Triple

T10128637
Position Surface form Disambiguated ID Type / Status
Subject Mangmi-dong E226278 entity
Predicate locatedInAdministrativeTerritory P40 FINISHED
Object Suyeong-gu E698278 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: Suyeong-gu | Statement: [Mangmi-dong, locatedInAdministrativeTerritory, Suyeong-gu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Suyeong-gu
Context triple: [Mangmi-dong, locatedInAdministrativeTerritory, Suyeong-gu]
  • A. Suyeong-gu chosen
    Suyeong-gu is a coastal district in the city of Busan, South Korea, known for its urban neighborhoods and proximity to popular beaches.
  • B. 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.
  • C. Gangseo-gu
    Gangseo-gu is a western coastal district of Busan, South Korea, known for its industrial complexes, logistics hubs, and proximity to Gimhae International Airport.
  • D. Geumjeong-gu
    Geumjeong-gu is a district in Busan, South Korea, known for its residential areas, transportation hubs, and proximity to Geumjeongsan Mountain.
  • 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 (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_69ca843057b48190a86730167f5d6b98 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cdd333186c819088bbf617967f24fa completed April 2, 2026, 2:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69de54d83e44819097abf6714bd48680 completed April 14, 2026, 2:53 p.m.
Created at: March 30, 2026, 9:05 p.m.