Labeled Dataset
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Labeled Dataset has 7 facts recorded in Dontopedia across 1 reference, with 2 live disagreements.
Mostly:contains(3), structure(2), rdf:type(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
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.
requiresRequires(3)
- Fine Tuning
ex:fine-tuning - Fine Tuning Process
ex:fine-tuning-process - Training Method
ex:training-method
Other facts (7)
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 |
|---|---|---|
| Contains | Original Query | [1] |
| Contains | Reformulated Query | [1] |
| Contains | Query Pairs | [1] |
| Structure | Paired Queries | [1] |
| Structure | Paired Format | [1] |
| Rdf:type | Training Data | [1] |
| Consists of | Paired Queries | [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/beam/63f3f6ff-b059-492e-954d-ccca67c2349d- full textbeam-chunktext/plain1020 B
doc:beam/63f3f6ff-b059-492e-954d-ccca67c2349dShow excerpt
However, I'm only achieving about 80% accuracy with this approach. I've studied LLM-based reformulation and noted a 25% intent accuracy boost for 6,000 complex queries. Can you help me improve my implementation to reach at least 92% detecti…
See also
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