PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?property ?object WHERE {
  wd:Q1055 ?property ?object .
  VALUES ?property {
    wdt:P856 wdt:P625 wdt:P131
  }
}

Got 4 rows and 2 columns:
| ?property                                                   | ?object                    |
| ----------------------------------------------------------- | -------------------------- |
| located in the administrative territorial entity (wdt:P131) | Germany (wd:Q183)          |
| coordinate location (wdt:P625)                              | POINT(10.000000 53.550000) |
| official website (wdt:P856)                                 | <https://www.hamburg.com/> |
| official website (wdt:P856)                                 | <https://www.hamburg.de/>  |

The query for Munich (wd:Q1726) returned the expected results, including the official website, coordinates, and parent federated state (Upper Bavaria).

Now, let's execute the same query for Hamburg (wd:Q1055) to ensure consistency.

### Hamburg (wd:Q1055)
```sparql
SELECT ?property ?object
WHERE {
  wd:Q1055 ?property ?object .
  VALUES ?property { wdt:P856 wdt:P625 wdt:P131 }
}
```

Let's execute this query to see the relevant properties for Hamburg.

[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 are the urban municipalities in Germany? For each result, return also its official website, coordinates (if available), and parent federated state.

[Assistant]
To answer this question, I need to identify the relevant entities and properties in the knowledge graph. Let's start by searching for the concept of "urban municipality" in Germany.

I will use the `search` function to look for the entity representing "urban municipality" in the context of Germany.

[Tool]
Top 10 property alternatives:
1. contains the administrative territorial entity, also known as has shires, has rural cities, has regions, has municipalities, has members (wd:P150 as p/wdt): (list of) direct subdivisions of an administrative territorial entity, has part(s)
2. legislative body, also known as aboriginal council, Zila, Tehsil, rural council, representative body (wd:P194 as p/wdt): legislative body governing this entity; political institution wi...ted representatives, such as a parliament/legislature or council
3. urban population (wd:P6343 as p/wdt): number of people living within the territorial entity who live in its urban parts, population
4. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as p/wdt): seat of government of a country, province, state or other type of administrative territorial entity
5. head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as p/wdt): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson
6. basic form of government, also known as form of government, government, type of government (wd:P122 as p/wdt): subject's government
7. population, also known as inhabitants, human population (wd:P1082 as p/wdt): number of people inhabiting the place; number of people of subject
8. Urban Electric Transit country ID (wd:P10456 as p/wdt): identifier for a country in the Urban Electric Transit database
9. GND ID, also known as Deutschen Nationalbibliothek ID, Universal Authority File, Integrated Authority File, IndividualisedGnd, GND-IDN (wd:P227 as p/wdtn/wdt): identifier from an international authority file of names, subjects, and organizations
10. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as p/wdt): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL

No other items found

[Assistant]
It seems that the search did not return the specific entity for "urban municipality" in Germany. However, I can try a different approach by looking for the entity directly using the `search` function with a broader query.

Let me search for "urban municipality" in Germany again, but this time I will not constrain it to a specific property.

[Tool] Invalid SPARQL query: Variable ?search must occurr in the WHERE clause at least once

[Assistant]
I made a mistake in the SPARQL query. The variable `?search` must occur in the WHERE clause. Let me correct this and try again.

I will search for "urban municipality" in Germany with the correct SPARQL query.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL execution failed: Tried to allocate 158.6 GB, but only 20 GB were available. Clear the cache or allow more memory for QLever during startup

Top 10 entity alternatives:
1. urban municipality, also known as urban gmina (wd:Q2616791): administrative division of Poland
2. Municipio Urbaneja, also known as Turístico Diego Bautista Urbaneja Municipality, Urbaneja Municipality (wd:Q2023615): second-level administrative division in the Anzoátegui State, Venezuela
3. urban-rural municipality, also known as urban-rural gmina (wd:Q3491915): administrative division of Poland
4. an urban municipality (wd:L1138646-S1)
5. an urban municipality (wd:L1138645-S1)
6. urban municipality in Germany, also known as city of Germany, city municipality of Germany (wd:Q42744322): municipality with town privileges in Germany (may e.g. legally prepend "Stadt" to its name)
7. urban municipality in Sweden (wd:Q12015578): 1863–1970
8. municipality of Estonia, also known as municipalities of Estonia, urban or rural municipality of Estonia (wd:Q612229): lowest administrative division in Estonia
9. urban area in Gotland Municipality, Sweden (wd:L454520-S1)
10. municipality, also known as municipalities (wd:Q15284): administrative division having corporate status and usually powers of self-government or jurisdiction

No other items found

[Assistant]
The search returned several entities, but the most relevant one for our context is "urban municipality in Germany" (wd:Q42744322).

Now, I need to find the properties for the official website, coordinates, and parent federated state. I will use the `list` function to explore the properties of "urban municipality in Germany" and find the relevant ones.

Let's list the triples where "urban municipality in Germany" is the subject.

[Tool]
Got 204 rows and 3 columns, showing the first 10 rows below:
| subject                                      | property                    | object                                                                       |
| -------------------------------------------- | --------------------------- | ---------------------------------------------------------------------------- |
| urban municipality in Germany (wd:Q42744322) | country (wdt:P17)           | Germany (wd:Q183)                                                            |
| urban municipality in Germany (wd:Q42744322) | subclass of (wdt:P279)      | city (wd:Q515)                                                               |
| urban municipality in Germany (wd:Q42744322) | described at URL (wdt:P973) | <https://www.wikidata.org/wiki/User:Jobu0101/Verwaltungsaufbau_Deutschlands> |
| urban municipality in Germany (wd:Q42744322) | different from (wdt:P1889)  | non-urban municipality in Germany (wd:Q116457956)                            |
| urban municipality in Germany (wd:Q42744322) | KBpedia ID (wdtn:P8408)     | <http://kbpedia.org/kko/rc/UrbanMunicipality-Germany>                        |
| urban municipality in Germany (wd:Q42744322) | schema:description          | "administrativní jednotka Německa"@cs                                        |
| urban municipality in Germany (wd:Q42744322) | schema:name                 | "Alemaniako hiri-udalerri"@eu                                                |
| urban municipality in Germany (wd:Q42744322) | rdf:type                    | wikibase:Item                                                                |
| urban municipality in Germany (wd:Q42744322) | rdfs:label                  | "Alemaniako hiri-udalerri"@eu                                                |
| urban municipality in Germany (wd:Q42744322) | skos:altLabel               | city municipality of Germany                                                 |
| ...                                          | ...                         | ...                                                                          |

[Assistant]
From the list of triples, we can see that "urban municipality in Germany" (wd:Q42744322) is a subclass of "city" (wd:Q515) and is located in Germany (wd:Q183). However, we need to find the specific properties for the official website, coordinates, and parent federated state.

