Triple

T7867004
Position Surface form Disambiguated ID Type / Status
Subject Igunga E182641 entity
Predicate locatedIn P40 FINISHED
Object Tabora Region E33430 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: Tabora Region | Statement: [Igunga, locatedIn, Tabora Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tabora Region
Context triple: [Igunga, locatedIn, Tabora Region]
  • A. Tabora Region chosen
    Tabora Region is an inland administrative region in western Tanzania known historically as a key hub for trade and rail transport.
  • B. Tanga Region
    Tanga Region is a coastal administrative region in northeastern Tanzania known for its port city of Tanga, Indian Ocean shoreline, and proximity to the Usambara Mountains.
  • C. Simiyu Region
    Simiyu Region is an administrative region in northern Tanzania known for its predominantly rural economy based on agriculture and livestock.
  • D. 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.
  • E. Iringa Region
    Iringa Region is an administrative area in south-central Tanzania known for its highland landscapes and as the gateway to Ruaha National Park, one of the country’s largest wildlife reserves.
  • 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_69ca82894d9081908a832bfce71a4714 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb38464274819080f182b53783fa84 completed March 31, 2026, 2:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce38d1e5508190abf808fa06f89627 completed April 2, 2026, 9:37 a.m.
Created at: March 30, 2026, 4:54 p.m.