Triple

T6219523
Position Surface form Disambiguated ID Type / Status
Subject High Court of Zimbabwe E139074 entity
Predicate hasSeatIn P3522 FINISHED
Object Chinhoyi E257663 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: Chinhoyi | Statement: [High Court of Zimbabwe, hasSeatIn, Chinhoyi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chinhoyi
Context triple: [High Court of Zimbabwe, hasSeatIn, Chinhoyi]
  • A. Chinhoyi chosen
    Chinhoyi is a town in northern Zimbabwe known as an administrative center and for the nearby Chinhoyi Caves.
  • B. Marondera
    Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
  • C. Chivhu, Zimbabwe
    Chivhu, Zimbabwe is a small town in central Zimbabwe known as an agricultural center and one of the country’s oldest European-settled communities.
  • D. Kasane
    Kasane is a small town in northern Botswana that serves as a key gateway and service hub for visitors to Chobe National Park and the surrounding wildlife areas.
  • E. Chiredzi
    Chiredzi is a town in southeastern Zimbabwe known as a center for sugarcane farming and a gateway to nearby wildlife and conservation areas.
  • 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_69c008aecb0c81909984b48f733ce8ae completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c062bbb768819099402d367f124639 completed March 22, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69c603e5b3d48190b156be02008a8c12 completed March 27, 2026, 4:13 a.m.
Created at: March 22, 2026, 4:21 p.m.