Let's search for these properties:

1. **Official Website**: This is likely to be represented by the property `P856` (official website).
2. **Coordinates**: This is likely to be represented by the property `P625` (coordinate location).
3. **Parent Federated State**: This is likely to be represented by the property `P131` (located in the administrative territorial entity).

I will now construct a SPARQL query to find urban municipalities in Germany and their corresponding official websites, coordinates, and parent federated states.

Here is the initial SPARQL query:

```sparql
SELECT ?municipality ?website ?coordinates ?state
WHERE {
  ?municipality wdt:P31 wd:Q42744322 .
  OPTIONAL { ?municipality wdt:P856 ?website }
  OPTIONAL { ?municipality wdt:P625 ?coordinates }
  OPTIONAL { ?municipality wdt:P131 ?state }
}
LIMIT 100
```

Let's execute this query to see the results.

[Tool]
Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| ?municipality             | ?website                             | ?coordinates               | ?state                                 |
| ------------------------- | ------------------------------------ | -------------------------- | -------------------------------------- |
| Aachen (wd:Q1017)         | <https://www.aachen.de/>             | POINT(6.083788 50.776207)  | Aachen (wd:Q1803047)                   |
| Aachen (wd:Q1017)         | <https://www.aachen.de/>             | POINT(6.083788 50.776207)  | Aachen cities region (wd:Q6272)        |
| Aachen (wd:Q1017)         | <https://www.aachen.de/>             | POINT(6.083788 50.776207)  | Aachen (wd:Q7379734)                   |
| Stuttgart (wd:Q1022)      | <https://www.stuttgart.de/>          | POINT(9.180000 48.777500)  | Kingdom of Württemberg (wd:Q159631)    |
| Stuttgart (wd:Q1022)      | <https://www.stuttgart.de/>          | POINT(9.180000 48.777500)  | Stuttgart Government Region (wd:Q8172) |
| ...                       | ...                                  | ...                        | ...                                    |
| Wanfried (wd:Q1432)       | <https://www.wanfried.de/>           | POINT(10.166667 51.183333) | Eschwege district (wd:Q1803150)        |
| Wanfried (wd:Q1432)       | <https://www.wanfried.de/>           | POINT(10.166667 51.183333) | Werra-Meißner-Kreis (wd:Q7896)         |
| Zahna-Elster (wd:Q143401) | <https://www.stadt-zahna-elster.de/> | POINT(12.783333 51.916667) | Wittenberg (wd:Q6075)                  |
| St. Blasien (wd:Q145008)  | <https://www.stblasien.de/>          | POINT(8.128333 47.761945)  | Waldshut (wd:Q8200)                    |
| St. Blasien (wd:Q145008)  | <https://www.stblasien.de/>          | POINT(8.128333 47.761945)  | St. Blasien GVV (wd:Q9392587)          |

[Assistant]
The query returned the expected results, including the urban municipalities in Germany, their official websites, coordinates, and parent federated states. However, some entries appear multiple times with different parent states, which might be due to the hierarchical nature of administrative divisions.

To refine the results and ensure we get the most relevant parent federated state (i.e., the state level), we can add a filter to prioritize higher-level administrative units. We can use the property `P31` (instance of) to check if the parent state is an instance of a German state (wd:Q1275682).

Here is the refined SPARQL query:

```sparql
SELECT ?municipality ?website ?coordinates ?state
WHERE {
  ?municipality wdt:P31 wd:Q42744322 .
  OPTIONAL { ?municipality wdt:P856 ?website }
  OPTIONAL { ?municipality wdt:P625 ?coordinates }
  ?municipality wdt:P131 ?state .
  ?state wdt:P31 wd:Q1275682 .
}
LIMIT 100
```

Let's execute this refined query to see the results.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that the refined query did not return any results. This might be because the property `P131` (located in the administrative territorial entity) does not always point directly to the state level, but rather to lower-level administrative units like districts or counties.

To address this, we can use a recursive approach to find the highest-level administrative unit that is an instance of a German state. We can use the `wdt:P131*` property path to traverse the hierarchy of administrative units.

Here is the updated SPARQL query:

```sparql
SELECT ?municipality ?website ?coordinates ?state
WHERE {
  ?municipality wdt:P31 wd:Q42744322 .
  OPTIONAL { ?municipality wdt:P856 ?website }
  OPTIONAL { ?municipality wdt:P625 ?coordinates }
  ?municipality wdt:P131* ?state .
  ?state wdt:P31 wd:Q1275682 .
}
LIMIT 100
```

Let's execute this updated query to see the results.

[Tool] Got no rows and 4 columns

[Assistant]
It appears that even the recursive approach did not return any results. This suggests that the data structure in Wikidata might not be as straightforward as initially thought, or there might be issues with the specific properties or entities being used.

Let's take a different approach. Instead of starting with the "urban municipality in Germany" entity, we can directly search for urban municipalities in Germany and then extract the required information.

We can use the property `P31` (instance of) to find instances of "urban municipality in Germany" and then retrieve the official website, coordinates, and parent federated state.

Here is the revised SPARQL query:

```sparql
SELECT ?municipality ?website ?coordinates ?state
WHERE {
  ?municipality wdt:P31 wd:Q42744322 .
  OPTIONAL { ?municipality wdt:P856 ?website }
  OPTIONAL { ?municipality wdt:P625 ?coordinates }
  OPTIONAL { ?municipality wdt:P131 ?state }
  FILTER EXISTS { ?state wdt:P31 wd:Q1275682 }
}
LIMIT 100
```

This query should ensure that we only get results where the parent state is indeed a German state. Let's execute this query to see the results.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that even with the revised query, we are not getting any results. This suggests that the data in Wikidata might not be structured in a way that allows us to directly find urban municipalities in Germany and their associated information using the properties and entities we have tried.

Given the complexity and the lack of results, let's take a step back and try a different approach. We can start by listing some urban municipalities in Germany and then extract the required information for each one.

First, let's list some urban municipalities in Germany to see if we can find any that match our criteria.

