Triple

T11596220
Position Surface form Disambiguated ID Type / Status
Subject Lusoga E275006 entity
Predicate hasWikipediaPage P7959 FINISHED
Object Lusoga language E935950 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: Lusoga language | Statement: [Lusoga, hasWikipediaPage, Lusoga language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lusoga language
Context triple: [Lusoga, hasWikipediaPage, Lusoga language]
  • A. Lusoga language chosen
    The Lusoga language is a Bantu language spoken primarily by the Basoga people in eastern Uganda.
  • B. Nsenga language
    The Nsenga language is a Bantu language spoken primarily in Zambia and neighboring regions, closely related to other languages of the area such as Tumbuka and Chewa.
  • C. Mwaghavul language
    The Mwaghavul language is a West Chadic language spoken primarily in Plateau State, central Nigeria, by the Mwaghavul people.
  • D. Lugbara language
    The Lugbara language is a Central Sudanic language spoken primarily by the Lugbara people of northwestern Uganda and northeastern Democratic Republic of the Congo.
  • E. Ngindo language
    The Ngindo language is a Bantu language spoken by the Ngindo people of southeastern Tanzania.
  • 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_69d6aae6b14c81908dc5a74bad7591f9 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8946790d08190924d60bb4b523250 completed April 10, 2026, 6:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69ee86f246848190a5b020c3e05d02dd completed April 26, 2026, 9:43 p.m.
Created at: April 8, 2026, 9:38 p.m.