PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?instanceOf ?instanceOfLabel ?subclassOf ?subclassOfLabel ?ortholog ?orthologLabel ?taxon ?taxonLabel ?genomicStart ?genomicEnd ?strandOrientation ?strandOrientationLabel ?expressedIn ?expressedInLabel ?encodes ?encodesLabel ?chromosome ?chromosomeLabel WHERE {
  wd:Q18032040 wdt:P31 ?instanceOf .
  OPTIONAL {
    ?instanceOf rdfs:label ?instanceOfLabel .
    FILTER ( LANG( ?instanceOfLabel ) = "en" )
  }
  wd:Q18032040 wdt:P279 ?subclassOf .
  OPTIONAL {
    ?subclassOf rdfs:label ?subclassOfLabel .
    FILTER ( LANG( ?subclassOfLabel ) = "en" )
  }
  wd:Q18032040 wdt:P684 ?ortholog .
  OPTIONAL {
    ?ortholog rdfs:label ?orthologLabel .
    FILTER ( LANG( ?orthologLabel ) = "en" )
  }
  wd:Q18032040 wdt:P703 ?taxon .
  OPTIONAL {
    ?taxon rdfs:label ?taxonLabel .
    FILTER ( LANG( ?taxonLabel ) = "en" )
  }
  wd:Q18032040 wdt:P644 ?genomicStart .
  wd:Q18032040 wdt:P645 ?genomicEnd .
  wd:Q18032040 wdt:P2548 ?strandOrientation .
  OPTIONAL {
    ?strandOrientation rdfs:label ?strandOrientationLabel .
    FILTER ( LANG( ?strandOrientationLabel ) = "en" )
  }
  wd:Q18032040 wdt:P5572 ?expressedIn .
  OPTIONAL {
    ?expressedIn rdfs:label ?expressedInLabel .
    FILTER ( LANG( ?expressedInLabel ) = "en" )
  }
  wd:Q18032040 wdt:P688 ?encodes .
  OPTIONAL {
    ?encodes rdfs:label ?encodesLabel .
    FILTER ( LANG( ?encodesLabel ) = "en" )
  }
  wd:Q18032040 wdt:P1057 ?chromosome .
  OPTIONAL {
    ?chromosome rdfs:label ?chromosomeLabel .
    FILTER ( LANG( ?chromosomeLabel ) = "en" )
  }
}

Got 120 rows and 18 columns, showing the first 5 and last 5 columns and the first 5 and last 5 rows below:
| ?instanceOf     | ?instanceOfLabel | ?subclassOf                        | ?subclassOfLabel    | ?ortholog              | ... | ?expressedInLabel       | ?encodes                                   | ?encodesLabel               | ?chromosome                      | ?chromosomeLabel    |
| --------------- | ---------------- | ---------------------------------- | ------------------- | ---------------------- | --- | ----------------------- | ------------------------------------------ | --------------------------- | -------------------------------- | ------------------- |
| gene (wd:Q7187) | gene             | protein-coding gene (wd:Q20747295) | protein-coding gene | Tnfaip1 (wd:Q18255043) | ... | smooth muscle tissue    | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 | human chromosome 17 (wd:Q220677) | human chromosome 17 |
| gene (wd:Q7187) | gene             | protein-coding gene (wd:Q20747295) | protein-coding gene | Tnfaip1 (wd:Q18255043) | ... | upper lobe of left lung | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 | human chromosome 17 (wd:Q220677) | human chromosome 17 |
| gene (wd:Q7187) | gene             | protein-coding gene (wd:Q20747295) | protein-coding gene | Tnfaip1 (wd:Q18255043) | ... | body of pancreas        | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 | human chromosome 17 (wd:Q220677) | human chromosome 17 |
| gene (wd:Q7187) | gene             | protein-coding gene (wd:Q20747295) | protein-coding gene | Tnfaip1 (wd:Q18255043) | ... | right lung              | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 | human chromosome 17 (wd:Q220677) | human chromosome 17 |
| gene (wd:Q7187) | gene             | protein-coding gene (wd:Q20747295) | protein-coding gene | Tnfaip1 (wd:Q18255043) | ... | gallbladder             | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 | human chromosome 17 (wd:Q220677) | human chromosome 17 |
| ...             | ...              | ...                                | ...                 | ...                    | ... | ...                     | ...                                        | ...                         | ...                              | ...                 |
| gene (wd:Q7187) | gene             | protein-coding gene (wd:Q20747295) | protein-coding gene | tnfaip1 (wd:Q29747183) | ... | right lobe of liver     | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 | human chromosome 17 (wd:Q220677) | human chromosome 17 |
| gene (wd:Q7187) | gene             | protein-coding gene (wd:Q20747295) | protein-coding gene | tnfaip1 (wd:Q29747183) | ... | islet of Langerhans     | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 | human chromosome 17 (wd:Q220677) | human chromosome 17 |
| gene (wd:Q7187) | gene             | protein-coding gene (wd:Q20747295) | protein-coding gene | tnfaip1 (wd:Q29747183) | ... | hair follicle           | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 | human chromosome 17 (wd:Q220677) | human chromosome 17 |
| gene (wd:Q7187) | gene             | protein-coding gene (wd:Q20747295) | protein-coding gene | tnfaip1 (wd:Q29747183) | ... | left ventricle          | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 | human chromosome 17 (wd:Q220677) | human chromosome 17 |
| gene (wd:Q7187) | gene             | protein-coding gene (wd:Q20747295) | protein-coding gene | tnfaip1 (wd:Q29747183) | ... | spleen                  | TNF alpha induced protein 1 (wd:Q21116711) | TNF alpha induced protein 1 | human chromosome 17 (wd:Q220677) | human chromosome 17 |

The query returned multiple rows with the same information, which suggests that there might be redundant data or multiple sources of the same information. To simplify the answer, I will filter out the duplicates and provide a concise response.

Let's refine the query to ensure we get unique values for each property and include optional labels for better readability.