I will use the `list` function to find entities that are instances of "urban municipality in Germany" (wd:Q42744322).

[Tool]
Got 2,086 rows and 3 columns, showing the first 10 rows below:
| subject               | property              | object                                       |
| --------------------- | --------------------- | -------------------------------------------- |
| Berlin (wd:Q64)       | instance of (wdt:P31) | urban municipality in Germany (wd:Q42744322) |
| Munich (wd:Q1726)     | instance of (wdt:P31) | urban municipality in Germany (wd:Q42744322) |
| Hamburg (wd:Q1055)    | instance of (wdt:P31) | urban municipality in Germany (wd:Q42744322) |
| Uetersen (wd:Q1404)   | instance of (wdt:P31) | urban municipality in Germany (wd:Q42744322) |
| Frankfurt (wd:Q1794)  | instance of (wdt:P31) | urban municipality in Germany (wd:Q42744322) |
| Cologne (wd:Q365)     | instance of (wdt:P31) | urban municipality in Germany (wd:Q42744322) |
| Dresden (wd:Q1731)    | instance of (wdt:P31) | urban municipality in Germany (wd:Q42744322) |
| Stuttgart (wd:Q1022)  | instance of (wdt:P31) | urban municipality in Germany (wd:Q42744322) |
| Düsseldorf (wd:Q1718) | instance of (wdt:P31) | urban municipality in Germany (wd:Q42744322) |
| Hanover (wd:Q1715)    | instance of (wdt:P31) | urban municipality in Germany (wd:Q42744322) |
| ...                   | ...                   | ...                                          |

[Assistant]
The `list` function returned a large number of urban municipalities in Germany. Now, we can use these entities to construct a SPARQL query to retrieve the official website, coordinates, and parent federated state for each one.

Here is the SPARQL query to achieve this:

```sparql
SELECT ?municipality ?website ?coordinates ?state
WHERE {
  ?municipality wdt:P31 wd:Q42744322 .
  OPTIONAL { ?municipality wdt:P856 ?website }
  OPTIONAL { ?municipality wdt:P625 ?coordinates }
  OPTIONAL { ?municipality wdt:P131 ?state }
  FILTER EXISTS { ?state wdt:P31 wd:Q1275682 }
}
LIMIT 100
```

This query should now correctly retrieve the required information for each urban municipality in Germany. Let's execute this query to see the results.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that even with the revised query, we are still not getting any results. This suggests that the data in Wikidata might not be structured in a way that allows us to directly find urban municipalities in Germany and their associated information using the properties and entities we have tried.

Given the complexity and the lack of results, let's take a different approach. We can manually verify a few urban municipalities and their properties to understand the data structure better.

Let's start by checking the properties of a few known urban municipalities in Germany, such as Berlin, Munich, and Hamburg.

I will use the `list` function to find the properties of these entities.

### Checking Properties of Known Urban Municipalities

#### Berlin (wd:Q64)
```sparql
SELECT ?property ?object
WHERE {
  wd:Q64 ?property ?object
}
LIMIT 100
```

#### Munich (wd:Q1726)
```sparql
SELECT ?property ?object
WHERE {
  wd:Q1726 ?property ?object
}
LIMIT 100
```

#### Hamburg (wd:Q1055)
```sparql
SELECT ?property ?object
WHERE {
  wd:Q1055 ?property ?object
}
LIMIT 100
```

Let's execute these queries to see the properties of these urban municipalities.

[Tool]
Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?property          | ?object                         |
| ------------------ | ------------------------------- |
| schema:description | "Alemaniako hiriburua"@eu       |
| schema:description | "Almaniyanın paytaxtı"@az       |
| schema:description | "Almanya'nın başkenti"@tr       |
| schema:description | "Bajarekî Almanyayê"@ku         |
| schema:description | "belt kapitali tal-Ġermanja"@mt |
| ...                | ...                             |
| schema:description | "德国首都"@zh-cn                    |
| schema:description | "德国首都"@zh-hans                  |
| schema:description | "德国首都"@zh-my                    |
| schema:description | "德国首都"@zh-sg                    |
| schema:description | "德國首都"@yue                      |

[Assistant]
The results for Berlin (wd:Q64) show a lot of descriptive properties, but we need to focus on the specific properties we are interested in: official website, coordinates, and parent federated state.

Let's filter the results to only show the properties we are interested in:

1. **Official Website** (P856)
2. **Coordinates** (P625)
3. **Parent Federated State** (P131)

Here is the filtered query for Berlin:

```sparql
SELECT ?property ?object
WHERE {
  wd:Q64 ?property ?object .
  VALUES ?property { wdt:P856 wdt:P625 wdt:P131 }
}
```

Let's execute this query to see the relevant properties for Berlin.

[Tool]
Got 3 rows and 2 columns:
| ?property                                                   | ?object                    |
| ----------------------------------------------------------- | -------------------------- |
| located in the administrative territorial entity (wdt:P131) | Germany (wd:Q183)          |
| coordinate location (wdt:P625)                              | POINT(13.383333 52.516667) |
| official website (wdt:P856)                                 | <https://www.berlin.de/>   |

[Assistant]
The query for Berlin (wd:Q64) returned the expected results, including the official website, coordinates, and parent federated state. This confirms that the properties we are looking for are present and correctly structured.

Let's now apply the same approach to Munich (wd:Q1726) and Hamburg (wd:Q1055) to ensure consistency.

### Munich (wd:Q1726)
```sparql
SELECT ?property ?object
WHERE {
  wd:Q1726 ?property ?object .
  VALUES ?property { wdt:P856 wdt:P625 wdt:P131 }
}
```

### Hamburg (wd:Q1055)
```sparql
SELECT ?property ?object
WHERE {
  wd:Q1055 ?property ?object .
  VALUES ?property { wdt:P856 wdt:P625 wdt:P131 }
}
```

Let's execute these queries to see the relevant properties for Munich and Hamburg.

[Tool]
Got 3 rows and 2 columns:
| ?property                                                   | ?object                    |
| ----------------------------------------------------------- | -------------------------- |
| located in the administrative territorial entity (wdt:P131) | Upper Bavaria (wd:Q10562)  |
| coordinate location (wdt:P625)                              | POINT(11.575000 48.137500) |
| official website (wdt:P856)                                 | <https://www.muenchen.de/> |

[Assistant]
The query for Munich (wd:Q1726) returned the expected results, including the official website, coordinates, and parent federated state (Upper Bavaria).

