Triple

T9096694
Position Surface form Disambiguated ID Type / Status
Subject Kwaluseni E218041 entity
Predicate partOf P40 FINISHED
Object Manzini urban area E397182 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: Manzini urban area | Statement: [Kwaluseni, partOf, Manzini urban area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manzini urban area
Context triple: [Kwaluseni, partOf, Manzini urban area]
  • A. Manzini chosen
    Manzini is a major city in Eswatini that serves as an important commercial and transport hub of the country.
  • B. Mogoditshane
    Mogoditshane is a rapidly growing suburban township located just outside Botswana’s capital, Gaborone.
  • C. Masindi
    Masindi is a town in western Uganda that serves as a key gateway and service center for visitors to Murchison Falls National Park.
  • D. Polokwane
    Polokwane is a city in South Africa’s Limpopo province that served as one of the venues for matches during the 2010 FIFA World Cup.
  • E. Mthatha
    Mthatha is a town in South Africa known as a regional economic and administrative center in the Eastern Cape and as the birthplace of Nelson Mandela.
  • 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_69ca83d9844081908e561e367fda6d45 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc96b7d0d48190a3b15f35bef087e3 completed April 1, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0181a9ae88190ab80d4e80e919f42 completed April 3, 2026, 7:42 p.m.
Created at: March 30, 2026, 7:15 p.m.