PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX up: <http://purl.uniprot.org/core/>
SELECT ?comment ( COUNT( * ) AS ?count ) WHERE {
  ?anno a up:Topological_Domain_Annotation ; rdfs:comment ?comment .
}
GROUP BY ?comment
ORDER BY DESC ( ?count )

Got 50 rows and 2 columns, showing the first 5 and last 5 rows below:
| comment                | count            |
| ---------------------- | ---------------- |
| Cytoplasmic            | 262787 (xsd:int) |
| Extracellular          | 75542 (xsd:int)  |
| Virion surface         | 74529 (xsd:int)  |
| Intravirion            | 67341 (xsd:int)  |
| Periplasmic            | 63410 (xsd:int)  |
| ...                    | ...              |
| Extracellular; loop 2  | 1 (xsd:int)      |
| Extracellular; loop L5 | 1 (xsd:int)      |
| Extracellular; loop 1  | 1 (xsd:int)      |
| Extracellular; loop L8 | 1 (xsd:int)      |
| Extracellular; loop L7 | 1 (xsd:int)      |

This query returns each unique topological domain comment text and the number of times it occurs in UniProtKB entries, ordered by decreasing count.

[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:
uniprot at https://qlever.cs.uni-freiburg.de/api/uniprot

You can use the following SPARQL prefixes implicitly in all functions:
ECO: <http://purl.obolibrary.org/obo/ECO_
annotation: <http://purl.uniprot.org/annotation/
bibo: <http://purl.org/ontology/bibo/
busco: <http://busco.ezlab.org/schema#
chebi: <http://purl.obolibrary.org/obo/CHEBI_
citation: <http://purl.uniprot.org/citations/
cito: <http://purl.org/spar/cito/
dcat: <http://www.w3.org/ns/dcat#
dcmit: <http://purl.org/dc/dcmitype/
dcterms: <http://purl.org/dc/terms/
disease: <http://purl.uniprot.org/diseases/
embl-cds: <http://purl.uniprot.org/embl-cds/
ensembl: <http://rdf.ebi.ac.uk/resource/ensembl/
enzyme: <http://purl.uniprot.org/enzyme/
faldo: <http://biohackathon.org/resource/faldo#
foaf: <http://xmlns.com/foaf/0.1/
go: <http://purl.obolibrary.org/obo/GO_
hs: <https://hamap.expasy.org/rdf/vocab#
isoform: <http://purl.uniprot.org/isoforms/
keywords: <http://purl.uniprot.org/keywords/
location: <http://purl.uniprot.org/locations/
obo: <http://purl.obolibrary.org/obo/
oboInOwl: <http://www.geneontology.org/formats/oboInOwl#
owl: <http://www.w3.org/2002/07/owl#
patent: <http://purl.uniprot.org/EPO/
pav: <http://purl.org/pav/
position: <http://purl.uniprot.org/position/
prism: <http://prismstandard.org/namespaces/basic/2.0/
pubmed: <http://purl.uniprot.org/pubmed/
range: <http://purl.uniprot.org/range/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
rh: <http://rdf.rhea-db.org/
schema: <http://schema.org/
sd: <http://www.w3.org/ns/sparql-service-description#
sh: <http://www.w3.org/ns/shacl#
skos: <http://www.w3.org/2004/02/skos/core#
sp: <http://spinrdf.org/sp#
ssmRegion: <http://purl.uniprot.org/signatureSequenceMatch/
stato: <http://purl.obolibrary.org/obo/STATO_
taxon: <http://purl.uniprot.org/taxonomy/
tissue: <http://purl.uniprot.org/tissues/
uniparc: <http://purl.uniprot.org/uniparc/
uniprot: <http://purl.uniprot.org/uniprot/
up: <http://purl.uniprot.org/core/
voag: <http://voag.linkedmodel.org/schema/voag#
void: <http://rdfs.org/ns/void#
xsd: <http://www.w3.org/2001/XMLSchema#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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] Find the co-occurrence count of topological domain comment texts in UniProtKB entries.

[Assistant]
Let's break down the question:

1. We are interested in "topological domain" comment texts in UniProtKB entries.
2. We want to find the co-occurrence count of these comment texts, i.e., how often each unique comment text appears across UniProtKB entries.

