Triple

T1565833
Position Surface form Disambiguated ID Type / Status
Subject Tabora Region E33430 entity
Predicate borderedBy P224 FINISHED
Object Kigoma Region E5115 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: Kigoma Region | Statement: [Tabora Region, borderedBy, Kigoma Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kigoma Region
Context triple: [Tabora Region, borderedBy, Kigoma Region]
  • A. Kigoma Region chosen
    Kigoma Region is a western Tanzanian administrative region along Lake Tanganyika, known for its biodiversity and as a center for primate research.
  • B. Kigoma
    Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
  • C. Tabora Region
    Tabora Region is an inland administrative region in western Tanzania known historically as a key hub for trade and rail transport.
  • D. South Kivu
    South Kivu is a conflict-affected province in the eastern Democratic Republic of the Congo, known for its ethnic tensions, mineral wealth, and presence of numerous armed groups.
  • E. 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.
  • 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_69a885f11b048190935025a035302715 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abb2308bec81909d1660934eff171b completed March 7, 2026, 5:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad469474c88190b80d6d7a30c9e19d completed March 8, 2026, 9:51 a.m.
Created at: March 4, 2026, 7:27 p.m.