Triple
T23258011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Internet in Sierra Leone |
E581923
|
entity |
| Predicate | usageContexts |
P2529
|
FINISHED |
| Object | mobile money services |
—
|
LITERAL 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: mobile money services | Statement: [Internet in Sierra Leone, usageContexts, mobile money services]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usageContexts Context triple: [Internet in Sierra Leone, usageContexts, mobile money services]
-
A.
modernUsageContext
Indicates the contemporary or current context in which something is used, applied, or functions.
-
B.
usageType
chosen
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
C.
usageCategory
Indicates the classification of how something is used or the type of usage it falls under.
-
D.
originalUseContext
Indicates the original situation, setting, or context in which something was intended to be used or applied.
-
E.
usageAmong
Indicates how frequently or in what manner something is used within a particular group, context, or population.
- F. None of above.
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_69e246079f58819085eaa9c260906880 |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f194c710c48190aff03d210642a043 |
completed | April 29, 2026, 5:19 a.m. |
| PD | Predicate disambiguation | batch_69effce4d704819092826931d430e8c4 |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:11 p.m.