Triple

T15542863
Position Surface form Disambiguated ID Type / Status
Subject Natal Mounted Police E370524 entity
Predicate headquartersLocation P62 FINISHED
Object Pietermaritzburg E42615 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: Pietermaritzburg | Statement: [Natal Mounted Police, headquartersLocation, Pietermaritzburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pietermaritzburg
Context triple: [Natal Mounted Police, headquartersLocation, Pietermaritzburg]
  • A. Pietermaritzburg chosen
    Pietermaritzburg is a major city in South Africa’s KwaZulu-Natal province, historically significant as a colonial administrative center and now known for its Victorian architecture and role as a regional economic and educational hub.
  • B. Grahamstown
    Grahamstown is a historic university town in South Africa, renowned for its colonial-era architecture and the annual National Arts Festival.
  • C. Pietersburg
    Pietersburg is the former name of Polokwane, a major city and administrative center in South Africa’s Limpopo province.
  • D. Durban
    Durban is a major coastal city in South Africa known for its busy port, subtropical climate, and significant Indian community.
  • 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_69d85cc521a08190921fb50319dddc34 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04432c3808190bb5b653bf8de30c6 completed April 16, 2026, 2:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff6ec6b5ac8190abeb944857d912e6 completed May 9, 2026, 5:28 p.m.
Created at: April 10, 2026, 4:07 a.m.