query

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

query has 80 facts recorded in Dontopedia across 24 references, with 9 live disagreements.

80 facts·31 predicates·24 sources·9 in dispute

Mostly:rdf:type(20), contains(7), has key(6)

Rdf:typein dispute`rdf:type`

Query^[1]sourceall time · 60ab9372 9811 442b 9f99 A99ec6e6717e
Dictionary^[2]all time · 57429c3d 6f92 4b7c 8afb 82c720fcbd3f
Dictionary^[5]all time · 575650b9 E31e 41c3 94b0 7445ce281a31
Elasticsearch Query^[6]all time · 6c82aa66 85bb 499a A5ca 004cfc98e7f3
Json Object^[7]all time · 7bd85e51 293e 474e 97e0 39e4f7463398
Json Object^[8]all time · Ef7935db F389 498e Baf5 Aff58f744d6b
Data Structure^[9]all time · 52477875 5368 4c2c 89e1 08b2f4d72518
Query^[10]all time · 5885d92f D822 4db1 Bdb7 D80fb7619783
Query Object^[12]all time · 2abe20aa 42dd 4960 A681 Dd7e97348329
Query Object^[13]all time · Ed2227ce 3ffd 49b1 92b7 C2205349c146

Inbound mentions (26)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

`contains`Contains(5)

Body Parameterex:body-parameter
Body Parameterex:body-parameter
Body Parameterex:body-parameter
Python Code Exampleex:python-code-example
Search Bodyex:search-body

`isPartOf`Is Part of(5)

Filter Clauseex:filter-clause
Must Clauseex:must-clause
Profile Parameterex:profile-parameter
Size Componentex:size-component
Source Componentex:source-component

`hasStructure`Has Structure(3)

Candidate Queryex:candidate-query
Intermediate Original Queryex:intermediate-original-query
Original Queryex:original-query

`returns`Returns(3)

Code Snippetex:code-snippet
Match Queryex:matchQuery
Query Methodex:query-method

`calledWith`Called With(2)

Dense Retrievalex:dense-retrieval
Sparse Retrievalex:sparse-retrieval

`containsObject`Contains Object(1)

Bool Query Json Structureex:bool-query-json-structure

`creates`Creates(1)

Code Execution Loopex:code-execution-loop

`defines`Defines(1)

Python Elasticsearch Queryex:python-elasticsearch-query

`exemplifiedBy`Exemplified by(1)

Nested Structureex:nested-structure

`hasValue`Has Value(1)

Query Key Valueex:query-key-value

`isTypeOf`Is Type of(1)

Match Queryex:match-query

`parentQuery`Parent Query(1)

Nested Queryex:nested-query

`receives`Receives(1)

Query Handlerex:QueryHandler

Other facts (52)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

52 facts

Predicate	Value	Ref
Contains	Match Phrase Query Object	[3]
Contains	Bool Must Query	[6]
Contains	Match Query	[11]
Contains	Match Clause	[17]
Contains	Match Query	[18]
Contains	Match Query	[20]
Contains	Match Query	[21]
Has Key	Param1 Key	[2]
Has Key	Param2 Key	[2]
Has Key	type	[5]
Has Key	query	[5]
Has Key	Bool Key	[19]
Has Key	Term Key	[23]
Passed to	Get From Cache	[11]
Passed to	Execute Query	[11]
Passed to	Put in Cache	[11]
Passed to	Sparse Retrieval	[14]
Passed to	Dense Retrieval	[14]
Has Component	Bool Query	[9]
Has Component	Filter Clause	[9]
Has Component	Source Component	[9]
Has Component	Size Component	[9]
Has Property	size	[12]
Has Property	query	[12]
Has Property	track_total_hits	[12]
Has Value for	Param1 Key	[2]
Has Value for	Param2 Key	[2]
Has Method	All Method	[13]
Has Method	Dict	[15]
Uses Class	Query	[1]
Parameter Relationship	Param2 Is Double Param1	[2]
Created Inside	Code Execution Loop	[2]
Has Attribute	Type Attribute	[4]
Accesses Key	Query Key	[5]
Targets Index	Example Index	[6]
Has Nested Structure	Bool Must Nested	[6]
Is Passed to	Es Search Call	[9]
Has Json Structure	Dictionary Structure	[9]
Has Size	10	[11]
Has Track Total Hits	false	[11]
Boolean Flag	track_total_hits	[11]
Boolean Value	false	[11]
Size Value	10	[12]
Track Total Hits Value	false	[12]
Has Nested Query	Nested Query	[12]
Is Part of	Python Code Block	[18]
Has Type	Match Query	[18]
Structured As	Match Query Structure	[18]
Has Query Type	Match Query	[20]
Has Key	term	[22]
Mutable	Python Dictionary	[23]
Contains Item	Match Key Value	[24]