Step 1: Identify how "topological domain" comments are represented in UniProt.
- "Topological domain" is a type of feature annotation in UniProt, often found in the "feature" or "comment" sections.
- We need to find the property or class representing "topological domain" and the property for its comment text.

Step 2: Search for the entity or property related to "topological domain".

I'll start by searching for the entity "topological domain" in UniProt.

[Tool]
Top 10 entity alternatives:
1. Topological Domain (up:Topological_Domain_Annotation): A topological domain.
2. Domain (up:Domain_Annotation): Description of the domain structure of a protein.
3. domains (up:domains)
4. 41294, also known as 9BRAD, alpha-2 proteobacteria, BANA domain, Bradyrhizobiaceae, Bradyrhizobiaceae Garrity et al. 2006 (taxon:41294)
5. nuclear speck, also known as nuclear speckles, splicing speckle, nuclear speckle, speckle focus, speckle domain (go:0016607): A discrete extra-nucleolar subnuclear domain, 20-50 in number, i...ctors are seen to be localized by immunofluorescence microscopy.
6. sterol sensor activity, also known as sterol-sensing domain, sterol sensing activity (go:0032935): Binding to and responding, e.g. by conformational change, to changes in the cellular level of a sterol.
7. POZ domain binding, also known as BTB domain, broad-complex, tramtrack, and bric-a-brac domain binding (go:0031208): Binding to a POZ (poxvirus and zinc finger) domain of a protein,...-protein interaction domain found in many transcription factors.
8. heterochromatin domain, also known as HOOD (go:1990343): A region of heterochromatin that is formed dynamically under spe..., and is enriched in histone H3 methylated on lysine 9 (H3K9me).
9. membrane raft, also known as GEM domain, lipid raft, glycolipid-enriched membrane domain (go:0045121): Any of the small (10-200 nm), heterogeneous, highly dynamic, ste...latforms through protein-protein and protein-lipid interactions.
10. Domain Extent (up:Domain_Extent_Annotation): Extent of a domain, which is defined as a specific combination o...nized into a characteristic three-dimensional structure or fold.

[Assistant]
The entity representing "Topological Domain" in UniProt is up:Topological_Domain_Annotation.

Next, I need to find how the comment text for this annotation is represented—likely as a property such as "description", "comment", or "text". I'll search for properties related to "text" or "description" for this entity.

[Tool]
Top 10 property alternatives:
1. comment (rdfs:comment)
2. label (rdfs:label)
3. type (rdf:type)
4. see also (rdfs:seeAlso)
5. is defined by (rdfs:isDefinedBy)
6. sub class of (rdfs:subClassOf)

No other items found

[Assistant]
The most relevant property for the comment text of a topological domain annotation appears to be rdfs:comment.

Now, I need to find UniProtKB entries that have a topological domain annotation and extract the rdfs:comment for each such annotation. Then, I will count the occurrences of each unique comment text.

To do this, I need to:
- Find UniProtKB entries (instances of up:Protein or similar) that have a topological domain annotation (up:Topological_Domain_Annotation).
- For each annotation, get its rdfs:comment.
- Count the number of times each unique rdfs:comment appears.

I'll first check how a UniProtKB entry is linked to a topological domain annotation.

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                          | property        | object                                                |
| ---------------------------------------------------------------- | --------------- | ----------------------------------------------------- |
| <http://purl.uniprot.org/uniprot/A0A009GRA3#SIP00680FCEDB47302E> | type (rdf:type) | Topological Domain (up:Topological_Domain_Annotation) |
| <http://purl.uniprot.org/uniprot/A0A009GRA3#SIP5BDF906594C22600> | type (rdf:type) | Topological Domain (up:Topological_Domain_Annotation) |
| <http://purl.uniprot.org/uniprot/A0A009GYA7#SIP74A24A9F74176049> | type (rdf:type) | Topological Domain (up:Topological_Domain_Annotation) |
| <http://purl.uniprot.org/uniprot/A0A009GYA7#SIP833202CBBD0748F4> | type (rdf:type) | Topological Domain (up:Topological_Domain_Annotation) |
| <http://purl.uniprot.org/uniprot/A0A009GYA7#SIPEC55680C01B0BC62> | type (rdf:type) | Topological Domain (up:Topological_Domain_Annotation) |
| <http://purl.uniprot.org/uniprot/A0A009HTG9#SIP0BE3BE4FA265DD30> | type (rdf:type) | Topological Domain (up:Topological_Domain_Annotation) |
| <http://purl.uniprot.org/uniprot/A0A009HTG9#SIP1B6B4787A0D4C9B0> | type (rdf:type) | Topological Domain (up:Topological_Domain_Annotation) |
| <http://purl.uniprot.org/uniprot/A0A009HTG9#SIPE4AB4DCDCE63AE65> | type (rdf:type) | Topological Domain (up:Topological_Domain_Annotation) |
| <http://purl.uniprot.org/uniprot/A0A009IFP0#SIP114CE1BCD382BF81> | type (rdf:type) | Topological Domain (up:Topological_Domain_Annotation) |
| <http://purl.uniprot.org/uniprot/A0A009IFP0#SIP2ED75CCE2A5F6314> | type (rdf:type) | Topological Domain (up:Topological_Domain_Annotation) |
| ...                                                              | ...             | ...                                                   |

