Triple

T759416
Position Surface form Disambiguated ID Type / Status
Subject Johannesburg E16031 entity
Predicate hasDistrict P459 FINISHED
Object Soweto E31807 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: Soweto | Statement: [Johannesburg, hasDistrict, Soweto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Soweto
Context triple: [Johannesburg, hasDistrict, Soweto]
  • A. Soweto chosen
    Soweto is a historically significant township in Johannesburg, South Africa, known for its central role in the struggle against apartheid and its rich urban culture.
  • B. Johannesburg, South Africa
    Johannesburg, South Africa is the country’s largest city and economic hub, known for its role in the gold mining industry and as a major urban center in Gauteng province.
  • C. Midrand
    Midrand is a rapidly growing commercial and residential area in South Africa strategically located between Johannesburg and Pretoria in the province of Gauteng.
  • D. Pretoria, South Africa
    Pretoria, South Africa is one of the country’s three capital cities, serving as the administrative capital and a major center for government, education, and culture.
  • E. Egoli
    Egoli is a common nickname for Johannesburg, South Africa’s major economic hub often referred to as the "City of Gold."
  • 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_69a493684ee48190bd43b7c78da4aec8 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a67f9778819098d3c144dd26b976 completed March 1, 2026, 8:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69a826c85e008190a7bba05607312192 completed March 4, 2026, 12:34 p.m.
Created at: March 1, 2026, 7:37 p.m.