Triple
T1583808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fipa people |
E34025
|
entity |
| Predicate | subregion |
P747
|
FINISHED |
| Object | Sumbawanga area |
E189396
|
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: Sumbawanga area | Statement: [Fipa people, subregion, Sumbawanga area]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sumbawanga area Context triple: [Fipa people, subregion, Sumbawanga area]
-
A.
Rukwa Region
chosen
Rukwa Region is an administrative region in southwestern Tanzania known for its location along Lake Rukwa and its largely rural, agricultural economy.
-
B.
Simiyu Region
Simiyu Region is an administrative region in northern Tanzania known for its predominantly rural economy based on agriculture and livestock.
-
C.
Kigoma Region
Kigoma Region is a western Tanzanian administrative region along Lake Tanganyika, known for its biodiversity and as a center for primate research.
-
D.
Singida Region
Singida Region is an administrative region in central Tanzania known for its semi-arid climate, agriculture, and role as a transport crossroads.
-
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_69a885fceb2c8190b47e0f7c0aefbff0 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a908f0e72c8190bb7a2a0c77379060 |
completed | March 5, 2026, 4:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada967e01481908802baf2de7ad6a7 |
completed | March 8, 2026, 4:52 p.m. |
Created at: March 4, 2026, 7:27 p.m.