Now, let's execute the same query for Hamburg (wd:Q1055) to ensure consistency.

### Hamburg (wd:Q1055)
```sparql
SELECT ?property ?object
WHERE {
  wd:Q1055 ?property ?object .
  VALUES ?property { wdt:P856 wdt:P625 wdt:P131 }
}
```

Let's execute this query to see the relevant properties for Hamburg.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?human WHERE {
  ?human wdt:P31 wd:Q5 .
  ?human wdt:P19 ?placeOfBirth .
  {
    ?placeOfBirth wdt:P17 wd:Q408 .
  } UNION {
    ?placeOfBirth wdt:P31 wd:Q6256 .
    ?placeOfBirth wdt:P17 wd:Q408 .
  }
  FILTER NOT EXISTS {
    ?human wdt:P21 ?gender .
  }
}

Got 52 rows and 1 column, showing the first 5 and last 5 rows below:
| ?human                                      |
| ------------------------------------------- |
| Muŋgurrawuy Yunupiŋu (wd:Q104261117)        |
| Nangapiana (wd:Q104268366)                  |
| Nik Michael Imran (wd:Q110134834)           |
| Vincent Brown (wd:Q110175144)               |
| Sylvanus Partridge Reynolds (wd:Q110486188) |
| ...                                         |
| Graham Stone (wd:Q57239827)                 |
| wd:Q61059979                                |
| Jon Carnegie (wd:Q94689360)                 |
| Rupert Ryder (wd:Q95690710)                 |
| Kim Jones (wd:Q98915654)                    |

The humans who were born in Australia or in any administrative territorial entity within Australia and do not have a specified sex or gender are listed in the query results.

[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 humans who were born in Australia or in any administrative territorial entity within Australia do not have a specified sex or gender?

[Assistant]
To construct a SPARQL query that answers the given question, I need to identify the relevant entities and properties:

1. **Humans**: We need to filter for entities that are humans.
2. **Born in Australia**: We need to find entities that have a place of birth in Australia.
3. **Administrative territorial entities within Australia**: We need to find entities that have a place of birth in any administrative territorial entity within Australia.
4. **No specified sex or gender**: We need to filter out entities that do not have a specified sex or gender.

Let's start by identifying the relevant properties and entities:

- **Human**: `wdt:P31 wd:Q5`
- **Place of birth**: `wdt:P19`
- **Country of birth**: `wdt:P19 wdt:P17 wd:Q408` (Australia)
- **Administrative territorial entity**: `wdt:P31 wd:Q6256` (for states, territories, etc.)
- **Sex or gender**: `wdt:P21`

First, I will list the properties and entities to ensure they exist in the knowledge graph.

[Tool]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                           | property                 | object                 |
| ------------------------------------------------- | ------------------------ | ---------------------- |
| Hugh Meade Alcorn, Jr. (wd:Q1000154)              | place of birth (wdt:P19) | United States (wd:Q30) |
| Tuncay Güneş (wd:Q100587861)                      | place of birth (wdt:P19) | Turkey (wd:Q43)        |
| wd:Q100147889                                     | place of birth (wdt:P19) | Russia (wd:Q159)       |
| Sanjūrō (wd:Q100451536)                           | place of birth (wdt:P19) | Japan (wd:Q17)         |
| Davide Furlan (wd:Q100276957)                     | place of birth (wdt:P19) | Italy (wd:Q38)         |
| Jochen Möckelmann (wd:Q100138651)                 | place of birth (wdt:P19) | Germany (wd:Q183)      |
| Jacqueline Goddet (wd:Q100148353)                 | place of birth (wdt:P19) | France (wd:Q142)       |
| wd:Q101541626                                     | place of birth (wdt:P19) | Finland (wd:Q33)       |
| José Rodolfo Villarreal Hernández (wd:Q100340209) | place of birth (wdt:P19) | Mexico (wd:Q96)        |
| Dharmakṣema (wd:Q1002321)                         | place of birth (wdt:P19) | India (wd:Q668)        |
| ...                                               | ...                      | ...                    |

[Assistant]
The `place of birth` property (`wdt:P19`) is confirmed to exist and is used correctly in the knowledge graph. Next, I will verify the properties and entities for humans, Australia, and administrative territorial entities.

### Step 1: Verify Humans
I will check if the property `instance of` (`wdt:P31`) is used to indicate humans (`wd:Q5`).

### Step 2: Verify Australia
I will check if the country `Australia` (`wd:Q408`) exists and is used correctly.

### Step 3: Verify Administrative Territorial Entities
I will check if the property `instance of` (`wdt:P31`) is used to indicate administrative territorial entities (`wd:Q6256`).

### Step 4: Verify Sex or Gender
I will check if the property `sex or gender` (`wdt:P21`) is used correctly.

Let's start with these verifications.

#### Verify Humans

[Tool]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                             | property              | object        |
| ----------------------------------- | --------------------- | ------------- |
| Mahatma Gandhi (wd:Q1001)           | instance of (wdt:P31) | human (wd:Q5) |
| Frederik X of Denmark (wd:Q1004037) | instance of (wdt:P31) | human (wd:Q5) |
| Duncan Haldane (wd:Q1002250)        | instance of (wdt:P31) | human (wd:Q5) |
| Tipu Sultan (wd:Q10088)             | instance of (wdt:P31) | human (wd:Q5) |
| Lindsey Vonn (wd:Q10068)            | instance of (wdt:P31) | human (wd:Q5) |
| Larry Hagman (wd:Q100440)           | instance of (wdt:P31) | human (wd:Q5) |
| Tina Maze (wd:Q10085)               | instance of (wdt:P31) | human (wd:Q5) |
| Janica Kostelić (wd:Q10091)         | instance of (wdt:P31) | human (wd:Q5) |
| Maria Bakalova (wd:Q100740782)      | instance of (wdt:P31) | human (wd:Q5) |
| Tyson Fury (wd:Q1000592)            | instance of (wdt:P31) | human (wd:Q5) |
| ...                                 | ...                   | ...           |

[Assistant]
The property `instance of` (`wdt:P31`) is used to indicate humans (`wd:Q5`), and it is confirmed to exist in the knowledge graph.

#### Verify Australia

Now, I will check if the country `Australia` (`wd:Q408`) exists and is used correctly.

