Dontopedia

brevity

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

brevity has 12 facts recorded in Dontopedia across 6 references, with 2 live disagreements.

12 facts·6 predicates·6 sources·2 in dispute

Mostly:rdf:type(5), soul of(1), demonstrated by(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (8)

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.

purposePurpose(3)

usedForUsed for(2)

identifiesIdentifies(1)

motivatedByMotivated by(1)

reasonForOmissionReason for Omission(1)

Other facts (10)

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.

10 facts
PredicateValueRef
Rdf:typeStylistic Feature[2]
Rdf:typeDisplay Consideration[3]
Rdf:typePresentation Consideration[4]
Rdf:typeGoal[5]
Rdf:typeDesign Goal[6]
Soul oflogic eloquence wit[1]
Demonstrated byMessage 2025 08 18 20 37 B[2]
Word Count3[2]
Motivation forSlice[5]
Achieved byLimited Output[6]

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.

soulOftrove-cooktown/cingalese
logic eloquence wit
typeblah/rust-TEST
ex:StylisticFeature
demonstratedByblah/rust-TEST
ex:message-2025-08-18-20-37-b
wordCountblah/rust-TEST
3
typebeam/4d8aaf8b-fb9e-4b75-8f18-106489b10190
ex:DisplayConsideration
labelbeam/4d8aaf8b-fb9e-4b75-8f18-106489b10190
Output Brevity
typebeam/49119412-4d42-4d3a-99ed-de20b950c7f2
ex:PresentationConsideration
typebeam/d795171e-b403-4d57-929d-378d01e57b2d
ex:Goal
labelbeam/d795171e-b403-4d57-929d-378d01e57b2d
brevity
motivationForbeam/d795171e-b403-4d57-929d-378d01e57b2d
ex:slice
typebeam/5a656395-eca3-4495-bbd0-31046aeca5e6
ex:DesignGoal
achievedBybeam/5a656395-eca3-4495-bbd0-31046aeca5e6
ex:limited-output

References (6)

6 references
  1. [1]Cingalese1 fact
    ctx:genes/trove-cooktown/cingalese
  2. [2]Rust Test3 facts
    discord/blah/rust-TEST
    • full textdiscord/blah/rust-TEST
      text/plain957 Bdoc:discord/blah/rust-TEST
      Show excerpt
      [2025-05-08 04:38] ajaxdavis: https://www.egui.rs/ [2025-05-08 04:43] ajaxdavis: https://github.com/leptos-rs/leptos [2025-05-09 07:58] lisamegawatts: https://github.com/igumnoff/shiva [2025-05-09 19:20] lisamegawatts: https://github.com/ze
  3. ctx:claims/beam/4d8aaf8b-fb9e-4b75-8f18-106489b10190
    • full textbeam-chunk
      text/plain1 KBdoc:beam/4d8aaf8b-fb9e-4b75-8f18-106489b10190
      Show excerpt
      - Use profiling tools like `cProfile` to identify bottlenecks in your code. - Benchmark different approaches to see which performs best for your specific use case. ### Example with Parallel Processing Here's an example using `concurre
  4. ctx:claims/beam/49119412-4d42-4d3a-99ed-de20b950c7f2
    • full textbeam-chunk
      text/plain1 KBdoc:beam/49119412-4d42-4d3a-99ed-de20b950c7f2
      Show excerpt
      end_time = time.time() print(f"Dask tokenization took {end_time - start_time} seconds") # Print first 5 results for brevity print(result.head()) ``` ### Explanation 1. **Load spaCy Model Once**: - Load the spaCy model once and reuse i
  5. ctx:claims/beam/d795171e-b403-4d57-929d-378d01e57b2d
    • full textbeam-chunk
      text/plain1 KBdoc:beam/d795171e-b403-4d57-929d-378d01e57b2d
      Show excerpt
      results = process_queries(queries) end_time = time.time() print(f"Processed 8,000 queries in {end_time - start_time} seconds") print(results[:5]) # Print first 5 results for brevity ``` ### Explanation 1. **Modular Design**: - `token
  6. ctx:claims/beam/5a656395-eca3-4495-bbd0-31046aeca5e6
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5a656395-eca3-4495-bbd0-31046aeca5e6
      Show excerpt
      with ProcessPoolExecutor(max_workers=max_workers) as executor: for token_freq in executor.map(tokenize_text, text_chunks): results.append(token_freq) return results # Example usage text_chunks = ["This is an exa

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