Dontopedia

Filter Action

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

Filter Action has 9 facts recorded in Dontopedia across 3 references, with 1 live disagreement.

9 facts·7 predicates·3 sources·1 in dispute

Mostly:rdf:type(3), filter by(1), target(1)

Maturity scale raw canonical shape-checked rule-derived certified

Other facts (9)

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.

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/d6064f19-9b41-4bd3-9a49-b0d4d0837739
ex:FilteringOperation
filterBybeam/d6064f19-9b41-4bd3-9a49-b0d4d0837739
ex:owner-account-id
typebeam/e37a7536-81bf-426c-bec2-f065816eeca3
ex:ProcessingAction
targetbeam/e37a7536-81bf-426c-bec2-f065816eeca3
ex:minor-discrepancies
resultbeam/e37a7536-81bf-426c-bec2-f065816eeca3
ex:significant-mismatches-remaining
removesbeam/e37a7536-81bf-426c-bec2-f065816eeca3
ex:minor-discrepancies
typebeam/b27efc86-7008-4384-852a-049d06d255cb
ex:SetRemoval
executesWhenbeam/b27efc86-7008-4384-852a-049d06d255cb
ex:filter-condition
removesFrombeam/b27efc86-7008-4384-852a-049d06d255cb
ex:filtered-synonyms

References (3)

3 references
  1. ctx:claims/beam/d6064f19-9b41-4bd3-9a49-b0d4d0837739
    • full textbeam-chunk
      text/plain1 KBdoc:beam/d6064f19-9b41-4bd3-9a49-b0d4d0837739
      Show excerpt
      If the AMI is owned by another AWS account, you need to ensure that it is shared with your account. The owner of the AMI can share it with you by specifying your account ID. #### Sharing an AMI: 1. **Log in to the AWS Management Console**
  2. ctx:claims/beam/e37a7536-81bf-426c-bec2-f065816eeca3
  3. ctx:claims/beam/b27efc86-7008-4384-852a-049d06d255cb
    • full textbeam-chunk
      text/plain1 KBdoc:beam/b27efc86-7008-4384-852a-049d06d255cb
      Show excerpt
      entities = [(ent.text, ent.label_) for ent in doc.ents] # Extract synonyms for each token synonyms = [] for token in tokens: pos = get_wordnet_pos(nltk.pos_tag([token])[0][1]) synsets = wordnet.synsets(t

See also

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