Triple

T20354431
Position Surface form Disambiguated ID Type / Status
Subject Morogoro Region E496104 entity
Predicate bordersRegion P224 FINISHED
Object Tanga 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: Tanga Region | Statement: [Morogoro Region, bordersRegion, Tanga Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tanga Region
Context triple: [Morogoro Region, bordersRegion, Tanga Region]
  • A. Tanga Region chosen
    Tanga Region is a coastal administrative region in northeastern Tanzania known for its port city of Tanga, Indian Ocean shoreline, and proximity to the Usambara Mountains.
  • B. Tabora Region
    Tabora Region is an inland administrative region in western Tanzania known historically as a key hub for trade and rail transport.
  • C. Lindi Region
    Lindi Region is a coastal administrative region in southern Tanzania known for its historical Swahili settlements and Indian Ocean shoreline.
  • D. Simiyu Region
    Simiyu Region is an administrative region in northern Tanzania known for its predominantly rural economy based on agriculture and livestock.
  • E. Nyanza region
    Nyanza region is an area in western Kenya along Lake Victoria, known for its predominantly Luo population and the city of Kisumu as its main urban center.
  • 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.