PREFIX pv: <http://ld.company.org/prod-vocab/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?supplier ( AVG( ?reliability ) AS ?avgReliability ) ( SAMPLE( ?name ) AS ?supplierName ) WHERE {
  ?product pv:hasSupplier ?supplier .
  ?product pv:reliabilityIndex ?reliability .
  OPTIONAL {
    ?supplier foaf:name ?name
  }
}
GROUP BY ?supplier
ORDER BY DESC ( ?avgReliability )
LIMIT 5

The top 5 suppliers with the best average reliability over their products are:
1. suppl-2a51afd9-a3de-45ee-8ada-b74203fed37b with average reliability 0.962
2. suppl-445081d6-305c-4fb7-b89e-82c86969d4bd with average reliability 0.962
3. suppl-11f1284a-d6f0-4822-b716-3e70d1fb91df with average reliability 0.951
4. suppl-888698ef-ad70-4022-9624-4dc111f56c01 with average reliability 0.945
5. suppl-0d183bba-b1df-4c41-be10-c0896378b406 with average reliability 0.942
Note: Supplier names are not available in the data.

What are the top 5 suppliers with best average reliability over their products.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
ck25 at http://localhost:9333

You can use the following SPARQL prefixes implicitly in all functions:
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
ecc: <https://ns.eccenca.com/
owl: <http://www.w3.org/2002/07/owl#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
dct: <http://purl.org/dc/terms/
void: <http://rdfs.org/ns/void#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
vann: <http://purl.org/vocab/vann/
dbpedia: <http://dbpedia.org/resource/
wgs84: <http://www.w3.org/2003/01/geo/wgs84_pos#
pv: <http://ld.company.org/prod-vocab/
prodi: <http://ld.company.org/prod-instances/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] What are the top 5 suppliers with best average reliability over their products.

[Assistant]
To answer this question, I need to identify the entities representing suppliers and their products, as well as the property representing the reliability of products. Then I will calculate the average reliability for each supplier over their products and return the top 5 suppliers with the best average reliability.

Step 1: Search for entities related to suppliers and products.
Step 2: Search for properties related to reliability of products.
Step 3: Construct a query to calculate average reliability per supplier and order by descending average reliability.
Step 4: Limit the results to the top 5 suppliers.

I will start by searching for entities related to "supplier".

[Tool]
Top 10 entity alternatives:
1. Supplier (pv:Supplier): The Supplier of some item(s).
2. supplier (pv:hasSupplier): The supplier of a product., has domain Product, has range Supplier

[Assistant]
I found an entity "Supplier" with the IRI prefix pv:Supplier representing suppliers. There is also a property pv:hasSupplier which relates a product to its supplier.

Next, I will search for properties related to "reliability" to find the property that represents the reliability of products.

[Tool]
Top 10 property alternatives:
1. has reliability index (pv:reliabilityIndex): A computed KPI to have an reliablity indicator in the context of a supply chain., has domain Hardware
2. compatible product (pv:compatibleProduct): A compatible product., has domain Product, has range Product
3. supplier (pv:hasSupplier): The supplier of a product., has domain Product, has range Supplier
4. quantity (pv:quantity): The quantity needed., has domain BOM Part
5. is Defined By (rdfs:isDefinedBy)
6. type (rdf:type)
7. name (foaf:name):

8. name (pv:name): The name of something.
9. range (rdfs:range)
10. country (pv:country): The country where the supplier is located., has domain Supplier, has range Country

[Assistant]
I found a property "has reliability index" with the IRI pv:reliabilityIndex which is described as a computed KPI to have a reliability indicator in the context of a supply chain. It has domain Hardware, which might be a type of product.

I will now check if products have this property and if products are linked to suppliers using pv:hasSupplier. I will do this by querying some products with their suppliers and reliability index.

[Tool]
Got 10 rows and 3 columns:
| product                                 | supplier                                                                                | reliability         |
| --------------------------------------- | --------------------------------------------------------------------------------------------- | ------------------- |
| hw A166 3766336 (prodi:hw-A166-3766336) | suppl 0301f65d 2290 456b a3f7 b2096a632198 (prodi:suppl-0301f65d-2290-456b-a3f7-b2096a632198) | 0.633 (xsd:decimal) |
| hw A181 1118563 (prodi:hw-A181-1118563) | suppl 816202d7 9e53 402c 94a2 fb21b0e37c0e (prodi:suppl-816202d7-9e53-402c-94a2-fb21b0e37c0e) | 0.544 (xsd:decimal) |
| hw A225 1988393 (prodi:hw-A225-1988393) | suppl 8943ee5e 5f73 4765 85cd 0c2c934f8877 (prodi:suppl-8943ee5e-5f73-4765-85cd-0c2c934f8877) | 0.921 (xsd:decimal) |
| hw A243 3332548 (prodi:hw-A243-3332548) | suppl b8b7794e 329e 4bdc 9fcb 5ddfe9b71248 (prodi:suppl-b8b7794e-329e-4bdc-9fcb-5ddfe9b71248) | 0.808 (xsd:decimal) |
| hw A315 1730287 (prodi:hw-A315-1730287) | suppl 9c9d9974 bea3 4bf9 882e c16a80b17e84 (prodi:suppl-9c9d9974-bea3-4bf9-882e-c16a80b17e84) | 0.768 (xsd:decimal) |
| hw A360 3041803 (prodi:hw-A360-3041803) | suppl 29cf8eca 50be 460e 9816 5ae505fc9491 (prodi:suppl-29cf8eca-50be-460e-9816-5ae505fc9491) | 0.921 (xsd:decimal) |
| hw A391 9508821 (prodi:hw-A391-9508821) | suppl 2b70d47e fa6a 4329 8112 22287096bc44 (prodi:suppl-2b70d47e-fa6a-4329-8112-22287096bc44) | 0.903 (xsd:decimal) |
| hw A403 4549719 (prodi:hw-A403-4549719) | suppl 9c9d9974 bea3 4bf9 882e c16a80b17e84 (prodi:suppl-9c9d9974-bea3-4bf9-882e-c16a80b17e84) | 0.768 (xsd:decimal) |
| hw A464 7310986 (prodi:hw-A464-7310986) | suppl 2f7172a0 e935 4dfc 98a6 b48dcec0ad18 (prodi:suppl-2f7172a0-e935-4dfc-98a6-b48dcec0ad18) | 0.446 (xsd:decimal) |
| hw A509 5571891 (prodi:hw-A509-5571891) | suppl afa65b2a f046 47f4 b352 97b579c77c21 (prodi:suppl-afa65b2a-f046-47f4-b352-97b579c77c21) | 0.921 (xsd:decimal) |

[Assistant]
I confirmed that products have the property pv:hasSupplier linking to suppliers and the property pv:reliabilityIndex representing the reliability of the product.

Now I will construct a query to calculate the average reliability index for each supplier over their products, order by descending average reliability, and limit the results to the top 5 suppliers. I will also optionally include the supplier's name if available.

[Tool]
SPARQL execution failed:
Invalid SPARQL query: The target ?supplierName of an AS clause was already used in the query body.

[Assistant] The error indicates that the variable ?supplierName is used both in the query body and as an alias in the AS clause, which is not allowed. I will fix this by using a different variable name for the alias of the supplier name sample.

[Tool]
Got 5 rows and 3 columns:
| supplier                                                                                | avgReliability      | supplierName |
| --------------------------------------------------------------------------------------------- | ------------------- | ------------ |
| suppl 2a51afd9 a3de 45ee 8ada b74203fed37b (prodi:suppl-2a51afd9-a3de-45ee-8ada-b74203fed37b) | 0.962 (xsd:decimal) |              |
| suppl 445081d6 305c 4fb7 b89e 82c86969d4bd (prodi:suppl-445081d6-305c-4fb7-b89e-82c86969d4bd) | 0.962 (xsd:decimal) |              |
| suppl 11f1284a d6f0 4822 b716 3e70d1fb91df (prodi:suppl-11f1284a-d6f0-4822-b716-3e70d1fb91df) | 0.951 (xsd:decimal) |              |
| suppl 888698ef ad70 4022 9624 4dc111f56c01 (prodi:suppl-888698ef-ad70-4022-9624-4dc111f56c01) | 0.945 (xsd:decimal) |              |
| suppl 0d183bba b1df 4c41 be10 c0896378b406 (prodi:suppl-0d183bba-b1df-4c41-be10-c0896378b406) | 0.942 (xsd:decimal) |              |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly calculates the average reliability for each supplier by aggregating over their products and orders the results in descending order to get the top 5 suppliers. The query also attempts to retrieve supplier names, but since they are not available in the data, the answer appropriately notes this. The execution results match the answer provided, listing the top 5 suppliers with their average reliability scores. Overall, the output is correct and complete for the given question.


