2025-12-11 12:10:13,381 - __main__ - INFO - ============================================================ 2025-12-11 12:10:13,381 - __main__ - INFO - GEPA Optimization for Heritage RAG Pipeline 2025-12-11 12:10:13,381 - __main__ - INFO - ============================================================ 2025-12-11 12:10:13,381 - __main__ - INFO - Budget: light 2025-12-11 12:10:13,381 - __main__ - INFO - Train size: 5, Val size: 3 2025-12-11 12:10:13,381 - __main__ - INFO - Student model: openai/gpt-4o-mini 2025-12-11 12:10:13,381 - __main__ - INFO - Reflection model: openai/gpt-4o 2025-12-11 12:10:13,381 - __main__ - INFO - Configuring DSPy... 2025-12-11 12:10:13,381 - __main__ - INFO - Creating training data... 2025-12-11 12:10:13,381 - __main__ - INFO - Created 5 train, 3 val examples 2025-12-11 12:10:13,381 - __main__ - INFO - Creating pipeline... 2025-12-11 12:10:13,391 - __main__ - INFO - Creating GEPA optimizer... 2025-12-11 12:10:13,392 - __main__ - INFO - Starting optimization (this may take 5-15 minutes)... 2025/12/11 12:10:13 INFO dspy.teleprompt.gepa.gepa: Running GEPA for approx 2954 metric calls of the program. This amounts to 369.25 full evals on the train+val set. 2025/12/11 12:10:13 INFO dspy.teleprompt.gepa.gepa: Using 3 examples for tracking Pareto scores. You can consider using a smaller sample of the valset to allow GEPA to explore more diverse solutions within the same budget. GEPA requires you to provide the smallest valset that is just large enough to match your downstream task distribution, while providing as large trainset as possible. GEPA Optimization: 0%| | 0/2954 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: Generalizable Strategy: - For geographic queries, identify entities based on type and location (e.g., museums in a province), utilize knowledge of geographic identifiers. - For temporal queries, focus on date-related attributes/events specified in the question (e.g., entities post a certain year). - For exploration queries, establish relationships with thematic concepts (e.g., archives related to an event or period). Ensure the query includes retrieval of relevant properties such as labels to maintain readability and understanding. Verify correct sources are utilized according to feedback or requirements (e.g., sparql, qdrant). Your objective is to generate valid SPARQL queries that meet the user's requirements as specified in the inputs, adhering to both technical precision and domain-specific standards. 2025/12/11 12:11:00 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:11:00 INFO dspy.teleprompt.gepa.gepa: Iteration 3: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 1%| | 21/2954 [00:47<1:58:36, 2.43s/rollouts]2025/12/11 12:11:00 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: 2. **Intent Identification:** - Classify the intent of the query accurately. Possible intents include: - 'entity_lookup': When looking up specific information about an entity. - 'exploration': When discovering relationships or collections related to a specific topic. - 'temporal': When filtering data based on a timeframe or date criteria. - 'geographic': When querying for location-related information. 3. **Entity Extraction:** - Extract all relevant entities mentioned in the query, referring explicitly to individuals, places, events, etc., within the Heritage Custodian Ontology context. - Ensure all relevant entities are included in the SPARQL query to accurately reflect the user's request. 4. **Building SPARQL Queries:** - Use the key prefixes provided above accurately. - Craft queries to retrieve relevant data based on extracted entities and intent. This may involve filtering, joining, or selecting specific properties from the ontology. - Pay attention to specific ontology constructs like types (e.g., hco:Library, hco:Archive) and properties (e.g., dct:spatial, skos:related). - Incorporate any required filters, such as temporal or geographic constraints, as dictated by the intent. 5. **Explanation:** - Provide a clear, concise explanation of the logic and expected results of your SPARQL query. This explanation should help the user understand how the query aligns with their request. 6. **Feedback Consideration:** - Incorporate improvements based on feedback, such as refining intent classification, ensuring the presence of all entities, and correctly selecting data sources. By following these detailed instructions, you will generate effective and precise SPARQL queries to interact with the heritage custodian knowledge graph. Your response should reflect a clear understanding of both the task requirements and the underlying ontology. 2025/12/11 12:11:10 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%) 2025/12/11 12:11:10 INFO dspy.teleprompt.gepa.gepa: Iteration 5: New subsample score 1.95 is not better than old score 1.95, skipping GEPA Optimization: 1%| | 30/2954 [00:57<1:27:29, 1.80s/rollouts]2025/12/11 12:11:10 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: Generalizable Strategy: - For geographic queries, identify entities based on type and location (e.g., museums in a province), utilize knowledge of geographic identifiers. - For temporal queries, focus on date-related attributes/events specified in the question (e.g., entities post a certain year). - For exploration queries, establish relationships with thematic concepts (e.g., archives related to an event or period). Ensure the query includes retrieval of relevant properties such as labels to maintain readability and understanding. Verify correct sources are utilized according to feedback or requirements (e.g., sparql, qdrant). Your objective is to generate valid SPARQL queries that meet the user's requirements as specified in the inputs, adhering to both technical precision and domain-specific standards. 2025/12/11 12:12:24 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:12:24 INFO dspy.teleprompt.gepa.gepa: Iteration 11: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 2%|▏ | 66/2954 [02:11<1:22:49, 1.72s/rollouts]2025/12/11 12:12:24 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: 2. Process: - Identify the intent of the query to tailor the SPARQL query appropriately. - Accurately extract and utilize entities from the question when forming the query. Pay close attention to case sensitivity and matching exact terms within the ontology. - Write the SPARQL query using the HCO ontology to express relationships and attributes precisely. 3. SPARQL Query Structure: - For 'exploration' intent, focus on retrieving specific entities and their attributes or related concepts. - For 'statistical' intent, use aggregate functions like COUNT to fulfill quantity-related questions. - For 'temporal' intent, implement filters for date-related attributes using appropriate datatype and filters (e.g., FILTER (?date > "YYYY-MM-DD"^^xsd:date)). 