Dontopedia

Bool query with filter context example

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

Bool query with filter context example has 12 facts recorded in Dontopedia across 3 references, with 2 live disagreements.

12 facts·8 predicates·3 sources·2 in dispute

Mostly:rdf:type(3), uses filter context(1), targets field(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (2)

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.

hasCodeExampleHas Code Example(1)

pairsPairs(1)

Other facts (10)

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.

10 facts
PredicateValueRef
Rdf:typeQuery Example[1]
Rdf:typeQuery[2]
Rdf:typePolitical Question[3]
Uses Filter Contexttrue[1]
Targets FieldMessage Keyword Field[1]
Searches forerror[1]
DemonstratesUse Filter Cache[1]
Uses Http MethodGet Http Method[1]
Has Json StructureFilter Context Json Structure[1]
Is Code Blocktrue[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.

typebeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
ex:QueryExample
labelbeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
Bool query with filter context example
usesFilterContextbeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
true
targetsFieldbeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
ex:message-keyword-field
searchesForbeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
error
demonstratesbeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
ex:use-filter-cache
usesHttpMethodbeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
ex:get-http-method
hasJSONStructurebeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
ex:filter-context-json-structure
isCodeBlockbeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
true
typebeam/18e6c5b9-2160-4b21-9330-265fbb84e19d
ex:Query
labelbeam/18e6c5b9-2160-4b21-9330-265fbb84e19d
I need to withdraw money from the bank.
typebeam/7330f1b5-3c62-486a-ba82-b5783b9e4936
ex:PoliticalQuestion

References (3)

3 references
  1. ctx:claims/beam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
    • full textbeam-chunk
      text/plain1 KBdoc:beam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
      Show excerpt
      Ensure your queries are optimized for performance. 1. **Use Efficient Query Types**: Prefer `term` and `terms` queries over `match` and `match_phrase` queries when possible. ```json { "query": { "bool": { "mu
  2. ctx:claims/beam/18e6c5b9-2160-4b21-9330-265fbb84e19d
  3. ctx:claims/beam/7330f1b5-3c62-486a-ba82-b5783b9e4936
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7330f1b5-3c62-486a-ba82-b5783b9e4936
      Show excerpt
      for future in as_completed(futures): results.extend(future.result()) return results # Example usage: queries = ["What is the capital of France?", "Who is the president of the United States?", ...] reformulated_q

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