PREFIX pv: <http://ld.company.org/prod-vocab/>
SELECT ?areaOfExpertise ( COUNT( ?agent ) AS ?count ) WHERE {
  ?agent pv:areaOfExpertise ?areaOfExpertise .
}
GROUP BY ?areaOfExpertise
ORDER BY DESC ( ?count )
LIMIT 3

Got 3 rows and 2 columns:
| areaOfExpertise                                   | count        |
| ------------------------------------------------- | ------------ |
| prod cat Compensator (prodi:prod-cat-Compensator) | 11 (xsd:int) |
| prod cat Breaker (prodi:prod-cat-Breaker)         | 9 (xsd:int)  |
| prod cat Crystal (prodi:prod-cat-Crystal)         | 9 (xsd:int)  |

The most relevant area of expertise among our products is 'Compensator' with 11 agents having this expertise. The top three skills or areas of expertise are 'Compensator' (11 agents), 'Breaker' (9 agents), and 'Crystal' (9 agents). These represent the product categories that are most frequently linked as areas of expertise to our agents.

Which area of expertise is most relevant among our products, what are the top three skills?

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
ck25 at http://localhost:9333

You can use the following SPARQL prefixes implicitly in all functions:
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
ecc: <https://ns.eccenca.com/
owl: <http://www.w3.org/2002/07/owl#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
dct: <http://purl.org/dc/terms/
void: <http://rdfs.org/ns/void#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
vann: <http://purl.org/vocab/vann/
dbpedia: <http://dbpedia.org/resource/
wgs84: <http://www.w3.org/2003/01/geo/wgs84_pos#
pv: <http://ld.company.org/prod-vocab/
prodi: <http://ld.company.org/prod-instances/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Which area of expertise is most relevant among our products, what are the top three skills?

[Assistant]
To answer this question, I need to understand the structure of the knowledge graph regarding products and their associated areas of expertise or skills. I will start by exploring the entities related to "product" and see if there are any properties linking products to areas of expertise or skills. Then I will try to identify how to determine the relevance or ranking of these skills.

Step 1: Search for entities related to "product" to understand how products are represented in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Product (pv:Product): Anything that we make and/or sell.
2. Product Category (pv:ProductCategory): The category of a product.
3. compatible product (pv:compatibleProduct): A compatible product., has domain Product, has range Product
4. has product manager (pv:hasProductManager): The employee acting as the product manager of the product., has range Employee, has domain Product
5. product outlined svg?color=#D6AF00&format=svg (<https://api.iconify.design/ant-design/product-outlined.svg?color=%23D6AF00&format=svg>)
6. Products - Integration (prodi:)
7. pv: Products - Vocab (pv:): Example vocabulary modeled from a simple sentence about products and services.

[Assistant]
The entity "pv:Product" represents anything that we make and/or sell. This seems to be the main entity for products in the knowledge graph.

Step 2: I will now search for properties of the entity "pv:Product" to find any properties related to areas of expertise or skills.

[Tool]
Top 10 property alternatives:
1. type (rdf:type)
2. is Defined By (rdfs:isDefinedBy)
3. comment (rdfs:comment)
4. label (rdfs:label)
5. depiction (foaf:depiction)

No other items found

[Tool]
Top 10 property alternatives:
1. is Defined By (rdfs:isDefinedBy)
2. type (rdf:type)
3. label (rdfs:label)
4. comment (rdfs:comment)

No other items found

[Tool]
Top 10 property alternatives:
1. is Defined By (rdfs:isDefinedBy)
2. type (rdf:type)
3. depiction (foaf:depiction)
4. label (rdfs:label)
5. comment (rdfs:comment)

No other items found

[Assistant] There are no direct properties linking products to skills or areas of expertise under the entity "pv:Product." I will now try to find entities related to "skill" or "expertise" to see if there is a separate entity representing skills or areas of expertise that might be linked to products indirectly.

[Tool] No entity items found

[Tool]
Top 10 entity alternatives:
1. area of expertise (pv:areaOfExpertise): The product category agent is expert for., has range Product Category, has domain Agent

[Assistant]
There is an entity "pv:areaOfExpertise" which is described as "The product category agent is expert for." It has the domain Agent and range Product Category. This might be related to areas of expertise.

Step 3: I will explore the properties of "pv:areaOfExpertise" to understand how it connects to products or skills.

[Tool]
Top 10 property alternatives:
1. type (rdf:type)
2. comment (rdfs:comment)
3. label (rdfs:label)
4. range (rdfs:range)
5. depiction (foaf:depiction)

No other items found

[Assistant]
The entity "pv:areaOfExpertise" does not have additional properties that clarify its connection to products or skills. Since it has the domain Agent and range Product Category, I will now check if there are any agents linked to products and if these agents have areas of expertise that can be considered skills.

Step 4: I will search for properties of "pv:Product" that might link to agents.

[Tool]
Top 10 property alternatives:
1. label (rdfs:label)
2. type (rdf:type)
3. is Defined By (rdfs:isDefinedBy)
4. depiction (foaf:depiction)
5. comment (rdfs:comment)

No other items found

[Tool]
Top 10 property alternatives:
1. label (rdfs:label)
2. type (rdf:type)
3. is Defined By (rdfs:isDefinedBy)

No other items found

[Assistant]
There is no direct property linking products to agents or managers that could have areas of expertise. I will now try to find the top skills or areas of expertise by looking at the product categories, as "pv:areaOfExpertise" has a range of Product Category. I will check if products have categories and if these categories can be linked to areas of expertise or skills.

Step 5: Search for properties of "pv:Product" related to categories.

[Tool]
Top 10 property alternatives:
1. label (rdfs:label)
2. type (rdf:type)
3. is Defined By (rdfs:isDefinedBy)
4. depiction (foaf:depiction)
5. comment (rdfs:comment)

No other items found

[Tool]
Top 10 property alternatives:
1. has category (pv:hasCategory): The category name of a product., has range Product Category, has domain Product
2. label (rdfs:label)
3. country (pv:country): The country where the supplier is located., has domain Supplier, has range Country
4. type (rdf:type)
5. part (pv:hasPart): has domain BOM Part, The Product needed., has range Product
6. vocabulary (void:vocabulary)
7. name (foaf:name):

8. name (pv:name): The name of something.
9. sub Class Of (rdfs:subClassOf)
10. is Defined By (rdfs:isDefinedBy)

[Assistant]
The property "pv:hasCategory" links a product to a product category. Since "pv:areaOfExpertise" has a range of Product Category, I will now check if there are any agents linked to areas of expertise and if these areas of expertise correspond to product categories.

Step 6: Search for agents with areas of expertise.

[Tool]
Got 142 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                               | object                                            |
| -------------------------------------------------------------------------------------------------------------------- | -------------------------------------- | ------------------------------------------------- |
| empl Arendt Beitel@company org (<http://ld.company.org/prod-instances/empl-Arendt.Beitel%40company.org>)             | area of expertise (pv:areaOfExpertise) | prod cat Compensator (prodi:prod-cat-Compensator) |
| empl Frauke Faerber@company org (<http://ld.company.org/prod-instances/empl-Frauke.Faerber%40company.org>)           | area of expertise (pv:areaOfExpertise) | prod cat Crystal (prodi:prod-cat-Crystal)         |
| empl Anamchara Foerstner@company org (<http://ld.company.org/prod-instances/empl-Anamchara.Foerstner%40company.org>) | area of expertise (pv:areaOfExpertise) | prod cat Inductor (prodi:prod-cat-Inductor)       |
| empl Adolfina Hoch@company org (<http://ld.company.org/prod-instances/empl-Adolfina.Hoch%40company.org>)             | area of expertise (pv:areaOfExpertise) | prod cat Transducer (prodi:prod-cat-Transducer)   |
| empl Baldwin Guenther@company org (<http://ld.company.org/prod-instances/empl-Baldwin.Guenther%40company.org>)       | area of expertise (pv:areaOfExpertise) | prod cat Multiplexer (prodi:prod-cat-Multiplexer) |
| empl Baldwin Dirksen@company org (<http://ld.company.org/prod-instances/empl-Baldwin.Dirksen%40company.org>)         | area of expertise (pv:areaOfExpertise) | prod cat Capacitor (prodi:prod-cat-Capacitor)     |
| empl Arnelle Gerber@company org (<http://ld.company.org/prod-instances/empl-Arnelle.Gerber%40company.org>)           | area of expertise (pv:areaOfExpertise) | prod cat Compensator (prodi:prod-cat-Compensator) |
| empl Bert Blumstein@company org (<http://ld.company.org/prod-instances/empl-Bert.Blumstein%40company.org>)           | area of expertise (pv:areaOfExpertise) | prod cat Compensator (prodi:prod-cat-Compensator) |
| empl Herr Burgh Eichel@company org (<http://ld.company.org/prod-instances/empl-Herr.Burgh.Eichel%40company.org>)     | area of expertise (pv:areaOfExpertise) | prod cat Compensator (prodi:prod-cat-Compensator) |
| empl Lili Geier@company org (<http://ld.company.org/prod-instances/empl-Lili.Geier%40company.org>)                   | area of expertise (pv:areaOfExpertise) | prod cat Compensator (prodi:prod-cat-Compensator) |
| ...                                                                                | ...                                    | ...                                               |

[Assistant]
There are agents (employees) with areas of expertise linked to product categories. This suggests that the areas of expertise are represented by product categories.

Step 7: To find the most relevant area of expertise among products, I will count the occurrences of each product category linked as an area of expertise to agents. This will give an indication of the top skills or areas of expertise.

