Dontopedia

Emotion

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

Emotion has 5 facts recorded in Dontopedia across 3 references, with 1 live disagreement.

5 facts·4 predicates·3 sources·1 in dispute

Mostly:superclass of(2), weight in southport(1), awakened by(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (12)

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.

semanticCategorySemantic Category(3)

semanticFieldSemantic Field(3)

beatBeat(1)

combinesCombines(1)

conveysConveys(1)

emphasizesEmphasizes(1)

hasNomineeHas Nominee(1)

sankUnderSank Under(1)

Other facts (5)

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.

5 facts
PredicateValueRef
Superclass ofPositive Emotion[3]
Superclass ofNegative Emotion[3]
Weight in Southport6 10[1]
Awakened byConstitutional Change[2]
Rdf:typeConcept[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.

weightInSouthporttrove-cooktown/reynolds
6 10
awakenedByblucher-uhr/trove--trove-cooktown-all--uhrs-camp--wednesday 8 september 1880--890855--warden-s-report-palmerville
ex:constitutional-change
typebeam/f0cc860e-7f75-4530-abef-84dc82b5e5ad
ex:Concept
superclassOfbeam/f0cc860e-7f75-4530-abef-84dc82b5e5ad
ex:positive_emotion
superclassOfbeam/f0cc860e-7f75-4530-abef-84dc82b5e5ad
ex:negative_emotion

References (3)

3 references
  1. [1]Reynolds1 fact
    ctx:genes/trove-cooktown/reynolds
  2. ctx:research/blucher-uhr/trove--trove-cooktown-all--uhrs-camp--wednesday 8 september 1880--890855--warden-s-report-palmerville
  3. ctx:claims/beam/f0cc860e-7f75-4530-abef-84dc82b5e5ad
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f0cc860e-7f75-4530-abef-84dc82b5e5ad
      Show excerpt
      term_embedding = get_contextual_embeddings(term) closest_synonyms = [] for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_context

See also

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