Triple

T2487113
Position Surface form Disambiguated ID Type / Status
Subject Bulawayo Polytechnic E55951 entity
Predicate city P40 FINISHED
Object Bulawayo E9766 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: Bulawayo | Statement: [Bulawayo Polytechnic, city, Bulawayo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bulawayo
Context triple: [Bulawayo Polytechnic, city, Bulawayo]
  • A. Bulawayo chosen
    Bulawayo is Zimbabwe’s second-largest city and a major industrial, cultural, and transport hub in the southwestern part of the country.
  • B. Maputo
    Maputo is the largest city and main economic and cultural center of Mozambique, located on the country’s southern coast along the Indian Ocean.
  • C. Bloemfontein
    Bloemfontein is a major South African city known as the seat of the country’s highest courts and one of its three national capitals.
  • D. Marondera
    Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
  • E. Pietermaritzburg
    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.
  • 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_69ab49e670a88190b928e08302381710 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd1782ca081909645164a6acf0ea0 completed March 7, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69b261c141f88190aaf340c92499b88b completed March 12, 2026, 6:48 a.m.
Created at: March 6, 2026, 9:45 p.m.