Triple

T4163403
Position Surface form Disambiguated ID Type / Status
Subject Hector Pieterson Museum E91583 entity
Predicate locatedIn P40 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: [Hector Pieterson Museum, locatedIn, Soweto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Soweto
Context triple: [Hector Pieterson Museum, locatedIn, 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. Gladysvale
    Gladysvale is a fossil-bearing cave site in South Africa known for its important hominin and animal fossil discoveries within the Cradle of Humankind World Heritage area.
  • C. Manenberg
    Manenberg is a township on the Cape Flats in Cape Town, South Africa, known for its history of forced removals under apartheid and ongoing social challenges alongside strong community activism and culture.
  • D. Bothasig
    Bothasig is a residential suburb in the northern part of Cape Town, South Africa.
  • E. Mitchells Plain
    Mitchells Plain is a large, predominantly residential township in Cape Town, South Africa, known for its dense population, socio-economic challenges, and vibrant community life.
  • 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_69aed9626ebc8190a39de631788bea3e completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af02a9bf348190b99cecd19fe65779 completed March 9, 2026, 5:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69b57f456bf88190b9b8678476ac3803 completed March 14, 2026, 3:31 p.m.
Created at: March 9, 2026, 3:44 p.m.