Triple

T22047782
Position Surface form Disambiguated ID Type / Status
Subject Seochon E544806 entity
Predicate hasPart P35 FINISHED
Object Nuha-dong
Nuha-dong is a neighborhood in central Seoul, South Korea, known for its traditional hanok houses, narrow alleyways, and proximity to historic and cultural sites.
 E1522929 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: Nuha-dong | Statement: [Seochon, hasPart, Nuha-dong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nuha-dong
Context triple: [Seochon, hasPart, Nuha-dong]
  • A. Sungin-dong
    Sungin-dong is a neighborhood in central Seoul, South Korea, known as a residential area within the city's Seongdong District.
  • B. Gaya-dong
    Gaya-dong is a neighborhood in Busan, South Korea, known as a residential and commercial area within the central urban zone of the city.
  • C. Yangsan-dong
    Yangsan-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
  • D. Seo-dong
    Seo-dong is a neighborhood within Busan’s Geumjeong District in South Korea, known primarily as a residential area with local commerce and community facilities.
  • E. Dangsan-dong
    Dangsan-dong is a neighborhood in western Seoul, South Korea, known for its residential areas, commercial facilities, and convenient access via major subway lines.
  • 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: Nuha-dong
Triple: [Seochon, hasPart, Nuha-dong]
Generated description
Nuha-dong is a neighborhood in central Seoul, South Korea, known for its traditional hanok houses, narrow alleyways, and proximity to historic and cultural sites.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nuha-dong
Target entity description: Nuha-dong is a neighborhood in central Seoul, South Korea, known for its traditional hanok houses, narrow alleyways, and proximity to historic and cultural sites.
  • A. Sungin-dong
    Sungin-dong is a neighborhood in central Seoul, South Korea, known as a residential area within the city's Seongdong District.
  • B. Gaya-dong
    Gaya-dong is a neighborhood in Busan, South Korea, known as a residential and commercial area within the central urban zone of the city.
  • C. Yangsan-dong
    Yangsan-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
  • D. Seo-dong
    Seo-dong is a neighborhood within Busan’s Geumjeong District in South Korea, known primarily as a residential area with local commerce and community facilities.
  • E. Dangsan-dong
    Dangsan-dong is a neighborhood in western Seoul, South Korea, known for its residential areas, commercial facilities, and convenient access via major subway lines.
  • 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_69e11e32445c8190ab97089b48a130bb completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f12830c674819080254d77ee02bc9f completed April 28, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a9eac8da08190a71f28985ca86519 completed May 18, 2026, 5:07 a.m.
NEDg Description generation batch_6a0a9f5a0a2c8190b0a8a4c56938cc02 completed May 18, 2026, 5:10 a.m.
NED2 Entity disambiguation (via description) batch_6a0aa01988b481909fe3bf966feb5916 completed May 18, 2026, 5:14 a.m.
Created at: April 16, 2026, 8:26 p.m.