Triple

T11258742
Position Surface form Disambiguated ID Type / Status
Subject Dr Kenneth Kaunda District E266506 entity
Predicate seat P75 FINISHED
Object Potchefstroom E408963 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: Potchefstroom | Statement: [Dr Kenneth Kaunda District, seat, Potchefstroom]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Potchefstroom
Context triple: [Dr Kenneth Kaunda District, seat, Potchefstroom]
  • A. Potchefstroom chosen
    Potchefstroom is a historic university town in South Africa known for its academic institutions, military base, and role in the North West province’s agriculture and industry.
  • B. Pietersburg
    Pietersburg is the former name of Polokwane, a major city and administrative center in South Africa’s Limpopo province.
  • C. Kroonstad
    Kroonstad is a town in the Free State province of South Africa, known as an agricultural and transport hub along the Vaal River.
  • D. Bloemfontein
    Bloemfontein is a major South African city known as the seat of the country’s highest courts and one of its three national capitals.
  • E. Uitenhage
    Uitenhage is a South African town in the Eastern Cape known historically for its automotive industry and as part of the greater Port Elizabeth (Gqeberha) urban area.
  • 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_69d6aac7953c8190b82caf9d7640fdf9 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e936cb048190b4d6fb2851ef8932 completed April 9, 2026, 6 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5d3049e788190a7caf324a4b793d2 completed April 20, 2026, 7:17 a.m.
Created at: April 8, 2026, 9:31 p.m.