Triple
T6120374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nyanja |
E136466
|
entity |
| Predicate | hasDialects |
P4251
|
FINISHED |
| Object | urban Nyanja |
E136466
|
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: urban Nyanja | Statement: [Nyanja, hasDialects, urban Nyanja]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: urban Nyanja Context triple: [Nyanja, hasDialects, urban Nyanja]
-
A.
Nyanja
chosen
Nyanja is a major Bantu language spoken primarily in Malawi, Zambia, Mozambique, and Zimbabwe, known for serving as a lingua franca in parts of southern Africa.
-
B.
Mathare
Mathare is a densely populated informal settlement and neighborhood in Nairobi, Kenya, known for its extensive slums and socio-economic challenges.
-
C.
Nairobi Metropolitan Region
Nairobi Metropolitan Region is the expansive urban and economic area centered on Kenya’s capital, Nairobi, encompassing the city and its surrounding counties and towns.
-
D.
Kibera
Kibera is one of Africa’s largest informal settlements, located in Nairobi, Kenya, known for its dense population, poverty, and vibrant community life.
-
E.
Mbare
Mbare is one of the oldest and most densely populated townships in Harare, Zimbabwe, known as a major transport hub and bustling market area.
- 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_69c0089f851c81909e5e189a617dcff6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05bef8dc08190b917ad7209188c62 |
completed | March 22, 2026, 9:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1257153748190947cd80589620f12 |
completed | March 23, 2026, 11:35 a.m. |
Created at: March 22, 2026, 4:14 p.m.