Here is the refined SPARQL query:

```sparql
SELECT DISTINCT ?instanceOf ?instanceOfLabel ?subclassOf ?subclassOfLabel ?ortholog ?orthologLabel ?taxon ?taxonLabel ?genomicStart ?genomicEnd ?strandOrientation ?strandOrientationLabel ?expressedIn ?expressedInLabel ?encodes ?encodesLabel ?chromosome ?chromosomeLabel
WHERE {
  wd:Q18032040 wdt:P31 ?instanceOf .
  OPTIONAL { ?instanceOf rdfs:label ?instanceOfLabel . FILTER(LANG(?instanceOfLabel) = "en") }
  wd:Q18032040 wdt:P279 ?subclassOf .
  OPTIONAL { ?subclassOf rdfs:label ?subclassOfLabel . FILTER(LANG(?subclassOfLabel) = "en") }
  wd:Q18032040 wdt:P684 ?ortholog .
  OPTIONAL { ?ortholog rdfs:label ?orthologLabel . FILTER(LANG(?orthologLabel) = "en") }
  wd:Q18032040 wdt:P703 ?taxon .
  OPTIONAL { ?taxon rdfs:label ?taxonLabel . FILTER(LANG(?taxonLabel) = "en") }
  wd:Q18032040 wdt:P644 ?genomicStart .
  wd:Q18032040 wdt:P645 ?genomicEnd .
  wd:Q18032040 wdt:P2548 ?strandOrientation .
  OPTIONAL { ?strandOrientation rdfs:label ?strandOrientationLabel . FILTER(LANG(?strandOrientationLabel) = "en") }
  wd:Q18032040 wdt:P5572 ?expressedIn .
  OPTIONAL { ?expressedIn rdfs:label ?expressedInLabel . FILTER(LANG(?expressedInLabel) = "en") }
  wd:Q18032040 wdt:P688 ?encodes .
  OPTIONAL { ?encodes rdfs:label ?encodesLabel . FILTER(LANG(?encodesLabel) = "en") }
  wd:Q18032040 wdt:P1057 ?chromosome .
  OPTIONAL { ?chromosome rdfs:label ?chromosomeLabel . FILTER(LANG(?chromosomeLabel) = "en") }
}
```

Let's execute this refined query.

<tool_call>
{"name": "execute", "arguments": {"kg": "wikidata", "sparql": "SELECT DISTINCT ?instanceOf ?instanceOfLabel ?subclassOf ?subclassOfLabel ?ortholog ?orthologLabel ?taxon ?taxonLabel ?genomicStart ?genomicEnd ?strandOrientation ?strandOrientationLabel ?expressedIn ?expressedInLabel ?encodes ?encodesLabel ?chromosome ?chromosomeLabel WHERE { wd:Q18032040 wdt:P31 ?instanceOf . OPTIONAL { ?instanceOf rdfs:label ?instanceOfLabel . FILTER(LANG(?instanceOfLabel) = \\"en\\") } wd:Q18032040 wdt:P279 ?subclassOf . OPTIONAL { ?subclassOf rdfs:label ?subclassOfLabel . FILTER(LANG(?subclassOfLabel) = \\"en\\") } wd:Q18032040 wdt:P684 ?ortholog . OPTIONAL { ?ortholog rdfs:label ?orthologLabel . FILTER(LANG(?orthologLabel) = \\"en\\") } wd:Q18032040 wdt:P703 ?taxon . OPTIONAL { ?taxon rdfs:label ?taxonLabel . FILTER(LANG(?taxonLabel) = \\"en\\") } wd:Q18032040 wdt:P644 ?genomicStart . wd:Q18032040 wdt:P645 ?genomicEnd . wd:Q18032040 wdt:P2548 ?strandOrientation . OPTIONAL { ?strandOrientation rdfs:label ?strandOrientationLabel . FILTER(LANG(?strandOrientationLabel) = \\"en\\") } wd:Q18032040 wdt:P5572 ?expressedIn . OPTIONAL { ?expressedIn rdfs:label ?expressedInLabel . FILTER(LANG(?expressedInLabel) = \\"en\\") } wd:Q18032040 wdt:P688 ?encodes . OPTIONAL { ?encodes rdfs:label ?encodesLabel . FILTER(LANG(?encodesLabel) = \\"en\\") } wd:Q18032040 wdt:P1057 ?chromosome . OPTIONAL { ?chromosome rdfs:label ?chromosomeLabel . FILTER(LANG(?chromosomeLabel) = \\"en\\") } }"}}
</tool_call>

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

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

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] What is TNFAIP1 ?

[Assistant]
To answer the question about what TNFAIP1 is, we need to identify the entity corresponding to TNFAIP1 and then gather relevant information about it. Let's start by searching for the entity TNFAIP1 in the knowledge graph.

I will use the `search_entity` function to find the entity.

[Tool]
Top 10 entity alternatives:
1. TNFAIP1, also known as B61, B12, TNF alpha induced protein 1, hBACURD2, BTBD34 (wd:Q18032040): protein-coding gene in the species Homo sapiens
2. TNF alpha induced protein 1, also known as Protein B12, Tumor necrosis factor, alpha-induced protein 1, endothelial, tumor necrosis factor, alpha-induced protein 1 (endothelial), tumor necrosis factor, alpha induced protein 1, TNFAIP1 (wd:Q21116711): mammalian protein found in Homo sapiens
3. TNF alpha induced protein 1, also known as BTB/POZ domain-containing adapte...iated RhoA degradation protein 2, BTB/POZ domain-containing protein TNFAIP1, Tnfaip1, tumor necrosis factor, alpha induced protein 1, tumor necrosis factor, alpha-induced protein 1 (endothelial) (wd:Q28561340): mammalian protein found in Rattus norvegicus
4. Tnfaip1, also known as Bacurd2, Edp-1, Edp1, Tnfip1, tumor necrosis factor, alpha-induced protein 1 (endothelial) (wd:Q18255043): protein-coding gene in the species Mus musculus
5. tnfaip1, also known as zgc:55302, wu:fi06b02, tumor necrosis factor, alpha-induced protein 1 (endothelial) (wd:Q29747183): protein-coding gene in the species Danio rerio
6. TNFAIP1 (wd:Q24590424): gene of the species Macaca nemestrina
7. Tnfaip1, also known as TNF alpha induced protein 1, Edp1 (wd:Q24361388): protein-coding gene in the species Rattus norvegicus
8. Tumor necrosis factor, alpha-induced protein 1 (endothelial), also known as Tnfaip1, tumor necrosis factor induced protein 1, Tumor necrosis factor, alpha-induced protein 1, endothelial, BTB/POZ domain-containing protein TNFAIP1, BTB/POZ domain-containing adapte...iated RhoA degradation protein 2 (wd:Q21496129): mammalian protein found in Mus musculus
9. Potassium channel tetramerization domain containing 13, also known as BTB/POZ domain-containing adapte...iated RhoA degradation protein 1, BTB/POZ domain-containing protein KCTD13, CTD-2574D22.4, KCTD13, polymerase delta-interacting protein 1 (wd:Q21112778): mammalian protein found in Homo sapiens
10. Estrogen is a novel regulator of Tnfaip1 in mouse hippocampus (wd:Q45741790): scientific article published on 11 April 2014

[Assistant]
From the search results, the most relevant entity for TNFAIP1 is `wd:Q18032040`, which is described as a protein-coding gene in the species Homo sapiens.

