Triple
T12155694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Senegambian languages |
E289568
|
entity |
| Predicate | hasNotableLanguage |
P7390
|
FINISHED |
| Object | Jola |
E193598
|
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: Jola | Statement: [Senegambian languages, hasNotableLanguage, Jola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jola Context triple: [Senegambian languages, hasNotableLanguage, Jola]
-
A.
Jola
chosen
Jola is a Niger–Congo language spoken primarily by the Jola people of southern Senegal and neighboring regions.
-
B.
Dyola
Dyola is an alternative name for the Jola language spoken by the Jola people of West Africa, primarily in Senegal, Gambia, and Guinea-Bissau.
-
C.
Murça
Murça is a small municipality in northern Portugal, known for its wine production and location within the Douro region.
-
D.
Kaloum
Kaloum is the central urban commune of Conakry, Guinea, encompassing the city’s historic core, main government institutions, and port area.
-
E.
Hendrina
Hendrina is a small town in Mpumalanga, South Africa, known for its coal mining and nearby power stations.
- 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_69d6ab4c6710819097a9d228382dde43 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915c1673c8190830cd15525d16869 |
completed | April 10, 2026, 3:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f69c8d408190abbc900deb534045 |
completed | May 2, 2026, 1:05 p.m. |
Created at: April 8, 2026, 9:50 p.m.