Dontopedia

dashes

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-07-01.)

dashes has 5 facts recorded in Dontopedia across 1 reference.

5 facts·4 predicates·1 sources

Mostly:rdf:type(1), dominates output of(1), is dominant in(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.

dominatesOutputsWithDominates Outputs With(1)

hasDominantMarkHas Dominant Mark(1)

hasHyponymHas Hyponym(1)

horrifiedByHorrified by(1)

hyponymOfHyponym of(1)

styleStyle(1)

usesLibraryUses Library(1)

usesPunctuationUses Punctuation(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:typePunctuation Mark[1]
Dominates Output ofAnchor Kan[1]
Is Dominant inPunctuation Loaded Style[1]
Hyponym ofPunctuation[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.

labeldocument/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
dashes
typedocument/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
ex:punctuation-mark
dominatesOutputOfdocument/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
ex:anchor-kan
isDominantIndocument/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
ex:punctuation-loaded-style
hyponymOfdocument/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
ex:punctuation

References (1)

1 references
  1. ctx:claims/document/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
    • full textxenonfun: well not that much speed up 46K now peak, think its memory bound already. 8K voc
      text/plain680 Bdiscord:msg/a3126764-fdd1-42f8-9653-a5170ea5bdef
      Show excerpt
      xenonfun: well not that much speed up 46K now peak, think its memory bound already. 8K vocab is signifigantly worse output at same training but makes sense, model was too lopsided with that much embeddings, but at 100K it did observe intere

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