Triple
T249684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kigoma Region |
E5115
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Uvinza
Uvinza is a town in western Tanzania known historically for its salt production and location along the Central Line railway in Kigoma Region.
|
E37146
|
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: Uvinza | Statement: [Kigoma Region, hasTown, Uvinza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uvinza Context triple: [Kigoma Region, hasTown, Uvinza]
-
A.
Kigoma
Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
-
B.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
-
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.
Kasulu
Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
-
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. 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: Uvinza Triple: [Kigoma Region, hasTown, Uvinza]
Generated description
Uvinza is a town in western Tanzania known historically for its salt production and location along the Central Line railway in Kigoma Region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Uvinza Target entity description: Uvinza is a town in western Tanzania known historically for its salt production and location along the Central Line railway in Kigoma Region.
-
A.
Kigoma
Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
-
B.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
-
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.
Kasulu
Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
-
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. 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_69a257c4bf688190a46ebbf411ab7473 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d3728f0819086214ccc2db2305a |
completed | Feb. 28, 2026, 3:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a39d0313b48190a3e8e1667a051610 |
completed | March 1, 2026, 1:57 a.m. |
| NEDg | Description generation | batch_69a39d732c64819086a6e11df23e60ce |
completed | March 1, 2026, 1:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a39de4511c8190bf668bc74ba57231 |
completed | March 1, 2026, 2:01 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.