I will now write a SPARQL query to count the top three most frequent areas of expertise (product categories) among agents.

[Tool]
Got 3 rows and 2 columns:
| areaOfExpertise                                   | count        |
| ------------------------------------------------- | ------------ |
| prod cat Compensator (prodi:prod-cat-Compensator) | 11 (xsd:int) |
| prod cat Breaker (prodi:prod-cat-Breaker)         | 9 (xsd:int)  |
| prod cat Crystal (prodi:prod-cat-Crystal)         | 9 (xsd:int)  |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the top three areas of expertise among agents by counting the number of agents per product category. The results match the answer provided, showing 'Compensator' as the most relevant area with 11 agents, followed by 'Breaker' and 'Crystal' with 9 agents each. The query uses the appropriate property and groups and orders the results correctly. The answer is clear, complete, and directly supported by the query results. No further refinement is needed.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX pv: <http://ld.company.org/prod-vocab/>
PREFIX prodi: <http://ld.company.org/prod-instances/>
SELECT ?compatibleProduct ?compatibleProductLabel WHERE {
  prodi:hw-U990-5234138 pv:compatibleProduct ?compatibleProduct .
  OPTIONAL {
    ?compatibleProduct rdfs:label ?compatibleProductLabel
  }
}

The products compatible with the U990 LCD Inductor (prodi:hw-U990-5234138) are:
1. A360-3041803 - Inductor Transformer Warp
2. A509-5571891 - Multiplexer Inductor
3. F675-6890144 - Resistor Inductor Strain
4. I264-7314323 - Film Inductor Memristor
5. J178-7002767 - Flow LCD Transistor Transducer
6. S113-2439377 - LCD Potentiometer

What products are compatible with the U990 LCD Inductor?

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
ck25 at http://localhost:9333

You can use the following SPARQL prefixes implicitly in all functions:
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
ecc: <https://ns.eccenca.com/
owl: <http://www.w3.org/2002/07/owl#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
dct: <http://purl.org/dc/terms/
void: <http://rdfs.org/ns/void#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
vann: <http://purl.org/vocab/vann/
dbpedia: <http://dbpedia.org/resource/
wgs84: <http://www.w3.org/2003/01/geo/wgs84_pos#
pv: <http://ld.company.org/prod-vocab/
prodi: <http://ld.company.org/prod-instances/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] What products are compatible with the U990 LCD Inductor?

[Assistant] To answer this question, I need to find the entity for "U990 LCD Inductor" in the knowledge graph and then look for properties or relations that indicate compatibility with products. I will start by searching for the entity "U990 LCD Inductor."

[Tool]
Top 10 entity alternatives:
1. prod cat LCD (prodi:prod-cat-LCD): is a Product Category
2. prod cat Inductor (prodi:prod-cat-Inductor): is a Product Category
3. hw U990 5234138 (prodi:hw-U990-5234138): is a Hardware
4. bom part 18 U990 5234138 (prodi:bom-part-18-U990-5234138): is a BOM Part
5. bom part 5 U990 5234138 (prodi:bom-part-5-U990-5234138): is a BOM Part
6. price hw U990 5234138 EUR (prodi:price-hw-U990-5234138-EUR): is a Price

[Assistant] The entity "U990 LCD Inductor" seems to correspond to "hw U990 5234138" (prodi:hw-U990-5234138) which is a hardware item. Next, I will look for properties or relations of this hardware entity that might indicate compatibility with products. I will search for properties of the entity prodi:hw-U990-5234138.

[Tool]
Top 10 property alternatives:
1. compatible product (pv:compatibleProduct): A compatible product., has domain Product, has range Product
2. supplier (pv:hasSupplier): The supplier of a product., has domain Product, has range Supplier
3. width (mm) (pv:width_mm): The width of an item measured in millimeter., has domain Hardware
4. type (rdf:type)
5. depth (mm) (pv:depth_mm): The depth of an item measured in millimeter., has domain Hardware
6. height (mm) (pv:height_mm): The height of an item measured in millimeter., has domain Hardware
7. ID (pv:id): The ID of a thing.
8. label (rdfs:label)
9. name (pv:name): The name of something.
10. has reliability index (pv:reliabilityIndex): A computed KPI to have an reliablity indicator in the context of a supply chain., has domain Hardware

No other items found

[Assistant] There is a property "compatible product" (pv:compatibleProduct) which indicates a compatible product and has domain and range as Product. This property seems relevant to find products compatible with the U990 LCD Inductor. I will now query for products that are compatible with the hardware entity prodi:hw-U990-5234138 using this property.

[Tool]
Got 6 rows and 2 columns:
| compatibleProduct                       | compatibleProductLabel                        |
| --------------------------------------- | --------------------------------------------- |
| hw A360 3041803 (prodi:hw-A360-3041803) | A360-3041803 - Inductor Transformer Warp      |
| hw A509 5571891 (prodi:hw-A509-5571891) | A509-5571891 - Multiplexer Inductor           |
| hw F675 6890144 (prodi:hw-F675-6890144) | F675-6890144 - Resistor Inductor Strain       |
| hw I264 7314323 (prodi:hw-I264-7314323) | I264-7314323 - Film Inductor Memristor        |
| hw J178 7002767 (prodi:hw-J178-7002767) | J178-7002767 - Flow LCD Transistor Transducer |
| hw S113 2439377 (prodi:hw-S113-2439377) | S113-2439377 - LCD Potentiometer              |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves all products compatible with the U990 LCD Inductor using the property pv:compatibleProduct. The query returns the expected 6 compatible products along with their labels, matching the user's question. The answer is clear, complete, and directly supported by the query results. No further refinement is needed.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX pv: <http://ld.company.org/prod-vocab/>
PREFIX prodi: <http://ld.company.org/prod-instances/>
SELECT ?coil ?label ?weight ?height ?width ?depth ( ( ?weight / ( ?height * ?width * ?depth ) ) AS ?density ) WHERE {
  ?coil pv:hasCategory prodi:prod-cat-Coil .
  ?coil pv:weight_g ?weight .
  ?coil pv:height_mm ?height .
  ?coil pv:width_mm ?width .
  ?coil pv:depth_mm ?depth .
  OPTIONAL {
    ?coil rdfs:label ?label
  }
}
ORDER BY DESC ( ?density )
LIMIT 1

The SPARQL query calculates the density for all coils with weight and dimension properties and returns the coil with the highest density.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
ck25 at http://localhost:9333

You can use the following SPARQL prefixes implicitly in all functions:
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
ecc: <https://ns.eccenca.com/
owl: <http://www.w3.org/2002/07/owl#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
dct: <http://purl.org/dc/terms/
void: <http://rdfs.org/ns/void#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
vann: <http://purl.org/vocab/vann/
dbpedia: <http://dbpedia.org/resource/
wgs84: <http://www.w3.org/2003/01/geo/wgs84_pos#
pv: <http://ld.company.org/prod-vocab/
prodi: <http://ld.company.org/prod-instances/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Which coil has the highest density?

[Assistant]
To answer the question "Which coil has the highest density?" I need to:

1. Identify the entity type "coil" in the knowledge graph.
2. Find the property that represents "density" for coils.
3. Query the coils and their densities.
4. Determine the coil with the highest density.

First, I will search for entities related to "coil" to understand how coils are represented in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. prod cat Coil (prodi:prod-cat-Coil): is a Product Category

[Assistant] The entity "Coil" is represented as a product category with the IRI prodi:prod-cat-Coil. Next, I will search for properties related to "density" for this product category or in general to find the relevant property that represents density.

[Tool]
Top 10 property alternatives:
1. quantity (pv:quantity): The quantity needed., has domain BOM Part
2. weight (g) (pv:weight_g): The weight of an item measured in grams., has domain Hardware
3. depth (mm) (pv:depth_mm): The depth of an item measured in millimeter., has domain Hardware
4. height (mm) (pv:height_mm): The height of an item measured in millimeter., has domain Hardware
5. type (rdf:type)
6. amount (pv:amount): has domain Price, The amount.
7. part (pv:hasPart): has domain BOM Part, The Product needed., has range Product
8. lat (wgs84:lat)
9. is Defined By (rdfs:isDefinedBy)
10. width (mm) (pv:width_mm): The width of an item measured in millimeter., has domain Hardware

[Assistant] I did not find a direct property named "density" in the top search results. I will try to search for properties related to "density" specifically for the product category "Coil" (prodi:prod-cat-Coil) to see if there is a more specific property related to density for coils.

[Tool]
Top 10 property alternatives:
1. type (rdf:type)
2. name (pv:name): The name of something.
3. label (rdfs:label)

No other items found

[Assistant] There are no specific properties related to "density" for the product category "Coil." I will try a broader search for properties related to "density" without restricting to the coil entity to see if there is any property that might represent density in the knowledge graph.

