Triple

T16260417
Position Surface form Disambiguated ID Type / Status
Subject Namutoni E394738 entity
Predicate hasLanguage P15 FINISHED
Object Afrikaans E5797 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: Afrikaans | Statement: [Namutoni, hasLanguage, Afrikaans]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Afrikaans
Context triple: [Namutoni, hasLanguage, 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. Afrikaansche Galey
    Afrikaansche Galey was a Dutch exploration ship notably associated with the early 18th-century Pacific voyages of navigator Jacob Roggeveen.
  • C. 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.
  • D. Tshivenda
    Tshivenda is a Bantu language spoken primarily by the Venda people in northern South Africa and neighboring regions.
  • E. Zulu
    Zulu is a Bantu language of the Nguni group spoken primarily in South Africa and widely influential in the country’s culture and other local languages.
  • 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_69d87f221d8081909b0b2063e7528ba2 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e245c3e5388190942b0237ab5d1f0f completed April 17, 2026, 2:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a000eee110c819088d99b80435ab70b completed May 10, 2026, 4:51 a.m.
Created at: April 10, 2026, 5:04 a.m.