Triple

T22047241
Position Surface form Disambiguated ID Type / Status
Subject Umlazi E544792 entity
Predicate hasSection P35 FINISHED
Object Umlazi J NE NERFINISHED

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: Umlazi J | Statement: [Umlazi, hasSection, Umlazi J]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Umlazi J
Context triple: [Umlazi, hasSection, Umlazi J]
  • A. Umlazi chosen
    Umlazi is a large township in the Durban area of South Africa, known as one of the country’s biggest urban settlements and a significant cultural and economic hub in KwaZulu-Natal.
  • B. Umluj
    Umluj is a coastal town in northwestern Saudi Arabia on the Red Sea, known for its pristine beaches and islands that have earned it the nickname "the Maldives of Saudi Arabia."
  • C. Ujaini
    Ujaini is a clan name variant associated with the Ujjainiya community, traditionally linked to the historic city of Ujjain in India.
  • D. UJ
    UJ is a major public university in Johannesburg, South Africa, known for its diverse academic programs and strong focus on research and innovation.
  • E. Umzinto
    Umzinto is a town in the KwaZulu-Natal province of South Africa, historically known for its sugar industry and diverse local community.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e32445c8190ab97089b48a130bb completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f12830c674819080254d77ee02bc9f completed April 28, 2026, 9:35 p.m.
Created at: April 16, 2026, 8:26 p.m.