Triple

T10567360
Position Surface form Disambiguated ID Type / Status
Subject Pusanjin-gu E249383 entity
Predicate locatedNear P294 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: [Pusanjin-gu, locatedNear, Sasang-gu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sasang-gu
Context triple: [Pusanjin-gu, locatedNear, 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. Geumjeong-gu
    Geumjeong-gu is a district in Busan, South Korea, known for its residential areas, transportation hubs, and proximity to Geumjeongsan Mountain.
  • D. Seo-gu
    Seo-gu is a coastal district in the southwestern part of Busan, South Korea, known for its port facilities, historic sites, and urban residential areas.
  • E. 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.
  • 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_69d381c8bd708190acf3d275c908251e completed April 6, 2026, 9:50 a.m.
NER Named-entity recognition batch_69d5272ef5848190b76d671ea2d26314 completed April 7, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69e623a9711081908eac4238a717305c completed April 20, 2026, 1:01 p.m.
Created at: April 6, 2026, 12:36 p.m.