[Tool]
Top 10 property alternatives:
1. quantity (pv:quantity): The quantity needed., has domain BOM Part
2. weight (g) (pv:weight_g): The weight of an item measured in grams., has domain Hardware
3. depth (mm) (pv:depth_mm): The depth of an item measured in millimeter., has domain Hardware
4. height (mm) (pv:height_mm): The height of an item measured in millimeter., has domain Hardware
5. type (rdf:type)
6. amount (pv:amount): has domain Price, The amount.
7. part (pv:hasPart): has domain BOM Part, The Product needed., has range Product
8. lat (wgs84:lat)
9. is Defined By (rdfs:isDefinedBy)
10. width (mm) (pv:width_mm): The width of an item measured in millimeter., has domain Hardware

[Assistant]
I did not find a direct property named "density" in the knowledge graph. However, density is typically calculated as mass (weight) divided by volume. I found properties for weight (pv:weight_g) and dimensions (depth, height, width in mm) which could be used to calculate volume.

I will now check if there are instances of coils (products under the category prodi:prod-cat-Coil) and if they have weight and dimension properties to calculate density. First, I will list some products that belong to the coil category.

[Tool]
Got 93 rows and 3 columns, showing the first 10 rows below:
| subject                                 | property                      | object                              |
| --------------------------------------- | ----------------------------- | ----------------------------------- |
| hw A145 1240844 (prodi:hw-A145-1240844) | has category (pv:hasCategory) | prod cat Coil (prodi:prod-cat-Coil) |
| hw A548 4778785 (prodi:hw-A548-4778785) | has category (pv:hasCategory) | prod cat Coil (prodi:prod-cat-Coil) |
| hw B120 9942467 (prodi:hw-B120-9942467) | has category (pv:hasCategory) | prod cat Coil (prodi:prod-cat-Coil) |
| hw B143 5457756 (prodi:hw-B143-5457756) | has category (pv:hasCategory) | prod cat Coil (prodi:prod-cat-Coil) |
| hw B150 4370781 (prodi:hw-B150-4370781) | has category (pv:hasCategory) | prod cat Coil (prodi:prod-cat-Coil) |
| hw B286 1369879 (prodi:hw-B286-1369879) | has category (pv:hasCategory) | prod cat Coil (prodi:prod-cat-Coil) |
| hw C171 3616793 (prodi:hw-C171-3616793) | has category (pv:hasCategory) | prod cat Coil (prodi:prod-cat-Coil) |
| hw C402 5072074 (prodi:hw-C402-5072074) | has category (pv:hasCategory) | prod cat Coil (prodi:prod-cat-Coil) |
| hw C717 1997689 (prodi:hw-C717-1997689) | has category (pv:hasCategory) | prod cat Coil (prodi:prod-cat-Coil) |
| hw C744 6535902 (prodi:hw-C744-6535902) | has category (pv:hasCategory) | prod cat Coil (prodi:prod-cat-Coil) |
| ...                                     | ...                           | ...                                 |

[Assistant] There are multiple coil products in the knowledge graph. Now I will check if these coil products have properties for weight (pv:weight_g) and dimensions (pv:depth_mm, pv:height_mm, pv:width_mm) to calculate density. I will check for these properties for one coil product (e.g., prodi:hw-A145-1240844).

[Tool]
Got 18 rows and 3 columns, showing the first 10 rows below:
| subject                                 | property                                   | object                                                                                |
| --------------------------------------- | ------------------------------------------ | -------------------------------------------------------------------------------------------------------- |
| hw A145 1240844 (prodi:hw-A145-1240844) | type (rdf:type)                            | Hardware (pv:Hardware)                                                                                |
| hw A145 1240844 (prodi:hw-A145-1240844) | label (rdfs:label)                         | A145-1240844 - Bipolar-junction Coil Compensator Transducer                                              |
| hw A145 1240844 (prodi:hw-A145-1240844) | has category (pv:hasCategory)              | prod cat Compensator (prodi:prod-cat-Compensator)                                                        |
| hw A145 1240844 (prodi:hw-A145-1240844) | compatible product (pv:compatibleProduct)  | hw D729 5737042 (prodi:hw-D729-5737042)                                                                  |
| hw A145 1240844 (prodi:hw-A145-1240844) | name (pv:name)                             | Bipolar-junction Coil Compensator Transducer                                                             |
| hw A145 1240844 (prodi:hw-A145-1240844) | ID (pv:id)                                 | A145-1240844                                                                                |
| hw A145 1240844 (prodi:hw-A145-1240844) | has product manager (pv:hasProductManager) | empl Ratt Hartmann@company org (<http://ld.company.org/prod-instances/empl-Ratt.Hartmann%40company.org>) |
| hw A145 1240844 (prodi:hw-A145-1240844) | price (pv:price)                           | price hw A145 1240844 EUR (prodi:price-hw-A145-1240844-EUR)                                              |
| hw A145 1240844 (prodi:hw-A145-1240844) | depth (mm) (pv:depth_mm)                   | 20 (xsd:int)                                                                                |
| hw A145 1240844 (prodi:hw-A145-1240844) | supplier (pv:hasSupplier)                  | suppl 42a20063 bd30 49d3 8506 ceac4ceb2d9e (prodi:suppl-42a20063-bd30-49d3-8506-ceac4ceb2d9e)            |
| ...                                     | ...                                        | ...                                                                                |

[Assistant] The coil product prodi:hw-A145-1240844 has a property for depth (pv:depth_mm) with a value of 20. I will now check if it has properties for weight (pv:weight_g), height (pv:height_mm), and width (pv:width_mm) as well.

[Tool]
Got 1 row and 3 columns:
| subject                                 | property                 | object       |
| --------------------------------------- | ------------------------ | ------------ |
| hw A145 1240844 (prodi:hw-A145-1240844) | weight (g) (pv:weight_g) | 14 (xsd:int) |

[Tool]
Got 1 row and 3 columns:
| subject                                 | property                   | object       |
| --------------------------------------- | -------------------------- | ------------ |
| hw A145 1240844 (prodi:hw-A145-1240844) | height (mm) (pv:height_mm) | 28 (xsd:int) |

[Tool]
Got 1 row and 3 columns:
| subject                                 | property                 | object       |
| --------------------------------------- | ------------------------ | ------------ |
| hw A145 1240844 (prodi:hw-A145-1240844) | width (mm) (pv:width_mm) | 17 (xsd:int) |

[Assistant] The coil product prodi:hw-A145-1240844 has weight (14 g), height (28 mm), width (17 mm), and depth (20 mm) properties. I can calculate density as weight / volume, where volume = height * width * depth (in cubic mm). To find the coil with the highest density, I will write a SPARQL query that calculates density for all coils with these properties and returns the coil with the highest density.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly calculates the density for all coils by dividing the weight by the product of height, width, and depth. It orders the results by density in descending order and limits the output to the coil with the highest density. The query returns the coil identifier, label, dimensions, weight, and calculated density, which matches the user's question. The execution result confirms that the query returns a single coil with the highest density. The answer is complete and accurate.


PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX pv: <http://ld.company.org/prod-vocab/>
PREFIX dc: <http://purl.org/dc/elements/1.1/>
SELECT ?hardware ?hardwareID ?name ?width WHERE {
  ?hardware rdf:type pv:Hardware .
  ?hardware pv:width_mm ?width .
  FILTER ( ?width >= 73 ) .
  OPTIONAL {
    ?hardware dc:identifier ?hardwareID
  }
  .
  OPTIONAL {
    ?hardware rdfs:label ?name
  }
  .
}
ORDER BY DESC ( ?width )

There are 103 hardware items with widths in the top 10% (width >= 73 mm). The query returns their IDs, names, and widths. Some examples include hardware items with widths of 80 mm and 73 mm, along with their respective IDs and names.

Which hardware items have dimensions in the top 10 % of all widths, and what are their IDs, names and widths?

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
ck25 at http://localhost:9333

You can use the following SPARQL prefixes implicitly in all functions:
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
ecc: <https://ns.eccenca.com/
owl: <http://www.w3.org/2002/07/owl#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
dct: <http://purl.org/dc/terms/
void: <http://rdfs.org/ns/void#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
vann: <http://purl.org/vocab/vann/
dbpedia: <http://dbpedia.org/resource/
wgs84: <http://www.w3.org/2003/01/geo/wgs84_pos#
pv: <http://ld.company.org/prod-vocab/
prodi: <http://ld.company.org/prod-instances/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Which hardware items have dimensions in the top 10 % of all widths, and what are their IDs, names and widths?

[Assistant]
To answer this question, I need to identify the following:
1. The class or type representing hardware items.
2. The property representing the width dimension of these hardware items.
3. Retrieve the widths of all hardware items to determine the top 10% threshold.
4. Retrieve the hardware items that have widths in the top 10%, along with their IDs, names, and widths.

Step 1: Search for the class representing hardware items. I will search for entities related to "hardware".

[Tool]
Top 10 entity alternatives:
1. Hardware (pv:Hardware): The hardware products produced., is subclass of Product
2. hardware chip outline svg?color=#D6AF00&format=svg (<https://api.iconify.design/ion/hardware-chip-outline.svg?color=%23D6AF00&format=svg>)

[Assistant] The class representing hardware items is likely "pv:Hardware" which is a subclass of Product. Next, I will search for properties related to "width" for the class pv:Hardware to identify the property representing the width dimension.

