Triple

T20354433
Position Surface form Disambiguated ID Type / Status
Subject Morogoro Region E496104 entity
Predicate bordersRegion P224 FINISHED
Object Njombe Region NE NERFINISHED

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: Njombe Region | Statement: [Morogoro Region, bordersRegion, Njombe Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Njombe Region
Context triple: [Morogoro Region, bordersRegion, Njombe Region]
  • A. Njombe Region chosen
    Njombe Region is an administrative region in southern Tanzania known for its highland climate, agriculture (especially tea and timber), and proximity to the Southern Highlands.
  • B. Rukwa Region
    Rukwa Region is an administrative region in southwestern Tanzania known for its location along Lake Rukwa and its largely rural, agricultural economy.
  • 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. Kigoma Region
    Kigoma Region is a western Tanzanian administrative region along Lake Tanganyika, known for its biodiversity and as a center for primate research.
  • E. Morogoro Region
    Morogoro Region is an administrative region in eastern Tanzania known for its diverse landscapes, agriculture, and proximity to major wildlife areas such as Mikumi National Park.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4a3f7f48190b37f354574028ca6 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e67852ca9881908a5af18005639859 completed April 20, 2026, 7:02 p.m.
Created at: April 16, 2026, 11:25 a.m.