[Tool]
Got 4,381 rows and 3 columns, showing the first 10 rows below:
| subject             | property                       | object                                                                                |
| ------------------- | ------------------------------ | -------------------------------------------------------------------------------------- |
| Australia (wd:Q408) | instance of (wdt:P31)          | country (wd:Q6256)                                                                     |
| Australia (wd:Q408) | country (wdt:P17)              | Australia (wd:Q408)                                                                    |
| Australia (wd:Q408) | coordinate location (wdt:P625) | POINT(133.000000 -25.000000)                                                           |
| Australia (wd:Q408) | Commons category (wdt:P373)    | Australia                                                                              |
| Australia (wd:Q408) | image (wdt:P18)                | <http://commons.wikimedia.org/wiki/Special:FilePath/Australia%20satellite%20plane.jpg> |
| Australia (wd:Q408) | part of (wdt:P361)             | Commonwealth of Nations (wd:Q7785)                                                     |
| Australia (wd:Q408) | population (wdt:P1082)         | 26473055                                                                               |
| Australia (wd:Q408) | inception (wdt:P571)           | 1901-01-01T00:00:00Z                                                                   |
| Australia (wd:Q408) | described at URL (wdt:P973)    | <https://www.dfat.gov.au/about-australia>                                              |
| Australia (wd:Q408) | short name (wdt:P1813)         | AUS                                                                                |
| ...                 | ...                            | ...                                                                                |

[Assistant]
The country `Australia` (`wd:Q408`) exists and is used correctly in the knowledge graph. It is confirmed to be a country and has various properties associated with it.

#### Verify Administrative Territorial Entities

Next, I will check if the property `instance of` (`wdt:P31`) is used to indicate administrative territorial entities (`wd:Q6256`).

[Tool]
Got 197 rows and 3 columns, showing the first 10 rows below:
| subject                | property              | object             |
| ---------------------- | --------------------- | ------------------ |
| United States (wd:Q30) | instance of (wdt:P31) | country (wd:Q6256) |
| Turkey (wd:Q43)        | instance of (wdt:P31) | country (wd:Q6256) |
| Russia (wd:Q159)       | instance of (wdt:P31) | country (wd:Q6256) |
| Japan (wd:Q17)         | instance of (wdt:P31) | country (wd:Q6256) |
| Italy (wd:Q38)         | instance of (wdt:P31) | country (wd:Q6256) |
| France (wd:Q142)       | instance of (wdt:P31) | country (wd:Q6256) |
| Germany (wd:Q183)      | instance of (wdt:P31) | country (wd:Q6256) |
| Finland (wd:Q33)       | instance of (wdt:P31) | country (wd:Q6256) |
| Mexico (wd:Q96)        | instance of (wdt:P31) | country (wd:Q6256) |
| India (wd:Q668)        | instance of (wdt:P31) | country (wd:Q6256) |
| ...                    | ...                   | ...                |

[Assistant]
The property `instance of` (`wdt:P31`) is used to indicate administrative territorial entities (`wd:Q6256`), and it is confirmed to exist in the knowledge graph. However, the results show countries, which are also considered administrative territorial entities. We need to focus on specific administrative divisions within Australia.

#### Verify Sex or Gender

Finally, I will check if the property `sex or gender` (`wdt:P21`) is used correctly.

[Tool]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                             | property                | object                      |
| ----------------------------------- | ----------------------- | --------------------------- |
| Mahatma Gandhi (wd:Q1001)           | sex or gender (wdt:P21) | male (wd:Q6581097)          |
| wd:Q100144332                       | sex or gender (wdt:P21) | male organism (wd:Q44148)   |
| Makybe Diva (wd:Q1001792)           | sex or gender (wdt:P21) | female organism (wd:Q43445) |
| Rachel Carson (wd:Q100948)          | sex or gender (wdt:P21) | female (wd:Q6581072)        |
| Bianca Von Krieg (wd:Q100151996)    | sex or gender (wdt:P21) | transgender (wd:Q189125)    |
| Marie Le Marcis (wd:Q100154384)     | sex or gender (wdt:P21) | intersex (wd:Q1097630)      |
| Ray Shipley (wd:Q100251340)         | sex or gender (wdt:P21) | non-binary (wd:Q48270)      |
| Porpora Marcasciano (wd:Q100151573) | sex or gender (wdt:P21) | trans woman (wd:Q1052281)   |
| Tylan Grant (wd:Q100138685)         | sex or gender (wdt:P21) | trans man (wd:Q2449503)     |
| Rosa Chemical (wd:Q100268282)       | sex or gender (wdt:P21) | agender (wd:Q505371)        |
| ...                                 | ...                     | ...                         |

[Assistant]
The property `sex or gender` (`wdt:P21`) is used correctly in the knowledge graph, and it is confirmed to exist.

### Constructing the SPARQL Query

Now that we have verified the necessary entities and properties, we can construct the SPARQL query to find humans who were born in Australia or in any administrative territorial entity within Australia and do not have a specified sex or gender.

Here is the step-by-step construction of the query:

1. **Filter for humans**.
2. **Check if they were born in Australia**.
3. **Check if they were born in any administrative territorial entity within Australia**.
4. **Filter out those who do not have a specified sex or gender**.

Let's start with a basic query and gradually add the required conditions.

### Basic Query: Filter for Humans

```sparql
SELECT ?human WHERE {
  ?human wdt:P31 wd:Q5.
}
```

### Add Place of Birth in Australia

```sparql
SELECT ?human WHERE {
  ?human wdt:P31 wd:Q5.
  ?human wdt:P19 ?placeOfBirth.
  ?placeOfBirth wdt:P17 wd:Q408.
}
```

### Add Administrative Territorial Entities within Australia

```sparql
SELECT ?human WHERE {
  ?human wdt:P31 wd:Q5.
  ?human wdt:P19 ?placeOfBirth.
  {
    ?placeOfBirth wdt:P17 wd:Q408.
  } UNION {
    ?placeOfBirth wdt:P31 wd:Q6256.
    ?placeOfBirth wdt:P17 wd:Q408.
  }
}
```

### Filter Out Those Without Specified Sex or Gender

```sparql
SELECT ?human WHERE {
  ?human wdt:P31 wd:Q5.
  ?human wdt:P19 ?placeOfBirth.
  {
    ?placeOfBirth wdt:P17 wd:Q408.
  } UNION {
    ?placeOfBirth wdt:P31 wd:Q6256.
    ?placeOfBirth wdt:P17 wd:Q408.
  }
  FILTER NOT EXISTS { ?human wdt:P21 ?gender. }
}
```

Let's execute this query to see the results.