[Tool]
Top 10 property alternatives:
1. type (rdf:type)
2. is Defined By (rdfs:isDefinedBy)
3. label (rdfs:label)
4. depiction (foaf:depiction)
5. sub Class Of (rdfs:subClassOf)
6. comment (rdfs:comment)

No other items found

[Assistant] No direct property related to "width" was found for the entity pv:Hardware. I will try a more general search for properties related to "width" in the knowledge graph to see if there is a property that can be used for dimensions.

[Tool]
Top 10 property alternatives:
1. width (mm) (pv:width_mm): The width of an item measured in millimeter., has domain Hardware
2. depth (mm) (pv:depth_mm): The depth of an item measured in millimeter., has domain Hardware
3. height (mm) (pv:height_mm): The height of an item measured in millimeter., has domain Hardware
4. type (rdf:type)
5. quantity (pv:quantity): The quantity needed., has domain BOM Part
6. range (rdfs:range)
7. lat (wgs84:lat)
8. weight (g) (pv:weight_g): The weight of an item measured in grams., has domain Hardware
9. country (pv:country): The country where the supplier is located., has domain Supplier, has range Country
10. created (dct:created)

[Assistant]
There is a property "pv:width_mm" which represents the width of an item measured in millimeters and has the domain Hardware. This is the property we need to use to get the widths of hardware items.

Next, I will retrieve the widths of all hardware items to determine the top 10% threshold. I will write a query to get all hardware items with their widths.

[Tool]
Got 1,000 rows and 2 columns, showing the first 5 and last 5 rows below:
| hardware                                | width        |
| --------------------------------------- | ------------ |
| hw B523 5464510 (prodi:hw-B523-5464510) | 80 (xsd:int) |
| hw C409 9349178 (prodi:hw-C409-9349178) | 80 (xsd:int) |
| hw F797 8658626 (prodi:hw-F797-8658626) | 80 (xsd:int) |
| hw M205 1376206 (prodi:hw-M205-1376206) | 80 (xsd:int) |
| hw M350 8985312 (prodi:hw-M350-8985312) | 80 (xsd:int) |
| ...                                     | ...          |
| hw Z553 4314789 (prodi:hw-Z553-4314789) | 11 (xsd:int) |
| hw X502 7135246 (prodi:hw-X502-7135246) | 11 (xsd:int) |
| hw T769 2350399 (prodi:hw-T769-2350399) | 11 (xsd:int) |
| hw T504 8448784 (prodi:hw-T504-8448784) | 11 (xsd:int) |
| hw W156 1748173 (prodi:hw-W156-1748173) | 11 (xsd:int) |

[Assistant]
There are 1000 hardware items with widths ranging from 11 to 80 mm. To find the top 10% of widths, I need to find the width value at the 90th percentile (i.e., the width value at the 100th position when sorted descending).

I will write a query to get the width value at the 90th percentile position.

[Tool]
Got 1 row and 1 column:
| width        |
| ------------ |
| 73 (xsd:int) |

[Assistant] The width value at the 90th percentile (top 10%) is 73 mm. Now I will write a query to retrieve the hardware items that have widths greater than or equal to 73 mm, along with their IDs, names (labels), and widths.

[Tool]
Got 103 rows and 4 columns, showing the first 5 and last 5 rows below:
| hardware                                | hardwareID | name                                              | width        |
| --------------------------------------- | ---------- | ------------------------------------------------- | ------------ |
| hw M205 1376206 (prodi:hw-M205-1376206) |            | M205-1376206 - Dipole Memristor Encoder           | 80 (xsd:int) |
| hw U967 6553099 (prodi:hw-U967-6553099) |            | U967-6553099 - Bipolar-junction Gauge Warp        | 80 (xsd:int) |
| hw N206 9059859 (prodi:hw-N206-9059859) |            | N206-9059859 - Polymer Memristor Capacitor        | 80 (xsd:int) |
| hw B523 5464510 (prodi:hw-B523-5464510) |            | B523-5464510 - Bipolar-junction Switch Transducer | 80 (xsd:int) |
| hw P337 4636612 (prodi:hw-P337-4636612) |            | P337-4636612 - Sensor Driver Crystal              | 80 (xsd:int) |
| ...                                     | ...        | ...                                               | ...          |
| hw V104 2082346 (prodi:hw-V104-2082346) |            | V104-2082346 - Flow Dipole Transformer            | 73 (xsd:int) |
| hw Q189 5857908 (prodi:hw-Q189-5857908) |            | Q189-5857908 - Phase Multiplexer Crystal Warp     | 73 (xsd:int) |
| hw P870 6495639 (prodi:hw-P870-6495639) |            | P870-6495639 - Inductor Transistor Warp           | 73 (xsd:int) |
| hw P844 4114854 (prodi:hw-P844-4114854) |            | P844-4114854 - Field-effect Strain Compensator    | 73 (xsd:int) |
| hw M522 9124638 (prodi:hw-M522-9124638) |            | M522-9124638 - Flow LCD Resistor Encoder          | 73 (xsd:int) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly identifies hardware items with widths in the top 10% (width >= 73 mm) and returns their IDs, names, and widths as requested. The query uses appropriate filtering and optional clauses for the hardware ID and name, and the results are ordered by descending width. The execution result confirms that the query returns 103 relevant items with widths ranging from 73 mm to 80 mm, matching the question's requirements. The answer is clear and provides examples as well. No further refinement is necessary.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX pv: <http://ld.company.org/prod-vocab/>
SELECT ?department ?departmentLabel WHERE {
  <http://ld.company.org/prod-instances/empl-Karen.Brant%40company.org> pv:memberOf ?department .
  OPTIONAL {
    ?department rdfs:label ?departmentLabel
  }
}

Ms. Brant (Karen Brant) is a member of the Engineering department.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
ck25 at http://localhost:9333

You can use the following SPARQL prefixes implicitly in all functions:
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
ecc: <https://ns.eccenca.com/
owl: <http://www.w3.org/2002/07/owl#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
dct: <http://purl.org/dc/terms/
void: <http://rdfs.org/ns/void#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
vann: <http://purl.org/vocab/vann/
dbpedia: <http://dbpedia.org/resource/
wgs84: <http://www.w3.org/2003/01/geo/wgs84_pos#
pv: <http://ld.company.org/prod-vocab/
prodi: <http://ld.company.org/prod-instances/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] In which department is Ms. Brant?

[Assistant]
To answer the question "In which department is Ms. Brant?" I need to find the entity representing Ms. Brant and then find the property that relates her to a department.

Step 1: Search for the entity "Ms. Brant" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. empl Karen Brant@company org (<http://ld.company.org/prod-instances/empl-Karen.Brant%40company.org>): is a Employee
2. empl Sylvester Brant@company org (<http://ld.company.org/prod-instances/empl-Sylvester.Brant%40company.org>): is a Employee

[Assistant]
There are two entities related to "Brant": Karen Brant and Sylvester Brant, both employees. Since the question uses "Ms. Brant," it is more likely referring to Karen Brant.

Step 2: Search for properties of the entity Karen Brant that might indicate a department.

[Tool]
Top 10 property alternatives:
1. area of expertise (pv:areaOfExpertise): The product category agent is expert for., has range Product Category, has domain Agent
2. label (rdfs:label)
3. has manager (pv:hasManager): The manager of the employee., has domain Employee, has range Manager
4. member of (pv:memberOf): The department to which an agents belongs., has range Department, has domain Agent
5. type (rdf:type)
6. name (pv:name): The name of something.
7. address text (pv:addressText): The address text., has domain Agent
8. email (pv:email): The e-mail of an agent., has domain Agent
9. phone number (pv:phone): has domain Agent, A phone number.

No other items found

[Assistant]
The property "member of" (pv:memberOf) is described as "The department to which an agent belongs" and has the domain Agent and range Department. This property seems to be the one that relates an employee to a department.

Step 3: Query the department of Karen Brant using the property pv:memberOf.

[Tool]
Got 1 row and 2 columns:
| department                    | departmentLabel |
| ----------------------------- | --------------- |
| dept 73191 (prodi:dept-73191) | Engineering     |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the department of Ms. Brant using the property pv:memberOf. The query returns the department identifier and its label, which is 'Engineering'. The answer provided matches the query result and is complete and accurate. No further refinement is needed.


