Triple

T17017900
Position Surface form Disambiguated ID Type / Status
Subject Seoul Metropolitan Government E412868 entity
Predicate hasSubdivision P747 FINISHED
Object Guro-gu NE NERFINISHED

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: Guro-gu | Statement: [Seoul Metropolitan Government, hasSubdivision, Guro-gu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guro-gu
Context triple: [Seoul Metropolitan Government, hasSubdivision, Guro-gu]
  • A. Guro-gu chosen
    Guro-gu is a district in southwestern Seoul, South Korea, known for its industrial areas, digital technology clusters, and dense urban residential neighborhoods.
  • B. Yongsan-gu
    Yongsan-gu is a central district of Seoul, South Korea, known for its diverse neighborhoods, major transportation hubs, and significant commercial and cultural centers.
  • C. Nowon-gu
    Nowon-gu is a northeastern district of Seoul, South Korea, known for its large residential areas and concentration of universities and educational institutions.
  • D. Dongdaemun-gu
    Dongdaemun-gu is a central district in Seoul, South Korea, known for its major commercial areas, historic sites, and the iconic Dongdaemun Design Plaza.
  • E. Eunpyeong-gu
    Eunpyeong-gu is a district in northwestern Seoul, South Korea, known for its mix of urban residential areas and access to nearby mountains and temples.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d886cc4170819093deddc7b8b4b6a7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d480a58c8190a3912d26debb4311 completed April 18, 2026, 6:59 p.m.
Created at: April 10, 2026, 5:33 a.m.