Triple

T2342282
Position Surface form Disambiguated ID Type / Status
Subject Arusha, Tanzania E45051 entity
Predicate region P40 FINISHED
Object Arusha Region E258867 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: Arusha Region | Statement: [Arusha, Tanzania, region, Arusha Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arusha Region
Context triple: [Arusha, Tanzania, region, Arusha Region]
  • A. Arusha Region chosen
    Arusha Region is an administrative region in northern Tanzania known for its tourism hub city of Arusha and proximity to major national parks and Mount Kilimanjaro.
  • B. Kilimanjaro Region
    Kilimanjaro Region is an administrative area in northeastern Tanzania best known for encompassing Africa’s highest peak, Mount Kilimanjaro, and serving as a major hub for tourism and agriculture.
  • C. Singida Region
    Singida Region is an administrative region in central Tanzania known for its semi-arid climate, agriculture, and role as a transport crossroads.
  • D. Rukwa Region
    Rukwa Region is an administrative region in southwestern Tanzania known for its location along Lake Rukwa and its largely rural, agricultural economy.
  • E. 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.
  • 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_69a88917935081909b755dbf38e81024 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abc6ad01fc81909e386986e9acc989 completed March 7, 2026, 6:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3c116088190b15fd12d5ac75594 completed March 9, 2026, 11:49 a.m.
Created at: March 4, 2026, 7:52 p.m.