Next, I will list the triples associated with this entity to gather more information about it.

[Tool]
Got 164 rows and 3 columns, showing the first 10 rows below:
| subject                | property                       | object                                     |
| ---------------------- | ------------------------------ | ------------------------------------------ |
| TNFAIP1 (wd:Q18032040) | instance of (wdt:P31)          | gene (wd:Q7187)                            |
| TNFAIP1 (wd:Q18032040) | subclass of (wdt:P279)         | protein-coding gene (wd:Q20747295)         |
| TNFAIP1 (wd:Q18032040) | ortholog (wdt:P684)            | Tnfaip1 (wd:Q18255043)                     |
| TNFAIP1 (wd:Q18032040) | found in taxon (wdt:P703)      | Homo sapiens (wd:Q15978631)                |
| TNFAIP1 (wd:Q18032040) | genomic start (wdt:P644)       | 26662628                                   |
| TNFAIP1 (wd:Q18032040) | genomic end (wdt:P645)         | 26674035                                   |
| TNFAIP1 (wd:Q18032040) | strand orientation (wdt:P2548) | forward strand (wd:Q22809680)              |
| TNFAIP1 (wd:Q18032040) | expressed in (wdt:P5572)       | spleen (wd:Q9371)                          |
| TNFAIP1 (wd:Q18032040) | encodes (wdt:P688)             | TNF alpha induced protein 1 (wd:Q21116711) |
| TNFAIP1 (wd:Q18032040) | chromosome (wdt:P1057)         | human chromosome 17 (wd:Q220677)           |
| ...                    | ...                            | ...                                        |

[Assistant]
Based on the triples listed, we can gather the following information about TNFAIP1:

- **Instance of**: Gene (wd:Q7187)
- **Subclass of**: Protein-coding gene (wd:Q20747295)
- **Ortholog**: Tnfaip1 (wd:Q18255043) in Mus musculus
- **Found in taxon**: Homo sapiens (wd:Q15978631)
- **Genomic start**: 26,662,628
- **Genomic end**: 26,674,035
- **Strand orientation**: Forward strand (wd:Q22809680)
- **Expressed in**: Spleen (wd:Q9371)
- **Encodes**: TNF alpha induced protein 1 (wd:Q21116711)
- **Chromosome**: Human chromosome 17 (wd:Q220677)

Now, let's construct a SPARQL query to retrieve this information and provide a clear answer to the user.

[Tool]
Got 120 rows and 10 columns, showing the first 5 and last 5 rows below:
| ?instanceOf     | ?subclassOf                        | ?ortholog              | ?taxon                      | ?genomicStart | ?genomicEnd | ?strandOrientation            | ?expressedIn                           | ?encodes                                   | ?chromosome                      |
| --------------- | ---------------------------------- | ---------------------- | --------------------------- | ------------- | ----------- | ----------------------------- | -------------------------------------- | ------------------------------------------ | -------------------------------- |
| gene (wd:Q7187) | protein-coding gene (wd:Q20747295) | Tnfaip1 (wd:Q18255043) | Homo sapiens (wd:Q15978631) | 26662628      | 26674035    | forward strand (wd:Q22809680) | smooth muscle tissue (wd:Q208453)      | TNF alpha induced protein 1 (wd:Q21116711) | human chromosome 17 (wd:Q220677) |
| gene (wd:Q7187) | protein-coding gene (wd:Q20747295) | Tnfaip1 (wd:Q18255043) | Homo sapiens (wd:Q15978631) | 28335602      | 26674035    | forward strand (wd:Q22809680) | smooth muscle tissue (wd:Q208453)      | TNF alpha induced protein 1 (wd:Q21116711) | human chromosome 17 (wd:Q220677) |
| gene (wd:Q7187) | protein-coding gene (wd:Q20747295) | Tnfaip1 (wd:Q18255043) | Homo sapiens (wd:Q15978631) | 26662628      | 28347009    | forward strand (wd:Q22809680) | smooth muscle tissue (wd:Q208453)      | TNF alpha induced protein 1 (wd:Q21116711) | human chromosome 17 (wd:Q220677) |
| gene (wd:Q7187) | protein-coding gene (wd:Q20747295) | Tnfaip1 (wd:Q18255043) | Homo sapiens (wd:Q15978631) | 28335602      | 28347009    | forward strand (wd:Q22809680) | smooth muscle tissue (wd:Q208453)      | TNF alpha induced protein 1 (wd:Q21116711) | human chromosome 17 (wd:Q220677) |
| gene (wd:Q7187) | protein-coding gene (wd:Q20747295) | Tnfaip1 (wd:Q18255043) | Homo sapiens (wd:Q15978631) | 26662628      | 26674035    | forward strand (wd:Q22809680) | upper lobe of left lung (wd:Q28656761) | TNF alpha induced protein 1 (wd:Q21116711) | human chromosome 17 (wd:Q220677) |
| ...             | ...                                | ...                    | ...                         | ...           | ...         | ...                           | ...                                    | ...                                        | ...                              |
| gene (wd:Q7187) | protein-coding gene (wd:Q20747295) | tnfaip1 (wd:Q29747183) | Homo sapiens (wd:Q15978631) | 28335602      | 28347009    | forward strand (wd:Q22809680) | left ventricle (wd:Q876089)            | TNF alpha induced protein 1 (wd:Q21116711) | human chromosome 17 (wd:Q220677) |
| gene (wd:Q7187) | protein-coding gene (wd:Q20747295) | tnfaip1 (wd:Q29747183) | Homo sapiens (wd:Q15978631) | 26662628      | 26674035    | forward strand (wd:Q22809680) | spleen (wd:Q9371)                      | TNF alpha induced protein 1 (wd:Q21116711) | human chromosome 17 (wd:Q220677) |
| gene (wd:Q7187) | protein-coding gene (wd:Q20747295) | tnfaip1 (wd:Q29747183) | Homo sapiens (wd:Q15978631) | 28335602      | 26674035    | forward strand (wd:Q22809680) | spleen (wd:Q9371)                      | TNF alpha induced protein 1 (wd:Q21116711) | human chromosome 17 (wd:Q220677) |
| gene (wd:Q7187) | protein-coding gene (wd:Q20747295) | tnfaip1 (wd:Q29747183) | Homo sapiens (wd:Q15978631) | 26662628      | 28347009    | forward strand (wd:Q22809680) | spleen (wd:Q9371)                      | TNF alpha induced protein 1 (wd:Q21116711) | human chromosome 17 (wd:Q220677) |
| gene (wd:Q7187) | protein-coding gene (wd:Q20747295) | tnfaip1 (wd:Q29747183) | Homo sapiens (wd:Q15978631) | 28335602      | 28347009    | forward strand (wd:Q22809680) | spleen (wd:Q9371)                      | TNF alpha induced protein 1 (wd:Q21116711) | human chromosome 17 (wd:Q220677) |

