Triple
T15146729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carte Noire |
E361826
|
entity |
| Predicate | hasLanguageOfBrandName |
P98842
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Carte Noire, hasLanguageOfBrandName, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageOfBrandName Context triple: [Carte Noire, hasLanguageOfBrandName, French]
-
A.
hasBrandName
Indicates that an entity is associated with or identified by a specific brand name.
-
B.
hasEnglishName
Indicates that an entity is associated with a name expressed in the English language.
-
C.
officialLanguageOfBranding
chosen
Indicates that a particular language is used as the official or primary language in the branding or marketing materials of an entity.
-
D.
brandLanguageVariant
Indicates that one language variant of a brand is related to or derived from another language version of the same brand.
-
E.
serviceBrandLanguage
Indicates the language or languages in which a service brand communicates or is presented.
- 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_69d85a0759908190b8a051d2e2a1cbe6 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005c825a481909d00098b0e743365 |
completed | April 15, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69deb9713fe881909dec2fd3f6c84b39 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:07 a.m.