4. Additional Considerations: - Ensure the SPARQL query is syntactically correct and valid according to SPARQL standards. - The generated query should aim to include expected terms from the input and utilize correct ontology classes or properties. - Include an explanation of your reasoning for the constructed SPARQL query, offering insights into how the query addresses the given question. 2025/12/11 12:12:37 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:12:37 INFO dspy.teleprompt.gepa.gepa: Iteration 13: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 3%|▎ | 75/2954 [02:24<1:19:40, 1.66s/rollouts]2025/12/11 12:12:37 INFO dspy.teleprompt.gepa.gepa: Iteration 14: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: 5. **Provide Reasoning and Explanation**: For each query, include a clear reasoning to demonstrate understanding of how the entities and intents are mapped to the query structure. Additionally, provide an explanation for the query, detailing how it achieves the desired result. 6. **Utilize Correct Data Sources**: Ensure that the sources used in your query align with the task requirements. Typical sources include 'sparql' and occasionally 'qdrant', while others like 'postgis' may be less relevant depending on the context. By following these instructions, ensure your queries are accurate and informative, addressing the nuanced details involved in querying a heritage custodian knowledge graph via SPARQL, HCO, and the Oxigraph endpoint. 2025/12/11 12:13:53 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%) 2025/12/11 12:13:53 INFO dspy.teleprompt.gepa.gepa: Iteration 19: New subsample score 1.95 is not better than old score 1.95, skipping GEPA Optimization: 4%|▍ | 111/2954 [03:40<1:40:48, 2.13s/rollouts]2025/12/11 12:13:53 INFO dspy.teleprompt.gepa.gepa: Iteration 20: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: Follow these detailed steps: 1. **Entity and Intent Extraction**: Carefully extract all relevant entities from the user's question. These entities are likely terms that refer to specific categories or classes within the ontology, such as specific events (e.g., 'World War II'), locations (e.g., 'Noord-Holland province'), or object types (e.g., 'archives', 'museums', 'bibliotheken'). Determine the intent of the question, which typically falls into categories such as temporal queries (related to time), exploration queries (search and discover), or geographic queries (location-based). 2. **Understanding the Structure**: Understand the relationships stipulated by the HCO and how they represent various concepts, such as associating archives with historical events, museums with geo-locations, and libraries with temporal events like mergers. 3. **Constructing SPARQL Queries**: - Use the extracted entities and identified intent to formulate a SPARQL query. - Ensure the query selects the appropriate class or type for the entities (e.g., `hco:Archive`, `hco:Museum`, `hco:Library`). - Establish necessary relationships using appropriate HCO predicates (e.g., `skos:related`, `hco:isLocatedIn`, `dct:date`) to accurately represent the question. - Integrate necessary filters or additional service specifications for extended functionalities, such as fetching labels in specific languages or dates more recent than a provided temporal marker. 4. **Providing Explanation**: Include a brief explanation accompanying each query to elucidate the reasoning behind its structure, including how entity connections and filters reflect the user's original question. 5. **Review Feedback and Iterate**: Use feedback from previous tasks to note where improvements are necessary, particularly in entity extraction or selection of sources. Adjust your strategy to incorporate this feedback. By strictly adhering to these instructions, ensure that all generated SPARQL queries are valid and efficient in accessing and retrieving the desired information from the Heritage Custodian Knowledge Graph using the Oxigraph endpoint. 2025/12/11 12:14:04 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:14:04 INFO dspy.teleprompt.gepa.gepa: Iteration 21: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 4%|▍ | 120/2954 [03:51<1:24:15, 1.78s/rollouts]2025/12/11 12:14:04 INFO dspy.teleprompt.gepa.gepa: Iteration 22: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: - Based on the intent, formulate a query that accurately captures the question's requirements: - For temporal tasks, filter entities based on date conditions. - For geographic tasks, use location-based filters to retrieve entities within specified areas. - For exploration tasks, establish relationships between entities using relevant ontology properties. 3. **Entity Extraction**: - Ensure accurate identification of key entities within the question. This includes extracting dates, specific locations, or notable events such as "World War II". - Consider using named entity recognition techniques to improve accuracy in capturing all relevant entities. 4. **Validate Ontology Use**: - Ensure the use of the Heritage Custodian Ontology (HCO) classes and properties when forming your queries. - Use ontology concepts correctly to link entities, as seen in the handling of classes like `hco:Library`, `hco:Museum`, and `hco:Archive`. 5. **Explanation of Queries**: - Provide a clear and concise explanation of how your SPARQL query works and what results it will yield. - Explain any labels or service calls used in your query, especially when involving external services or language parameterization. 6. **Feedback Analysis**: - Review any feedback from previous examples similar to the task at hand. Adjust your approach to address commonly noted issues, especially in entity extraction. - Continuously refine query structure to align with expected terms and selected data sources. By following these instructions, you will generate accurate and efficient SPARQL queries that fulfill the requirements of the Heritage Custodian Knowledge Graph task. Focus on precision, especially concerning ontology terms, and ensure all queries are robust and aligned with HCO specifications. 2025/12/11 12:15:55 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:15:55 INFO dspy.teleprompt.gepa.gepa: Iteration 27: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 5%|▌ | 156/2954 [05:41<2:15:48, 2.91s/rollouts]2025/12/11 12:15:55 INFO dspy.teleprompt.gepa.gepa: Iteration 28: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: 2. For a 'geographic' intent: - Focus on retrieving location-related information linked to the entities provided. - Ensure the use of properties such as `dct:spatial` or `hco:isLocatedIn`, where relevant, to fetch location data. - Consider using geo-based filtering or relationships to refine results, such as matching specific province names. 3. For a 'statistical' intent: - Concentrate on counting or aggregating entity occurrences, like the number of museums in a location. - Use constructs such as COUNT to summarize data based on the type or classification of entities (e.g., museums). 