Triple

T3111977
Position Surface form Disambiguated ID Type / Status
Subject Mutare Museum E64971 entity
Predicate serves P98 FINISHED
Object Manicaland region E64969 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: Manicaland region | Statement: [Mutare Museum, serves, Manicaland region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manicaland region
Context triple: [Mutare Museum, serves, Manicaland region]
  • A. Manicaland Province chosen
    Manicaland Province is an eastern region of Zimbabwe known for its mountainous landscapes, rich mineral resources, and proximity to the border with Mozambique.
  • B. Mashonaland region
    Mashonaland region is a historical and agricultural region in northern Zimbabwe that includes the capital city, Harare, and is a key center of the country’s population and political life.
  • C. Harare Province
    Harare Province is the metropolitan province in Zimbabwe that encompasses the capital city, Harare, and its surrounding urban areas.
  • D. Mashonaland East Province
    Mashonaland East Province is an administrative region in northeastern Zimbabwe known for its agricultural activities and rural landscapes.
  • E. Mashonaland West Province
    Mashonaland West Province is a region in northern Zimbabwe known for its rich agricultural lands, mineral resources, and wildlife areas along the Zambezi River.
  • 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_69ad857eeaf48190b34ebfdaa7a264cf completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada43b0b3c8190a828c9cfcf730ed9 completed March 8, 2026, 4:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4dae657ec81909e8a99e4ccd95bfc completed March 14, 2026, 3:49 a.m.
Created at: March 8, 2026, 3:04 p.m.