Triple

T2533939
Position Surface form Disambiguated ID Type / Status
Subject Atlantic–Congo languages E56225 entity
Predicate areSpokenIn P7445 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: [Atlantic–Congo languages, areSpokenIn, Uganda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uganda
Context triple: [Atlantic–Congo languages, areSpokenIn, 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. Nzera
    Nzera is a settlement located within Tanzania’s Geita Region in East Africa.
  • C. 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.
  • D. Democratic Republic of the Congo
    The Democratic Republic of the Congo is a vast, resource-rich Central African nation known for the Congo River basin, extensive rainforests, and a history marked by colonial exploitation and ongoing political instability.
  • E. Rwanda
    Rwanda is a landlocked East African nation known for its dramatic recovery from the 1994 genocide, rapid economic growth, and strong conservation efforts, particularly for mountain gorillas.
  • 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_69ab4a49b6508190bc467fbef4bac334 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd27afe7c8190984e10d3f3d5586b completed March 7, 2026, 7:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69af2bb9c37081909128d7a227651c8b completed March 9, 2026, 8:21 p.m.
Created at: March 6, 2026, 9:47 p.m.