Triple
T10924587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Habiba Akumu Nyanjoga |
E258032
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Nyanjoga |
E258032
|
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: Nyanjoga | Statement: [Habiba Akumu Nyanjoga, familyName, Nyanjoga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nyanjoga Context triple: [Habiba Akumu Nyanjoga, familyName, Nyanjoga]
-
A.
Nyanjoga
chosen
Nyanjoga is a Kenyan surname associated with individuals such as Habiba Akumu Nyanjoga.
-
B.
Nyanda
Nyanda is the former name of Masvingo, a historic city in southeastern Zimbabwe known for its proximity to the Great Zimbabwe ruins.
-
C.
Cinyanja
Cinyanja is a Bantu language spoken primarily in Malawi, Zambia, Mozambique, and Zimbabwe, where it serves as an important lingua franca in parts of southern Africa.
-
D.
Manyoni
Manyoni is a town and district headquarters in central Tanzania known for its location along major road and rail routes in the Singida Region.
-
E.
Kanyaga
"Kanyaga" is a popular Tanzanian Bongo Flava hit song by Diamond Platnumz known for its energetic beat and danceable style.
- 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7708f7ab48190b60a4bb8fdb17c8e |
completed | April 9, 2026, 9:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e217369b648190914c58db6f6e0200 |
completed | April 17, 2026, 11:19 a.m. |
Created at: April 8, 2026, 9:22 p.m.