Triple

T7723985
Position Surface form Disambiguated ID Type / Status
Subject mainland Tanzania E175082 entity
Predicate hasRegion P285 FINISHED
Object Mwanza Region E208934 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: Mwanza Region | Statement: [mainland Tanzania, hasRegion, Mwanza Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mwanza Region
Context triple: [mainland Tanzania, hasRegion, Mwanza Region]
  • A. Mwanza Region chosen
    Mwanza Region is an administrative region in northwestern Tanzania, located along the southern shores of Lake Victoria and known as a major economic and cultural center, including for the Sukuma people.
  • B. Nyanza region
    Nyanza region is an area in western Kenya along Lake Victoria, known for its predominantly Luo population and the city of Kisumu as its main urban center.
  • C. Mafinga region
    Mafinga Region is an administrative area in Tanzania that includes Mafinga Central and surrounding localities.
  • D. Ruvuma Region
    Ruvuma Region is a largely rural administrative area in southern Tanzania known for its wildlife, forests, and proximity to major conservation areas.
  • E. Vumba region
    The Vumba region is a scenic highland area in eastern Zimbabwe known for its lush forests, cool misty climate, and rich biodiversity, attracting nature lovers and tourists.
  • 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_69c6995d541c81909eaa646b1a8369a9 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c7031279708190a3a5fb64f9206974 completed March 27, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69c9743540b88190b8e8e0f3f7f303b0 completed March 29, 2026, 6:49 p.m.
Created at: March 27, 2026, 4:05 p.m.