[Assistant]
The query returned multiple rows with the same information, which suggests that there might be redundant data or multiple sources of the same information. To simplify the answer, I will filter out the duplicates and provide a concise response.

Let's refine the query to ensure we get unique values for each property and include optional labels for better readability.

Here is the refined SPARQL query:

```sparql
SELECT DISTINCT ?instanceOf ?instanceOfLabel ?subclassOf ?subclassOfLabel ?ortholog ?orthologLabel ?taxon ?taxonLabel ?genomicStart ?genomicEnd ?strandOrientation ?strandOrientationLabel ?expressedIn ?expressedInLabel ?encodes ?encodesLabel ?chromosome ?chromosomeLabel
WHERE {
  wd:Q18032040 wdt:P31 ?instanceOf .
  OPTIONAL { ?instanceOf rdfs:label ?instanceOfLabel . FILTER(LANG(?instanceOfLabel) = "en") }
  wd:Q18032040 wdt:P279 ?subclassOf .
  OPTIONAL { ?subclassOf rdfs:label ?subclassOfLabel . FILTER(LANG(?subclassOfLabel) = "en") }
  wd:Q18032040 wdt:P684 ?ortholog .
  OPTIONAL { ?ortholog rdfs:label ?orthologLabel . FILTER(LANG(?orthologLabel) = "en") }
  wd:Q18032040 wdt:P703 ?taxon .
  OPTIONAL { ?taxon rdfs:label ?taxonLabel . FILTER(LANG(?taxonLabel) = "en") }
  wd:Q18032040 wdt:P644 ?genomicStart .
  wd:Q18032040 wdt:P645 ?genomicEnd .
  wd:Q18032040 wdt:P2548 ?strandOrientation .
  OPTIONAL { ?strandOrientation rdfs:label ?strandOrientationLabel . FILTER(LANG(?strandOrientationLabel) = "en") }
  wd:Q18032040 wdt:P5572 ?expressedIn .
  OPTIONAL { ?expressedIn rdfs:label ?expressedInLabel . FILTER(LANG(?expressedInLabel) = "en") }
  wd:Q18032040 wdt:P688 ?encodes .
  OPTIONAL { ?encodes rdfs:label ?encodesLabel . FILTER(LANG(?encodesLabel) = "en") }
  wd:Q18032040 wdt:P1057 ?chromosome .
  OPTIONAL { ?chromosome rdfs:label ?chromosomeLabel . FILTER(LANG(?chromosomeLabel) = "en") }
}
```

Let's execute this refined query.

<tool_call>
{"name": "execute", "arguments": {"kg": "wikidata", "sparql": "SELECT DISTINCT ?instanceOf ?instanceOfLabel ?subclassOf ?subclassOfLabel ?ortholog ?orthologLabel ?taxon ?taxonLabel ?genomicStart ?genomicEnd ?strandOrientation ?strandOrientationLabel ?expressedIn ?expressedInLabel ?encodes ?encodesLabel ?chromosome ?chromosomeLabel WHERE { wd:Q18032040 wdt:P31 ?instanceOf . OPTIONAL { ?instanceOf rdfs:label ?instanceOfLabel . FILTER(LANG(?instanceOfLabel) = \\"en\\") } wd:Q18032040 wdt:P279 ?subclassOf . OPTIONAL { ?subclassOf rdfs:label ?subclassOfLabel . FILTER(LANG(?subclassOfLabel) = \\"en\\") } wd:Q18032040 wdt:P684 ?ortholog . OPTIONAL { ?ortholog rdfs:label ?orthologLabel . FILTER(LANG(?orthologLabel) = \\"en\\") } wd:Q18032040 wdt:P703 ?taxon . OPTIONAL { ?taxon rdfs:label ?taxonLabel . FILTER(LANG(?taxonLabel) = \\"en\\") } wd:Q18032040 wdt:P644 ?genomicStart . wd:Q18032040 wdt:P645 ?genomicEnd . wd:Q18032040 wdt:P2548 ?strandOrientation . OPTIONAL { ?strandOrientation rdfs:label ?strandOrientationLabel . FILTER(LANG(?strandOrientationLabel) = \\"en\\") } wd:Q18032040 wdt:P5572 ?expressedIn . OPTIONAL { ?expressedIn rdfs:label ?expressedInLabel . FILTER(LANG(?expressedInLabel) = \\"en\\") } wd:Q18032040 wdt:P688 ?encodes . OPTIONAL { ?encodes rdfs:label ?encodesLabel . FILTER(LANG(?encodesLabel) = \\"en\\") } wd:Q18032040 wdt:P1057 ?chromosome . OPTIONAL { ?chromosome rdfs:label ?chromosomeLabel . FILTER(LANG(?chromosomeLabel) = \\"en\\") } }"}}
</tool_call>


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( ?borderingCountry ) AS ?count ) WHERE {
  wd:Q408 wdt:P47 ?borderingCountry .
}

Got 1 row and 1 column:
| ?count |
| ------ |
| 7      |

Australia shares a border with 7 countries.

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

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

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] With how many countries does Australia share a border?

[Assistant]
To answer the question about how many countries Australia shares a border with, we need to identify the relevant properties and entities in the knowledge graph. Specifically, we need to find the property that indicates bordering countries and then count the number of such countries for Australia.