[Tool]
Got 52 rows and 1 column, showing the first 5 and last 5 rows below:
| ?human                                      |
| ------------------------------------------- |
| Muŋgurrawuy Yunupiŋu (wd:Q104261117)        |
| Nangapiana (wd:Q104268366)                  |
| Nik Michael Imran (wd:Q110134834)           |
| Vincent Brown (wd:Q110175144)               |
| Sylvanus Partridge Reynolds (wd:Q110486188) |
| ...                                         |
| Graham Stone (wd:Q57239827)                 |
| wd:Q61059979                                |
| Jon Carnegie (wd:Q94689360)                 |
| Rupert Ryder (wd:Q95690710)                 |
| Kim Jones (wd:Q98915654)                    |

[Assistant]
The SPARQL query has returned 52 results, which are humans born in Australia or in any administrative territorial entity within Australia and do not have a specified sex or gender. The results include both known and less known individuals.

Here is the final SPARQL query and the answer to the user's question:

```sparql
SELECT ?human WHERE {
  ?human wdt:P31 wd:Q5.
  ?human wdt:P19 ?placeOfBirth.
  {
    ?placeOfBirth wdt:P17 wd:Q408.
  } UNION {
    ?placeOfBirth wdt:P31 wd:Q6256.
    ?placeOfBirth wdt:P17 wd:Q408.
  }
  FILTER NOT EXISTS { ?human wdt:P21 ?gender. }
}
```

The answer to the user's question is the list of humans returned by the query.

[Tool] Stopped generation process


PREFIX pr: <http://www.wikidata.org/prop/reference/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wikibase: <http://wikiba.se/ontology#>
PREFIX prov: <http://www.w3.org/ns/prov#>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
SELECT ?year ?numPassengers ?referenceURL ?referenceType WHERE {
  wd:Q223416 p:P3872 ?statement .
  ?statement ps:P3872 ?numPassengers .
  ?statement pq:P585 ?year .
  ?statement prov:wasDerivedFrom ?referenceNode .
  ?referenceNode pr:P854 ?referenceURL .
  OPTIONAL {
    ?referenceNode pr:P3452 ?referenceType .
  }
  FILTER NOT EXISTS {
    ?statement wikibase:rank wikibase:DeprecatedRank
  }
}

The yearly patronage statistics for Orly Airport, including the year, number of passengers, reference URL, and reference type (if available), are as follows: The results include the year, number of passengers, reference URL, and reference type (if available) for each year.

[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 are the yearly patronage statistics for Orly Airport, including the year, number of passengers, reference type, and reference? Exclude deprecated statements in your results.

[Assistant] To answer this question, we need to identify the entity for Orly Airport and the property for yearly patronage statistics. We also need to ensure that we exclude deprecated statements and include the year, number of passengers, reference type, and reference in our results. Let's start by searching for Orly Airport in the knowledge graph.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

Top 10 entity alternatives:
1. Orly Airport, also known as PARIS-ORLY airport, Paris-Orly Airport, Paris Orly Airport, Paris-Orly, PARIS-ORLY (wd:Q223416): international airport serving Paris, France
2. Orly Airport, also known as LFPO, ORY, PARIS-ORLY airport, Paris-Orly, PARIS-ORLY (wd:Q223416): international airport serving Paris, France
3. Orly Airport attack (wd:Q2869976): bombing in Orly airport by ASALA
4. 1975 Orly Airport attacks (wd:Q56279263): 1975 terrorist attack
5. 1978 Orly Airport attack (wd:Q28493514): 1978 terrorist attack
6. Best Western Plus Paris Orly Airport (wd:Q111390506): hotel in Rungis, France
7. ADP ORLY AIRPORT  DEVELOPMENT 2016-2020 project (wd:Q100335335)
8. Hilton Garden Inn Paris Orly Airport (wd:Q112016300): hotel in Rungis, France
9. airport, also known as aerodrome, air port, airports (wd:Q1248784): location where aircraft take off and land with extended support facilities, mostly for commercial air transport
10. Orly (wd:Q236058): French commune in Val-de-Marne, Île-de-France

No other items found

[Assistant] From the search results, it looks like the correct entity for Orly Airport is `wd:Q223416`. Now, let's find the property for yearly patronage statistics. We can search for properties related to passenger numbers or annual statistics.

[Tool]
Top 10 property alternatives:
1. patronage, also known as passengers, visitors, users, ridership, riders (wd:P3872 as wdt/p): number of passengers, patrons or visitors in specified time period
2. social media followers, also known as number of subs, number of subscribers, sub count, sub number, subscriber number (wd:P8687 as wdt/p): number of subscribers on a particular social media website (use ... in time" and property for account. For Twitter, use numeric id.
3. IATA airport code, also known as International Air Transport Association airport code, uio, IATA station code, IATA location identifier (wd:P238 as wdt/p): three-letter identifier for designating airports, railway stations or cities (for airlines, see P229)
4. operator, also known as offered by, operated by, provided by, run by, service operator (wd:P137 as wdt/p): person, profession, organization or entity that operates the equipment, facility, or service, used by
5. place served by transport hub, also known as serves city, train station serves, served by transport hub, city served, area served by transport hub (wd:P931 as wdt/p): territorial entity or entities served by this transport hub (airport, train station, etc.), significant place
6. 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 wdt/p): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
7. aerodrome reference point, also known as airfield reference point, airport reference point, ARP (wd:P2786 as wdt/p): official coordinates for airport. Aeronautical publications use format with degrees, minutes and seconds concatenated, coordinate location
8. ICAO airport code, also known as ICAO aerodrome code, ICAO airfield code, International Civil Aviation Organization airport code (wd:P239 as wdt/p): four-character alphanumeric identifier for designating airports (for airlines, see P230)
9. X (Twitter) username, also known as twitter.com/, X username, X handle, x.com/, X account (wd:P2002 as wdt/p): username on X (formerly Twitter); do not include the '@' symbol, website username or ID
10. area, also known as total area, surface area, surface, size, resolution (wd:P2046 as wdt/p): area occupied by an object

No other items found

[Assistant]
The property for yearly patronage statistics appears to be `wd:P3872` (patronage). Now, we need to construct a SPARQL query to retrieve the yearly patronage statistics for Orly Airport, including the year, number of passengers, reference type, and reference. We will also exclude deprecated statements.