[Assistant]
Each topological domain annotation is represented as a unique URI (e.g., http://purl.uniprot.org/uniprot/A0A009GRA3#SIP00680FCEDB47302E) and is typed as up:Topological_Domain_Annotation. These URIs are likely linked to their parent UniProtKB entry (e.g., http://purl.uniprot.org/uniprot/A0A009GRA3).

Now, I need to find the rdfs:comment for each of these annotation URIs and count the occurrences of each unique comment text.

I'll check if these annotation URIs have an rdfs:comment property.

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property               | object                                                |
| ---------------------------------------------------------------------------------------- | ---------------------- | ----------------------------------------------------- |
| <http://abcm2.hegelab.org/search>                                                        | comment (rdfs:comment) | ABCMdb; Database for mutations in ABC proteins        |
| <http://androgendb.mcgill.ca>                                                            | comment (rdfs:comment) | Androgen receptor gene mutations database             |
| <http://arup.utah.edu/database/ACVRL1/ACVRL1_welcome.php>                                | comment (rdfs:comment) | Hereditary Hemorrhagic Telangiectasia and ENG         |
| <http://bioinformatica.isa.cnr.it/galactosemia-proteins-db/index3.html>                  | comment (rdfs:comment) | Galactosemia Proteins Database                        |
| <http://dramp.cpu-bioinfor.org/browse/All_Information.php?id=DRAMP01646&dataset=General> | comment (rdfs:comment) | The data repository of antimicrobial peptides (DRAMP) |
| <http://genome.weizmann.ac.il/horde/card/index/symbol:OR10A2>                            | comment (rdfs:comment) | Human Olfactory Receptor Data Exploratorium (HORDE)   |
| <http://identifiers.org/reactome/R-BTA-1059683>                                          | comment (rdfs:comment) | Interleukin-6 signaling                               |
| <http://identifiers.org/reactome/R-BTA-109704>                                           | comment (rdfs:comment) | PI3K Cascade                                          |
| <http://identifiers.org/reactome/R-BTA-110056>                                           | comment (rdfs:comment) | MAPK3 (ERK1) activation                               |
| <http://identifiers.org/reactome/R-BTA-110312>                                           | comment (rdfs:comment) | Translesion synthesis by REV1                         |
| ...                                                                                | ...                    | ...                                                   |

[Tool]
Got 3 rows and 3 columns:
| subject                                                          | property               | object                                                |
| ---------------------------------------------------------------- | ---------------------- | ----------------------------------------------------- |
| <http://purl.uniprot.org/uniprot/A0A009GRA3#SIP00680FCEDB47302E> | type (rdf:type)        | Topological Domain (up:Topological_Domain_Annotation) |
| <http://purl.uniprot.org/uniprot/A0A009GRA3#SIP00680FCEDB47302E> | comment (rdfs:comment) | Cytoplasmic                                           |
| <http://purl.uniprot.org/uniprot/A0A009GRA3#SIP00680FCEDB47302E> | range (up:range)       | range:-9218586922016551890tt1tt3                      |

[Assistant]
The annotation URI for a topological domain does have an rdfs:comment property, which contains the comment text (e.g., "Cytoplasmic").

Now, to answer the question, I need to:
- Select all subjects of type up:Topological_Domain_Annotation.
- For each, get the rdfs:comment.
- Count the number of occurrences of each unique rdfs:comment.

