Triple

T1340474
Position Surface form Disambiguated ID Type / Status
Subject Kwa languages E28451 entity
Predicate spokenIn P2266 FINISHED
Object Nigeria E2050 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: Nigeria | Statement: [Kwa languages, spokenIn, Nigeria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nigeria
Context triple: [Kwa languages, spokenIn, Nigeria]
  • A. Nigeria chosen
    Nigeria is a populous West African country known for its diverse ethnic groups, rich cultural heritage, and status as Africa’s largest economy and oil producer.
  • B. Cameroon
    Cameroon is a Central African country known for its cultural and linguistic diversity, varied geography from coast to rainforest and savanna, and a mixed French-English colonial heritage.
  • C. Benin
    Benin is a West African country on the Gulf of Guinea known for its historical Kingdom of Dahomey and as a key region in the transatlantic slave trade.
  • D. Ghana
    Ghana is a West African nation known for being the first sub-Saharan African country to gain independence from colonial rule and for its stable democracy and rich cultural heritage.
  • E. Niger
    Niger is a landlocked West African country in the Sahel region, known for its vast desert landscapes, uranium resources, and predominantly rural population.
  • 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_69a49854eb3481908c7d56b2e449a290 completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c21490488190b4281a16c87677d1 completed March 1, 2026, 10:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69acc6305c988190830dd535726c6338 completed March 8, 2026, 12:43 a.m.
Created at: March 1, 2026, 7:56 p.m.