context
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)
context has 7 facts recorded in Dontopedia across 3 references, with 1 live disagreement.
Mostly:rdf:type(2), validated by(1), has field name(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
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.
hasMemberHas Member(2)
- Query Context Intent
ex:query-context-intent - Three Fields
ex:three-fields
appliesToApplies to(1)
- 200 Character Limit
ex:200-character-limit
containsContains(1)
- Fields Variable
ex:fields-variable
hasFieldHas Field(1)
- Task Input Arguments
ex:task-input-arguments
Other facts (6)
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Context Field | [1] |
| Rdf:type | Field | [2] |
| Validated by | Context Field Validator | [2] |
| Has Field Name | context | [3] |
| Has Field Value | The full source code is available at https://raw.githubusercontent.com/thomasdavis/omega/main/file-library/mairy_pipeline_a931128e.py. It implements MAIRY v3.0 pipeline with semantic understanding, triadic scoring across kindness, freedom, and truth, and safety constraints based on semantic, geometric, and mathematical reasoning. The code uses dataclasses, numpy, and external imported components like memory_engine, RLMD, triad_projection, api_clients, and shell_manager. The pipeline produces scored response candidates and manages multi-step safety checks in | [3] |
| References Entity | Mairy Pipeline File | [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.
References (3)
ctx:claims/beam/6d683f5a-8ab1-4007-8981-58fa4633ea6f- full textbeam-chunktext/plain1 KB
doc:beam/6d683f5a-8ab1-4007-8981-58fa4633ea6fShow excerpt
[Turn 2506] User: I'm designing input structures for our LLM queries, and I'm proposing 6 context fields to improve answer relevance by 15%. I want to ensure that these fields are properly validated and sanitized to prevent any potential se…
ctx:claims/beam/5f3ffea8-fcd4-40f8-9533-21786a778a47ctx:discord/blah/omega/842- full textomega-842text/plain2 KB
doc:agent/omega-842/fc438eee-4b61-4419-afd1-0054d3c2eff3Show excerpt
[2026-01-12 20:53] omega [bot]: 🔧 2/2: axllmExecutor ✅ Success **Args:** ```json { "task": "Analyze the architecture, style, and key concepts of the mairy_pipeline.py code. Provide a detailed summary explaining its main components, workfl…
See also
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