Triple

T9369460
Position Surface form Disambiguated ID Type / Status
Subject Nkumba University E225491 entity
Predicate locatedIn P40 FINISHED
Object Wakiso District E225487 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: Wakiso District | Statement: [Nkumba University, locatedIn, Wakiso District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wakiso District
Context triple: [Nkumba University, locatedIn, Wakiso District]
  • A. Wakiso District chosen
    Wakiso District is a central Ugandan district that encompasses rapidly urbanizing areas around Kampala, including the town of Entebbe on the shores of Lake Victoria.
  • B. 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.
  • C. Kampala District
    Kampala District is the central administrative and urban district of Uganda that encompasses the nation’s capital city, Kampala.
  • 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. Mubende District
    Mubende District is an administrative district in central Uganda known for its agricultural activities and strategic location along major transport routes.
  • 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_69ca842cbddc819099d71ecec48cf9e5 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd5080f55c8190bd5ca0dc0a4ea989 completed April 1, 2026, 5:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14bdb000c81909b11b3bf2398bf0f completed April 4, 2026, 5:35 p.m.
Created at: March 30, 2026, 7:43 p.m.