PREFIX pv: <http://ld.company.org/prod-vocab/>
SELECT DISTINCT ?employee ?name ?email ?phone WHERE {
  ?employee a pv:Employee .
  OPTIONAL {
    ?employee pv:name ?name
  }
  OPTIONAL {
    ?employee pv:email ?email
  }
  OPTIONAL {
    ?employee pv:phone ?phone
  }
  FILTER NOT EXISTS {
    ?otherEmployee pv:hasManager ?employee
  }
}
ORDER BY ?name

Got 47 rows and 4 columns, showing the first 5 and last 5 rows below:
| employee                                                                                | name                | email                           | phone             |
| -------------------------------------------------------------------------------------------------------------------- | ------------------- | ------------------------------- | ----------------- |
| empl Adolfina Hoch@company org (<http://ld.company.org/prod-instances/empl-Adolfina.Hoch%40company.org>)             | Adolfina Hoch       | Adolfina.Hoch@company.org       | +49-109-5719002   |
| empl Anamchara Foerstner@company org (<http://ld.company.org/prod-instances/empl-Anamchara.Foerstner%40company.org>) | Anamchara Foerstner | Anamchara.Foerstner@company.org | +49-360-5655698   |
| empl Arendt Beitel@company org (<http://ld.company.org/prod-instances/empl-Arendt.Beitel%40company.org>)             | Arendt Beitel       | Arendt.Beitel@company.org       | +49-8763-66514379 |
| empl Arnelle Gerber@company org (<http://ld.company.org/prod-instances/empl-Arnelle.Gerber%40company.org>)           | Arnelle Gerber      | Arnelle.Gerber@company.org      | (03586) 7240528   |
| empl Baldwin Dirksen@company org (<http://ld.company.org/prod-instances/empl-Baldwin.Dirksen%40company.org>)         | Baldwin Dirksen     | Baldwin.Dirksen@company.org     | +49-6200-33069465 |
| ...                                                                                | ...                 | ...                             | ...               |
| empl Valda Everhart@company org (<http://ld.company.org/prod-instances/empl-Valda.Everhart%40company.org>)           | Valda Everhart      | Valda.Everhart@company.org      | +49-04-969-14255  |
| empl Wanja Hoffmann@company org (<http://ld.company.org/prod-instances/empl-Wanja.Hoffmann%40company.org>)           | Wanja Hoffmann      | Wanja.Hoffmann@company.org      | +49-1083-38194095 |
| empl Wolfgang Martin@company org (<http://ld.company.org/prod-instances/empl-Wolfgang.Martin%40company.org>)         | Wolfgang Martin     | Wolfgang.Martin@company.org     | +49-701-5973041   |
| empl Xochitl Aue@company org (<http://ld.company.org/prod-instances/empl-Xochitl.Aue%40company.org>)                 | Xochitl Aue         | Xochitl.Aue@company.org         |                   |
| empl Yanka Schreiber@company org (<http://ld.company.org/prod-instances/empl-Yanka.Schreiber%40company.org>)         | Yanka Schreiber     | Yanka.Schreiber@company.org     |                   |

Here is the phone directory of everyone on staff who does not manage anyone, including their name, email, and phone number, sorted by name.

Give me a phone directory of everyone on staff who does not manage anyone, I need name, email, and phone, sorted by name?

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
ck25 at http://localhost:9333

You can use the following SPARQL prefixes implicitly in all functions:
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
ecc: <https://ns.eccenca.com/
owl: <http://www.w3.org/2002/07/owl#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
dct: <http://purl.org/dc/terms/
void: <http://rdfs.org/ns/void#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
vann: <http://purl.org/vocab/vann/
dbpedia: <http://dbpedia.org/resource/
wgs84: <http://www.w3.org/2003/01/geo/wgs84_pos#
pv: <http://ld.company.org/prod-vocab/
prodi: <http://ld.company.org/prod-instances/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Give me a phone directory of everyone on staff who does not manage anyone, I need name, email, and phone, sorted by name?

[Tool]
Top 10 property alternatives:
1. contributor (dct:contributor)
2. has manager (pv:hasManager): The manager of the employee., has domain Employee, has range Manager
3. member of (pv:memberOf): The department to which an agents belongs., has range Department, has domain Agent
4. name (foaf:name):

5. name (pv:name): The name of something.
6. responsible for (pv:responsibleFor): The product for which a department is responsible., has domain Department, has range Product
7. area of expertise (pv:areaOfExpertise): The product category agent is expert for., has range Product Category, has domain Agent
8. country (pv:country): The country where the supplier is located., has domain Supplier, has range Country
9. creator (dct:creator)
10. created (dct:created)

[Tool] No entity items found

[Tool]
Top 10 entity alternatives:
1. Employee (pv:Employee): An employee in my company., is subclass of Agent
2. employee line svg?color=#D6AF00&format=svg (<https://api.iconify.design/clarity/employee-line.svg?color=%23D6AF00&format=svg>)

[Tool]
Top 10 property alternatives:
1. label (rdfs:label)
2. type (rdf:type)
3. comment (rdfs:comment)
4. is Defined By (rdfs:isDefinedBy)
5. depiction (foaf:depiction)
6. sub Class Of (rdfs:subClassOf)

No other items found

[Tool]
Top 10 property alternatives:
1. comment (rdfs:comment)
2. label (rdfs:label)
3. type (rdf:type)

No other items found

[Tool]
Top 10 property alternatives:
1. type (rdf:type)
2. label (rdfs:label)
3. comment (rdfs:comment)
4. depiction (foaf:depiction)
5. sub Class Of (rdfs:subClassOf)
6. is Defined By (rdfs:isDefinedBy)

No other items found

[Tool]
Top 10 property alternatives:
1. email (pv:email): The e-mail of an agent., has domain Agent
2. address text (pv:addressText): The address text., has domain Agent
3. address country (pv:addressCountry): The address country., has domain Agent
4. comment (rdfs:comment)
5. phone number (pv:phone): has domain Agent, A phone number.
6. name (foaf:name):

7. name (pv:name): The name of something.
8. license (<http://creativecommons.org/ns#license>)
9. label (rdfs:label)
10. ID (pv:id): The ID of a thing.

[Tool]
Top 10 entity alternatives:
1. Agent (pv:Agent): Abstract base class for people, robots or other things that can act.
2. Agent (dct:Agent)

[Tool]
Top 10 property alternatives:
1. label (rdfs:label)
2. type (rdf:type)
3. comment (rdfs:comment)
4. is Defined By (rdfs:isDefinedBy)
5. depiction (foaf:depiction)

No other items found

[Tool]
Top 10 property alternatives:
1. comment (rdfs:comment)
2. label (rdfs:label)
3. type (rdf:type)

No other items found

[Tool]
Top 10 property alternatives:
1. type (rdf:type)
2. label (rdfs:label)
3. comment (rdfs:comment)
4. depiction (foaf:depiction)
5. is Defined By (rdfs:isDefinedBy)

No other items found

[Tool]
Got 47 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                    | object                                                                                |
| -------------------------------------------------------------------------------------------------------------- | --------------------------- | ---------------------------------------------------------------------------------------------------------------- |
| empl Wolfgang Martin@company org (<http://ld.company.org/prod-instances/empl-Wolfgang.Martin%40company.org>)   | has manager (pv:hasManager) | empl Elena Herzog@company org (<http://ld.company.org/prod-instances/empl-Elena.Herzog%40company.org>)           |
| empl Corinna Ludwig@company org (<http://ld.company.org/prod-instances/empl-Corinna.Ludwig%40company.org>)     | has manager (pv:hasManager) | empl Thomas Mueller@company org (<http://ld.company.org/prod-instances/empl-Thomas.Mueller%40company.org>)       |
| empl Franziska Acker@company org (<http://ld.company.org/prod-instances/empl-Franziska.Acker%40company.org>)   | has manager (pv:hasManager) | empl Franz Kornhaeusel@company org (<http://ld.company.org/prod-instances/empl-Franz.Kornhaeusel%40company.org>) |
| empl Bert Blumstein@company org (<http://ld.company.org/prod-instances/empl-Bert.Blumstein%40company.org>)     | has manager (pv:hasManager) | empl Dietlinde Boehme@company org (<http://ld.company.org/prod-instances/empl-Dietlinde.Boehme%40company.org>)   |
| empl Elisabeth Harman@company org (<http://ld.company.org/prod-instances/empl-Elisabeth.Harman%40company.org>) | has manager (pv:hasManager) | empl Waldtraud Kuttner@company org (<http://ld.company.org/prod-instances/empl-Waldtraud.Kuttner%40company.org>) |
| empl Baldwin Guenther@company org (<http://ld.company.org/prod-instances/empl-Baldwin.Guenther%40company.org>) | has manager (pv:hasManager) | empl Reiner Widmann@company org (<http://ld.company.org/prod-instances/empl-Reiner.Widmann%40company.org>)       |
| empl Gretel Roth@company org (<http://ld.company.org/prod-instances/empl-Gretel.Roth%40company.org>)           | has manager (pv:hasManager) | empl Elena Herzog@company org (<http://ld.company.org/prod-instances/empl-Elena.Herzog%40company.org>)           |
| empl Herr Haan Bader@company org (<http://ld.company.org/prod-instances/empl-Herr.Haan.Bader%40company.org>)   | has manager (pv:hasManager) | empl Thomas Mueller@company org (<http://ld.company.org/prod-instances/empl-Thomas.Mueller%40company.org>)       |
| empl Karch Moeller@company org (<http://ld.company.org/prod-instances/empl-Karch.Moeller%40company.org>)       | has manager (pv:hasManager) | empl Thomas Mueller@company org (<http://ld.company.org/prod-instances/empl-Thomas.Mueller%40company.org>)       |
| empl Karen Brant@company org (<http://ld.company.org/prod-instances/empl-Karen.Brant%40company.org>)           | has manager (pv:hasManager) | empl Thomas Mueller@company org (<http://ld.company.org/prod-instances/empl-Thomas.Mueller%40company.org>)       |
| ...                                                                                | ...                         | ...                                                                                |