I'll write a SPARQL query that does this aggregation.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves each unique topological domain comment text and counts its occurrences in UniProtKB entries, as requested. The query uses the appropriate class (up:Topological_Domain_Annotation) and property (rdfs:comment), and the results are grouped and ordered as specified. The answer is accurate and complete according to the user's question.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX up: <http://purl.uniprot.org/core/>
PREFIX taxon: <http://purl.uniprot.org/taxonomy/>
SELECT ?taxon ?name WHERE {
  ?taxon rdfs:subClassOf + taxon:2 .
  OPTIONAL {
    ?taxon up:scientificName ?name
  }
}

This query selects all bacterial taxa (descendants of taxon:2, the Bacteria domain) in the UniProt taxonomy and their scientific names, if available.

[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:
uniprot at https://qlever.cs.uni-freiburg.de/api/uniprot

You can use the following SPARQL prefixes implicitly in all functions:
ECO: <http://purl.obolibrary.org/obo/ECO_
annotation: <http://purl.uniprot.org/annotation/
bibo: <http://purl.org/ontology/bibo/
busco: <http://busco.ezlab.org/schema#
chebi: <http://purl.obolibrary.org/obo/CHEBI_
citation: <http://purl.uniprot.org/citations/
cito: <http://purl.org/spar/cito/
dcat: <http://www.w3.org/ns/dcat#
dcmit: <http://purl.org/dc/dcmitype/
dcterms: <http://purl.org/dc/terms/
disease: <http://purl.uniprot.org/diseases/
embl-cds: <http://purl.uniprot.org/embl-cds/
ensembl: <http://rdf.ebi.ac.uk/resource/ensembl/
enzyme: <http://purl.uniprot.org/enzyme/
faldo: <http://biohackathon.org/resource/faldo#
foaf: <http://xmlns.com/foaf/0.1/
go: <http://purl.obolibrary.org/obo/GO_
hs: <https://hamap.expasy.org/rdf/vocab#
isoform: <http://purl.uniprot.org/isoforms/
keywords: <http://purl.uniprot.org/keywords/
location: <http://purl.uniprot.org/locations/
obo: <http://purl.obolibrary.org/obo/
oboInOwl: <http://www.geneontology.org/formats/oboInOwl#
owl: <http://www.w3.org/2002/07/owl#
patent: <http://purl.uniprot.org/EPO/
pav: <http://purl.org/pav/
position: <http://purl.uniprot.org/position/
prism: <http://prismstandard.org/namespaces/basic/2.0/
pubmed: <http://purl.uniprot.org/pubmed/
range: <http://purl.uniprot.org/range/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
rh: <http://rdf.rhea-db.org/
schema: <http://schema.org/
sd: <http://www.w3.org/ns/sparql-service-description#
sh: <http://www.w3.org/ns/shacl#
skos: <http://www.w3.org/2004/02/skos/core#
sp: <http://spinrdf.org/sp#
ssmRegion: <http://purl.uniprot.org/signatureSequenceMatch/
stato: <http://purl.obolibrary.org/obo/STATO_
taxon: <http://purl.uniprot.org/taxonomy/
tissue: <http://purl.uniprot.org/tissues/
uniparc: <http://purl.uniprot.org/uniparc/
uniprot: <http://purl.uniprot.org/uniprot/
up: <http://purl.uniprot.org/core/
voag: <http://voag.linkedmodel.org/schema/voag#
void: <http://rdfs.org/ns/void#
xsd: <http://www.w3.org/2001/XMLSchema#
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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] Select all bacterial taxa and their scientific names from the UniProt taxonomy.

[Assistant]
Let's break down the question:

1. We need to find all taxa that are bacteria in the UniProt taxonomy.
2. For each taxon, we want its identifier and its scientific name.

Step 1: Identify how bacteria are represented in UniProt taxonomy. Usually, there is a root taxon for Bacteria, likely with a specific IRI (e.g., taxon:2 for Bacteria in NCBI taxonomy).

Step 2: Find all taxa that are descendants of Bacteria.

Step 3: For each, retrieve its identifier and scientific name.

First, let's search for the entity representing "Bacteria" in the UniProt taxonomy.

