Dontopedia

do

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

do has 17 facts recorded in Dontopedia across 10 references, with 2 live disagreements.

17 facts·5 predicates·10 sources·2 in dispute

Mostly:rdf:type(10), returns(1), executes query(1)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Inbound mentions (11)

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.

chainedMethodChained Method(2)

callsDoCalls Do(1)

callsMethodCalls Method(1)

chainedWithChained With(1)

chainedWithDoMethodChained With Do Method(1)

executesQueryExecutes Query(1)

executesWithExecutes With(1)

hasMethodHas Method(1)

invokesDoMethodInvokes Do Method(1)

usesMethodUses Method(1)

Other facts (4)

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.

4 facts
PredicateValueRef
ReturnsQuery Result Object[1]
Executes QueryQuery Operation[3]
ExecutesQuery Operation[8]
Invoked onQuery Builder[9]

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.

typebeam/70bbc43a-27da-4ee6-abde-0b83af52d874
ex:ExecutionMethod
labelbeam/70bbc43a-27da-4ee6-abde-0b83af52d874
do
returnsbeam/70bbc43a-27da-4ee6-abde-0b83af52d874
ex:query-result-object
typebeam/5649feba-310c-425b-9ed5-db5583522d98
ex:Method
typebeam/e3b0d393-cb26-4e01-b5f0-47981803de05
ex:ExecutionMethod
executesQuerybeam/e3b0d393-cb26-4e01-b5f0-47981803de05
ex:query-operation
typebeam/cbaeb875-e16f-44dd-bc0f-36b3945d0935
ex:MethodCall
labelbeam/cbaeb875-e16f-44dd-bc0f-36b3945d0935
Do Method
typebeam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0
ex:Method
typebeam/131a150d-00ba-472b-bdc7-209aa22bc91d
ex:ExecutionMethod
typebeam/df58a3ab-2df5-43d0-a3c7-d866e2d0138c
ex:ExecutionMethod
typebeam/ea34a816-3421-425e-97a9-50206b2c6248
ex:MethodCall
labelbeam/ea34a816-3421-425e-97a9-50206b2c6248
.do()
executesbeam/ea34a816-3421-425e-97a9-50206b2c6248
ex:query-operation
typebeam/7930b608-9757-4a86-9aa2-c6ca10571913
ex:MethodCall
invokedOnbeam/7930b608-9757-4a86-9aa2-c6ca10571913
ex:query-builder
typebeam/9d96f8cb-54e9-48bd-a699-50a1796601b9
ex:QueryExecutionMethod

References (10)

10 references
  1. ctx:claims/beam/70bbc43a-27da-4ee6-abde-0b83af52d874
  2. ctx:claims/beam/5649feba-310c-425b-9ed5-db5583522d98
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5649feba-310c-425b-9ed5-db5583522d98
      Show excerpt
      client.data_object.create(data[0], "MyClass") print("Data inserted successfully.") except Exception as e: print(f"Failed to insert data: {e}") ``` #### 4. Check Query Implementation Ensure the query is correctly implemented and
  3. ctx: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
  4. ctx: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
  5. ctx: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
  6. ctx:claims/beam/131a150d-00ba-472b-bdc7-209aa22bc91d
  7. ctx: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
  8. ctx:claims/beam/ea34a816-3421-425e-97a9-50206b2c6248
  9. ctx:claims/beam/7930b608-9757-4a86-9aa2-c6ca10571913
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7930b608-9757-4a86-9aa2-c6ca10571913
      Show excerpt
      self.name = name self.vector = vector # Add some test data test_data = [ TestData("Test 1", [0.1, 0.2, 0.3]), TestData("Test 2", [0.4, 0.5, 0.6]), ] # Upload the test data to Weaviate for data in test_data: cli
  10. ctx:claims/beam/9d96f8cb-54e9-48bd-a699-50a1796601b9

See also

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