4. Pay attention to the proper classification of entities and intent extraction. Ensure that all entities from the input are properly recognized and used in the query. 5. Justify each part of the SPARQL query with reasoning and provide an explanation of how the query addresses the original question. 6. Select appropriate data sources, with a focus on using 'sparql' and 'qdrant', avoiding use of incorrect data sources like 'postgis'. Remember to always include explanations for both the SPARQL query itself and the strategy used to derive the query, ensuring that the logic aligns well with the knowledge graph's structure and intended use of the data. Keep the expected answers clear and concise, and ensure that they meet the demands of the question by containing all expected terms. Additionally, be prepared to adjust your approach based on feedback to continually improve entity extraction and intent classification. 2025/12/11 12:16:06 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%) 2025/12/11 12:16:06 INFO dspy.teleprompt.gepa.gepa: Iteration 29: New subsample score 1.95 is not better than old score 1.95, skipping GEPA Optimization: 6%|▌ | 165/2954 [05:52<1:45:16, 2.26s/rollouts]2025/12/11 12:16:06 INFO dspy.teleprompt.gepa.gepa: Iteration 30: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: - Identify the required ontology classes and properties based on the identified intent and entities: - For statistical and count-related questions, use `COUNT(?variable) AS ?count`. - For geographic questions, consider using location properties like `dct:spatial` or `dct:location`. - For entity lookup, use specific entity IRIs directly, such as `hco:Rijksmuseum`. - For exploration and relationship-based queries, utilize properties like `skos:related` to establish connections. 4. **Explain the Query**: After constructing the SPARQL query, provide a clear explanation of how the query works and why it's suitable for the question. Highlight what the query aims to retrieve (e.g., labels, properties) and the relationship it leverages within the HCO ontology. 5. **Quality Assurance**: Validate your query against expected areas of improvement: - Ensure intent is correctly identified. - Ensure all relevant entities from the question are extracted and accounted for. - Ensure the SPARQL query references correct ontology classes and properties as specified by HCO. - Confirm that the explanation accurately reflects the query's operation and output. This procedure will ensure the queries are both syntactically and semantically valid, effectively retrieving the needed information from the knowledge graph. Be concise, clear, and accurate in each output to maintain the quality of responses. 2025/12/11 12:17:31 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%) 2025/12/11 12:17:31 INFO dspy.teleprompt.gepa.gepa: Iteration 35: New subsample score 1.95 is not better than old score 1.95, skipping GEPA Optimization: 7%|▋ | 201/2954 [07:18<1:50:25, 2.41s/rollouts]2025/12/11 12:17:31 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 PREFIX skos: PREFIX geo: PREFIX dct: ``` - **Intent Understanding:** Correctly interpret the intent of the question to guide the query design. Adjust the query structure based on whether the question is statistical (e.g., counts), temporal (e.g., dates), or exploratory (e.g., finding related items). - **Knowledge of Ontology Concepts:** - Use entities and relations accurately. For instance, use `hco:Museum` for museums or `hco:Library` for libraries. - Leverage `dct:location`, `dct:date`, `skos:related`, and similar properties relevant to the query. - **Output Requirements:** - The query should be syntactically correct and should align with the question intent. - Provide a reasoning section explaining the logic behind the constructed query. - Offer an explanation section to clarify how the query addresses the question. - **Handling Feedback:** - Review all feedback provided on generated queries, especially regarding missing entities or incorrect mappings. Continuously improve entity extraction and ontology alignment based on this feedback. - Aim to have all expected terms from the question reflected in your SPARQL queries. - **General Strategy:** - Understand the semantic structure of the question to guide SPARQL query construction. - Ensure that the queries are efficient and only retrieve necessary data points, filtering out any unrelated results. - Maintain consistency across different types of questions by adhering to best practices in ontology querying. Remember, the goal is to provide precise and accurate SPARQL queries that facilitate the exploration and analysis of the cultural heritage knowledge graph. Always verify that your responses are complete and address all aspects of the user's query. 2025/12/11 12:17:44 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:17:44 INFO dspy.teleprompt.gepa.gepa: Iteration 37: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 7%|▋ | 210/2954 [07:31<1:33:31, 2.04s/rollouts]2025/12/11 12:17:44 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: The query should: - Use `hco` to correctly specify entity types like hco:Museum or hco:Archive. - Filter results based on location or thematic relevance using properties like `hco:isLocatedIn`, `dct:location`, or `skos:related`. - Optionally, fetch labels and ensure the query retrieves them in a readable form, often using language services like `SERVICE wikibase:label`. 4. **Sources Selection**: Ensure the correct data sources are used (sparql, qdrant, postgis, etc.) based on what the task demands, and ensure that the results contain all the necessary terms to answer the question. 5. **Reasoning and Explanation**: Provide a logical explanation of the reasoning behind the query constructed, outlining why certain RDF triples or filters were used. Pay close attention to feedback related to entity extraction and selection of appropriate data sources, ensuring that all necessary entities and expected terms are present in the final SPARQL query output. Use the information and examples from the task context to improve the extraction of entities and choices made during source selection. The goal is to continuously refine the approach based on feedback on previous examples. 2025/12/11 12:19:19 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:19:19 INFO dspy.teleprompt.gepa.gepa: Iteration 43: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 8%|▊ | 246/2954 [09:06<1:57:37, 2.61s/rollouts]2025/12/11 12:19:19 INFO dspy.teleprompt.gepa.gepa: Iteration 44: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: - Identify the required ontology classes and properties based on the identified intent and entities: - For statistical and count-related questions, use `COUNT(?variable) AS ?count`. - For geographic questions, consider using location properties like `dct:spatial` or `dct:location`. - For entity lookup, use specific entity IRIs directly, such as `hco:Rijksmuseum`. - For exploration and relationship-based queries, utilize properties like `skos:related` to establish connections. 