Triple

T15612314
Position Surface form Disambiguated ID Type / Status
Subject Manyara Region E375327 entity
Predicate borders P224 FINISHED
Object Dodoma Region E472363 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: Dodoma Region | Statement: [Manyara Region, borders, Dodoma Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dodoma Region
Context triple: [Manyara Region, borders, Dodoma Region]
  • A. Dodoma Region chosen
    Dodoma Region is an administrative region in central Tanzania that includes the national capital city, Dodoma.
  • B. Arusha Region
    Arusha Region is an administrative region in northern Tanzania known for its tourism hub city of Arusha and proximity to major national parks and Mount Kilimanjaro.
  • 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. Pwani Region
    Pwani Region is a coastal administrative region in eastern Tanzania known for its Swahili culture, Indian Ocean shoreline, and proximity to Dar es Salaam.
  • E. Dar es Salaam Region
    Dar es Salaam Region is a coastal administrative region in eastern Tanzania that encompasses the country’s largest city and main economic hub.
  • 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_69d85ccf2794819096cda4cbcb02d478 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e8148a0819087d6d69cc84487ca completed April 16, 2026, 2:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffb0335a0c8190ade4c2f78df3d113 completed May 9, 2026, 10:07 p.m.
Created at: April 10, 2026, 4:13 a.m.