Triple

T13077634
Position Surface form Disambiguated ID Type / Status
Subject Lira E329618 entity
Predicate locatedIn P40 FINISHED
Object Lango sub-region E810857 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: Lango sub-region | Statement: [Lira, locatedIn, Lango sub-region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lango sub-region
Context triple: [Lira, locatedIn, Lango sub-region]
  • A. Lango sub-region chosen
    The Lango sub-region is an area in northern Uganda that serves as the traditional homeland of the Lango people, known for its distinct Luo-related language and culture.
  • B. Teso sub-region
    The Teso sub-region is an area in eastern Uganda that serves as the cultural and historical homeland of the Iteso people.
  • C. Buganda sub-region
    Buganda sub-region is a historically significant and populous area in central Uganda that largely overlaps with the traditional Kingdom of Buganda, including the capital city Kampala.
  • D. Mafinga region
    Mafinga Region is an administrative area in Tanzania that includes Mafinga Central and surrounding localities.
  • E. Ohangwena Region
    Ohangwena Region is an administrative region in northern Namibia, known for its dense rural population and location along the border with Angola.
  • 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.