Triple

T1565868
Position Surface form Disambiguated ID Type / Status
Subject Tabora Region E33430 entity
Predicate hasSettlement P1068 FINISHED
Object Igunga
Igunga is a town and district in central Tanzania known for its agricultural activities, particularly cotton and livestock farming, within the Tabora Region.
 E182641 NE FINISHED

How this triple was built (4 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: Igunga | Statement: [Tabora Region, hasSettlement, Igunga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Igunga
Context triple: [Tabora Region, hasSettlement, Igunga]
  • A. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • B. Apswa
    Apswa is the endonym used by the Abkhaz people to refer to themselves and their language.
  • C. Nzega
    Nzega is a town and district in western Tanzania that serves as an important commercial and transport hub within the Tabora Region.
  • D. Njuká
    Njuká is an alternative name for the Ndyuka language, a creole spoken primarily by the Ndyuka Maroon community in Suriname and French Guiana.
  • E. Murambi
    Murambi is a residential suburb of Mutare, a major city in eastern Zimbabwe.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Igunga
Triple: [Tabora Region, hasSettlement, Igunga]
Generated description
Igunga is a town and district in central Tanzania known for its agricultural activities, particularly cotton and livestock farming, within the Tabora Region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Igunga
Target entity description: Igunga is a town and district in central Tanzania known for its agricultural activities, particularly cotton and livestock farming, within the Tabora Region.
  • A. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • B. Apswa
    Apswa is the endonym used by the Abkhaz people to refer to themselves and their language.
  • C. Nzega
    Nzega is a town and district in western Tanzania that serves as an important commercial and transport hub within the Tabora Region.
  • D. Njuká
    Njuká is an alternative name for the Ndyuka language, a creole spoken primarily by the Ndyuka Maroon community in Suriname and French Guiana.
  • E. Murambi
    Murambi is a residential suburb of Mutare, a major city in eastern Zimbabwe.
  • F. None of above. chosen

Provenance (5 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_69ad51af2de8819087d287d65aabbf1a completed March 8, 2026, 10:38 a.m.
NEDg Description generation batch_69ad523ae04c819099431e09cf1eb953 completed March 8, 2026, 10:40 a.m.
NED2 Entity disambiguation (via description) batch_69ad52b3dcf081909e73fba891e985b2 completed March 8, 2026, 10:43 a.m.
Created at: March 4, 2026, 7:27 p.m.