Triple

T9122008
Position Surface form Disambiguated ID Type / Status
Subject R103 E218875 entity
Predicate connects P390 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: [R103, connects, Pietermaritzburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pietermaritzburg
Context triple: [R103, connects, 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_69ca83dddd548190983b96c664f7f367 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cca8b46dac8190bde88205c85f596a completed April 1, 2026, 5:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d190b5bb70819082b8eeb18bd5f1f6 completed April 4, 2026, 10:29 p.m.
Created at: March 30, 2026, 7:17 p.m.