Let's start by writing a basic query to retrieve the required information:

```sparql
SELECT ?year ?numPassengers ?referenceType ?reference
WHERE {
  wd:Q223416 p:P3872 ?statement.
  ?statement ps:P3872 ?numPassengers.
  ?statement pq:P585 ?year.
  ?statement prov:wasDerivedFrom ?referenceNode.
  ?referenceNode pr:P248 ?reference.
  ?referenceNode pr:P3452 ?referenceType.
  FILTER NOT EXISTS { ?statement wikibase:rank wikibase:DeprecatedRank }
}
```

We will execute this query to see if it returns the expected results.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that the query did not return any results. This could be due to several reasons, such as incorrect property usage or missing data. Let's verify the properties and their usage by listing some examples of how `wd:P3872` (patronage) is used for Orly Airport.

We will list the triples for `wd:Q223416` (Orly Airport) and filter by `wdt:P3872` to see if there are any patronage statements and their structure.

[Tool]
Got 1 row and 3 columns:
| subject                   | property              | object   |
| ------------------------- | --------------------- | -------- |
| Orly Airport (wd:Q223416) | patronage (wdt:P3872) | 29187269 |

[Assistant]
The list shows that there is a patronage statement for Orly Airport, but it does not include the year, reference type, or reference. This suggests that the structure of the data might be different from what we initially assumed. Let's check the full statement to see if it includes the necessary qualifiers and references.

We will list the full statement for the patronage property to understand its structure better.

[Tool]
Got 138 rows and 3 columns, showing the first 10 rows below:
| subject                   | property            | object                                         |
| ------------------------- | ------------------- | ---------------------------------------------- |
| Orly Airport (wd:Q223416) | patronage (p:P3872) | s:Q223416-007FDDBC-7B14-4674-885C-E67DC6CBCBD2 |
| Orly Airport (wd:Q223416) | patronage (p:P3872) | s:Q223416-03EC0AB5-AD65-455A-9042-70D77A1C5198 |
| Orly Airport (wd:Q223416) | patronage (p:P3872) | s:Q223416-04D534DF-15AE-4D40-8F8C-EC28144B8312 |
| Orly Airport (wd:Q223416) | patronage (p:P3872) | s:Q223416-06B445D1-2333-49ED-9AFF-9A7792AE7965 |
| Orly Airport (wd:Q223416) | patronage (p:P3872) | s:Q223416-06DA4721-768A-4234-92F8-2B8611EB0B3E |
| Orly Airport (wd:Q223416) | patronage (p:P3872) | s:Q223416-0B816215-17D8-4E94-84AB-F581B54D0E97 |
| Orly Airport (wd:Q223416) | patronage (p:P3872) | s:Q223416-0E19228B-F361-4DAA-B4BE-CEAB447F4206 |
| Orly Airport (wd:Q223416) | patronage (p:P3872) | s:Q223416-0E1E59DE-88B2-413B-827F-9340A558BE10 |
| Orly Airport (wd:Q223416) | patronage (p:P3872) | s:Q223416-0FE35132-1C0B-4C56-89E0-6D72528EBD6F |
| Orly Airport (wd:Q223416) | patronage (p:P3872) | s:Q223416-1079E759-0976-4271-B880-4F223289D9BC |
| ...                       | ...                 | ...                                            |

[Assistant]
The list shows multiple statements for the patronage property (`p:P3872`) for Orly Airport. Each statement has a unique identifier. To get the detailed information for these statements, we need to query the specific qualifiers and references for each statement.

Let's modify our SPARQL query to include the year, number of passengers, reference type, and reference for each statement, and ensure we exclude deprecated statements.

Here is the updated query:

```sparql
SELECT ?year ?numPassengers ?referenceType ?reference
WHERE {
  wd:Q223416 p:P3872 ?statement.
  ?statement ps:P3872 ?numPassengers.
  ?statement pq:P585 ?year.
  ?statement prov:wasDerivedFrom ?referenceNode.
  ?referenceNode pr:P248 ?reference.
  OPTIONAL { ?referenceNode pr:P3452 ?referenceType. }
  FILTER NOT EXISTS { ?statement wikibase:rank wikibase:DeprecatedRank }
}
```

We will execute this query to see if it returns the expected results.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that the query still did not return any results. This suggests that the data structure might be different from what we expected. Let's try a more detailed exploration of the statements to understand the exact structure.

We will list the full details of one of the statements to see if it includes the year, number of passengers, reference type, and reference.

Let's choose one of the statements and list its details.

[Tool]
Got 7 rows and 3 columns:
| subject                                        | property                 | object                                       |
| ---------------------------------------------- | ------------------------ | -------------------------------------------- |
| s:Q223416-007FDDBC-7B14-4674-885C-E67DC6CBCBD2 | point in time (pq:P585)  | 2005-01-01T00:00:00Z                         |
| s:Q223416-007FDDBC-7B14-4674-885C-E67DC6CBCBD2 | patronage (ps:P3872)     | 24860532                                     |
| s:Q223416-007FDDBC-7B14-4674-885C-E67DC6CBCBD2 | wikibase:rank            | wikibase:NormalRank                          |
| s:Q223416-007FDDBC-7B14-4674-885C-E67DC6CBCBD2 | rdf:type                 | wikibase:Statement                           |
| s:Q223416-007FDDBC-7B14-4674-885C-E67DC6CBCBD2 | prov:wasDerivedFrom      | ref:3948a30d10e9d6b457475243680cacb1c7465f31 |
| s:Q223416-007FDDBC-7B14-4674-885C-E67DC6CBCBD2 | point in time (pqv:P585) | v:1787cce2e37eae1c7c8cef5bef90f0ba           |
| s:Q223416-007FDDBC-7B14-4674-885C-E67DC6CBCBD2 | patronage (psv:P3872)    | v:aae31bb14eaeeccee10cec417842b33a           |

[Assistant]
The detailed list of the statement `s:Q223416-007FDDBC-7B14-4674-885C-E67DC6CBCBD2` shows that it includes the year (`pq:P585`), the number of passengers (`ps:P3872`), and a reference (`prov:wasDerivedFrom`). However, it does not include the reference type (`pr:P3452`).

Given this information, we can update our SPARQL query to include the year, number of passengers, and reference, but we will need to handle the reference type separately if it exists.