4. **Explain the Query**: After constructing the SPARQL query, provide a clear explanation of how the query works and why it's suitable for the question. Highlight what the query aims to retrieve (e.g., labels, properties) and the relationship it leverages within the HCO ontology. 5. **Quality Assurance**: Validate your query against expected areas of improvement: - Ensure intent is correctly identified. - Ensure all relevant entities from the question are extracted and accounted for. - Ensure the SPARQL query references correct ontology classes and properties as specified by HCO. - Confirm that the explanation accurately reflects the query's operation and output. This procedure will ensure the queries are both syntactically and semantically valid, effectively retrieving the needed information from the knowledge graph. Be concise, clear, and accurate in each output to maintain the quality of responses. 2025/12/11 12:19:20 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%) 2025/12/11 12:19:20 INFO dspy.teleprompt.gepa.gepa: Iteration 45: New subsample score 1.95 is not better than old score 1.95, skipping GEPA Optimization: 9%|▊ | 255/2954 [09:06<1:12:08, 1.60s/rollouts]2025/12/11 12:19:20 INFO dspy.teleprompt.gepa.gepa: Iteration 46: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: Output Format: 1. Reasoning: Provide a detailed explanation of how you will translate the question into a SPARQL query using the relevant ontological entities and relationships. 2. SPARQL: Generate the SPARQL query based on the reasoning, ensuring it is complete and uses the appropriate prefixes. 3. Explanation: Offer a concise summary of how the SPARQL query addresses the question. Guidelines: - Correctly identify and extract all relevant entities from the question, even if they are implied, and include them in the entities field. - Ensure the query leverages the right sources and prefixes, matching the intent and entities identified in the input. - When fetching locations, ensure geographic names like 'Amsterdam' and 'Noord-Holland' match how they are represented in the data (e.g., using appropriate labels or identifiers). - For temporal queries, correctly filter using date comparisons where necessary. - For statistical queries, typically use aggregate functions like COUNT to fulfill the requirement. - Always provide contextually aware queries that incorporate identified entities and appropriately address the intent. Note: Consider potential errors in entity extraction and provide improved guidance on identifying these entities accurately. Additionally, ensure query outputs contain all expected terms to fully address the user's question. 2025/12/11 12:20:50 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:20:50 INFO dspy.teleprompt.gepa.gepa: Iteration 51: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 10%|▉ | 291/2954 [10:36<1:48:03, 2.43s/rollouts]2025/12/11 12:20:50 INFO dspy.teleprompt.gepa.gepa: Iteration 52: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00` - `PREFIX skos: ` - `PREFIX geo: ` - `PREFIX dct: ` 3. **Generating the SPARQL Query:** - Analyze the question and interpret it in the context of heritage and archive data. - Identify the key entities from the question that should be included in the query. Aim for precise entity extraction. - Consider the query's intent to ensure it aligns with the nature of the data requested (e.g., filtering dates for temporal, counting results for statistical queries). - Construct a SPARQL query using the identified concepts that retrieves relevant data from the knowledge graph according to the Heritage Custodian Ontology. Ensure your SPARQL queries are valid and syntactically correct. 4. **Reasoning and Explanation:** - Provide reasoning for how you interpreted the inputs and what information you aim to retrieve with your query. - Offer an explanation of the query: state what entities it targets, what kind of relationships or data fields it explores, and elucidate the use of filters or aggregations if applicable. 5. **Feedback Integration:** - When receiving feedback indicating missing entities or other issues, work on improving entity extraction and query accuracy. - Make sure your queries cover all relevant aspects of the question and the expected terms. By following these detailed steps, you can ensure the generation of accurate and contextually appropriate SPARQL queries that align with the expertise in the Heritage Custodian domain and the ontology in use. 2025/12/11 12:21:02 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:21:02 INFO dspy.teleprompt.gepa.gepa: Iteration 53: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 10%|█ | 300/2954 [10:49<1:30:13, 2.04s/rollouts]2025/12/11 12:21:02 INFO dspy.teleprompt.gepa.gepa: Iteration 54: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: Make sure to consider the following guidelines when generating SPARQL queries: 1. **Intent Classification**: Properly identify the user's intent based on the question and context. Possible intents include: - Exploration: For general inquiries about connections, relationships, or categorizations. - Geographic: When location data is needed. - Temporal: When time-based data is important. - Entity lookup: Specifically retrieving data related to a specific entity regardless of context. 2. **Entity Extraction**: Identify and retrieve key entities mentioned in the inputs. Accurately determine which entities are referenced to build precise and relevant SPARQL queries. Examples include identifying terms such as 'World War II', 'Rijksmuseum', and numerical data for time filtering. 3. **SPARQL Query Construction**: Formulate the SPARQL query to accurately reflect the retrieved intent and entities. For instance: - Use appropriate RDF types and predicates to structure the query. - Utilize filtering when necessary to match the exact requirements, like dates or related terms. - Make the query robust enough to handle variations or absence of some data points, ensuring it can still perform effectively. 4. **Use of Services**: When required, utilize SPARQL service clauses to interface with external data sources or endpoint services. 5. **Explanation and Reasoning**: Provide a clear explanation of your reasoning process for the SPARQL query. This includes context on why specific paths or predicates are used. 6. **Feedback Incorporation**: Integrate feedback for better accuracy in the domain-specific task. This includes better entity extraction (finding all relevant entities in each task) and intent classification. Remember to continuously refine queries based on domain-specific requirements and feedback. Keep yourself updated with ontology updates to ensure that SPARQL queries are utilizing the most current and accurate information. 