MyClass
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)
MyClass has 10 facts recorded in Dontopedia across 3 references, with 2 live disagreements.
Mostly:rdf:type(3), has property(3), has property type(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 Querying
ex:data-querying
belongsToManyBelongs to Many(1)
- Data Object
ex:data-object
definesClassDefines Class(1)
- Schema Creation
ex:schema-creation
inverseOfInverse of(1)
- My Property
ex:my-property
Other facts (8)
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 | Class | [1] |
| Rdf:type | Weaviate Class | [2] |
| Rdf:type | Data Model Class | [3] |
| Has Property | My Property | [1] |
| Has Property | My Text Property | [3] |
| Has Property | Vector Data | [3] |
| Has Property Type | Text Data Type | [1] |
| Has Name | MyClass | [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 (3)
ctx:claims/beam/d48f6c81-cdac-45b4-b6d4-28dd17a6eaaa- full textbeam-chunktext/plain1 KB
doc:beam/d48f6c81-cdac-45b4-b6d4-28dd17a6eaaaShow excerpt
client = weaviate.Client("http://localhost:8080") # Create a new schema for my data schema = { "class": "MyClass", "properties": [ {"name": "my_property", "dataType": ["text"]} ] } # Create the schema in Weaviate clien…
ctx:claims/beam/131a150d-00ba-472b-bdc7-209aa22bc91dctx:claims/beam/b199aa18-2d4a-4e37-a971-f1f5b557a5b8- full textbeam-chunktext/plain821 B
doc:beam/b199aa18-2d4a-4e37-a971-f1f5b557a5b8Show excerpt
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 = {"vector": query_vector_256} result_256 = ( client.query.get("MyC…
See also
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