Triple
T38179140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cameroon |
E1005103
|
entity |
| Predicate | regionalLanguagesCount |
P14732
|
FINISHED |
| Object | over 200 local languages |
—
|
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: over 200 local languages | Statement: [Cameroon, regionalLanguagesCount, over 200 local languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionalLanguagesCount Context triple: [Cameroon, regionalLanguagesCount, over 200 local languages]
-
A.
languageRegionsRepresented
Indicates that certain geographic or cultural regions are represented or covered through specific languages.
-
B.
regionOfMajorLanguage
Indicates the geographic region where a particular language is predominantly spoken or holds major usage.
-
C.
estimatedNumberOfLanguages
chosen
Indicates the approximate count of distinct languages associated with an entity, typically based on estimation rather than an exact measurement.
-
D.
languageVarietyRegionOf
Indicates that a particular language variety is associated with or used in a specific geographic region.
-
E.
numberOfOfficialLanguagesInCountry
Indicates the total count of officially recognized languages within a given country.
- 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_69f76dbc22c481908139b694ffde7a0c |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a0087de41c48190b2743a26b6d65409 |
completed | May 10, 2026, 1:27 p.m. |
| PD | Predicate disambiguation | batch_6a00870a8bc48190be1385579b8cc1dd |
completed | May 10, 2026, 1:24 p.m. |
Created at: May 3, 2026, 4:29 p.m.