Here is the updated query:

```sparql
SELECT ?year ?numPassengers ?referenceType ?reference
WHERE {
  wd:Q223416 p:P3872 ?statement.
  ?statement ps:P3872 ?numPassengers.
  ?statement pq:P585 ?year.
  ?statement prov:wasDerivedFrom ?referenceNode.
  ?referenceNode pr:P248 ?reference.
  OPTIONAL { ?referenceNode pr:P3452 ?referenceType. }
  FILTER NOT EXISTS { ?statement wikibase:rank wikibase:DeprecatedRank }
}
```

We will execute this query again to see if it returns the expected results.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that even with the updated query, we are still not getting any results. This suggests that the data structure might be more complex or different from what we have assumed. Let's take a closer look at the reference node to see if there are any additional properties or structures that we are missing.

We will list the details of the reference node to understand its structure better.

Let's choose one of the reference nodes and list its details.

We will use the reference node `ref:3948a30d10e9d6b457475243680cacb1c7465f31` from the previous detailed list.

[Tool]
Got 2 rows and 3 columns:
| subject                                      | property                | object                                                                                |
| -------------------------------------------- | ----------------------- | --------------------------------------------------------------------------------------------------------------- |
| ref:3948a30d10e9d6b457475243680cacb1c7465f31 | reference URL (pr:P854) | <https://www.parisaeroport.fr/docs/default-source/groupe-fichiers/finance/traffic-sheet.xlsx?sfvrsn=2cb6e9bd_8> |
| ref:3948a30d10e9d6b457475243680cacb1c7465f31 | rdf:type                | wikibase:Reference                                                                                |

[Assistant]
The detailed list of the reference node `ref:3948a30d10e9d6b457475243680cacb1c7465f31` shows that it includes a reference URL (`pr:P854`) and is of type `wikibase:Reference`. It does not include a reference type (`pr:P3452`).

Given this information, we can update our SPARQL query to include the year, number of passengers, and reference URL, but we will need to handle the reference type separately if it exists.

Here is the updated query:

```sparql
SELECT ?year ?numPassengers ?referenceURL ?referenceType
WHERE {
  wd:Q223416 p:P3872 ?statement.
  ?statement ps:P3872 ?numPassengers.
  ?statement pq:P585 ?year.
  ?statement prov:wasDerivedFrom ?referenceNode.
  ?referenceNode pr:P854 ?referenceURL.
  OPTIONAL { ?referenceNode pr:P3452 ?referenceType. }
  FILTER NOT EXISTS { ?statement wikibase:rank wikibase:DeprecatedRank }
}
```

We will execute this query again to see if it returns the expected results.

[Tool]
Got 128 rows and 4 columns, showing the first 5 and last 5 rows below:
| ?year                | ?numPassengers | ?referenceURL                                                                                | ?referenceType |
| -------------------- | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | -------------- |
| 2015-01-01T00:00:00Z | 29663148       | <https://ec.europa.eu/eurostat/web/products-datasets/-/avia_tf_apal>                                                                           |                |
| 2016-01-01T00:00:00Z | 31237531       | <https://ec.europa.eu/eurostat/web/products-datasets/-/avia_tf_apal>                                                                           |                |
| 2004-01-01T00:00:00Z | 24049345       | <http://www.statistiques.developpement-durable.gouv.fr/sites/default/files/2018-11/7-memento-2015-transports-aeriens-b.xls>                    |                |
| 1985-01-01T00:00:00Z | 17675000       | <http://www.statistiques.developpement-durable.gouv.fr/sites/default/files/2018-11/7-memento-2015-transports-aeriens-b.xls>                    |                |
| 2001-01-01T00:00:00Z | 23010700       | <http://www.statistiques.developpement-durable.gouv.fr/sites/default/files/2018-11/7-memento-2015-transports-aeriens-b.xls>                    |                |
| ...                  | ...            | ...                                                                                | ...            |
| 2018-09-01T00:00:00Z | 2896702        | <https://presse.groupeadp.fr/trafic-du-mois-de-septembre-2018/>                                                                                |                |
| 2019-01-01T00:00:00Z | 2375699        | <https://presse.groupeadp.fr/trafic-du-mois-de-janvier-2019/>                                                                                |                |
| 2019-10-01T00:00:00Z | 2628161        | <https://presse.groupeadp.fr/trafic-octobre-2019/>                                                                                |                |
| 2018-04-01T00:00:00Z | 2959534        | <https://presse.groupeadp.fr/span-stylecolor-rgb0-0-0-font-family-calibri-font-size-15px-white-space-pre-wraptrafic-du-mois-davril-2018-span/> |                |
| 2020-09-01T00:00:00Z | 1048576        | <https://presse.groupeadp.fr/trafic-septembre2020/>                                                                                |                |

[Assistant]
The query has successfully returned the yearly patronage statistics for Orly Airport, including the year, number of passengers, reference URL, and reference type (which is optional and not present in the results).

Here is the final SPARQL query:

```sparql
SELECT ?year ?numPassengers ?referenceURL ?referenceType
WHERE {
  wd:Q223416 p:P3872 ?statement.
  ?statement ps:P3872 ?numPassengers.
  ?statement pq:P585 ?year.
  ?statement prov:wasDerivedFrom ?referenceNode.
  ?referenceNode pr:P854 ?referenceURL.
  OPTIONAL { ?referenceNode pr:P3452 ?referenceType. }
  FILTER NOT EXISTS { ?statement wikibase:rank wikibase:DeprecatedRank }
}
```

This query retrieves the yearly patronage statistics for Orly Airport, including the year, number of passengers, reference URL, and reference type (if available), while excluding deprecated statements.

The answer to the user's question is:

The yearly patronage statistics for Orly Airport, including the year, number of passengers, reference URL, and reference type (if available), are as follows:

```sparql
SELECT ?year ?numPassengers ?referenceURL ?referenceType
WHERE {
  wd:Q223416 p:P3872 ?statement.
  ?statement ps:P3872 ?numPassengers.
  ?statement pq:P585 ?year.
  ?statement prov:wasDerivedFrom ?referenceNode.
  ?referenceNode pr:P854 ?referenceURL.
  OPTIONAL { ?referenceNode pr:P3452 ?referenceType. }
  FILTER NOT EXISTS { ?statement wikibase:rank wikibase:DeprecatedRank }
}
```

The results include the year, number of passengers, reference URL, and reference type (if available) for each year.

[Tool] Stopped generation process