Triple

T2450813
Position Surface form Disambiguated ID Type / Status
Subject Ukerewe Island E53698 entity
Predicate locatedIn P40 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: [Ukerewe Island, locatedIn, Mwanza Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mwanza Region
Context triple: [Ukerewe Island, locatedIn, 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. Simiyu Region
    Simiyu Region is an administrative region in northern Tanzania known for its predominantly rural economy based on agriculture and livestock.
  • D. Singida Region
    Singida Region is an administrative region in central Tanzania known for its semi-arid climate, agriculture, and role as a transport crossroads.
  • E. Kunene Region
    Kunene Region is a sparsely populated, northwestern region of Namibia known for its rugged landscapes, desert-adapted wildlife, and remote Atlantic coastline.
  • 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_69ab495d227c8190b26ae6548eeb1019 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd0f402b48190b871b2475983af7e completed March 7, 2026, 7:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69af65436c2c8190a8f1aba2852b52e8 completed March 10, 2026, 12:26 a.m.
Created at: March 6, 2026, 9:43 p.m.