Triple

T7978279
Position Surface form Disambiguated ID Type / Status
Subject Mpanda District E185500 entity
Predicate near P350 FINISHED
Object Rukwa Region E189396 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: Rukwa Region | Statement: [Mpanda District, near, Rukwa Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rukwa Region
Context triple: [Mpanda District, near, Rukwa Region]
  • A. Rukwa Region chosen
    Rukwa Region is an administrative region in southwestern Tanzania known for its location along Lake Rukwa and its largely rural, agricultural economy.
  • B. 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.
  • C. 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.
  • 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. Mbeya Region
    Mbeya Region is an administrative region in southwestern Tanzania known for its mountainous landscapes, agriculture, and role as a transport hub near the borders with Malawi and Zambia.
  • 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_69ca829851908190b4e03829353ee7c3 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3bf84b1081908e60a556d984aad6 completed March 31, 2026, 3:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce8828faf48190927b2a6680f6b4d8 completed April 2, 2026, 3:15 p.m.
Created at: March 30, 2026, 5:14 p.m.