[Tool]
Got 53 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property         | object                       |
| -------------------------------------------------------------------------------------------------------------- | ---------------- | ---------------------------- |
| empl Wolfgang Martin@company org (<http://ld.company.org/prod-instances/empl-Wolfgang.Martin%40company.org>)   | email (pv:email) | Wolfgang.Martin@company.org  |
| empl Gretel Roth@company org (<http://ld.company.org/prod-instances/empl-Gretel.Roth%40company.org>)           | email (pv:email) | Gretel.Roth@company.org      |
| empl Thomas Mueller@company org (<http://ld.company.org/prod-instances/empl-Thomas.Mueller%40company.org>)     | email (pv:email) | Thomas.Mueller@company.org   |
| empl Franziska Acker@company org (<http://ld.company.org/prod-instances/empl-Franziska.Acker%40company.org>)   | email (pv:email) | Franziska.Acker@company.org  |
| empl Bert Blumstein@company org (<http://ld.company.org/prod-instances/empl-Bert.Blumstein%40company.org>)     | email (pv:email) | Bert.Blumstein@company.org   |
| empl Elena Herzog@company org (<http://ld.company.org/prod-instances/empl-Elena.Herzog%40company.org>)         | email (pv:email) | Elena.Herzog@company.org     |
| empl Elisabeth Harman@company org (<http://ld.company.org/prod-instances/empl-Elisabeth.Harman%40company.org>) | email (pv:email) | Elisabeth.Harman@company.org |
| empl Kevin Feigenbaum@company org (<http://ld.company.org/prod-instances/empl-Kevin.Feigenbaum%40company.org>) | email (pv:email) | Kevin.Feigenbaum@company.org |
| empl Manfred Foth@company org (<http://ld.company.org/prod-instances/empl-Manfred.Foth%40company.org>)         | email (pv:email) | Manfred.Foth@company.org     |
| empl Adolfina Hoch@company org (<http://ld.company.org/prod-instances/empl-Adolfina.Hoch%40company.org>)       | email (pv:email) | Adolfina.Hoch@company.org    |
| ...                                                                                | ...              | ...                          |

[Tool]
Got 42 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                | object            |
| ---------------------------------------------------------------------------------------------------------------- | ----------------------- | ----------------- |
| empl Wolfgang Martin@company org (<http://ld.company.org/prod-instances/empl-Wolfgang.Martin%40company.org>)     | phone number (pv:phone) | +49-701-5973041   |
| empl Gretel Roth@company org (<http://ld.company.org/prod-instances/empl-Gretel.Roth%40company.org>)             | phone number (pv:phone) | +49-9456-94517553 |
| empl Thomas Mueller@company org (<http://ld.company.org/prod-instances/empl-Thomas.Mueller%40company.org>)       | phone number (pv:phone) | +49-8200-38218301 |
| empl Franziska Acker@company org (<http://ld.company.org/prod-instances/empl-Franziska.Acker%40company.org>)     | phone number (pv:phone) | (01908) 6892372   |
| empl Elena Herzog@company org (<http://ld.company.org/prod-instances/empl-Elena.Herzog%40company.org>)           | phone number (pv:phone) | +49-7608-83268331 |
| empl Elisabeth Harman@company org (<http://ld.company.org/prod-instances/empl-Elisabeth.Harman%40company.org>)   | phone number (pv:phone) | +49-4278-55203507 |
| empl Adolfina Hoch@company org (<http://ld.company.org/prod-instances/empl-Adolfina.Hoch%40company.org>)         | phone number (pv:phone) | +49-109-5719002   |
| empl Corinna Ludwig@company org (<http://ld.company.org/prod-instances/empl-Corinna.Ludwig%40company.org>)       | phone number (pv:phone) | +49-1743-24836762 |
| empl Jarvis Jans@company org (<http://ld.company.org/prod-instances/empl-Jarvis.Jans%40company.org>)             | phone number (pv:phone) | (0322) 517636576  |
| empl Franz Kornhaeusel@company org (<http://ld.company.org/prod-instances/empl-Franz.Kornhaeusel%40company.org>) | phone number (pv:phone) | (02065) 4946239   |
| ...                                                                                | ...                     | ...               |

[Tool]
Got 1 row and 3 columns:
| subject                          | property         | object       |
| -------------------------------- | ---------------- | ------------ |
| eccenca Gmb H (ecc:eccenca-GmbH) | name (foaf:name) | eccenca GmbH |

[Tool]
Got 1,364 rows and 3 columns, showing the first 10 rows below:
| subject                                           | property       | object                 |
| ------------------------------------------------- | -------------- | ---------------------- |
| srv P516 8211068 (prodi:srv-P516-8211068)         | name (pv:name) | IoT Data Marketing     |
| srv Y704 9764759 (prodi:srv-Y704-9764759)         | name (pv:name) | Product Analysis       |
| srv Y274 1029755 (prodi:srv-Y274-1029755)         | name (pv:name) | Industry-Grade Hacking |
| srv D215 3449390 (prodi:srv-D215-3449390)         | name (pv:name) | Manual Inspection      |
| srv P925 8919074 (prodi:srv-P925-8919074)         | name (pv:name) | Predictive Maintenance |
| srv N558 1730215 (prodi:srv-N558-1730215)         | name (pv:name) | Sensor Adjustment      |
| srv O662 4012383 (prodi:srv-O662-4012383)         | name (pv:name) | Involuntary Service    |
| prod cat Compensator (prodi:prod-cat-Compensator) | name (pv:name) | Compensator            |
| prod cat Crystal (prodi:prod-cat-Crystal)         | name (pv:name) | Crystal                |
| prod cat Encoder (prodi:prod-cat-Encoder)         | name (pv:name) | Encoder                |
| ...                                               | ...            | ...                    |

[Tool]
Got 2,620 rows and 3 columns, showing the first 10 rows below:
| subject                                   | property           | object                                |
| ----------------------------------------- | ------------------ | ------------------------------------- |
| Hardware (pv:Hardware)                    | label (rdfs:label) | Hardware (en)                         |
| Price (pv:Price)                          | label (rdfs:label) | Price (en)                            |
| srv P516 8211068 (prodi:srv-P516-8211068) | label (rdfs:label) | P516-8211068 - IoT Data Marketing     |
| srv Y704 9764759 (prodi:srv-Y704-9764759) | label (rdfs:label) | Y704-9764759 - Product Analysis       |
| srv Y274 1029755 (prodi:srv-Y274-1029755) | label (rdfs:label) | Y274-1029755 - Industry-Grade Hacking |
| srv D215 3449390 (prodi:srv-D215-3449390) | label (rdfs:label) | D215-3449390 - Manual Inspection      |
| Supplier (pv:Supplier)                    | label (rdfs:label) | Supplier (en)                         |
| srv P925 8919074 (prodi:srv-P925-8919074) | label (rdfs:label) | P925-8919074 - Predictive Maintenance |
| srv N558 1730215 (prodi:srv-N558-1730215) | label (rdfs:label) | N558-1730215 - Sensor Adjustment      |
| BOM Part (pv:BomPart)                     | label (rdfs:label) | BOM Part (en)                         |
| ...                                       | ...                | ...                                   |

[Tool]
Got 47 rows and 4 columns, showing the first 5 and last 5 rows below:
| employee                                                                                | name                | email                           | phone             |
| -------------------------------------------------------------------------------------------------------------------- | ------------------- | ------------------------------- | ----------------- |
| empl Adolfina Hoch@company org (<http://ld.company.org/prod-instances/empl-Adolfina.Hoch%40company.org>)             | Adolfina Hoch       | Adolfina.Hoch@company.org       | +49-109-5719002   |
| empl Anamchara Foerstner@company org (<http://ld.company.org/prod-instances/empl-Anamchara.Foerstner%40company.org>) | Anamchara Foerstner | Anamchara.Foerstner@company.org | +49-360-5655698   |
| empl Arendt Beitel@company org (<http://ld.company.org/prod-instances/empl-Arendt.Beitel%40company.org>)             | Arendt Beitel       | Arendt.Beitel@company.org       | +49-8763-66514379 |
| empl Arnelle Gerber@company org (<http://ld.company.org/prod-instances/empl-Arnelle.Gerber%40company.org>)           | Arnelle Gerber      | Arnelle.Gerber@company.org      | (03586) 7240528   |
| empl Baldwin Dirksen@company org (<http://ld.company.org/prod-instances/empl-Baldwin.Dirksen%40company.org>)         | Baldwin Dirksen     | Baldwin.Dirksen@company.org     | +49-6200-33069465 |
| ...                                                                                | ...                 | ...                             | ...               |
| empl Valda Everhart@company org (<http://ld.company.org/prod-instances/empl-Valda.Everhart%40company.org>)           | Valda Everhart      | Valda.Everhart@company.org      | +49-04-969-14255  |
| empl Wanja Hoffmann@company org (<http://ld.company.org/prod-instances/empl-Wanja.Hoffmann%40company.org>)           | Wanja Hoffmann      | Wanja.Hoffmann@company.org      | +49-1083-38194095 |
| empl Wolfgang Martin@company org (<http://ld.company.org/prod-instances/empl-Wolfgang.Martin%40company.org>)         | Wolfgang Martin     | Wolfgang.Martin@company.org     | +49-701-5973041   |
| empl Xochitl Aue@company org (<http://ld.company.org/prod-instances/empl-Xochitl.Aue%40company.org>)                 | Xochitl Aue         | Xochitl.Aue@company.org         |                   |
| empl Yanka Schreiber@company org (<http://ld.company.org/prod-instances/empl-Yanka.Schreiber%40company.org>)         | Yanka Schreiber     | Yanka.Schreiber@company.org     |                   |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves all employees who do not manage anyone by using a FILTER NOT EXISTS clause to exclude those who are managers. It selects the employee's name, email, and phone number with optional clauses, ensuring that missing data does not exclude employees from the result. The results are ordered by name as requested. The query returns 47 rows, which seems reasonable for a staff directory. The answer is clear and matches the query results. No further refinement is necessary.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX pv: <http://ld.company.org/prod-vocab/>
SELECT ?department ?departmentName ( COUNT( ?employee ) AS ?employeeCount ) WHERE {
  ?employee pv:memberOf ?department .
  ?department a pv:Department .
  OPTIONAL {
    ?department rdfs:label ?departmentName
  }
}
GROUP BY ?department ?departmentName
HAVING ( COUNT( ?employee ) > 5 )

