Triple
T20659184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mwenga Territory |
E507710
|
entity |
| Predicate | administrativeCenter |
P1474
|
FINISHED |
| Object | Mwenga |
—
|
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: Mwenga | Statement: [Mwenga Territory, administrativeCenter, Mwenga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mwenga Context triple: [Mwenga Territory, administrativeCenter, Mwenga]
-
A.
Mwenga
chosen
Mwenga is a town in the South Kivu province of the Democratic Republic of the Congo that serves as an administrative and commercial center for the surrounding region.
-
B.
Mbugwe
Mbugwe are an ethnic group in northern Tanzania known for their distinct Bantu language and cultural traditions.
-
C.
Lugazi
Lugazi is a town in central Uganda known for its sugar plantations and location along the Kampala–Jinja highway.
-
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.
Mumbwa
Mumbwa is a town in central Zambia known as an agricultural and mining hub west of the capital, Lusaka.
- 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_69e0b4bf58c081908e52a4500e03ff83 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b2eff7a88190be0bdea227616e02 |
completed | April 20, 2026, 11:12 p.m. |
Created at: April 16, 2026, 11:43 a.m.