Triple

T10652367
Position Surface form Disambiguated ID Type / Status
Subject Justice Mandela E251003 entity
Predicate language P15 FINISHED
Object Xhosa language E11008 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: Xhosa language | Statement: [Justice Mandela, language, Xhosa language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xhosa language
Context triple: [Justice Mandela, language, Xhosa language]
  • A. Xhosa chosen
    Xhosa is a Bantu language of South Africa, known for its distinctive click consonants and as one of the country’s major official languages.
  • B. Xitsonga
    Xitsonga is a Bantu language spoken primarily by the Tsonga people in southern Africa, notably in South Africa, Mozambique, and Zimbabwe.
  • C. Tshivenda
    Tshivenda is a Bantu language spoken primarily by the Venda people in northern South Africa and neighboring regions.
  • D. Sesotho
    Sesotho is a Southern Bantu language spoken primarily in Lesotho and South Africa, where it holds official status and serves as a major medium of communication and cultural identity.
  • E. Swazi language
    Swazi language is a Bantu language of the Nguni group spoken primarily in Eswatini and parts of South Africa.
  • 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_69d6aa5a4c4881908f39be6efe5981e5 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6dff78ec88190a4d1863fe87245f6 completed April 8, 2026, 11:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69d988530f288190b8150d159f723a74 completed April 10, 2026, 11:31 p.m.
Created at: April 8, 2026, 9:06 p.m.