Dontopedia

Cache Ttl

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

Cache Ttl has 5 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

5 facts·4 predicates·2 sources·1 in dispute

Mostly:rdf:type(2), specifies(1), duration(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (1)

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.

representsRepresents(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
Rdf:typeConfiguration[1]
Rdf:typeTime to Live[2]
SpecifiesTime-to-live for cached responses[1]
Duration3600[2]
Unitseconds[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/24a296d9-7611-44d2-8eab-457851631404
ex:Configuration
specifiesbeam/24a296d9-7611-44d2-8eab-457851631404
Time-to-live for cached responses
typebeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
ex:TimeToLive
durationbeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
3600
unitbeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
seconds

References (2)

2 references
  1. ctx:claims/beam/24a296d9-7611-44d2-8eab-457851631404
    • full textbeam-chunk
      text/plain1 KBdoc:beam/24a296d9-7611-44d2-8eab-457851631404
      Show excerpt
      Tagging cache entries can help you invalidate specific sets of data when underlying data changes. #### Example with Tags ```python # Tag the cache entry tag_key = f"tag:{request.query}" r.sadd(tag_key, cache_key) # Invalidate cache entri
  2. ctx:claims/beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
      Show excerpt
      for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_contextual_embeddings(syn)) for syn in synonyms] closest_synonyms.extend([synon

See also

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