Triple

T17107000
Position Surface form Disambiguated ID Type / Status
Subject David Kato E415126 entity
Predicate countryOfCitizenship P2 FINISHED
Object Uganda E10768 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: Uganda | Statement: [David Kato, countryOfCitizenship, Uganda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uganda
Context triple: [David Kato, countryOfCitizenship, Uganda]
  • A. Uganda chosen
    Uganda is a landlocked country in East Africa known for its diverse landscapes, abundant wildlife, and location along the equator.
  • B. Uganda and Democratic Republic of the Congo
    Uganda and the Democratic Republic of the Congo are neighboring Central-East African countries that share extensive natural frontiers, rich biodiversity, and significant cross-border cultural and economic ties.
  • C. Oluganda
    Oluganda is the endonym for Luganda, a major Bantu language spoken primarily by the Baganda people in central Uganda.
  • D. Nzera
    Nzera is a settlement located within Tanzania’s Geita Region in East Africa.
  • E. Kenya
    Kenya is an East African country known for its diverse wildlife, scenic landscapes from savannas to highlands, and a coastline along the Indian Ocean.
  • 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_69d886cfc8e88190b05ba466edd35591 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dc2750b481908de18e8cb8f2195c completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a013a019540819083ce6100b24f8cfb completed May 11, 2026, 2:08 a.m.
Created at: April 10, 2026, 5:35 a.m.