words
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
words has 18 facts recorded in Dontopedia across 3 references, with 3 live disagreements.
Mostly:contains(3), rdf:type(3), simulates(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (10)
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.
iteratesOverIterates Over(2)
- For Loop
ex:for-loop - Word Iteration
ex:word-iteration
createsCreates(1)
- Query Split
ex:query-split
definesDefines(1)
- Test Code
ex:test-code
hasIteratorHas Iterator(1)
- For Loop
ex:for-loop
iterationTargetIteration Target(1)
- Word Loop
ex:word-loop
processesProcesses(1)
- Correct Query Function
ex:correct-query-function
Other facts (15)
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 |
|---|---|---|
| Contains | Happy | [1] |
| Contains | Sad | [1] |
| Contains | Angry | [1] |
| Rdf:type | Variable | [1] |
| Rdf:type | Array Variable | [2] |
| Rdf:type | Variable | [3] |
| Simulates | Larger Dataset | [1] |
| Repeats | 100 | [1] |
| Is Iterated by | For Loop | [1] |
| Has Element Type | String | [1] |
| Has Iterator | For Loop | [1] |
| Assignment | query.split() | [3] |
| Iteration Source | Word Loop | [3] |
| Processed by | Correct Query Function | [3] |
| Created by | Query Split | [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/534be9d2-c97a-4867-8efb-8f090879be4b- full textbeam-chunktext/plain1 KB
doc:beam/534be9d2-c97a-4867-8efb-8f090879be4bShow excerpt
logging.info(f"Thesaurus lookup for '{word}' took {end_time - start_time:.6f} seconds") return ["synonym1", "synonym2"] # Test the lookup words = ["happy", "sad", "angry"] * 100 # Simulate a larger dataset for word in words: …
ctx:claims/beam/8f327b3d-bdda-4eb4-8da7-5bd63a1fcd03- full textbeam-chunktext/plain1 KB
doc:beam/8f327b3d-bdda-4eb4-8da7-5bd63a1fcd03Show excerpt
Based on the analysis, we can make targeted optimizations to improve performance. ### Example Code with Profiling Here's an example of how you can profile your code to identify the bottleneck: ```python import time import cProfile import…
ctx:claims/beam/d16bbca9-cb9f-45c2-ad1b-8c00fc936a5c- full textbeam-chunktext/plain1 KB
doc:beam/d16bbca9-cb9f-45c2-ad1b-8c00fc936a5cShow excerpt
1. **Dictionary Mismatch**: If dictionary mismatches are causing delays, consider expanding the dictionary or using a more comprehensive dictionary. 2. **Tokenization**: Ensure that the tokenization step is efficient. 3. **Batch Processing*…
See also
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