Triple

T14886557
Position Surface form Disambiguated ID Type / Status
Subject Jongno District E350135 entity
Predicate contains P35 FINISHED
Object Sogyeok-dong E570221 NE FINISHED

How this triple was built (2 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: Sogyeok-dong | Statement: [Jongno District, contains, Sogyeok-dong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sogyeok-dong
Context triple: [Jongno District, contains, Sogyeok-dong]
  • A. Sogyeok-dong chosen
    Sogyeok-dong is a neighborhood in central Seoul, South Korea, known for its traditional Korean houses (hanok), art galleries, and proximity to historic palaces.
  • B. Sogong-dong
    Sogong-dong is a central neighborhood in Seoul known for its major hotels, shopping areas, and proximity to key business and cultural sites.
  • C. Seongho-dong
    Seongho-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
  • D. Yeonsu-dong
    Yeonsu-dong is a neighborhood within Incheon, South Korea, known as a residential and local commercial area of Yeonsu District.
  • E. Hwanghak-dong
    Hwanghak-dong is a neighborhood in central Seoul, South Korea, known for its traditional flea markets and dense urban streetscape.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d822ee4f408190b6ac3b2fa434f0df completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded5f5b1c88190815f3585770cb135 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69fff28b707481908611ae8e23de3294 completed May 10, 2026, 2:50 a.m.
Created at: April 10, 2026, 1:56 a.m.