Triple

T19117321
Position Surface form Disambiguated ID Type / Status
Subject Ui LRT E467938 entity
Predicate locale P387 FINISHED
Object Dobong District 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: Dobong District | Statement: [Ui LRT, locale, Dobong District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dobong District
Context triple: [Ui LRT, locale, Dobong District]
  • A. Dobong District chosen
    Dobong District is a northeastern borough of Seoul, South Korea, known for its residential neighborhoods and the popular hiking areas of Bukhansan and Dobongsan mountains.
  • B. Yeongdeungpo District
    Yeongdeungpo District is a major administrative and commercial area in southwestern Seoul, South Korea, known for its government institutions, business centers, and dense urban development.
  • C. Seongbuk District
    Seongbuk District is a residential and educational borough in northern Seoul, South Korea, known for its universities, cultural sites, and traditional neighborhoods.
  • D. Gangseo District
    Gangseo District is a western coastal district of Busan, South Korea, known for its industrial complexes, logistics hubs, and proximity to Gimhae International Airport.
  • E. Gangseo District
    Gangseo District is a western administrative district of Seoul, South Korea, known for its residential areas, transportation hubs, and proximity to Gimpo International Airport.
  • 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_69d8dd06a26481908039e2a1bae8c597 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e399a6d8819090a9501ff1637b9d completed April 20, 2026, 8:28 a.m.
Created at: April 10, 2026, 12:05 p.m.