Dontopedia

file_ext

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-05.)

file_ext has 8 facts recorded in Dontopedia across 3 references, with 3 live disagreements.

8 facts·3 predicates·3 sources·3 in dispute
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.

assignsVariableAssigns Variable(1)

extractsExtensionExtracts Extension(1)

Other facts (6)

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.

6 facts
PredicateValueRef
Rdf:typeVariable[1]
Rdf:typeVariable[2]
Rdf:typePython Variable[3]
Assigned FromSplitext Result[1]
Assigned FromLowercase Conversion[1]
Derived FromFile Variable[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/6bfba55e-cd71-49d1-b357-965037533de2
ex:Variable
labelbeam/6bfba55e-cd71-49d1-b357-965037533de2
file_ext variable
assignedFrombeam/6bfba55e-cd71-49d1-b357-965037533de2
ex:splitext-result
assignedFrombeam/6bfba55e-cd71-49d1-b357-965037533de2
ex:lowercase-conversion
typebeam/e7e7c796-91be-4632-bd3f-500b94e7a62e
ex:Variable
labelbeam/e7e7c796-91be-4632-bd3f-500b94e7a62e
file_ext
derivedFrombeam/e7e7c796-91be-4632-bd3f-500b94e7a62e
ex:file-variable
typebeam/e3b7ad28-c610-499f-b527-47a2d7f6872f
ex:PythonVariable

References (3)

3 references
  1. ctx:claims/beam/6bfba55e-cd71-49d1-b357-965037533de2
  2. ctx:claims/beam/e7e7c796-91be-4632-bd3f-500b94e7a62e
  3. ctx:claims/beam/e3b7ad28-c610-499f-b527-47a2d7f6872f
    • full textbeam-chunk
      text/plain1 KBdoc:beam/e3b7ad28-c610-499f-b527-47a2d7f6872f
      Show excerpt
      Let's walk through an example that combines semi-supervised learning and active learning to handle documents without clear labels. #### Step 1: Load and Prepare Data ```python import os import re import pandas as pd from sklearn.feature_e

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