Triple
T19351880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regio |
E484040
|
entity |
| Predicate | brandLanguageContext |
P56949
|
FINISHED |
| Object | German‑speaking Switzerland |
—
|
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: German‑speaking Switzerland | Statement: [Regio, brandLanguageContext, German‑speaking Switzerland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: brandLanguageContext Context triple: [Regio, brandLanguageContext, German‑speaking Switzerland]
-
A.
brandLanguageVariant
Indicates that one language variant of a brand is related to or derived from another language version of the same brand.
-
B.
languageSpecifies
Indicates that one entity defines or constrains the syntax, semantics, or usage rules that govern how another language or linguistic system is expressed or interpreted.
-
C.
serviceBrandLanguage
chosen
Indicates the language or languages in which a service brand communicates or is presented.
-
D.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
-
E.
languageOfInstitutionalContext
Indicates the language used as the primary medium of communication within an institutional setting or context.
- 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_69d8e8d244f8819080eb1f3491300db2 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e61904a878819084d58ed3b7d8a978 |
completed | April 20, 2026, 12:16 p.m. |
| PD | Predicate disambiguation | batch_69e4dd13a8cc81909cd02668564c9f29 |
completed | April 19, 2026, 1:48 p.m. |
Created at: April 10, 2026, 1:34 p.m.