Triple

T13077849
Position Surface form Disambiguated ID Type / Status
Subject Nyakalengija E329624 entity
Predicate locatedIn P40 FINISHED
Object Kasese District E332728 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: Kasese District | Statement: [Nyakalengija, locatedIn, Kasese District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kasese District
Context triple: [Nyakalengija, locatedIn, Kasese District]
  • A. Kasese chosen
    Kasese is a town in western Uganda that serves as a key gateway to Queen Elizabeth National Park and the Rwenzori Mountains.
  • B. Kibaale District
    Kibaale District is a rural administrative district in western Uganda known for its agriculture and forested landscapes.
  • C. Mubende District
    Mubende District is an administrative district in central Uganda known for its agricultural activities and strategic location along major transport routes.
  • D. Lyantonde District
    Lyantonde District is an administrative district in southern Uganda known for its location along the Kampala–Mbarara highway and its predominantly agricultural economy.
  • E. Kiryandongo District
    Kiryandongo District is a central Ugandan district known for its agricultural communities and strategic location along major transport routes between Kampala and northern Uganda.
  • 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_69d80771749c81909a6d9197b9504872 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d9811828448190ac6ddd3e9c221251 completed April 10, 2026, 11 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e27058dc8190a64e1a929f296619 completed May 3, 2026, 5:51 a.m.
Created at: April 9, 2026, 9:01 p.m.