[Tool]
Top 10 entity alternatives:
1. 629395, also known as Bacteria Latreille et al. 1825, Bacteria stick insect, Bacteria Latreille, Peletier de ...Fargeau, Serville & Guerin, 1825, Bacteria (stick insect), Bacteria (taxon:629395)
2. 2, also known as Bacteria, prokaryotes, prokaryote, Prokaryotae, Bacteria Woese et al. 2024 (taxon:2)
3. 426670, also known as Bacteriastrum (taxon:426670)
4. 629399, also known as Bacteria aetolus Westwood, 1859, Bacteria aetolus (taxon:629399)
5. 629396, also known as Bacteria abnormis (Brunner von Wattenwyl, 1907), Bacteria abnormis (taxon:629396)
6. 629397, also known as Bacteria aborigena Giglio-Tos, 1910, Bacteria aborigena (taxon:629397)
7. 629398, also known as Bacteria acuminatocercata (Brunner von Wattenwyl, 1907), Bacteria acuminatocercata (taxon:629398)
8. 629400, also known as Bacteria amazonica (Brunner von Wattenwyl, 1907), Bacteria amazonica (taxon:629400)
9. 629401, also known as Bacteria ambigua (Brunner von Wattenwyl, 1907), Bacteria ambigua (taxon:629401)
10. 629402, also known as Bacteria annulicornis Philippi, 1863, Bacteria annulicornis (taxon:629402)

[Assistant]
There are two main candidates for "Bacteria" in the UniProt taxonomy search results:

- taxon:2, labeled as "Bacteria, prokaryotes, Prokaryotae, Bacteria Woese et al. 2024" (this is the biological domain Bacteria).
- taxon:629395, labeled as "Bacteria Latreille et al. 1825" (this is a stick insect genus, not the domain).

For the biological domain, taxon:2 is the correct root for Bacteria.

Next, I need to find all taxa that are descendants of taxon:2 (Bacteria) and retrieve their identifiers and scientific names.

To do this, I need to know the property that links a taxon to its parent (e.g., "parent taxon" or "subclass of"). Let's search for properties related to "parent" in the UniProt taxonomy.

[Tool]
Top 10 property alternatives:
1. host (up:host): has domain Taxon, has range Taxon
2. creator (dcterms:creator)
3. end (faldo:end)
4. created (up:created): The date a resource was created.
5. method (up:method): The experimental method that was used., has range Method
6. object (rdf:object)
7. author (up:author): The author of a publication., has domain Citation
8. name (up:name)
9. interaction (up:interaction): has range Interaction, has domain Protein
10. type (rdf:type)

[Tool]
Top 10 property alternatives:
1. sub class of (rdfs:subClassOf)
2. equivalent class (owl:equivalentClass)
3. sub property of (rdfs:subPropertyOf)
4. classified with (up:classifiedWith): A concept that classifies this resource., has domain Protein, has range Concept
5. type (rdf:type)
6. on class (owl:onClass)
7. enzyme class (up:enzymeClass): has domain Catalytic Activity, has range Enzyme
8. object (rdf:object)
9. part of lineage (up:partOfLineage): True for taxa that can appear as part of an organism's non abbre...ntry (as well as at INSDC) only these taxonomic nodes are shown., has domain Taxon
10. some members classified with (up:someMembersClassifiedWith): Indicates which GO terms are somewhat consistently used to annotate UniProtKB members of this cluster., has domain Cluster (UniRef)

[Tool]
Got 57 rows and 3 columns, showing the first 10 rows below:
| subject     | property                                      | object                         |
| ----------- | --------------------------------------------- | ------------------------------ |
| 2 (taxon:2) | type (rdf:type)                               | Taxon (up:Taxon)               |
| 2 (taxon:2) | Mnemonic (up:mnemonic)                        | 9BACT                          |
| 2 (taxon:2) | sub class of (rdfs:subClassOf)                | 131567 (taxon:131567)          |
| 2 (taxon:2) | part of lineage (up:partOfLineage)            | true (xsd:boolean)             |
| 2 (taxon:2) | narrower transitive (skos:narrowerTransitive) | 1224 (taxon:1224)              |
| 2 (taxon:2) | scientific name (up:scientificName)           | Bacteria                       |
| 2 (taxon:2) | rank (up:rank)                                | up:Superkingdom                |
| 2 (taxon:2) | other name (up:otherName)                     | "Bacteria" Cavalier-Smith 1987 |
| 2 (taxon:2) | narrower transitive (skos:narrowerTransitive) | 1297 (taxon:1297)              |
| 2 (taxon:2) | narrower transitive (skos:narrowerTransitive) | 1783257 (taxon:1783257)        |
| ...         | ...                                           | ...                            |

[Assistant]
From the triples for taxon:2, I see that:

- The property skos:narrowerTransitive links taxon:2 to other taxa (e.g., taxon:1224, taxon:1297, etc.). This property is likely used to indicate descendant taxa in the taxonomy.

