Triple
T11397613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yakoma language |
E270017
|
entity |
| Predicate | primaryArea |
P19488
|
FINISHED |
| Object | Bangui area |
E148403
|
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: Bangui area | Statement: [Yakoma language, primaryArea, Bangui area]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bangui area Context triple: [Yakoma language, primaryArea, Bangui area]
-
A.
Bangui
chosen
Bangui is the capital and largest city of the Central African Republic, serving as its political, economic, and cultural center.
-
B.
Bunia
Bunia is a city in northeastern Democratic Republic of the Congo that has been a focal point of regional conflict and international peacekeeping efforts.
-
C.
Bamenda
Bamenda is a prominent city in northwestern Cameroon known as a cultural and commercial hub of the Anglophone region.
-
D.
Butembo
Butembo is a major commercial city in eastern Democratic Republic of the Congo, known as a trading hub and economic center in North Kivu.
-
E.
Ekondo-Titi
Ekondo-Titi is a coastal town and commune in Cameroon's Southwest Region, known for its agricultural activities and location near the Ndian River and the Atlantic coast.
- 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_69d6aacdbc6c8190af6dc3d5f5d22836 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d80019d3d48190a2f473deb6eae33a |
completed | April 9, 2026, 7:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58cd74280819092f8c420630f4889 |
completed | April 20, 2026, 2:17 a.m. |
Created at: April 8, 2026, 9:34 p.m.