Triple

T14088991
Position Surface form Disambiguated ID Type / Status
Subject Jung District, Seoul E339072 entity
Predicate romanization P2508 FINISHED
Object Jung-gu E687873 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: Jung-gu | Statement: [Jung District, Seoul, romanization, Jung-gu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jung-gu
Context triple: [Jung District, Seoul, romanization, Jung-gu]
  • 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. Jung-gu
    Jung-gu is a central district of Busan, South Korea, known for its historic markets, port-side location, and dense urban commercial areas.
  • E. Jung-gu chosen
    Jung-gu is a central urban district name used in several major South Korean cities, typically encompassing key commercial, administrative, and cultural areas.
  • 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_69d81c687b0c819087fd9ed4198403f8 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5ee1ce88819091c983286289337e completed April 14, 2026, 3:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd466febb88190986eb8f033d29279 completed May 8, 2026, 2:12 a.m.
Created at: April 9, 2026, 10:21 p.m.