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

80 undated

—

typebeam/60ab9372-9811-442b-9f99-a99ec6e6717e

ex:Query

—

usesClassbeam/60ab9372-9811-442b-9f99-a99ec6e6717e

ex:Query

—

typebeam/57429c3d-6f92-4b7c-8afb-82c720fcbd3f

ex:Dictionary

—

hasKeybeam/57429c3d-6f92-4b7c-8afb-82c720fcbd3f

ex:param1-key

—

hasKeybeam/57429c3d-6f92-4b7c-8afb-82c720fcbd3f

ex:param2-key

—

hasValueForbeam/57429c3d-6f92-4b7c-8afb-82c720fcbd3f

ex:param1-key

—

hasValueForbeam/57429c3d-6f92-4b7c-8afb-82c720fcbd3f

ex:param2-key

—

parameterRelationshipbeam/57429c3d-6f92-4b7c-8afb-82c720fcbd3f

ex:param2-is-double-param1

—

createdInsidebeam/57429c3d-6f92-4b7c-8afb-82c720fcbd3f

ex:code-execution-loop

—

containsbeam/df7c58f3-fbec-47d0-9088-2916d03b14b6

ex:match-phrase-query-object

—

hasAttributebeam/5cb8f644-7a7b-4b3d-afd1-e7d85b36637e

ex:type-attribute

—

typebeam/575650b9-e31e-41c3-94b0-7445ce281a31

ex:Dictionary

—

labelbeam/575650b9-e31e-41c3-94b0-7445ce281a31

query dictionary

—

hasKeybeam/575650b9-e31e-41c3-94b0-7445ce281a31

type

—

hasKeybeam/575650b9-e31e-41c3-94b0-7445ce281a31

query

—

accessesKeybeam/575650b9-e31e-41c3-94b0-7445ce281a31

ex:query-key

—

containsbeam/6c82aa66-85bb-499a-a5ca-004cfc98e7f3

ex:bool-must-query

—

targetsIndexbeam/6c82aa66-85bb-499a-a5ca-004cfc98e7f3

ex:example-index

—

typebeam/6c82aa66-85bb-499a-a5ca-004cfc98e7f3

ex:elasticsearch-query

—

hasNestedStructurebeam/6c82aa66-85bb-499a-a5ca-004cfc98e7f3

ex:bool-must-nested

—

typebeam/7bd85e51-293e-474e-97e0-39e4f7463398

ex:JSONObject

—

typebeam/ef7935db-f389-498e-baf5-aff58f744d6b

ex:JSONObject

—

labelbeam/ef7935db-f389-498e-baf5-aff58f744d6b

query object

—

typebeam/52477875-5368-4c2c-89e1-08b2f4d72518

ex:DataStructure

—

labelbeam/52477875-5368-4c2c-89e1-08b2f4d72518

query

—

hasComponentbeam/52477875-5368-4c2c-89e1-08b2f4d72518

ex:bool-query

—

hasComponentbeam/52477875-5368-4c2c-89e1-08b2f4d72518

ex:filter-clause

—

hasComponentbeam/52477875-5368-4c2c-89e1-08b2f4d72518

ex:source-component

—

hasComponentbeam/52477875-5368-4c2c-89e1-08b2f4d72518

ex:size-component

—

isPassedTobeam/52477875-5368-4c2c-89e1-08b2f4d72518

ex:es-search-call

—

hasJsonStructurebeam/52477875-5368-4c2c-89e1-08b2f4d72518

ex:dictionary-structure

—

typebeam/5885d92f-d822-4db1-bdb7-d80fb7619783

ex:Query

—

containsbeam/5bf33c44-db58-4937-b48b-2e0fbb169a1b

ex:match-query

—

hasSizebeam/5bf33c44-db58-4937-b48b-2e0fbb169a1b

—

hasTrackTotalHitsbeam/5bf33c44-db58-4937-b48b-2e0fbb169a1b

false

—

passedTobeam/5bf33c44-db58-4937-b48b-2e0fbb169a1b

ex:get_from_cache

—

passedTobeam/5bf33c44-db58-4937-b48b-2e0fbb169a1b

ex:execute_query

—

passedTobeam/5bf33c44-db58-4937-b48b-2e0fbb169a1b

ex:put_in_cache

—

booleanFlagbeam/5bf33c44-db58-4937-b48b-2e0fbb169a1b

track_total_hits

—

