Triple

T6802730
Position Surface form Disambiguated ID Type / Status
Subject Sasang-gu Office E156223 entity
Predicate governs P760 FINISHED
Object Sasang-gu E689213 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: Sasang-gu | Statement: [Sasang-gu Office, governs, Sasang-gu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sasang-gu
Context triple: [Sasang-gu Office, governs, Sasang-gu]
  • A. Sasang-gu chosen
    Sasang-gu is an administrative district in Busan, South Korea, known for its transportation hubs, industrial areas, and mixed residential-commercial neighborhoods.
  • B. Kangseo-gu
    Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
  • C. Seo-gu
    Seo-gu is a district of the metropolitan city of Daejeon in South Korea, known for its residential areas, commercial centers, and educational institutions.
  • D. Seo-gu
    Seo-gu is an administrative district in the city of Daegu, South Korea, known primarily as a residential and commercial urban area.
  • E. Pusanjin-gu
    Pusanjin-gu is a central urban district of Busan, South Korea, known for its major commercial areas, transportation hubs, and dense residential neighborhoods.
  • 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_69c68826e6a48190a3d220b541e639de completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d2e714d4819084c8109c4de7de72 completed March 27, 2026, 6:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8fa179c008190a6bba55cb62b7fe8 completed March 29, 2026, 10:08 a.m.
Created at: March 27, 2026, 2:16 p.m.