Dontopedia

representative subset

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

representative subset has 4 facts recorded in Dontopedia across 2 references.

4 facts·2 predicates·2 sources
Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (3)

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.

capturesCaptures(2)

ensuresEnsures(1)

Other facts (3)

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.

3 facts
PredicateValueRef
Rdf:typeData Subset[1]
Rdf:typeData Subset[2]
Property ofLookup Operations[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.

typebeam/fdf83faa-03c9-4e80-9792-6fa66000e80d
ex:DataSubset
propertyOfbeam/fdf83faa-03c9-4e80-9792-6fa66000e80d
ex:lookup-operations
typebeam/534be9d2-c97a-4867-8efb-8f090879be4b
ex:DataSubset
labelbeam/534be9d2-c97a-4867-8efb-8f090879be4b
representative subset

References (2)

2 references
  1. ctx:claims/beam/fdf83faa-03c9-4e80-9792-6fa66000e80d
    • full textbeam-chunk
      text/plain1 KBdoc:beam/fdf83faa-03c9-4e80-9792-6fa66000e80d
      Show excerpt
      logging.basicConfig(level=logging.INFO) def thesaurus_lookup(word): start_time = time.time() # Simulate the lookup time.sleep(0.1) end_time = time.time() logging.info(f"Lookup took {end_time - start_time} seconds")
  2. ctx:claims/beam/534be9d2-c97a-4867-8efb-8f090879be4b
    • full textbeam-chunk
      text/plain1 KBdoc:beam/534be9d2-c97a-4867-8efb-8f090879be4b
      Show excerpt
      logging.info(f"Thesaurus lookup for '{word}' took {end_time - start_time:.6f} seconds") return ["synonym1", "synonym2"] # Test the lookup words = ["happy", "sad", "angry"] * 100 # Simulate a larger dataset for word in words:

See also

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