word
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
word has 20 facts recorded in Dontopedia across 8 references, with 3 live disagreements.
Mostly:rdf:type(7), assigned from(2), max length127 chars(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.
hasArgumentHas Argument(2)
- Trie Insert Call
ex:trie-insert-call - Trie Search Call
ex:trie-search-call
assignsAssigns(1)
- Load Dictionary Loop
ex:load-dictionary-loop
assignsKeyAssigns Key(1)
- Dictionary Assignment
ex:dictionary-assignment
assignsToAssigns to(1)
- Word Variable Assignment
ex:word-variable-assignment
calledWithCalled With(1)
- Insert Method
ex:insert-method
comparesCompares(1)
- Word Comparison Condition
ex:word-comparison-condition
unpackedAsUnpacked As(1)
- Context Word Tuples
ex:context-word-tuples
unpacksAsUnpacks As(1)
- Context and Word
ex:context-and-word
usesUses(1)
- Code Example
ex:code-example
Other facts (14)
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 | String | [2] |
| Rdf:type | Loop Variable | [3] |
| Rdf:type | Variable | [4] |
| Rdf:type | Variable | [6] |
| Rdf:type | Variable | [7] |
| Rdf:type | String Value | [7] |
| Rdf:type | Loop Variable | [8] |
| Assigned From | Line Strip | [5] |
| Assigned From | Line Stripping | [6] |
| Max Length127 Chars | null | [1] |
| Declared As | char[128] | [1] |
| Removes Trailing Newline | null | [1] |
| Type | String | [5] |
| Has Value | happy | [7] |
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 (8)
ctx:discord/blah/omega/part-561ctx:claims/beam/892c7b9e-a360-4951-a1bd-65dd1b7048dcctx:claims/beam/731b8e8a-1f12-4ab1-a853-9852e66bc19ectx:claims/beam/1eefc249-ab97-4ee4-83ca-d08dafe70606ctx:claims/beam/ec325d43-e9a5-4bd8-934d-599822520612ctx:claims/beam/dbb91cd4-736d-4452-9b19-46651567b10b- full textbeam-chunktext/plain1 KB
doc:beam/dbb91cd4-736d-4452-9b19-46651567b10bShow excerpt
Here's an example of how you can implement these best practices in Python: #### 1. Use Efficient Data Structures ```python class TrieNode: def __init__(self): self.children = {} self.is_end_of_word = False class Trie:…
ctx:claims/beam/e29476c7-671a-4bcf-a12e-6777683543f3- full textbeam-chunktext/plain1 KB
doc:beam/e29476c7-671a-4bcf-a12e-6777683543f3Show excerpt
best_synonym = synonym return best_synonym word = 'happy' context_sentence = 'She felt happy after receiving the gift.' best_synonym = get_context_aware_synonyms(word, context_sentence) print(best_synonym) ``` ### 3. …
ctx:claims/beam/ba8f0f6e-4076-45ec-b8ac-81b951e5391d- full textbeam-chunktext/plain1 KB
doc:beam/ba8f0f6e-4076-45ec-b8ac-81b951e5391dShow excerpt
nltk.download('words') word_list = set(words.words()) # Define a function to correct a query using NLTK def correct_query_nltk(query): # Split the query into words words = query.split() # Correct each word corrected_wo…
See also
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