MyClass
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)
MyClass has 8 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Mostly:rdf:type(2), has part(1), has property count(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
targetsClassTargets Class(2)
- Data Insertion
ex:data-insertion - Data Query
ex:data-query
belongsToClassBelongs to Class(1)
- Property My Property
ex:property-my-property
causesCauses(1)
- Schema Creation
ex:schema-creation
definesDefines(1)
- Example Schema Definition
ex:example-schema-definition
Other facts (7)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Schema Class | [1] |
| Rdf:type | Weaviate Class | [2] |
| Has Part | Property My Property | [1] |
| Has Property Count | 1 | [1] |
| Belongs to Tenant | Default Tenant | [1] |
| Is Custom Class | true | [1] |
| Has Property | Property My Text | [2] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (2)
ctx:claims/beam/76ef050f-d3ad-4526-bb06-9c01f7701d3a- full textbeam-chunktext/plain1 KB
doc:beam/76ef050f-d3ad-4526-bb06-9c01f7701d3aShow excerpt
print(f"Failed to create schema: {e}") # Add some data to the schema data = [{"my_property": "Hello World"}] try: client.data_object.create(data[0], "MyClass") print("Data inserted successfully.") except Exception as e: pr…
ctx:claims/beam/df58a3ab-2df5-43d0-a3c7-d866e2d0138c- full textbeam-chunktext/plain1 KB
doc:beam/df58a3ab-2df5-43d0-a3c7-d866e2d0138cShow excerpt
.with_near_vector(near_vector_128) .with_limit(10) .do() ) print("Vector search query successful (size 128):") print(result_128) query_vector_256 = [0.5, 0.6, 0.7, 0.8] * 64 # Example query vector of size 256 near_vector_256 …
See also
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