276bc81908e9f6c6596880a8d completed May 18, 2026, 4:39 a.m.
NED2 Entity disambiguation (via description) batch_6a0a986105bc819082556e8dec156ab6 completed May 18, 2026, 4:41 a.m.
Created at: April 16, 2026, 12:46 p.m.