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Rekognition

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-05.)

Rekognition has 6 facts recorded in Dontopedia across 2 references.

6 facts·6 predicates·2 sources

Mostly:consumes(1), processes(1), analyzes(1)

Maturity scale raw canonical shape-checked rule-derived certified

Consumesconsumes

Processesprocesses

Analyzesanalyzes

Performs ActionperformsAction

Service TypeserviceType

  • AWS Rekognition[2]sourceall time · 8d71f190 64f4 4bef 8354 27133ff0c62b

Created bycreatedBy

  • boto3.client[2]sourceall time · 8d71f190 64f4 4bef 8354 27133ff0c62b

Inbound mentions (2)

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.

methodOfMethod of(1)

producedByProduced by(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.

6 undated
—
analyzesbeam/743f61f8-3cd3-4037-a174-3456ebb9ddeb
ex:image-segments
—
consumesbeam/743f61f8-3cd3-4037-a174-3456ebb9ddeb
ex:image-segments
—
createdBybeam/8d71f190-64f4-4bef-8354-27133ff0c62b
boto3.client
—
performsActionbeam/743f61f8-3cd3-4037-a174-3456ebb9ddeb
ex:analyze-image-segments
—
processesbeam/743f61f8-3cd3-4037-a174-3456ebb9ddeb
ex:each-segment
—
serviceTypebeam/8d71f190-64f4-4bef-8354-27133ff0c62b
AWS Rekognition

References (2)

2 references
  1. [1]beam-chunk4 facts
    customctx:claims/beam/743f61f8-3cd3-4037-a174-3456ebb9ddeb
    • full textbeam-chunk
      text/plain1 KBdoc:beam/743f61f8-3cd3-4037-a174-3456ebb9ddeb
      Show excerpt
      "SegmentImages": { "Type": "Task", "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:SegmentImagesLambdaFunction", "Parameters": { "bucket": "my-bucket", "key": "large-image.jpg" }, "Ne…
  2. [2]beam-chunk2 facts
    customctx:claims/beam/8d71f190-64f4-4bef-8354-27133ff0c62b
    • full textbeam-chunk
      text/plain1 KBdoc:beam/8d71f190-64f4-4bef-8354-27133ff0c62b
      Show excerpt
      # Define the size of each chunk chunk_size = 1024 # Adjust as needed # Segment the image height, width, _ = image.shape for i in range(0, height, chunk_size): for j in range(0, width, chunk_size): …

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