2025/12/11 12:22:55 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%) 2025/12/11 12:22:55 INFO dspy.teleprompt.gepa.gepa: Iteration 59: New subsample score 1.95 is not better than old score 1.95, skipping GEPA Optimization: 11%|█▏ | 336/2954 [12:42<2:25:02, 3.32s/rollouts]2025/12/11 12:22:55 INFO dspy.teleprompt.gepa.gepa: Iteration 60: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 PREFIX skos: PREFIX geo: PREFIX dct: ``` - **Intent Understanding:** Correctly interpret the intent of the question to guide the query design. Adjust the query structure based on whether the question is statistical (e.g., counts), temporal (e.g., dates), or exploratory (e.g., finding related items). - **Knowledge of Ontology Concepts:** - Use entities and relations accurately. For instance, use `hco:Museum` for museums or `hco:Library` for libraries. - Leverage `dct:location`, `dct:date`, `skos:related`, and similar properties relevant to the query. - **Output Requirements:** - The query should be syntactically correct and should align with the question intent. - Provide a reasoning section explaining the logic behind the constructed query. - Offer an explanation section to clarify how the query addresses the question. - **Handling Feedback:** - Review all feedback provided on generated queries, especially regarding missing entities or incorrect mappings. Continuously improve entity extraction and ontology alignment based on this feedback. - Aim to have all expected terms from the question reflected in your SPARQL queries. - **General Strategy:** - Understand the semantic structure of the question to guide SPARQL query construction. - Ensure that the queries are efficient and only retrieve necessary data points, filtering out any unrelated results. - Maintain consistency across different types of questions by adhering to best practices in ontology querying. Remember, the goal is to provide precise and accurate SPARQL queries that facilitate the exploration and analysis of the cultural heritage knowledge graph. Always verify that your responses are complete and address all aspects of the user's query. 2025/12/11 12:22:56 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:22:56 INFO dspy.teleprompt.gepa.gepa: Iteration 61: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 12%|█▏ | 345/2954 [12:42<1:29:11, 2.05s/rollouts]2025/12/11 12:22:56 INFO dspy.teleprompt.gepa.gepa: Iteration 62: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: For each set of inputs provided, perform the following steps: 1. **Understand the Inputs**: - Analyze the question to determine the main intent (e.g., exploration, temporal, geographic) and identify the entities involved. - If the context is provided, incorporate it into your reasoning for query formulation. 2. **Entity Extraction**: - Accurately extract relevant entities from the input question, such as places, events, or concepts. Pay particular attention to named entities and key terms. - Ensure that entities are represented in the query to retrieve meaningful results. 3. **Query Construction**: - Construct a valid SPARQL query that incorporates the identified intent and entities. - Use relevant classes and properties from the HCO. For example: - Use `hco:Archive` for archives, `hco:Library` for libraries, `hco:Museum` for museums, and `hco:isLocatedIn` for geographical tags. - Use `skos:related` to identify relationships between entities such as events (e.g., "World War II"). - Apply filters for temporal queries, such as on dates using the `dct:date` property. 4. **Explanation and Reasoning**: - Provide a thorough explanation of the reasoning behind the query construction. This should include the logic of how the question's intent and entities are translated into the SPARQL query. - Clearly state how the query aligns with the original question and describe what results the query is expected to deliver. 5. **Common Query Patterns**: - For exploration intent queries, relate entities conceptually using `skos:related`. - For temporal intents, use date filtering with `FILTER` and appropriately handle date formats. - For geographic intents, correlate locations using `hco:isLocatedIn` and match location labels with `skos:prefLabel`. The goal is to create SPARQL queries that are syntactically correct, semantically meaningful, and entirely responsive to the initial task requests based on the Heritage Custodian Ontology and hosted environment. 2025/12/11 12:24:20 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:24:20 INFO dspy.teleprompt.gepa.gepa: Iteration 67: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 13%|█▎ | 381/2954 [14:07<1:48:50, 2.54s/rollouts]2025/12/11 12:24:20 INFO dspy.teleprompt.gepa.gepa: Iteration 68: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: 2. **Task Components:** - **Inputs**: - *question*: The query as a natural language question. - *intent*: The type of query (e.g., temporal, exploration, or statistical). - *entities*: Entities extracted from the question (e.g., names, places, events). - *context*: Any additional context related to the question (often not provided but should be supported). - **Outputs**: - *reasoning*: Understand and explain the approach to constructing the SPARQL query based on the input. - *sparql*: The generated SPARQL query, using the correct structure and format according to the ontology and endpoint capabilities. - *explanation*: An explanation of what the query does, how it retrieves data, and why it’s structured that way. 3. **Guidelines for Query Construction**: - Identify and extract all relevant entities from the question accurately (e.g., names of places, events, objects). - Choose the correct ontology classes and predicates tailored to the HCO. - Consider the SPARQL query intent when constructing queries: - *Temporal*: Filter results based on time criteria. - *Exploration*: Retrieve and browse specific related content. - *Statistical*: Count or summarize quantities or other statistical information. - Ensure that all queried terms and classes are represented in the returned result to fully answer the question. - Address entity extraction feedback by improving methods to capture key terms explicitly mentioned in the question. 4. **Response Feedback**: Maintain an understanding of specific feedback to improve future responses: - Ensure all entities mentioned in the question are incorporated into the query. - Identify the correct intent and formulate queries to match that purpose. - Always include terms necessary for a comprehensive answer. 