Dontopedia

User Direction

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

User Direction has 4 facts recorded in Dontopedia across 2 references, with 2 live disagreements.

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

Inbound mentions (4)

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.

requestsRequests(2)

alignsWithAligns With(1)

expressesWillingnessToProceedExpresses Willingness to Proceed(1)

Other facts (4)

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.

4 facts
PredicateValueRef
Rdf:typeInstruction[1]
Rdf:typeInteraction Choice[2]
Involves GoalCentralize Data Persistence[1]
Involves GoalCleanup Artifact Stuff[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.

typeblah/omega/575
ex:Instruction
involvesGoalblah/omega/575
ex:centralize-data-persistence
involvesGoalblah/omega/575
ex:cleanup-artifact-stuff
typebeam/0e4dede6-52a5-49ce-a450-4813d1738359
ex:InteractionChoice

References (2)

2 references
  1. [1]5753 facts
    ctx:discord/blah/omega/575
    • full textomega-575
      text/plain3 KBdoc:agent/omega-575/bcb4c498-42e8-46a3-b22e-9b111f702f30
      Show excerpt
      [2025-12-04 15:40] omega [bot]: I've created issue #659 to remove all artifact-related tools and mentions from prompts, and shift fully to using PostgreSQL for storage and management. New tools will build on PostgreSQL tables with correspon
  2. ctx:claims/beam/0e4dede6-52a5-49ce-a450-4813d1738359
    • full textbeam-chunk
      text/plain990 Bdoc:beam/0e4dede6-52a5-49ce-a450-4813d1738359
      Show excerpt
      - Load and split the dataset into training and testing sets. - Tokenize the data using the tokenizer. 2. **Model Fine-Tuning**: - Define a custom dataset class to handle the tokenized data. - Set up training arguments and defin

See also

Keep researching

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