Entity Recognition Benchmark
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-19.)
Entity Recognition Benchmark has 2 facts recorded in Dontopedia across 1 reference, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (1)
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.
hasBenchmarkHas Benchmark(1)
- Spacy
ex:spacy
Other facts (2)
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 |
|---|---|---|
| Compares | Spacy | [1] |
| Compares | Nltk With Stanford Corenlp | [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 (1)
ctx:claims/lme/2a578673-5ce7-4f89-8d29-0595b9609db0- full textbeam-chunktext/plain22 KB
doc:beam/2a578673-5ce7-4f89-8d29-0595b9609db0Show excerpt
[Session date: 2023/05/21 (Sun) 15:59] User: I'm trying to work on a project that involves text analysis and sentiment analysis. Can you recommend some popular NLP libraries in Python that I can use for this project? By the way, I've been b…
See also
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