Dontopedia

Entities Variable

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

Entities Variable has 7 facts recorded in Dontopedia across 2 references.

7 facts·7 predicates·2 sources

Mostly:is list(1), contains tuples(1), rdf:type(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (3)

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.

constructsConstructs(1)

hasValueHas Value(1)

mapsToMaps to(1)

Other facts (7)

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.

7 facts
PredicateValueRef
Is Listtrue[1]
Contains Tuplestrue[1]
Rdf:typePython List[2]
Initialized byEntity Extraction[2]
Result ofEntity Extraction[2]
ContainsText Label Tuples[2]
Included inDictionary Object[2]

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.

isListbeam/b27efc86-7008-4384-852a-049d06d255cb
true
containsTuplesbeam/b27efc86-7008-4384-852a-049d06d255cb
true
typebeam/75da3500-669d-461a-9314-c433678ef083
ex:PythonList
initializedBybeam/75da3500-669d-461a-9314-c433678ef083
ex:entity-extraction
resultOfbeam/75da3500-669d-461a-9314-c433678ef083
ex:entity-extraction
containsbeam/75da3500-669d-461a-9314-c433678ef083
ex:text-label-tuples
includedInbeam/75da3500-669d-461a-9314-c433678ef083
ex:dictionary-object

References (2)

2 references
  1. ctx:claims/beam/b27efc86-7008-4384-852a-049d06d255cb
    • full textbeam-chunk
      text/plain1 KBdoc:beam/b27efc86-7008-4384-852a-049d06d255cb
      Show excerpt
      entities = [(ent.text, ent.label_) for ent in doc.ents] # Extract synonyms for each token synonyms = [] for token in tokens: pos = get_wordnet_pos(nltk.pos_tag([token])[0][1]) synsets = wordnet.synsets(t
  2. ctx:claims/beam/75da3500-669d-461a-9314-c433678ef083
    • full textbeam-chunk
      text/plain1 KBdoc:beam/75da3500-669d-461a-9314-c433678ef083
      Show excerpt
      nlp = spacy.load('en_core_web_sm') def process_query(query): doc = nlp(query) # Tokenization and Lemmatization tokens = [token.lemma_.lower() for token in doc if token.is_alpha and token.lemma_.lower() not in STOP_WORDS]

See also

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