Got 5 rows and 3 columns:
| department                    | departmentName     | employeeCount |
| ----------------------------- | ------------------ | ------------- |
| dept 22183 (prodi:dept-22183) | Product Management | 13 (xsd:int)  |
| dept 41622 (prodi:dept-41622) | Data Services      | 10 (xsd:int)  |
| dept 73191 (prodi:dept-73191) | Engineering        | 6 (xsd:int)   |
| dept 84279 (prodi:dept-84279) | Procurement        | 9 (xsd:int)   |
| dept 85880 (prodi:dept-85880) | Marketing          | 10 (xsd:int)  |

The departments that have more than 5 employees along with their names and the number of employees are:
1. Product Management with 13 employees
2. Data Services with 10 employees
3. Engineering with 6 employees
4. Procurement with 9 employees
5. Marketing with 10 employees

Which department have more than 5 employees? I need their names and the number of employees.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
ck25 at http://localhost:9333

You can use the following SPARQL prefixes implicitly in all functions:
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
ecc: <https://ns.eccenca.com/
owl: <http://www.w3.org/2002/07/owl#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
dct: <http://purl.org/dc/terms/
void: <http://rdfs.org/ns/void#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
vann: <http://purl.org/vocab/vann/
dbpedia: <http://dbpedia.org/resource/
wgs84: <http://www.w3.org/2003/01/geo/wgs84_pos#
pv: <http://ld.company.org/prod-vocab/
prodi: <http://ld.company.org/prod-instances/
skos: <http://www.w3.org/2004/02/skos/core#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Which department have more than 5 employees? I need their names and the number of employees.

[Assistant]
To answer this question, I need to find the entity representing "department" and the property that relates departments to their employees. Then I will count the number of employees for each department and filter those with more than 5 employees. Finally, I will retrieve the names of those departments and the number of employees.

Step 1: Search for entities related to "department" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Department (pv:Department): A department in an organization.
2. emoji department store svg?color=#D6AF00&format=svg (<https://api.iconify.design/arcticons/emoji-department-store.svg?color=%23D6AF00&format=svg>)

[Assistant] I found an entity "Department" with the IRI prefix pv:Department that represents a department in an organization. Next, I will search for properties related to "Department" that might link it to employees.

[Tool]
Top 10 property alternatives:
1. label (rdfs:label)
2. type (rdf:type)
3. comment (rdfs:comment)
4. is Defined By (rdfs:isDefinedBy)
5. depiction (foaf:depiction)

No other items found

[Assistant] There are no direct properties linking "Department" to employees found by the search. I will try searching for the entity "employee" to see if there is a property linking employees to departments.

[Tool]
Top 10 entity alternatives:
1. Employee (pv:Employee): An employee in my company., is subclass of Agent
2. employee line svg?color=#D6AF00&format=svg (<https://api.iconify.design/clarity/employee-line.svg?color=%23D6AF00&format=svg>)

[Assistant] I found an entity "Employee" with the IRI prefix pv:Employee. Now I will search for properties related to "Employee" that might link an employee to a department.

[Tool]
Top 10 property alternatives:
1. label (rdfs:label)
2. sub Class Of (rdfs:subClassOf)
3. type (rdf:type)
4. depiction (foaf:depiction)
5. is Defined By (rdfs:isDefinedBy)

No other items found

[Assistant] There are no direct properties linking "Employee" to "Department" found by the search. I will try a more general search for properties related to "department" to see if there is any property that links employees to departments.

[Tool]
Top 10 property alternatives:
1. area of expertise (pv:areaOfExpertise): The product category agent is expert for., has range Product Category, has domain Agent
2. label (rdfs:label)
3. country (pv:country): The country where the supplier is located., has domain Supplier, has range Country
4. part (pv:hasPart): has domain BOM Part, The Product needed., has range Product
5. has manager (pv:hasManager): The manager of the employee., has domain Employee, has range Manager
6. responsible for (pv:responsibleFor): The product for which a department is responsible., has domain Department, has range Product
7. sub Class Of (rdfs:subClassOf)
8. quantity (pv:quantity): The quantity needed., has domain BOM Part
9. member of (pv:memberOf): The department to which an agents belongs., has range Department, has domain Agent
10. type (rdf:type)

[Assistant] I found a property "member of" (pv:memberOf) which has the domain Agent and range Department. Since Employee is a subclass of Agent, this property might link employees to departments. I will verify if employees are linked to departments using this property by querying some triples.

[Tool] Got no rows and 3 columns

[Assistant] No triples found with property pv:memberOf linking to pv:Department. I will try to find employees that have the property pv:memberOf with any object to see if employees are linked to departments or other entities.

[Tool]
Got 53 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                | object                        |
| -------------------------------------------------------------------------------------------------------------- | ----------------------- | ----------------------------- |
| empl Wolfgang Martin@company org (<http://ld.company.org/prod-instances/empl-Wolfgang.Martin%40company.org>)   | member of (pv:memberOf) | dept 41622 (prodi:dept-41622) |
| empl Thomas Mueller@company org (<http://ld.company.org/prod-instances/empl-Thomas.Mueller%40company.org>)     | member of (pv:memberOf) | dept 73191 (prodi:dept-73191) |
| empl Franziska Acker@company org (<http://ld.company.org/prod-instances/empl-Franziska.Acker%40company.org>)   | member of (pv:memberOf) | dept 22183 (prodi:dept-22183) |
| empl Bert Blumstein@company org (<http://ld.company.org/prod-instances/empl-Bert.Blumstein%40company.org>)     | member of (pv:memberOf) | dept 85880 (prodi:dept-85880) |
| empl Elisabeth Harman@company org (<http://ld.company.org/prod-instances/empl-Elisabeth.Harman%40company.org>) | member of (pv:memberOf) | dept 84279 (prodi:dept-84279) |
| empl Reiner Widmann@company org (<http://ld.company.org/prod-instances/empl-Reiner.Widmann%40company.org>)     | member of (pv:memberOf) | dept 66469 (prodi:dept-66469) |
| empl Gretel Roth@company org (<http://ld.company.org/prod-instances/empl-Gretel.Roth%40company.org>)           | member of (pv:memberOf) | dept 41622 (prodi:dept-41622) |
| empl Elena Herzog@company org (<http://ld.company.org/prod-instances/empl-Elena.Herzog%40company.org>)         | member of (pv:memberOf) | dept 41622 (prodi:dept-41622) |
| empl Kevin Feigenbaum@company org (<http://ld.company.org/prod-instances/empl-Kevin.Feigenbaum%40company.org>) | member of (pv:memberOf) | dept 85880 (prodi:dept-85880) |
| empl Manfred Foth@company org (<http://ld.company.org/prod-instances/empl-Manfred.Foth%40company.org>)         | member of (pv:memberOf) | dept 73191 (prodi:dept-73191) |
| ...                                                                                | ...                     | ...                           |

[Assistant] I found that employees are linked to departments using the property pv:memberOf. The employees have IRIs starting with "empl" and the departments have IRIs starting with "dept". Now I will write a query to count the number of employees per department and filter those with more than 5 employees. I will also try to get the department names if available.

[Tool]
Got 5 rows and 3 columns:
| department                    | departmentName     | employeeCount |
| ----------------------------- | ------------------ | ------------- |
| dept 22183 (prodi:dept-22183) | Product Management | 13 (xsd:int)  |
| dept 41622 (prodi:dept-41622) | Data Services      | 10 (xsd:int)  |
| dept 73191 (prodi:dept-73191) | Engineering        | 6 (xsd:int)   |
| dept 84279 (prodi:dept-84279) | Procurement        | 9 (xsd:int)   |
| dept 85880 (prodi:dept-85880) | Marketing          | 10 (xsd:int)  |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves departments with more than 5 employees, including their names and the count of employees. The query uses appropriate grouping and filtering with HAVING, and the execution result matches the answer provided. The answer is complete and accurate based on the data. No further refinement is needed.