Ground Truth Documents
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Ground Truth Documents has 4 facts recorded in Dontopedia across 4 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
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.
derivedFromDerived From(1)
- True Vector
ex:true-vector
extractsExtracts(1)
- Evaluation Execution
ex:evaluation-execution
involvesGatheringInvolves Gathering(1)
- Collect Data Step
ex:collect-data-step
iteratesOverIterates Over(1)
- True Vector Calculation
ex:true-vector-calculation
unpacksRowUnpacks Row(1)
- Evaluation Execution
ex:evaluation-execution
Other facts (4)
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 | Dataset Component | [1] |
| Rdf:type | Data Entity | [4] |
| Is Split by | Comma | [2] |
| Allows Multiple | true | [3] |
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 (4)
ctx:claims/beam/241122f8-dc34-4876-8384-3647f4796af6- full textbeam-chunktext/plain1 KB
doc:beam/241122f8-dc34-4876-8384-3647f4796af6Show excerpt
self.tokenizer = tokenizer def process_query(self, query, context=None): # Reformulate the query reformulated_query = reformulate_query(query, context) # Process the reformulated query (e.g., retrieve r…
ctx:claims/beam/34a1dce2-ecc2-4241-ad4a-235e8625b612- full textbeam-chunktext/plain1 KB
doc:beam/34a1dce2-ecc2-4241-ad4a-235e8625b612Show excerpt
retrieved_documents = rag_system.process_query(reformulated_query, context) return reformulated_query, retrieved_documents # Apply the function to each row df[['reformulated_query', 'retrieved_documents']] = df.apply( lambda ro…
ctx:claims/beam/4cc521bd-2791-4334-88dc-f5e3519e2d92- full textbeam-chunktext/plain1 KB
doc:beam/4cc521bd-2791-4334-88dc-f5e3519e2d92Show excerpt
2. **Split the Dataset**: Divide the dataset into training and testing sets. 3. **Evaluate Precision and Recall**: Use precision and recall to evaluate the relevance of the retrieved documents. 4. **User Feedback**: Optionally, collect user…
ctx:claims/beam/4b0e94ef-084d-4363-8931-568f755392e6- full textbeam-chunktext/plain1 KB
doc:beam/4b0e94ef-084d-4363-8931-568f755392e6Show excerpt
true_vector = [doc in ground_truth_documents for doc in retrieved_documents] pred_vector = [True] * len(retrieved_documents) y_true.extend(true_vector) y_pred.extend(pred_vector) # Calculate precision and recall precision …
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.