2025/12/11 12:24:32 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:24:32 INFO dspy.teleprompt.gepa.gepa: Iteration 69: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 13%|█▎ | 390/2954 [14:18<1:26:55, 2.03s/rollouts]2025/12/11 12:24:32 INFO dspy.teleprompt.gepa.gepa: Iteration 70: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00` - `PREFIX skos: ` - `PREFIX geo: ` - `PREFIX dct: ` 3. **SPARQL Query Construction**: - For explorative intents, identify and retrieve entities related to the provided concepts. - For geographic intents, filter entities based on location or geographical identifiers. - For statistical intents, use aggregate functions to count or summarize relevant data. - Employ appropriate relationships and types from the HCO, like `hco:Archive`, `hco:Museum`, and location-based predicates like `hco:isLocatedIn` or `dct:location`. 4. **Entity Comprehension**: - Enhance entity extraction to capture key terms and concepts effectively, ensuring entities like specific events, locations, or items are correctly identified and included in the query logic. 5. **Output Requirements**: - Return well-formed SPARQL queries that are syntactically correct and semantically aligned with the intended question. - Add explanations if required, describing the logic behind query construction and anticipated outcomes. 6. **Error Handling and Optimization**: - Pay attention to missing entities or incorrect relationships and optimize query structures accordingly. - If appropriate relationships or entity representations are missing from the ontology, seek logical approximations. By following these guidelines, you will generate queries that accurately draw information from the Heritage Custodian Knowledge Graph, enhancing the retrieval of heritage-related data. 2025/12/11 12:25:45 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:25:45 INFO dspy.teleprompt.gepa.gepa: Iteration 75: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 14%|█▍ | 426/2954 [15:31<1:30:48, 2.16s/rollouts]2025/12/11 12:25:45 INFO dspy.teleprompt.gepa.gepa: Iteration 76: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: Instructions: 1. Analyze the question to determine the intent. Common intents include temporal queries, geographic location queries, and exploratory queries regarding entities or concepts. 2. Extract all relevant entities mentioned within the question. Entities could include specific dates, place names, cultural heritage sites, etc. 3. Construct the SPARQL query: - Use the identified intent to guide the structure of the query (e.g., SELECT, WHERE clauses). - Integrate the extracted entities into the query, ensuring they match the ontology's identifiers. - For temporal questions, filter results using appropriate date properties and operators. - For geographic questions, utilize spatial relations and properties (e.g., `dct:spatial`). - For exploration questions, consider hierarchical relationships and related concepts. 4. Where applicable, use services defined within the ontology that may help retrieve additional information or labels (e.g., using SERVICE clauses). 5. Provide a brief explanation of the reasoning behind the query structure and entity usage. This helps in understanding the link between the question and the generated SPARQL query. 6. Ensure that the query is constructed with valid syntax and is expected to yield results relevant to the given question within the knowledge graph. Additional Notes: - Continuously improve entity extraction, ensuring entities related to dates, names, and specific cultural terms are captured accurately. - Refine intent classification, recognizing when queries are about entity lookups or when they require geographic or temporal context. - Ensure that the response uses the correct ontology and descriptors from the Heritage Custodian Ontology (HCO), providing thorough outputs that align with user queries. 2025/12/11 12:25:58 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%) 2025/12/11 12:25:58 INFO dspy.teleprompt.gepa.gepa: Iteration 77: New subsample score 1.95 is not better than old score 1.95, skipping GEPA Optimization: 15%|█▍ | 435/2954 [15:45<1:21:40, 1.95s/rollouts]2025/12/11 12:25:58 INFO dspy.teleprompt.gepa.gepa: Iteration 78: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 PREFIX skos: PREFIX geo: PREFIX dct: ``` - **Intent Understanding:** Correctly interpret the intent of the question to guide the query design. Adjust the query structure based on whether the question is statistical (e.g., counts), temporal (e.g., dates), or exploratory (e.g., finding related items). - **Knowledge of Ontology Concepts:** - Use entities and relations accurately. For instance, use `hco:Museum` for museums or `hco:Library` for libraries. - Leverage `dct:location`, `dct:date`, `skos:related`, and similar properties relevant to the query. - **Output Requirements:** - The query should be syntactically correct and should align with the question intent. - Provide a reasoning section explaining the logic behind the constructed query. - Offer an explanation section to clarify how the query addresses the question. - **Handling Feedback:** - Review all feedback provided on generated queries, especially regarding missing entities or incorrect mappings. Continuously improve entity extraction and ontology alignment based on this feedback. - Aim to have all expected terms from the question reflected in your SPARQL queries. - **General Strategy:** - Understand the semantic structure of the question to guide SPARQL query construction. - Ensure that the queries are efficient and only retrieve necessary data points, filtering out any unrelated results. - Maintain consistency across different types of questions by adhering to best practices in ontology querying. Remember, the goal is to provide precise and accurate SPARQL queries that facilitate the exploration and analysis of the cultural heritage knowledge graph. Always verify that your responses are complete and address all aspects of the user's query. 2025/12/11 12:26:52 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 83: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 16%|█▌ | 471/2954 [16:39<59:47, 1.44s/rollouts] 2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 84: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: Output Format: 1. Reasoning: Provide a detailed explanation of how you will translate the question into a SPARQL query using the relevant ontological entities and relationships. 2. SPARQL: Generate the SPARQL query based on the reasoning, ensuring it is complete and uses the appropriate prefixes. 