Triple
T6138972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Senate of Rwanda |
E136909
|
entity |
| Predicate | languageUsed |
P238
|
FINISHED |
| Object | Kinyarwanda |
E65621
|
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: Kinyarwanda | Statement: [Senate of Rwanda, languageUsed, Kinyarwanda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kinyarwanda Context triple: [Senate of Rwanda, languageUsed, Kinyarwanda]
-
A.
Kinyarwanda
chosen
Kinyarwanda is a Bantu language spoken primarily in Rwanda, where it serves as a national and widely used lingua franca.
-
B.
Kirundi
Kirundi is a Bantu language primarily spoken in Burundi and neighboring regions of East Africa.
-
C.
Kitwe
Kitwe is a major mining and industrial city in Zambia’s Copperbelt Province, known as one of the country’s largest urban and economic centers.
-
D.
Kikongo
Kikongo is a Bantu language widely spoken in Central Africa, particularly in the western regions of the Democratic Republic of the Congo and neighboring countries.
-
E.
Kimbundu
Kimbundu is a major Bantu language spoken primarily in northwestern Angola, especially around the capital Luanda, by the Ambundu people.
- 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_69c008a179388190a3b5a081bbf46d55 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05c855a2481909801de9fd55686a4 |
completed | March 22, 2026, 9:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c14173d2288190920b719e221a929c |
completed | March 23, 2026, 1:34 p.m. |
Created at: March 22, 2026, 4:15 p.m.