re.findall
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
re.findall has 13 facts recorded in Dontopedia across 3 references, with 4 live disagreements.
Mostly:rdf:type(3), returns(2), called with(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (6)
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.
invokesInvokes(2)
- Check Gdpr Compliance
ex:check_gdpr_compliance - Check Gdpr Compliance Function
ex:check-gdpr-compliance-function
usesUses(2)
- Check Gdpr Compliance Function
ex:check-gdpr-compliance-function - Tokenize Text
ex:tokenize_text
isAssignedByIs Assigned by(1)
- Matches Variable
ex:matches-variable
storesResultOfStores Result of(1)
- Matches Variable
ex:matches-variable
Other facts (11)
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Python Function | [1] |
| Rdf:type | Python Function | [2] |
| Rdf:type | Python Function | [3] |
| Returns | Match List | [1] |
| Returns | Matches Variable | [2] |
| Called With | Regex Pattern | [2] |
| Called With | Config Parameter | [2] |
| Inverse Called With | Regex Pattern | [2] |
| Inverse Called With | Config Parameter | [2] |
| Has Purpose | search-for-matches | [2] |
| Member of | Re Module | [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.
References (3)
ctx:claims/beam/363aadc6-5a9a-4ccb-a386-0fe724d1392bctx:claims/beam/7129b9a2-9346-401d-906f-668abd5f5110ctx:claims/beam/e7c6aa25-11df-495a-974c-9dbc5aca18ac- full textbeam-chunktext/plain1 KB
doc:beam/e7c6aa25-11df-495a-974c-9dbc5aca18acShow excerpt
[Turn 10780] User: I've improved tokenization accuracy by 13% for 5,000 queries after rule adjustments, but I'm struggling to optimize the code for better performance; can you help me identify bottlenecks and suggest improvements? ```python…
See also
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