Dontopedia

Return Tokens

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

Return Tokens has 3 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

3 facts·2 predicates·2 sources·1 in dispute
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.

containsContains(1)

hasReturnStatementHas Return Statement(1)

includesStepIncludes Step(1)

Other facts (3)

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.

3 facts
PredicateValueRef
Rdf:typeReturn Statement[1]
Rdf:typeReturn Statement[2]
Returns Valuetokens[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.

typebeam/d477eb96-b50c-45ea-ad52-922235fbbd94
ex:ReturnStatement
typebeam/80fec442-58d4-4a91-973a-5fde191c5879
ex:ReturnStatement
returnsValuebeam/80fec442-58d4-4a91-973a-5fde191c5879
tokens

References (2)

2 references
  1. ctx:claims/beam/d477eb96-b50c-45ea-ad52-922235fbbd94
    • full textbeam-chunk
      text/plain1 KBdoc:beam/d477eb96-b50c-45ea-ad52-922235fbbd94
      Show excerpt
      except OSError as e: logging.error(f"Failed to load SpaCy model: {e}") raise # Define a class to handle language tokenization class LanguageTokenizer: def __init__(self): self.nlp = nlp @lru_cache(maxsize=1000)
  2. ctx:claims/beam/80fec442-58d4-4a91-973a-5fde191c5879
    • full textbeam-chunk
      text/plain1 KBdoc:beam/80fec442-58d4-4a91-973a-5fde191c5879
      Show excerpt
      logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Load spaCy model nlp = spacy.load('en_core_web_sm') def tokenize_text(text): try: doc = nlp(text) tokens = [token.text for t

See also

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