Triple

T6853287
Position Surface form Disambiguated ID Type / Status
Subject Namdong District E158074 entity
Predicate romanizationRevised P23170 FINISHED
Object Namdong-gu E158074 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: Namdong-gu | Statement: [Namdong District, romanizationRevised, Namdong-gu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Namdong-gu
Context triple: [Namdong District, romanizationRevised, Namdong-gu]
  • A. Namdong District chosen
    Namdong District is a major administrative and commercial hub of Incheon, South Korea, known for housing the city hall and various industrial and residential areas.
  • B. 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.
  • C. Dong-gu
    Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
  • D. Dong-gu
    Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
  • E. Yeonsu-gu
    Yeonsu-gu is an administrative district of Incheon, South Korea, known for its coastal location, modern residential areas, and proximity to the Songdo International Business District.
  • 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_69c6882fae988190864cbba788c5ebb4 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d84fffbc8190943ca7f3f03937e9 completed March 27, 2026, 7:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69c91b1532808190a0b85fa98ef24cfa completed March 29, 2026, 12:29 p.m.
Created at: March 27, 2026, 2:20 p.m.