Ent
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
Ent has 3 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
3 facts·2 predicates·2 sources·1 in dispute
Maturity scale
raw canonical shape-checked rule-derived certifiedOther 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.
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.
—
hasAttributebeam/96604915-ce41-4197-9dc1-48f60db96e2f
text
—
hasAttributebeam/96604915-ce41-4197-9dc1-48f60db96e2f
label_
—
hasAttributebeam/82dc87bd-74b8-4fb6-be5d-469ed934c86c
ex:label
References (2)
2 references
ctx:claims/beam/96604915-ce41-4197-9dc1-48f60db96e2f- full textbeam-chunktext/plain1 KB
doc:beam/96604915-ce41-4197-9dc1-48f60db96e2fShow excerpt
# Load multi-language model nlp = spacy.load("xx_ent_wiki_sm") def process_text(text, lang): doc = nlp(text) entities = [(ent.text, ent.label_) for ent in doc.ents] pos_tags = [(token.text, token.pos_) for token in …
ctx:claims/beam/82dc87bd-74b8-4fb6-be5d-469ed934c86c- full textbeam-chunktext/plain1 KB
doc:beam/82dc87bd-74b8-4fb6-be5d-469ed934c86cShow excerpt
nlp = spacy.load("en_core_web_sm") lemmatizer = WordNetLemmatizer() def get_wordnet_pos(treebank_tag): """Converts treebank POS tags to WordNet POS tags.""" if treebank_tag.startswith('J'): return wordnet.ADJ elif treeb…
See also
Keep researching
Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.