### Step 1: Identify the Entity for Australia
First, let's find the entity for Australia in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Australia, also known as AU, The Commonwealth of Australia, Stria, Straya, Stralia (wd:Q408): country in Oceania
2. Australian continent, also known as New Holland, Sahul, Nova Hollandia, Meganesia, Australinea (wd:Q3960): continent in the Earth's Southern Hemisphere
3. Australia (wd:Q275180): 2008 film by Baz Luhrmann
4. Australia (wd:Q205546): 1989 film by Jean-Jacques Andrien
5. Australia (wd:Q4823546): 1996 single by Manic Street Preachers
6. Australia (wd:Q781244): board game
7. Australia (wd:Q16835533): Racehorse
8. Australia, also known as Australia (KA–5) (wd:Q4823541): yacht that raced in the Americas Cup
9. Australia (wd:Q4823543): 2000 album by Howie Day
10. Australia (wd:Q25907685): albedo feature on the planet Mercury

[Assistant]
The correct entity for Australia is `wd:Q408`, which represents the country in Oceania.

### Step 2: Identify the Property for Bordering Countries
Next, we need to find the property that indicates bordering countries. We can search for properties related to borders or neighboring countries.

Let's search for the property related to bordering countries.

[Tool]
Top 10 property alternatives:
1. has boundary, also known as has border, has 2D boundary, frontier, delimited by, boundary (wd:P4777 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): element that's on the two dimensional border that surrounds the subject; the limit of an entity
2. shares border with, also known as shares borders with, next to, bordered by, border, borders clockwise (wd:P47 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): countries or administrative subdivisions, of equal level, that t...rders, either by land or water. A single common point is enough.
3. territory overlaps, also known as partially overlaps, partially coincident with, partly coincident with, partly located in territorial entity, partly overlaps (wd:P3179 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): part or all of the area associated with (this) entity overlaps part or all of the area associated with that entity, partially coincident with
4. located in or next to body of water, also known as located on body of water, ocean, on bay, next to lake, on coast of (wd:P206 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): body of water on or next to which a place is located, location
5. contains the administrative territorial entity, also known as has shires, has rural cities, has regions, has municipalities, has members (wd:P150 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): (list of) direct subdivisions of an administrative territorial entity, has part(s)
6. gained territory from, also known as receives territory from, territory from (wd:P7903 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): (qualifier) territorial entity from which the subject item gained territory as a result of the event in the statement
7. territory claimed by, also known as claimed by, sovereignty claimed by, territorial claim by (wd:P1336 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): administrative divisions that claim control of a given area
8. coextensive with, also known as territory identical with, coterminous with, coincident territory, same territory as (wd:P3403 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): this item has the same boundary as the target item; area associa...s) entity is identical with the area associated with that entity
9. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
10. diplomatic relation, also known as political relation, political relations, foreign relations, foreign relation, diplomatic relations (wd:P530 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): diplomatic relations of the country

[Assistant]
The property `wd:P47` (shares border with) is the most appropriate for our purpose. It indicates countries or administrative subdivisions that share a border, either by land or water.

### Step 3: Build and Execute the SPARQL Query
Now, we can build a SPARQL query to count the number of countries that share a border with Australia.

Let's execute the query.

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 7      |

[Assistant]
The SPARQL query returned a count of 7 countries that share a border with Australia.

### Final Answer
Australia shares a border with 7 countries.

Let's provide the final answer using the `answer` function.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?deathDate WHERE {
  wd:Q43347 wdt:P570 ?deathDate .
}

Rumi died on December 17, 1273.

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

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

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] When did Rumi die?

[Assistant] To answer the question "When did Rumi die?", we need to identify the entity for Rumi and then find the property that represents the date of death. Let's start by searching for the entity Rumi in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Rumi, also known as Mawlana, Mawlana Rumi, Mawlawi, Jallal ed-Din Muhammad Balkhy, Jelaluddin Rumi (wd:Q43347): Sufi and Persian poet, philosopher, literary, writer, mystic, ʿālim, poet
2. Rum, also known as Arwām, Rūm, Rūmī (wd:Q3943294): autonym
3. Rūmī (wd:Q25908371): crater on planet Mercury
4. POGLUT1, also known as MDSRP, Rumi, protein O-glucosyltransferase 1, MDS010, LGMDR21 (wd:Q18042749): protein-coding gene in the species Homo sapiens
5. Lanmewko, also known as Rumi (wd:Q109342876): Taiwanese Virtual YouTuber, Twitch streamer, VTuber
6. Rumi Rumiyuq (wd:Q25104391): mountain in Bolivia
7. Rumi, also known as るみ (wd:Q97655647): Japanese female given name (るみ)
8. Rumi (wd:Q11241326): Japanese master of ceremonies, rapper
9. Rumi (wd:Q11578550): Japanese actress, actor
10. Rumi (wd:Q19949377)

[Assistant] From the search results, the first option "Rumi, also known as Mawlana, Mawlana Rumi, Mawlawi, Jallal ed-Din Muhammad Balkhy, Jelaluddin Rumi (wd:Q43347)" is the correct entity for the Sufi poet Rumi. Now, let's find the property that represents the date of death for this entity.

[Tool]
Top 10 property alternatives:
1. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as p/wdt): dissolved, abolished or demolished date, date on which the subject died, end time
2. place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as p/wdt): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location
3. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception
4. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
5. place of burial, also known as interment, interment location, interment place, interred at, location of burial (wd:P119 as p/wdt): location of grave, resting place, place of ash-scattering, etc. ...veral places: e.g., re-burials, parts of body buried separately., location
6. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
7. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as p/wdt): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
8. pseudonym, also known as nom de plume, nom-de-plume, nome de guerre, pen name, ring name (wd:P742 as p/wdt): alias used by someone (for nickname use P1449), name
9. significant person, also known as significant people, sigper, sign. person, relevant person, relevant people (wd:P3342 as p/wdt): person linked to the item in any possible way
10. Oxford Reference overview ID, also known as OR ID, ORO ID, Overview Oxford Reference ID, Overview Reference Oxford ID, Oxford Reference ID (wd:P12582 as p/wdt): identifier for an overview page for a topic in the Oxford Reference

No other items found

[Assistant]
The top property alternative for "date of death" is `wd:P570`. We can now construct a SPARQL query to retrieve the date of death for Rumi (wd:Q43347).

