Triple

T20131493
Position Surface form Disambiguated ID Type / Status
Subject Northern Tanzania E490904 entity
Predicate hasIndigenousPeople P194 FINISHED
Object Chagga 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: Chagga | Statement: [Northern Tanzania, hasIndigenousPeople, Chagga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chagga
Context triple: [Northern Tanzania, hasIndigenousPeople, Chagga]
  • A. Chagga chosen
    Chagga is a Bantu language spoken primarily by the Chagga people on the slopes of Mount Kilimanjaro in northern Tanzania.
  • B. Murang’a
    Murang’a is a town in central Kenya that serves as an important commercial and cultural hub in a region historically associated with the Kikuyu community.
  • C. Mhangura
    Mhangura is a small mining town in northern Zimbabwe known historically for its copper production.
  • D. Chang'ombe
    Chang'ombe is a neighborhood in Dar es Salaam, Tanzania, known for its residential areas and educational institutions, including the University of Dar es Salaam’s constituent colleges.
  • E. Wazaramo
    Wazaramo are a Bantu-speaking ethnic group native to the coastal and near-coastal regions around Dar es Salaam in eastern Tanzania.
  • 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_69da62651a0c8190a3e05e95e056a66b completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e66762f0448190b7dbbc665e179ffc completed April 20, 2026, 5:50 p.m.
Created at: April 11, 2026, 11:31 p.m.