Dontopedia

Ner Extraction

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

Ner Extraction has 8 facts recorded in Dontopedia across 2 references.

8 facts·8 predicates·2 sources

Mostly:across articles(1), confirms absence(1), found person entity(1)

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.

usedInContextUsed in Context(1)

usesUses(1)

Other facts (8)

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.

8 facts
PredicateValueRef
Across Articles191K[1]
Confirms AbsenceBrackenridge at Cooktown[1]
Found Person EntityBrackenridge[1]
Scoped toCooktown[1]
Used ModelspaCy en_core_web_lg[1]
Reliable Evidencetrue[1]
Rdf:typeExpansion Method[2]
UsesSpacy[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.

acrossArticlesgeorge-brackenridge/exhaustive-newspaper-search
191K
confirmsAbsencegeorge-brackenridge/exhaustive-newspaper-search
ex:brackenridge-at-cooktown
foundPersonEntitygeorge-brackenridge/exhaustive-newspaper-search
ex:brackenridge
scopedTogeorge-brackenridge/exhaustive-newspaper-search
ex:cooktown
usedModelgeorge-brackenridge/exhaustive-newspaper-search
spaCy en_core_web_lg
reliableEvidencegeorge-brackenridge/exhaustive-newspaper-search
true
typebeam/b27efc86-7008-4384-852a-049d06d255cb
ex:ExpansionMethod
usesbeam/b27efc86-7008-4384-852a-049d06d255cb
ex:spacy

References (2)

2 references
  1. ctx:genes/george-brackenridge/exhaustive-newspaper-search
  2. ctx:claims/beam/b27efc86-7008-4384-852a-049d06d255cb
    • full textbeam-chunk
      text/plain1 KBdoc:beam/b27efc86-7008-4384-852a-049d06d255cb
      Show 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

See also

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