booleanValuebeam/5bf33c44-db58-4937-b48b-2e0fbb169a1b

false

—

typebeam/2abe20aa-42dd-4960-a681-dd7e97348329

ex:QueryObject

—

hasPropertybeam/2abe20aa-42dd-4960-a681-dd7e97348329

size

—

sizeValuebeam/2abe20aa-42dd-4960-a681-dd7e97348329

—

hasPropertybeam/2abe20aa-42dd-4960-a681-dd7e97348329

query

—

hasPropertybeam/2abe20aa-42dd-4960-a681-dd7e97348329

track_total_hits

—

track_total_hitsValuebeam/2abe20aa-42dd-4960-a681-dd7e97348329

false

—

hasNestedQuerybeam/2abe20aa-42dd-4960-a681-dd7e97348329

ex:nested-query

—

typebeam/ed2227ce-3ffd-49b1-92b7-c2205349c146

ex:QueryObject

—

labelbeam/ed2227ce-3ffd-49b1-92b7-c2205349c146

Query Object

—

hasMethodbeam/ed2227ce-3ffd-49b1-92b7-c2205349c146

ex:all-method

—

typebeam/0ffdb47f-7355-4044-a040-123b60076c23

ex:SearchQuery

—

passedTobeam/0ffdb47f-7355-4044-a040-123b60076c23

ex:sparse-retrieval

—

passedTobeam/0ffdb47f-7355-4044-a040-123b60076c23

ex:dense-retrieval

—

hasMethodbeam/f7efd7d0-3d68-4ac6-841d-644f98af804e

ex:dict

—

typebeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86

ex:JSONObject

—

labelbeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86

query object

—

containsbeam/5f26f8c5-dfd9-40e7-a81f-f613a88eead6

ex:match-clause

—

typebeam/5f26f8c5-dfd9-40e7-a81f-f613a88eead6

ex:JSONObject

—

typebeam/8f0d7477-3a02-46e9-a340-4c293e908ebc

ex:JSONObject

—

labelbeam/8f0d7477-3a02-46e9-a340-4c293e908ebc

Elasticsearch query object

—

isPartOfbeam/8f0d7477-3a02-46e9-a340-4c293e908ebc

ex:python-code-block

—

containsbeam/8f0d7477-3a02-46e9-a340-4c293e908ebc

ex:match-query

—

hasTypebeam/8f0d7477-3a02-46e9-a340-4c293e908ebc

ex:match-query

—

structuredAsbeam/8f0d7477-3a02-46e9-a340-4c293e908ebc

ex:match-query-structure

—

typebeam/63484f14-f077-4119-aad4-2ec5f59e1801

ex:CodeElement

—

labelbeam/63484f14-f077-4119-aad4-2ec5f59e1801

query object

—

hasKeybeam/63484f14-f077-4119-aad4-2ec5f59e1801

ex:bool-key

—

typebeam/64bee5ce-b7c5-4343-9213-164b1fc9c66e

ex:QueryStructure

—

labelbeam/64bee5ce-b7c5-4343-9213-164b1fc9c66e

Query Object

—

containsbeam/64bee5ce-b7c5-4343-9213-164b1fc9c66e

ex:match-query

—

hasQueryTypebeam/64bee5ce-b7c5-4343-9213-164b1fc9c66e

ex:match-query

—

typebeam/32482dcb-f293-412a-8ea0-a9dfc518165e

ex:QueryContainer

—

containsbeam/32482dcb-f293-412a-8ea0-a9dfc518165e

ex:match-query

—

typebeam/9a83a47a-e47d-4467-bbab-2f9a27e7d3bf

ex:DataStructure

—

has-keybeam/9a83a47a-e47d-4467-bbab-2f9a27e7d3bf

term

—

typebeam/866cc857-ac06-46bc-8040-c98e5126053f

ex:dictionary

—

hasKeybeam/866cc857-ac06-46bc-8040-c98e5126053f

ex:term-key

—

mutablebeam/866cc857-ac06-46bc-8040-c98e5126053f

ex:python-dictionary

—

typebeam/dc43e263-ae12-4ebe-aaee-b46ef58b17d0

ex:PythonDictionary

—

containsItembeam/dc43e263-ae12-4ebe-aaee-b46ef58b17d0

ex:match-key-value

References (24)

