Triple
T4621331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German-speaking Denmark |
E100991
|
entity |
| Predicate | includesLanguageVariety |
P2177
|
FINISHED |
| Object | Standard German |
—
|
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: Standard German | Statement: [German-speaking Denmark, includesLanguageVariety, Standard German]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesLanguageVariety Context triple: [German-speaking Denmark, includesLanguageVariety, Standard German]
-
A.
includesLanguage
chosen
Indicates that one entity contains, supports, or makes use of a specified language as part of its content, functionality, or representation.
-
B.
primaryLanguageVariety
Indicates the main dialect or specific variety of a language that an entity primarily uses.
-
C.
languageDiversity
Indicates the degree to which multiple distinct languages are present and used within a given context or population.
-
D.
linguisticVariant
Indicates that one linguistic form is an alternative version or expression of another within the same or closely related language context.
-
E.
hasColloquialVariety
Indicates that one linguistic form, expression, or variety is an informal, colloquial counterpart or version of another.
- 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_69bd43d0497c8190ac23c65c5804846a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd59e560f481908abb1a97b4ff5795 |
completed | March 20, 2026, 2:29 p.m. |
| PD | Predicate disambiguation | batch_69bd5231db7c8190b38d4fdbad8bf842 |
completed | March 20, 2026, 1:57 p.m. |
Created at: March 20, 2026, 1:12 p.m.