Triple

T1583869
Position Surface form Disambiguated ID Type / Status
Subject Kigoma E34026 entity
Predicate regionCapitalOf P204 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: [Kigoma, regionCapitalOf, Kigoma Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kigoma Region
Context triple: [Kigoma, regionCapitalOf, 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. 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. Simiyu Region
    Simiyu Region is an administrative region in northern Tanzania known for its predominantly rural economy based on agriculture and livestock.
  • D. Kagera Region
    Kagera Region is a northwestern region of Tanzania bordering Lake Victoria and several East African countries, known for its diverse ethnic groups, agriculture, and historical significance.
  • E. Singida Region
    Singida Region is an administrative region in central Tanzania known for its semi-arid climate, agriculture, and role as a transport crossroads.
  • 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_69a885fceb2c8190b47e0f7c0aefbff0 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a908f0e72c8190bb7a2a0c77379060 completed March 5, 2026, 4:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc98b2b0081909d10b22d59c5e653 completed March 8, 2026, 7:10 p.m.
Created at: March 4, 2026, 7:27 p.m.