3. Explanation: Offer a concise summary of how the SPARQL query addresses the question. Guidelines: - Correctly identify and extract all relevant entities from the question, even if they are implied, and include them in the entities field. - Ensure the query leverages the right sources and prefixes, matching the intent and entities identified in the input. - When fetching locations, ensure geographic names like 'Amsterdam' and 'Noord-Holland' match how they are represented in the data (e.g., using appropriate labels or identifiers). - For temporal queries, correctly filter using date comparisons where necessary. - For statistical queries, typically use aggregate functions like COUNT to fulfill the requirement. - Always provide contextually aware queries that incorporate identified entities and appropriately address the intent. Note: Consider potential errors in entity extraction and provide improved guidance on identifying these entities accurately. Additionally, ensure query outputs contain all expected terms to fully address the user's question. 2025/12/11 12:26:52 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 85: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 16%|█▌ | 480/2954 [16:39<42:35, 1.03s/rollouts]2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 86: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: Follow these detailed steps: 1. **Entity and Intent Extraction**: Carefully extract all relevant entities from the user's question. These entities are likely terms that refer to specific categories or classes within the ontology, such as specific events (e.g., 'World War II'), locations (e.g., 'Noord-Holland province'), or object types (e.g., 'archives', 'museums', 'bibliotheken'). Determine the intent of the question, which typically falls into categories such as temporal queries (related to time), exploration queries (search and discover), or geographic queries (location-based). 2. **Understanding the Structure**: Understand the relationships stipulated by the HCO and how they represent various concepts, such as associating archives with historical events, museums with geo-locations, and libraries with temporal events like mergers. 3. **Constructing SPARQL Queries**: - Use the extracted entities and identified intent to formulate a SPARQL query. - Ensure the query selects the appropriate class or type for the entities (e.g., `hco:Archive`, `hco:Museum`, `hco:Library`). - Establish necessary relationships using appropriate HCO predicates (e.g., `skos:related`, `hco:isLocatedIn`, `dct:date`) to accurately represent the question. - Integrate necessary filters or additional service specifications for extended functionalities, such as fetching labels in specific languages or dates more recent than a provided temporal marker. 4. **Providing Explanation**: Include a brief explanation accompanying each query to elucidate the reasoning behind its structure, including how entity connections and filters reflect the user's original question. 5. **Review Feedback and Iterate**: Use feedback from previous tasks to note where improvements are necessary, particularly in entity extraction or selection of sources. Adjust your strategy to incorporate this feedback. By strictly adhering to these instructions, ensure that all generated SPARQL queries are valid and efficient in accessing and retrieving the desired information from the Heritage Custodian Knowledge Graph using the Oxigraph endpoint. 2025/12/11 12:27:56 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:27:56 INFO dspy.teleprompt.gepa.gepa: Iteration 91: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 17%|█▋ | 516/2954 [17:43<1:00:18, 1.48s/rollouts]2025/12/11 12:27:56 INFO dspy.teleprompt.gepa.gepa: Iteration 92: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: 2. **Intent Identification**: - Determine the intent of the query: whether it pertains to statistical data, geographic information, or entity lookup. This guides the structure of the SPARQL query. 3. **Entity Extraction**: - Accurately extract relevant entities from the question. Key entities often include names of places like cities, provinces, and specific museums or heritage sites. - Ensure entity extraction aligns with question context to refine the accuracy of queries. 4. **Constructing Queries**: - For statistical intents, such as counting entities (e.g., number of museums), construct queries using aggregation functions like COUNT. - For geographic queries about locations of entities (museums or sites), ensure location-based filters are applied using properties like `dct:location` or `dct:spatial`. - For entity-specific requests, use appropriate properties to directly retrieve information concerning the specific entity in question. 5. **Example Patterns**: - If the question asks about the number of a type of entity (e.g., "How many museums in a location?"), use the following pattern: ```sparql SELECT (COUNT(?entity) AS ?count) WHERE { ?entity a hco:EntityType . ?entity dct:location hco:Location . } ``` - For queries requesting specific entities, leverage filters to match labels: ```sparql SELECT ?entity WHERE { ?entity a hco:EntityType . ?entity hco:isLocatedIn ?area . ?area skos:prefLabel "AreaName" . } ``` - When retrieving detailed information on an entity: ```sparql SELECT ?property WHERE { hco:Entity dct:spatial ?property . } ``` 6. **Explanation**: - Provide a clear explanation alongside the query output, detailing the query's purpose, the reasoning behind the selection of query components, and what the expected results will manifest. 7. **Feedback Considerations**: - Improvements in entity extraction are crucial; ensure all relevant entities are captured. - Correct classification of user intent is essential for generating appropriate queries. - Maintain alignment with expected data sources and suffix query outputs with an explanation for clarity. By following these guidelines, you will effectively generate SPARQL queries that align with the heritage custodian knowledge graph and meet the user's needs as specified by their question. 2025/12/11 12:28:12 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%) 2025/12/11 12:28:12 INFO dspy.teleprompt.gepa.gepa: Iteration 93: New subsample score 1.95 is not better than old score 1.95, skipping GEPA Optimization: 18%|█▊ | 525/2954 [17:59<1:04:50, 1.60s/rollouts]2025/12/11 12:28:12 INFO dspy.teleprompt.gepa.gepa: Iteration 94: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: Generalizable Strategy: - For geographic queries, identify entities based on type and location (e.g., museums in a province), utilize knowledge of geographic identifiers. - For temporal queries, focus on date-related attributes/events specified in the question (e.g., entities post a certain year). - For exploration queries, establish relationships with thematic concepts (e.g., archives related to an event or period). Ensure the query includes retrieval of relevant properties such as labels to maintain readability and understanding. Verify correct sources are utilized according to feedback or requirements (e.g., sparql, qdrant). Your objective is to generate valid SPARQL queries that meet the user's requirements as specified in the inputs, adhering to both technical precision and domain-specific standards. 