Triple

T9127017
Position Surface form Disambiguated ID Type / Status
Subject Kasha Jacqueline Nabagesera E218990 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: [Kasha Jacqueline Nabagesera, countryOfCitizenship, Uganda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uganda
Context triple: [Kasha Jacqueline Nabagesera, 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_69ca83debfc0819095800583e97ab10f completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cca8c93d3c8190b003b2b1af2003b2 completed April 1, 2026, 5:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d030a1042c8190a31c76638a95a2cf completed April 3, 2026, 9:26 p.m.
Created at: March 30, 2026, 7:18 p.m.