Dontopedia
Explore

Hashlib.md5

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

Hashlib.md5 has 9 facts recorded in Dontopedia across 5 references, with 3 live disagreements.

9 facts·4 predicates·5 sources·3 in dispute

Mostly:rdf:type(4), rdfs:label(2), returns(2)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Rdfs:labelin disputerdfs:label

  • hashlib.md5[2]all time · Bb357b6e 614f 43e0 B1e5 9e7f1b67a8ab
  • MD5 Hash Function[3]all time · 0b0e3d9f 0f06 4562 A8ee 1d3f71c4c557

Returnsin disputereturns

  • Hexdigest[5]sourceall time · 52dd23cb 1e9b 4862 A465 9116450bfe75
  • Md5 Object[2]all time · Bb357b6e 614f 43e0 B1e5 9e7f1b67a8ab

Called oncalledOn

Inbound mentions (7)

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.

usesUses(4)

createdByCreated by(1)

providesFunctionProvides Function(1)

used-byUsed 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.

9 undated
—
calledOnbeam/e2022965-f15d-4b5b-b4ae-0988973392db
ex:text-parameter
—
labelbeam/bb357b6e-614f-43e0-b1e5-9e7f1b67a8ab
hashlib.md5
—
labelbeam/0b0e3d9f-0f06-4562-a8ee-1d3f71c4c557
MD5 Hash Function
—
typebeam/0b0e3d9f-0f06-4562-a8ee-1d3f71c4c557
ex:Function
—
typebeam/9a30ba69-a5d9-4112-8a96-910a73b0346c
ex:HashFunction
—
typebeam/52dd23cb-1e9b-4862-a465-9116450bfe75
ex:HashFunction
—
typebeam/bb357b6e-614f-43e0-b1e5-9e7f1b67a8ab
ex:PythonFunction
—
returnsbeam/52dd23cb-1e9b-4862-a465-9116450bfe75
ex:hexdigest
—
returnsbeam/bb357b6e-614f-43e0-b1e5-9e7f1b67a8ab
ex:MD5Object

References (5)

5 references
  1. [1]beam-chunk1 fact
    customctx:claims/beam/e2022965-f15d-4b5b-b4ae-0988973392db
    • full textbeam-chunk
      text/plain923 Bdoc:beam/e2022965-f15d-4b5b-b4ae-0988973392db
      Show excerpt
      - **Profiling**: Use profiling tools to measure the performance of your code and identify any remaining bottlenecks. By implementing these optimizations, you should be able to reduce the processing time for your text chunks significantly. …
  2. [2]Bb357b6e 614f 43e0 B1e5 9e7f1b67a8ab3 facts
    customctx:claims/beam/bb357b6e-614f-43e0-b1e5-9e7f1b67a8ab
  3. [3]0b0e3d9f 0f06 4562 A8ee 1d3f71c4c5572 facts
    customctx:claims/beam/0b0e3d9f-0f06-4562-a8ee-1d3f71c4c557
  4. [4]beam-chunk1 fact
    customctx:claims/beam/9a30ba69-a5d9-4112-8a96-910a73b0346c
    • full textbeam-chunk
      text/plain929 Bdoc:beam/9a30ba69-a5d9-4112-8a96-910a73b0346c
      Show excerpt
      index = int(digest, 16) % self.size self.bit_array[index] = True def check(self, item): for i in range(self.hash_count): digest = hashlib.md5((str(item) + str(i)).encode()).hexdiges…
  5. [5]beam-chunk2 facts
    customctx:claims/beam/52dd23cb-1e9b-4862-a465-9116450bfe75
    • full textbeam-chunk
      text/plain1 KBdoc:beam/52dd23cb-1e9b-4862-a465-9116450bfe75
      Show excerpt
      # Calculate the hash of the data hash_value = hashlib.md5(data.encode()).hexdigest() # Convert the hash to an integer hash_int = int(hash_value, 16) # Determine which node to use based on the hash node_index = hash_i…

See also

Keep researching

Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.

↑