Dontopedia

Architectural Decision

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

Architectural Decision has 4 facts recorded in Dontopedia across 3 references, with 1 live disagreement.

4 facts·3 predicates·3 sources·1 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

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.

framesAsDecisionFrames As Decision(1)

involvesInvolves(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:typeEngineering Decision[1]
Rdf:typeDesign Choice[3]
Principleseparation of concerns[2]
InvolvesModule Separation[3]

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/d7d024f4-215e-46ae-af59-a9812a458db0
ex:EngineeringDecision
principlebeam/1eb8aa09-e959-4141-bc61-fdce4119df7f
separation of concerns
typebeam/c0baa754-c67c-42a8-a024-5dc692e78f75
ex:DesignChoice
involvesbeam/c0baa754-c67c-42a8-a024-5dc692e78f75
ex:module-separation

References (3)

3 references
  1. ctx:claims/beam/d7d024f4-215e-46ae-af59-a9812a458db0
    • full textbeam-chunk
      text/plain1 KBdoc:beam/d7d024f4-215e-46ae-af59-a9812a458db0
      Show excerpt
      [Turn 2182] User: I'm trying to implement a microservices architecture with Patricia, and we're discussing the trade-offs between monoliths and microservices. I've heard that microservices can be more scalable, but I'm not sure how to appro
  2. ctx:claims/beam/1eb8aa09-e959-4141-bc61-fdce4119df7f
    • full textbeam-chunk
      text/plain1 KBdoc:beam/1eb8aa09-e959-4141-bc61-fdce4119df7f
      Show excerpt
      document_embeddings = vectorization_module.vectorize(documents) # Add the document embeddings to the index indexing_module.add_to_index(document_embeddings) ``` ->-> 4,24 [Turn 4863] Assistant: Certainly! To design a modular architecture
  3. ctx:claims/beam/c0baa754-c67c-42a8-a024-5dc692e78f75

See also

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