Triple

T1710869
Position Surface form Disambiguated ID Type / Status
Subject Busan Metro E36979 entity
Predicate alsoServes P6337 FINISHED
Object Saha District E28721 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: Saha District | Statement: [Busan Metro, alsoServes, Saha District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saha District
Context triple: [Busan Metro, alsoServes, Saha District]
  • A. Saha District chosen
    Saha District is an administrative district (gu) in the southwestern part of Busan, South Korea, known for its coastal areas and residential neighborhoods.
  • B. Sabha District
    Sabha District is an administrative region in southwestern Libya centered around the city of Sabha, a key hub in the Fezzan desert area.
  • C. Arun District
    Arun District is a local government district in West Sussex, England, named after the River Arun and encompassing coastal towns such as Bognor Regis and Littlehampton.
  • D. Sibi District
    Sibi District is an administrative district in the Balochistan province of Pakistan, centered around the historic town of Sibi.
  • E. Bubi District
    Bubi District is an administrative district in Matabeleland North Province in western Zimbabwe, known largely for its rural communities and mining activities.
  • 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_69a88617439c819094ffb5d16a0f6307 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa63149288819082e7055d0d292d1d completed March 6, 2026, 5:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada0d1882c81908e02e36ab28e7fdc completed March 8, 2026, 4:16 p.m.
Created at: March 4, 2026, 7:30 p.m.