Let's execute the SPARQL query:

```sparql
SELECT ?deathDate WHERE {
  wd:Q43347 wdt:P570 ?deathDate .
}
```

This query will return the date of death for Rumi.

[Tool]
Got 1 row and 1 column:
| ?deathDate           |
| -------------------- |
| 1273-12-17T00:00:00Z |

[Assistant]
The SPARQL query returned the date of death for Rumi as `1273-12-17T00:00:00Z`. This is the date when Rumi died.

Now, let's format the answer and provide it to the user.

Rumi died on December 17, 1273.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?animal ?operation WHERE {
  ?operation wdt:P710 wd:Q625657 .
  ?animal wdt:P1344 ?operation .
  ?animal wdt:P31 wd:Q729 .
}

We could not find a specific animal that participated in a military operation with the Australian Defence Force in the knowledge graph. The data might be incomplete or structured differently, making it difficult to find such information.

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

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

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] Which animal participated in a military operation with the Australian Defence Force?

[Assistant]
To answer this question, we need to identify the animal that has participated in a military operation with the Australian Defence Force. We can break this down into the following steps:

1. Identify the Australian Defence Force (ADF) in the knowledge graph.
2. Find military operations involving the ADF.
3. Identify animals that participated in those operations.

Let's start by searching for the Australian Defence Force in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Australian Defence Force, also known as ADF, military of Australia, mil Australia, armed forces of Australia, Australian Armed Forces (wd:Q625657): combined military forces of Australia
2. Australian Defence Force Academy, also known as ADFA (wd:Q371370): staff college
3. Australian Defence Force ranks (wd:Q3113099): ranks of officers and enlisted personnel
4. Australian Defence Force Ensign (wd:Q12409908): flag of the Australian Defence Force
5. Australian Defence Force Cadets, also known as ADFC, Australian Service Cadet Scheme (wd:Q4824077): youth organization operated by the Australian military
6. Australian Defence Force Journal...of Australian profession of arms, also known as Australian defence force journal (wd:Q15746503): journal
7. Australian Defence Force Parachuting School, also known as Parachute Training School, PTS (wd:Q7133934): unit of the Australian Army
8. Australian Defence Force Investigative Service (wd:Q4824075): investigative arm of Australian Defence Force Police
9. Australian Defence Force Helicopter School (wd:Q48817728): military unit
10. Flags of the Australian Defence Force (wd:Q55611758)

[Assistant]
The top result for "Australian Defence Force" is `wd:Q625657`. Now, let's find military operations involving the Australian Defence Force. We can search for properties related to military operations and then look for specific operations involving the ADF.

First, let's search for properties related to military operations.

[Tool]
Top 10 property alternatives:
1. military unit, also known as member of military unit, unit (wd:P7779 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): smallest military unit that a person is/was in, member of
2. conflict, also known as war, theatre (military), theater (military), participated in conflict, military conflict (wd:P607 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): battles, wars or other military engagements in which the person or item participated, participant in
3. target, also known as attack target, military target, target of attack (wd:P533 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): target of an attack or military operation, object of occurrence
4. military branch, also known as unit branch, service branch, formation, branch (wd:P241 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): branch to which this military unit, award, office, or person belongs, e.g. Royal Navy, part of
5. operating area, also known as province served, serves jurisdiction, serves geographic region, serves geographic area, serves area (wd:P2541 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, location
6. military designation, also known as designation, 1962 US military aerospace vehicle designation, USDOD aircraft designation, Tri-Service aerospace vehicle designation, mission design series designation (wd:P798 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): officially assigned designation for a vehicle in military service
7. has use, also known as utility, used in, used for, used as, use (wd:P366 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): main use of the subject (includes current and former usage)
8. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): location where persons or organisations were actively participating in employment, business or other work, significant place, location
9. commanded by, also known as captain, commander, commanding officer (wd:P4791 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): commander of a military unit/army/security service, operation, etc.
10. service entry, also known as commissioned, time of service entry, service introduction, launch date, in service (wd:P729 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): date or point in time on which a piece or class of equipment entered operational service, start time

[Assistant]
The property `wd:P607` (conflict) seems to be the most relevant for finding military operations. Let's use this property to find military operations involving the Australian Defence Force (`wd:Q625657`).

We will execute a SPARQL query to find the military operations in which the Australian Defence Force participated.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that there are no direct links between the Australian Defence Force and military operations using the `wd:P607` property. This might be because the ADF is not directly linked to specific operations, or the data might be structured differently.

Let's try another approach. We can search for properties related to participation in military operations and see if there are any other relevant properties. Additionally, we can look for specific military operations and then check if the ADF was involved in them.

First, let's search for properties related to participation in military operations.

[Tool]
Top 10 property alternatives:
1. conflict, also known as war, theatre (military), theater (military), participated in conflict, military conflict (wd:P607 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): battles, wars or other military engagements in which the person or item participated, participant in
2. military unit, also known as member of military unit, unit (wd:P7779 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): smallest military unit that a person is/was in, member of
3. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
4. military branch, also known as unit branch, service branch, formation, branch (wd:P241 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): branch to which this military unit, award, office, or person belongs, e.g. Royal Navy, part of
5. military designation, also known as designation, 1962 US military aerospace vehicle designation, USDOD aircraft designation, Tri-Service aerospace vehicle designation, mission design series designation (wd:P798 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): officially assigned designation for a vehicle in military service
6. target, also known as attack target, military target, target of attack (wd:P533 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): target of an attack or military operation, object of occurrence
7. has use, also known as utility, used in, used for, used as, use (wd:P366 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): main use of the subject (includes current and former usage)
8. organization directed by the office or position, also known as leader of, head of, leads, organization directed by, organization directed from the office (wd:P2389 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): organization which is directed by this office
9. operating area, also known as province served, serves jurisdiction, serves geographic region, serves geographic area, serves area (wd:P2541 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, location
10. service entry, also known as commissioned, time of service entry, service introduction, launch date, in service (wd:P729 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): date or point in time on which a piece or class of equipment entered operational service, start time

[Assistant]
The property `wd:P1344` (participant in) seems to be a good candidate for finding military operations in which the Australian Defence Force participated. Let's use this property to find the military operations involving the ADF.

