Triple
T24817762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dyula language |
E620970
|
entity |
| Predicate | majorCountryOfUse |
P135380
|
FINISHED |
| Object | Côte d’Ivoire |
—
|
NE NERFINISHED |
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: Côte d’Ivoire | Statement: [Dyula language, majorCountryOfUse, Côte d’Ivoire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorCountryOfUse Context triple: [Dyula language, majorCountryOfUse, Côte d’Ivoire]
-
A.
primaryUseCountry
chosen
Indicates the country in which something is primarily used or most commonly utilized.
-
B.
countryOrRegionUsed
Indicates that something is used within, or applies to, a specific country or geographic region.
-
C.
mainCountry
Indicates that one country is the primary or most significant country associated with a given entity or context.
-
D.
associatedCountry
Indicates that there is a relevant connection or linkage between an entity and a specific country, such as origin, operation, or affiliation.
-
E.
countryOfSymbolicUse
Indicates the country in which something (such as a symbol, object, or element) is used in a symbolic or representative way.
- 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_69e2fabfd4648190bd0e5c7f4dbb6cab |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f44a417a58819081777e18dda149fd |
completed | May 1, 2026, 6:37 a.m. |
| PD | Predicate disambiguation | batch_69f442b8479c8190a7c8e416ac9e28a0 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 5:03 a.m.