Triple

T11310916
Position Surface form Disambiguated ID Type / Status
Subject Black South African E267831 entity
Predicate hasEthnicSubgroup P14193 FINISHED
Object Venda E52951 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: Venda | Statement: [Black South African, hasEthnicSubgroup, Venda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Venda
Context triple: [Black South African, hasEthnicSubgroup, Venda]
  • A. Venda chosen
    Venda is a Bantu language of the Venda people of South Africa and Zimbabwe, recognized as one of South Africa’s official languages.
  • B. Loja
    Loja is a city in southern Ecuador known as a cultural and musical center nestled in the Andean highlands.
  • C. Loja
    Loja is a historic town in the province of Granada, Spain, known for its strategic location between Granada and the coast and its role in the final stages of the Reconquista.
  • D. Vendas Novas
    Vendas Novas is a Portuguese town and municipality in the Alentejo region, known for its strategic location between Lisbon and Évora and its traditional bifanas (pork sandwiches).
  • E. Feira Nova
    Feira Nova is a small municipality located in the semi-arid interior region of the state of Sergipe, Brazil.
  • 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_69d6aaca5c24819083db46a30d86cb34 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9c0b3b88190ac0e3d6a5ad3b9bc completed April 9, 2026, 6:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69e50a7fc06881909afe85a600d25ff2 completed April 19, 2026, 5:01 p.m.
Created at: April 8, 2026, 9:32 p.m.