We will execute a SPARQL query to find the military operations in which the Australian Defence Force participated using the `wd:P1344` property.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the Australian Defence Force is not directly linked to any military operations using the `wd:P1344` property either. This suggests that the data might be structured differently, or the ADF might be involved in operations through its sub-units or specific units.

Let's try another approach. We can search for specific military operations and then check if the ADF or any of its units were involved in those operations. We can also look for animals that participated in military operations and then check if they were associated with the ADF.

First, let's search for properties related to animals in military operations.

[Tool]
Top 10 property alternatives:
1. military unit, also known as member of military unit, unit (wd:P7779 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): smallest military unit that a person is/was in, member of
2. target, also known as attack target, military target, target of attack (wd:P533 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): target of an attack or military operation, object of occurrence
3. animal breed, also known as breed, sheep breed, horse breed, goat breed, duck breed (wd:P4743 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): subject item belongs to a specific group of domestic animals, generally given by association
4. taxon especially protected in area, also known as animal protected, plant protected, protected animal, protected plant, protected species (wd:P6569 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): species and other taxa especially protected in a area, whether s...ountries/subnational entities: use red lists properties instead.
5. has pet, also known as pet, keeps pet, owns pet (wd:P1429 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): pet that a person owns
6. conflict, also known as war, theatre (military), theater (military), participated in conflict, military conflict (wd:P607 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): battles, wars or other military engagements in which the person or item participated, participant in
7. item operated, also known as fleet, item used, operated, operates, operator of (wd:P121 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): equipment, installation or service operated by the subject, uses
8. military designation, also known as designation, 1962 US military aerospace vehicle designation, USDOD aircraft designation, Tri-Service aerospace vehicle designation, mission design series designation (wd:P798 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): officially assigned designation for a vehicle in military service
9. military branch, also known as unit branch, service branch, formation, branch (wd:P241 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): branch to which this military unit, award, office, or person belongs, e.g. Royal Navy, part of
10. species kept, also known as plant taxon in botanical garden, resident animals, plant taxon in collection, resident animal species, rose varity in rose garden (wd:P1990 as pqn/prn/psn/psv/pqv/p/wdt/ps/pq/pr/wdtn/prv): taxa, preferably species, present at a zoo, botanical garden, co...stitution. NOT specific animals, not for any geographic location, has part(s) of the class

[Assistant]
None of the properties listed seem to directly relate to animals participating in military operations. However, we can try to find specific animals that have been involved in military operations and then check if they were associated with the Australian Defence Force.

Let's search for animals that have participated in military operations. We can use the `wd:P607` (conflict) property to find animals that have been involved in military conflicts.

We will execute a SPARQL query to find animals that have participated in military operations.

[Tool]
SPARQL execution failed:
failed to parse input: parse error at position 78 for token } with content '}' (the input most likely does not follow the grammar)

[Assistant]
It looks like there was a syntax error in the SPARQL query. Let's correct the query and try again. We need to ensure that the query is properly formatted.

Here is the corrected query to find animals that have participated in military operations:

```sparql
SELECT ?animal WHERE {
  ?animal wdt:P607 ?operation .
  ?animal wdt:P31 wd:Q729 .
}
```

This query will find animals (`?animal`) that have participated in military operations (`?operation`) and are instances of the animal class (`wd:Q729`).

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that there are no animals in the knowledge graph that are directly linked to military operations using the `wd:P607` property and are instances of the animal class (`wd:Q729`).

Given this, let's try a different approach. We can search for specific animals that are known to have participated in military operations and then check if they were associated with the Australian Defence Force.

Let's search for well-known military animals and see if any of them are associated with the ADF.

We will execute a SPARQL query to find animals that are known to have participated in military operations and then check if they are associated with the ADF.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that there are no animals in the knowledge graph that are directly linked to military operations using the `wd:P1344` property and are instances of the animal class (`wd:Q729`).

Given the lack of direct links, let's try a more specific approach. We can search for individual animals that are known to have participated in military operations and then check if they are associated with the Australian Defence Force.

Let's search for specific animals that are known to have participated in military operations. We can use the `search_entity` function to find such animals.

For example, let's search for "Sergeant Stubby," a famous military dog, and see if there is any information about him being associated with the ADF.

### Step 1: Search for Sergeant Stubby
Let's search for "Sergeant Stubby" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Sergeant Stubby, also known as Stubby (wd:Q687727): military personnel, American war dog
2. sergeant, also known as SGT, Sgt, Sergeant, Sgt. (wd:Q157696): military rank
3. Feldwebel, also known as Sergeant (wd:Q429703): German military rank
4. The Sergeant, also known as Sergeant (wd:Q3227111): 1968 film by John Flynn
5. The Sergeant, also known as Sergeant, The Sergeant (1910 film) (wd:Q27958245): 1910 film by Francis Boggs
6. Sergeant (wd:Q16882981): family name
7. sergeant, also known as kersantti (wd:Q7453376): military rank in Sweden and Finland
8. Sergeant, also known as Sergeant, PA, Sergeant, Pennsylvania (wd:Q22083131): human settlement in United States of America
9. Sergeant (wd:Q4417595): 1988 film by Stanislav Gajduk
10. Sergeant (wd:Q120298478): 2023 Indian film

[Assistant]
The top result for "Sergeant Stubby" is `wd:Q687727`, which is the entity for the famous military dog. Let's check if Sergeant Stubby was associated with the Australian Defence Force.

We will execute a SPARQL query to find if Sergeant Stubby participated in any military operations and if those operations involved the Australian Defence Force.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that Sergeant Stubby, while a famous military dog, was not associated with the Australian Defence Force. This suggests that the specific animal we are looking for might not be as well-known or might be associated with a different military operation.

Let's broaden our search to include any animals that have participated in military operations and then check if they are associated with the Australian Defence Force. We can also look for specific military operations involving the ADF and then check if any animals were involved in those operations.

### Step 1: Find military operations involving the Australian Defence Force
We will execute a SPARQL query to find military operations involving the Australian Defence Force using the `wd:P1344` property.

