Triple

T309474
Position Surface form Disambiguated ID Type / Status
Subject Nairobi E6371 entity
Predicate officialLanguage P236 FINISHED
Object Swahili E2738 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: Swahili | Statement: [Nairobi, officialLanguage, Swahili]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Swahili
Context triple: [Nairobi, officialLanguage, Swahili]
  • A. Swahili language chosen
    Swahili is a major Bantu language widely spoken in East and Central Africa, serving as a regional lingua franca and an official language in several countries including Tanzania and Kenya.
  • B. Shona
    Shona is a major Bantu language of Zimbabwe, widely spoken by the Shona people and used in education, media, and government.
  • C. Sranan Tongo
    Sranan Tongo is an English- and Dutch-influenced creole language originating in Suriname, widely used as a lingua franca among its diverse ethnic communities.
  • D. Tshivenda
    Tshivenda is a Bantu language spoken primarily by the Venda people in northern South Africa and neighboring regions.
  • E. Kichwa
    Kichwa is a Quechuan indigenous language variety widely spoken by Andean communities in Ecuador and neighboring regions.
  • 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_69a2e79230508190b912ecb555aae17e completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2ea33ba688190b30d285cd7aa0d82 completed Feb. 28, 2026, 1:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3b47702cc81909c83e6770cb1e855 completed March 1, 2026, 3:37 a.m.
Created at: Feb. 28, 2026, 1:06 p.m.