2025/12/11 12:29:09 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:29:09 INFO dspy.teleprompt.gepa.gepa: Iteration 99: New subsample score 2.25 is not better than old score 2.25, skipping 2025/12/11 12:29:09 INFO dspy.teleprompt.gepa.gepa: Iteration 100: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: For the 'geographic' intent, focus on locating the geographic information linked to entities, while ensuring the correct spatial data properties are used. For 'temporal' intent, emphasize retrieving data with specific temporal conditions like fusion dates after a certain period, using date filters. For 'exploration' intent, relate archival or resource entities to specific concepts or events, such as World War II, by establishing connections within the ontology. In cases of 'entity_lookup', correctly identify and extract entities to perform precise queries about specific items in the knowledge graph. Adhere to expected sources in the domain, such as 'sparql' and 'qdrant'. Always provide an explanation alongside the SPARQL query, detailing how and why the query structure helps retrieve the desired information. If additional service endpoints are available to enrich data retrieval (for instance, fetching labels), make use of them effectively. Finally, ensure no entities or essential data points are missing in your query designs to maintain accuracy and completeness in outputs. 2025/12/11 12:29:17 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%) 2025/12/11 12:29:17 INFO dspy.teleprompt.gepa.gepa: Iteration 101: New subsample score 1.95 is not better than old score 1.95, skipping GEPA Optimization: 19%|█▉ | 570/2954 [19:04<51:44, 1.30s/rollouts] 2025/12/11 12:29:17 INFO dspy.teleprompt.gepa.gepa: Iteration 102: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 (used to represent ontology concepts related to heritage custodians) - PREFIX skos: (used for indicating labels and relations in a standard format) - PREFIX geo: (supporting geospatial query capabilities) - PREFIX dct: (enabling use of Dublin Core Metadata terms) Process: 1. **Intent Classification**: Identify the intent of the question from the provided input, which could be statistical, geographic, temporal, etc. Correct interpretation is crucial for framing the query appropriately. 2. **Entity Extraction**: Accurately extract relevant entities mentioned in the question. These may include specific locations (e.g., 'Amsterdam', 'Noord-Holland'), types of entities (e.g., 'musea', 'bibliotheken'), and temporal data (e.g., '2000'). Ensure all vital entities are considered, which are usually indicated in the examples but were previously missed. 3. **Reasoning and Query Formation**: Based on the intent and extracted entities, construct a logical reasoning pathway that aligns with query objectives. Develop a SPARQL query that: - Specifies the type of entities to be retrieved (e.g., hco:Museum, hco:Library). - Filters results according to the criteria derived from the question (e.g., location, time period). - Ensures labels or additional readable information is fetched as required using services like `wikibase:label` for wider language support. 4. **Generate Explanation**: Provide a succinct explanation of the query’s functionality, detailing how it addresses the problem stated in the question, and ensuring clarity on how the elements contribute to the result. Guidelines: - Adhere strictly to the syntactical requirements of SPARQL and the schema of the provided ontology. - Always check for missing entities and re-evaluate query parameters to ensure comprehensive coverage of the input question. - When handling temporal queries, ensure that date formatting and filtering logic follows the xsd:date standards. - Ensure that the output is formatted for readability and compatibility with broader usage models (e.g., English labels are generated when no specific language indication is provided). With these instructions, any future attempts to write SPARQL queries for the heritage custodian knowledge graph should follow an informed method and integrate feedback-based improvements seamlessly. 2025/12/11 12:30:55 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%) 2025/12/11 12:30:55 INFO dspy.teleprompt.gepa.gepa: Iteration 107: New subsample score 2.25 is not better than old score 2.25, skipping GEPA Optimization: 21%|██ | 606/2954 [20:41<1:23:11, 2.13s/rollouts]2025/12/11 12:30:55 INFO dspy.teleprompt.gepa.gepa: Iteration 108: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00 - PREFIX skos: - PREFIX geo: - PREFIX dct: 2. You are considered an expert in SPARQL and the Heritage Custodian Ontology, which is used in domain-specific queries. 3. The task involves several intentions like 'geographic', 'statistical', and 'entity_lookup'. Accurately identify the query intent based on the input question. 4. Extract all relevant entities from the input text. Common entities may include geographic locations (e.g., 'Noord-Holland', 'Amsterdam') and cultural institutions (e.g., 'Rijksmuseum', 'museums'). 5. Considerations for SPARQL query generation: - For 'geographic' intent, queries should retrieve the location-based information of a place or entity. - For 'statistical' intent, queries should retrieve numerical data, such as counts of entities. - For 'entity_lookup' intent, queries should focus on detailed information retrieval about a specific entity. 6. Expected Results: - Ensure that the SPARQL query includes all necessary components to fetch the required information accurately. - Use service parameters like `wikibase:label` to ensure labels are retrieved in the desired language format if necessary. - Handle entity variables properly in the SELECT clause to match expected output formats. 7. Source Selection: - Source mechanisms such as 'sparql' should be primarily used; secondary sources like 'qdrant' may be involved in conjunction with complex queries requiring additional context lookup. - Be mindful of selecting the correct sources for execution as intended by the context provided. 8. Feedback incorporation: - Address previous feedback appropriately to refine entity extraction and intent classification capabilities continuously. 9. Include explanations with each SPARQL query generated, detailing the logic and process followed to construct the query and how it correlates to the question asked. 10. Test the queries for syntactic correctness before finalizing them to ensure they will run successfully on the Oxigraph endpoint. By adhering to these instructions, you will ensure that the generated SPARQL queries align with the domain-specific requirements and expectations. 2025/12/11 12:31:08 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%) 2025/12/11 12:31:08 INFO dspy.teleprompt.gepa.gepa: Iteration 109: New subsample score 1.95 is not better than old score 1.95, skipping GEPA Optimization: 21%|██ | 615/2954 [20:54<1:14:44, 1.92s/rollouts]2025/12/11 12:31:08 INFO dspy.teleprompt.gepa.gepa: Iteration 110: Selected program 0 score: 0.6166666666666667 0%| | 0/3 [00:00