Triple

T1565844
Position Surface form Disambiguated ID Type / Status
Subject Tabora Region E33430 entity
Predicate contains P35 FINISHED
Object Tabora Urban District E180266 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: Tabora Urban District | Statement: [Tabora Region, contains, Tabora Urban District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tabora Urban District
Context triple: [Tabora Region, contains, Tabora Urban District]
  • A. Tabora chosen
    Tabora is a historic town in western Tanzania known as a regional trade center and former hub of 19th-century caravan routes.
  • 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. Montaza district
    Montaza district is a coastal area in Alexandria, Egypt, known for its expansive royal gardens, beaches, and historic palaces.
  • D. Urambo District
    Urambo District is an administrative district in western Tanzania known for its rural communities and tobacco farming, located within the Tabora Region.
  • E. Nzega District
    Nzega District is an administrative district in central Tanzania known for its rural communities and agriculture, located within the country’s Tabora Region.
  • 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_69ad58bb94888190bcf5ea49e7638cb4 completed March 8, 2026, 11:08 a.m.
Created at: March 4, 2026, 7:27 p.m.