[Tool]
Got 53 rows and 1 column, showing the first 5 and last 5 rows below:
| ?operation                                      |
| ----------------------------------------------- |
| Emu War (wd:Q14665)                             |
| Cope North (wd:Q29325071)                       |
| Exercise Talisman Sabre 2005 (wd:Q41061291)     |
| Exercise Talisman Sabre 2007 (wd:Q41061297)     |
| Exercise Talisman Sabre 2009 (wd:Q41061301)     |
| ...                                             |
| Exercise Cooperation Spirit 2017 (wd:Q45388821) |
| Exercise Phoenix Spirit 2013 (wd:Q45389964)     |
| Exercise Cooperation Spirit 2012 (wd:Q45390541) |
| Exercise Cooperation Spirit 2011 (wd:Q45390638) |
| Exercise Talisman Sabre (wd:Q5420046)           |

[Assistant]
We have found several military operations involving the Australian Defence Force. Now, let's check if any animals were involved in these operations.

We will execute a SPARQL query to find animals that participated in these operations. We will use the `wd:P1344` property to find participants and then filter for animals using the `wd:P31` property.

### Step 2: Find animals involved in these operations
Let's execute the following SPARQL query:

```sparql
SELECT ?animal ?operation WHERE {
  ?operation wdt:P710 wd:Q625657 .
  ?animal wdt:P1344 ?operation .
  ?animal wdt:P31 wd:Q729 .
}
```

[Tool] Got no rows and 2 columns

[Assistant]
It appears that there are no animals in the knowledge graph that are directly linked to the military operations involving the Australian Defence Force using the `wd:P1344` property and are instances of the animal class (`wd:Q729`).

Given this, it seems that the specific animal we are looking for might not be well-documented in the knowledge graph, or the data might be structured differently.

Since we have exhausted the direct approaches and found no relevant results, we can conclude that there is no clear evidence in the knowledge graph of an animal that participated in a military operation with the Australian Defence Force.

Therefore, we will provide a response indicating that we could not find a satisfactory answer.

### Final Answer
We could not find a specific animal that participated in a military operation with the Australian Defence Force in the knowledge graph. The data might be incomplete or structured differently, making it difficult to find such information.

If you have any additional details or specific operations in mind, please provide them, and we can try to refine the search further.

Thank you for your understanding.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?mainSubject ?mainSubjectLabel WHERE {
  wd:Q636399 wdt:P921 ?mainSubject .
  OPTIONAL {
    ?mainSubject rdfs:label ?mainSubjectLabel .
    FILTER ( LANG( ?mainSubjectLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| ?mainSubject                   | ?mainSubjectLabel |
| ------------------------------ | ----------------- |
| Montreux Casino (wd:Q81085137) | Montreux Casino   |

The main subject addressed in the Deep Purple song "Smoke on the Water" is the Montreux Casino.

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

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

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] What was the main subject addressed in the Deep Purple song “Smoke on the water”?

[Assistant] To answer this question, we need to identify the song "Smoke on the Water" by Deep Purple and then find out what the main subject of the song is. Let's start by searching for the song "Smoke on the Water" by Deep Purple in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Smoke on the Water: a Tribute to Deep Purple (wd:Q3963036): album by various artists
2. Smoke on the Water (wd:Q636399): original song written and composed by Deep Purple; first recorded by Deep Purple
3. Smoke on the Water (wd:Q3435148): compilation album by Deep Purple
4. Smoke on the Water (wd:Q24206648): 2014 television film directed by Dominik Graf
5. Smoke on the Water (wd:Q7545910): 1943 song by Earl Nunn and Zeke Clements
6. Smoke on the Water / Child in Time / Woman from Tokyo, also known as Child in Time, Woman from Tokyo, Smoke on the Water (wd:Q15931285): 1977 3-track single by Deep Purple
7. Smoke on the Water (wd:Q114495787): episode of seaQuest DSV (S3 E3)
8. Smoke on the Water (wd:Q94008225): vocal track by Deep Purple; 1972 studio recording
9. Smoke on the Water (wd:Q94003891): 1973 single by Deep Purple
10. Smoke on the Water (wd:Q93971258): vocal track by Pat Boone; 1996 studio recording; cover version

[Assistant]
From the search results, the most relevant entity is "Smoke on the Water" (wd:Q636399), which is the original song written and composed by Deep Purple. Now, let's find out what the main subject of this song is. We can look for properties related to the song's content or lyrics.

Let's search for properties related to the content or subject of the song "Smoke on the Water".

[Tool]
Top 10 property alternatives:
1. main subject, also known as primary topic, primary subject, primary keyword, plot keyword, of (wd:P921 as p/wdt): primary topic of a work (see also P180: depicts), depicts
2. title, also known as name, titled, page name, original title, known as (wd:P1476 as p/wdt): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, name
3. lyricist, also known as text poet, writer (of song lyrics), words by, text writer, text by (wd:P676 as p/wdt): author of song lyrics, author
4. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as p/wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
5. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
6. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as p/wdt): date or point in time when a work was first published or released, point in time
7. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as p/wdt): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
8. part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as p/wdt): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))
9. composer, also known as music by, written by, songwriter, musical score by, film score by (wd:P86 as p/wdt): person(s) who wrote the music [for lyricist, use "lyrics by" (P676)], creator
10. form of creative work, also known as work type, work form, type of work, artform, type of reference work (wd:P7937 as p/wdt): structure of a creative work

No other items found

[Assistant]
The most relevant property for finding the main subject of the song is `main subject` (wd:P921). Let's use this property to find the main subject of the song "Smoke on the Water".

I will now construct a SPARQL query to retrieve the main subject of the song "Smoke on the Water" (wd:Q636399).

```sparql
SELECT ?mainSubject
WHERE {
  wd:Q636399 wdt:P921 ?mainSubject .
  OPTIONAL { ?mainSubject rdfs:label ?mainSubjectLabel . FILTER(LANG(?mainSubjectLabel) = "en") }
}
```

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| ?mainSubject                   | ?mainSubjectLabel |
| ------------------------------ | ----------------- |
| Montreux Casino (wd:Q81085137) | Montreux Casino   |

[Assistant]
The main subject of the song "Smoke on the Water" by Deep Purple is the Montreux Casino.

Therefore, the answer to the question is:

The main subject addressed in the Deep Purple song "Smoke on the Water" is the Montreux Casino.

Here is the final SPARQL query:

```sparql
SELECT ?mainSubject ?mainSubjectLabel
WHERE {
  wd:Q636399 wdt:P921 ?mainSubject .
  OPTIONAL { ?mainSubject rdfs:label ?mainSubjectLabel . FILTER(LANG(?mainSubjectLabel) = "en") }
}
```

And the answer:

The main subject addressed in the Deep Purple song "Smoke on the Water" is the Montreux Casino.

[Tool] Stopped generation process