Triple

T23390920
Position Surface form Disambiguated ID Type / Status
Subject Natal region E594014 entity
Predicate languageSpoken P151 FINISHED
Object Afrikaans NE NERFINISHED

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: Afrikaans | Statement: [Natal region, languageSpoken, Afrikaans]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Afrikaans
Context triple: [Natal region, languageSpoken, Afrikaans]
  • A. Afrikaans chosen
    Afrikaans is a West Germanic language spoken mainly in South Africa and Namibia, originating from 17th-century Dutch and influenced by various African and Asian languages.
  • B. Siswati
    Siswati is a Bantu language of the Nguni group spoken primarily in Eswatini and South Africa, where it holds official status.
  • C. Afrikaansche Galey
    Afrikaansche Galey was a Dutch exploration ship notably associated with the early 18th-century Pacific voyages of navigator Jacob Roggeveen.
  • D. Xhosa
    Xhosa is a Bantu language of South Africa, known for its distinctive click consonants and as one of the country’s major official languages.
  • E. Tshivenda
    Tshivenda is a Bantu language spoken primarily by the Venda people in northern South Africa and neighboring regions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e25d2754fc819085deea939bde60ab completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1a49bdfec8190afa592c66660c279 completed April 29, 2026, 6:26 a.m.
Created at: April 17, 2026, 5:36 p.m.