Tuple Construction
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Tuple Construction has 3 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound 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.
appliesApplies(2)
- Entity Extraction
ex:entity-extraction - List Comprehension Pos
ex:list-comprehension-pos
Other facts (3)
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 Tuple | [1] |
| Rdf:type | Data Structure Operation | [2] |
| Constructs | Entities Variable | [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.
References (2)
ctx:claims/beam/b27efc86-7008-4384-852a-049d06d255cb- full textbeam-chunktext/plain1 KB
doc:beam/b27efc86-7008-4384-852a-049d06d255cbShow excerpt
entities = [(ent.text, ent.label_) for ent in doc.ents] # Extract synonyms for each token synonyms = [] for token in tokens: pos = get_wordnet_pos(nltk.pos_tag([token])[0][1]) synsets = wordnet.synsets(t…
ctx:claims/beam/75da3500-669d-461a-9314-c433678ef083- full textbeam-chunktext/plain1 KB
doc:beam/75da3500-669d-461a-9314-c433678ef083Show excerpt
nlp = spacy.load('en_core_web_sm') def process_query(query): doc = nlp(query) # Tokenization and Lemmatization tokens = [token.lemma_.lower() for token in doc if token.is_alpha and token.lemma_.lower() not in STOP_WORDS] …
See also
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