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Expected Outcomes has 32 facts recorded in Dontopedia across 8 references, with 4 live disagreements.
Mostly:rdf:type(6), has member(4), is appended to(2)
isAppendedTocontainshasMemberisAppendedByisAssignedBystoresinitializesAsisProducedByisInputToisParameterOfinitialValueOther 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.
hasParameterHas Parameter(3)ex:evaluate_modelex:evaluate_modelex:evaluate_modelappliesToApplies to(2)ex:outputTruncationex:subtask-1pairedWithPaired With(2)ex:test_queriesex:test_queriesrequiresRequires(2)ex:evaluate_modelex:step-1containsListDefinitionContains List Definition(1)ex:source-documentcontainsVariableContains Variable(1)ex:code-snippetdescribesDescribes(1)ex:explanation_sectionmodifiesModifies(1)ex:generate_test_datareturnsReturns(1)ex:generate_test_dataThe 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 |
|---|---|---|
| Array Length | 10 | [1] |
| Is Argument to | Evaluate Model | [1] |
| Data Structure | list | [3] |
| Parameter of | evaluate_model | [3] |
| Is List of | Resized Query | [2] |
| Corresponds to One to One | Test Queries | [2] |
| Corresponds to | Test Queries | [2] |
| Rdfs:label | expected_outcomes | [2] |
| Length | 4 | [4] |
| Ordered Sequence | Outcome 1 Then 2 Then 3 Then 4 | [4] |
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.
doc:beam/88a09d82-6475-43c6-b318-5038c7d69d1e"How many people live in New York City?", "Explain the theory of relativity and its implications.", "What is the weather like today?", "Can you provide a detailed explanation of quantum mechanics?", "Who is the current p…
doc:beam/8f949948-6f9e-4109-8cf9-0e2453f1a6ddprint(f"Precision: {precision}") ``` ### Explanation 1. **Expected Outcomes**: - `expected_outcomes` is a list of expected resized queries corresponding to each test query. 2. **Calculate Complexity**: - The `calculate_complexity` …
doc:beam/73cf5b25-5967-4e9d-b001-95f229bcbab5```python def evaluate_model(test_queries, expected_outcomes): # Evaluate model on test queries correct_count = 0 for query, expected in zip(test_queries, expected_outcomes): # Calculate complexity complexity = c…
doc:beam/f3fab465-2260-4fa0-9bdc-b6b05a461a72if resized_query == expected: correct_count += 1 # Compute precision precision = correct_count / len(test_queries) return precision def calculate_complexity(query): # Calculate complexity based on q…
doc:beam/8f7cbd67-ee5f-4dd4-87a3-f48bc3b5ce32precision = correct_count / len(test_queries) return precision # Generate test data def generate_test_data(num_queries): test_queries = [] expected_outcomes = [] for _ in range(num_queries): query_length = rando…
doc:beam/67f41409-4cd1-4781-8f85-fae844b4b736query = ''.join(np.random.choice(list(string.ascii_letters + string.digits), size=query_length)) test_queries.append(query) # Simulate complexity calculation and resizing complexity = len(query) / 20…
doc:beam/f307c285-b34b-4883-acff-f7cccfa37760"Explain the theory of relativity and its impl", "What is the weather like today?", "Can you provide a detailed explanation of quantum mechan", "Who is the current president of the United States?", "What are the main com…
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