Triple
T2118003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mwanza |
E43851
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Mwanza Region |
E208934
|
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: Mwanza Region | Statement: [Mwanza, partOf, Mwanza Region]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mwanza Region Context triple: [Mwanza, partOf, Mwanza Region]
-
A.
Mwanza Region
chosen
Mwanza Region is an administrative region in northwestern Tanzania, located along the southern shores of Lake Victoria and known as a major economic and cultural center, including for the Sukuma people.
-
B.
Simiyu Region
Simiyu Region is an administrative region in northern Tanzania known for its predominantly rural economy based on agriculture and livestock.
-
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.
Kunene Region
Kunene Region is a sparsely populated, northwestern region of Namibia known for its rugged landscapes, desert-adapted wildlife, and remote Atlantic coastline.
-
E.
Tabora Region
Tabora Region is an inland administrative region in western Tanzania known historically as a key hub for trade and rail transport.
- 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_69a88717cfe48190b7ecdd68c824848a |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abbb3117c081908c5e748a869d1f9f |
completed | March 7, 2026, 5:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae307b08148190aa201ac038ce9944 |
completed | March 9, 2026, 2:29 a.m. |
Created at: March 4, 2026, 7:44 p.m.