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My Class

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)

My Class has 13 facts recorded in Dontopedia across 5 references, with 3 live disagreements.

13 facts·8 predicates·5 sources·3 in dispute

Mostly:rdf:type(3), is referenced in(3), used in(2)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Is Referenced inin disputeisReferencedIn

Used inin disputeusedIn

Has PropertyhasProperty

Is Used ConsistentlyisUsedConsistently

  • true[4]all time · 131a150d 00ba 472b Bdc7 209aa22bc91d

Rdfs:labelrdfs:label

  • MyClass[5]sourceall time · F80d8de8 0d2a 446e Ac9c Fc4672dce4f0

Inverse ofinverseOf

Has VectorizerhasVectorizer

Inbound mentions (10)

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.

belongsToManyBelongs to Many(2)

specifiesClassSpecifies Class(2)

storedInStored in(2)

classArgumentClass Argument(1)

createsClassCreates Class(1)

definesClassDefines Class(1)

retrievesFromRetrieves From(1)

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.

13 undated
—
hasPropertybeam/df58a3ab-2df5-43d0-a3c7-d866e2d0138c
ex:property-vector-data
—
hasVectorizerbeam/e3b0d393-cb26-4e01-b5f0-47981803de05
ex:text2vec-contextionary
—
inverseOfbeam/cbaeb875-e16f-44dd-bc0f-36b3945d0935
ex:definesClass
—
isReferencedInbeam/131a150d-00ba-472b-bdc7-209aa22bc91d
ex:data-insertion-section
—
isReferencedInbeam/131a150d-00ba-472b-bdc7-209aa22bc91d
ex:data-querying-section
—
isReferencedInbeam/131a150d-00ba-472b-bdc7-209aa22bc91d
ex:vector-search-section
—
isUsedConsistentlybeam/131a150d-00ba-472b-bdc7-209aa22bc91d
true
—
labelbeam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0
MyClass
—
typebeam/cbaeb875-e16f-44dd-bc0f-36b3945d0935
ex:Class
—
typebeam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0
ex:ClassDefinition
—
typebeam/df58a3ab-2df5-43d0-a3c7-d866e2d0138c
ex:WeaviateClass
—
usedInbeam/e3b0d393-cb26-4e01-b5f0-47981803de05
ex:data-insertion-step
—
usedInbeam/e3b0d393-cb26-4e01-b5f0-47981803de05
ex:data-query-step

References (5)

5 references
  1. [1]beam-chunk2 facts
    customctx:claims/beam/df58a3ab-2df5-43d0-a3c7-d866e2d0138c
    • full textbeam-chunk
      text/plain1 KBdoc:beam/df58a3ab-2df5-43d0-a3c7-d866e2d0138c
      Show 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 …
  2. [2]beam-chunk3 facts
    customctx:claims/beam/e3b0d393-cb26-4e01-b5f0-47981803de05
    • full textbeam-chunk
      text/plain1 KBdoc:beam/e3b0d393-cb26-4e01-b5f0-47981803de05
      Show excerpt
      client = weaviate.Client("http://localhost:8080") # Define the schema schema = { "class": "MyClass", "properties": [ {"name": "my_text_property", "dataType": ["text"]}, {"name": "my_vector_property", "dataType": ["v…
  3. [3]beam-chunk2 facts
    customctx:claims/beam/cbaeb875-e16f-44dd-bc0f-36b3945d0935
    • full textbeam-chunk
      text/plain1 KBdoc:beam/cbaeb875-e16f-44dd-bc0f-36b3945d0935
      Show excerpt
      print("Query successful:") print(result) ``` ### Example with Vector Search If you want to perform a vector search and retrieve both text and vector data, you can use the `nearVector` filter: ```python # Perform a vector search query_vec…
  4. [4]131a150d 00ba 472b Bdc7 209aa22bc91d4 facts
    customctx:claims/beam/131a150d-00ba-472b-bdc7-209aa22bc91d
  5. [5]beam-chunk2 facts
    customctx:claims/beam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0
      Show excerpt
      # Create the schema in Weaviate client.schema.create_class(schema) print("Schema created successfully.") ``` #### Inserting Data When inserting data, you can specify which vector property to use based on the vector size. ```python # Add …

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