Triple

T21588037
Position Surface form Disambiguated ID Type / Status
Subject Kisukuma language E532703 entity
Predicate region P40 FINISHED
Object Shinyanga Region 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: Shinyanga Region | Statement: [Kisukuma language, region, Shinyanga Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shinyanga Region
Context triple: [Kisukuma language, region, Shinyanga Region]
  • A. Shinyanga Region chosen
    Shinyanga Region is an administrative region in northwestern Tanzania known for its agriculture, mining activities, and proximity to Lake Victoria.
  • B. Nyanga Province
    Nyanga Province is a sparsely populated, resource-rich administrative region in southern Gabon known for its forests, rivers, and coastal areas along the Atlantic Ocean.
  • C. Rakai District
    Rakai District is a rural administrative district in southern Uganda known for its agricultural economy and its early prominence in the country’s HIV/AIDS epidemic.
  • D. Luweero District
    Luweero District is an administrative district in Uganda known for its role as a key battleground area during the Ugandan Bush War in the 1980s.
  • E. Mpigi District
    Mpigi District is an administrative district in central Uganda known for its agricultural activities and proximity to the capital, Kampala.
  • 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_69e0c46251648190876f0427cf2d321b completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69eeeb621ab88190a33a943424ffb306 completed April 27, 2026, 4:51 a.m.
Created at: April 16, 2026, 6:31 p.m.