- The property up:scientificName gives the scientific name.

Therefore, to get all bacterial taxa and their scientific names, I should:

- Select all taxa (?taxon) such that ?taxon skos:narrowerTransitive* taxon:2 (using the transitive closure to get all descendants).
- For each ?taxon, get its up:scientificName.

To avoid including taxon:2 itself, I can filter out ?taxon = taxon:2.

Now, I'll write the SPARQL query accordingly.

[Tool]
Got 1 row and 2 columns:
| taxon                 | name               |
| --------------------- | ------------------ |
| 131567 (taxon:131567) | cellular organisms |

[Assistant]
The query only returned one row: taxon:131567 ("cellular organisms"). This suggests that skos:narrowerTransitive* taxon:2 is not the correct direction; it actually finds ancestors, not descendants.

To find all descendants of Bacteria (taxon:2), I should reverse the direction: ?descendant skos:narrowerTransitive taxon:2 (or use the property in the other direction).

Let me check which taxa have taxon:2 as their ancestor using skos:narrowerTransitive.

[Tool]
Got 1 row and 3 columns:
| subject               | property                                      | object      |
| --------------------- | --------------------------------------------- | ----------- |
| 131567 (taxon:131567) | narrower transitive (skos:narrowerTransitive) | 2 (taxon:2) |

[Assistant]
It appears that only "cellular organisms" (taxon:131567) has taxon:2 as a skos:narrowerTransitive. This suggests that skos:narrowerTransitive is not used to encode the child relationships in the expected direction.

Let's try another approach: perhaps there is a "subclass of" (rdfs:subClassOf) or similar property used for the taxonomic hierarchy. Let's check which taxa have taxon:2 as their rdfs:subClassOf.

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                 | property                       | object      |
| ----------------------- | ------------------------------ | ----------- |
| 10 (taxon:10)           | sub class of (rdfs:subClassOf) | 2 (taxon:2) |
| 100 (taxon:100)         | sub class of (rdfs:subClassOf) | 2 (taxon:2) |
| 100000 (taxon:100000)   | sub class of (rdfs:subClassOf) | 2 (taxon:2) |
| 1000000 (taxon:1000000) | sub class of (rdfs:subClassOf) | 2 (taxon:2) |
| 1000001 (taxon:1000001) | sub class of (rdfs:subClassOf) | 2 (taxon:2) |
| 1000002 (taxon:1000002) | sub class of (rdfs:subClassOf) | 2 (taxon:2) |
| 1000003 (taxon:1000003) | sub class of (rdfs:subClassOf) | 2 (taxon:2) |
| 1000004 (taxon:1000004) | sub class of (rdfs:subClassOf) | 2 (taxon:2) |
| 1000005 (taxon:1000005) | sub class of (rdfs:subClassOf) | 2 (taxon:2) |
| 1000006 (taxon:1000006) | sub class of (rdfs:subClassOf) | 2 (taxon:2) |
| ...                     | ...                            | ...         |

[Assistant]
There are many taxa that have taxon:2 as their rdfs:subClassOf, indicating that this property is used to represent the taxonomic hierarchy (parent-child relationships).

To get all bacterial taxa (descendants of taxon:2), I should use the transitive closure of rdfs:subClassOf, i.e., ?taxon rdfs:subClassOf+ taxon:2.

For each such taxon, I will retrieve its scientific name using up:scientificName.

Here is the improved SPARQL query.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves all bacterial taxa (descendants of taxon:2) and their scientific names from the UniProt taxonomy. It returns the actual IRIs/identifiers for each taxon and includes the scientific name as an optional field, which is appropriate. The query follows all specified rules and the result matches the user's request. No further refinement is needed.