24 references

[1]60ab9372 9811 442b 9f99 A99ec6e6717e2 facts
ctx:claims/beam/60ab9372-9811-442b-9f99-a99ec6e6717e
- full textbeam-chunk
  text/plain1 KBdoc:beam/60ab9372-9811-442b-9f99-a99ec6e6717e
  Show excerpt
  {"name": "vector", "dataType": ["vector", "512"]} # Adjust vector size as needed ] } ) # Add data data_object = DataObject(client) data_object.create( { "class": "Article", "properties": { …
[2]57429c3d 6f92 4b7c 8afb 82c720fcbd3f7 facts
ctx:claims/beam/57429c3d-6f92-4b7c-8afb-82c720fcbd3f
- full textbeam-chunk
  text/plain1 KBdoc:beam/57429c3d-6f92-4b7c-8afb-82c720fcbd3f
  Show excerpt
  7. **Technology and Tools**: - Use project management software and automate routine tasks to reduce risks. By implementing these strategies, you can better handle unexpected costs and maintain project control throughout the implementati…
[3]Df7c58f3 Fbec 47d0 9088 2916d03b14b61 fact
ctx:claims/beam/df7c58f3-fbec-47d0-9088-2916d03b14b6
- full textbeam-chunk
  text/plain1 KBdoc:beam/df7c58f3-fbec-47d0-9088-2916d03b14b6
  Show excerpt
  "number_of_shards": 5, "number_of_replicas": 1, "analysis": { "analyzer": { "default": { "type": "standard", " stopwords…
[4]5cb8f644 7a7b 4b3d Afd1 E7d85b36637e1 fact
ctx:claims/beam/5cb8f644-7a7b-4b3d-afd1-e7d85b36637e
- full textbeam-chunk
  text/plain1 KBdoc:beam/5cb8f644-7a7b-4b3d-afd1-e7d85b36637e
  Show excerpt
  print(f'Database: {database_name}, Indexing Strategy: {strategy}, Query: {query["query"]}, Time: {elapsed_time:.6f} seconds') elif database_name == 'mongodb': db = databases[database_name] …
[5]575650b9 E31e 41c3 94b0 7445ce281a315 facts
ctx:claims/beam/575650b9-e31e-41c3-94b0-7445ce281a31
[6]6c82aa66 85bb 499a A5ca 004cfc98e7f34 facts
ctx:claims/beam/6c82aa66-85bb-499a-a5ca-004cfc98e7f3
- full textbeam-chunk
  text/plain1 KBdoc:beam/6c82aa66-85bb-499a-a5ca-004cfc98e7f3
  Show excerpt
  [Turn 3212] User: I'm evaluating Elasticsearch 8.9.0 for our project, and I've noted a need for 2 experts with 95% query optimization skills. I want to create a sample query to test the optimization skills of potential candidates. Here's an…
[7]7bd85e51 293e 474e 97e0 39e4f74633981 fact
ctx:claims/beam/7bd85e51-293e-474e-97e0-39e4f7463398
- full textbeam-chunk
  text/plain1 KBdoc:beam/7bd85e51-293e-474e-97e0-39e4f7463398
  Show excerpt
  "bool": { "must": [ { "match": { "title": "example" } }, { "match": { "content": "example" } } ], "filter": [ { "term": { "status": "active" }} ] …
[8]Ef7935db F389 498e Baf5 Aff58f744d6b2 facts
ctx:claims/beam/ef7935db-f389-498e-baf5-aff58f744d6b
[9]52477875 5368 4c2c 89e1 08b2f4d725188 facts
ctx:claims/beam/52477875-5368-4c2c-89e1-08b2f4d72518
- full textbeam-chunk
  text/plain1 KBdoc:beam/52477875-5368-4c2c-89e1-08b2f4d72518
  Show excerpt
  - **Filter Cache**: Use the filter cache for frequently used filters. ### 4. **Monitor and Profile** - **Use the Explain API**: Use the `_explain` API to understand how Elasticsearch is executing your query. - **Use the Profile API**: Use…
[10]5885d92f D822 4db1 Bdb7 D80fb76197831 fact
ctx:claims/beam/5885d92f-d822-4db1-bdb7-d80fb7619783
[11]5bf33c44 Db58 4937 B48b 2e0fbb169a1b8 facts
ctx:claims/beam/5bf33c44-db58-4937-b48b-2e0fbb169a1b
- full textbeam-chunk
  text/plain1 KBdoc:beam/5bf33c44-db58-4937-b48b-2e0fbb169a1b
  Show excerpt
  # Example usage es = Elasticsearch(["http://localhost:9200"]) indexer = Indexer(es) query_handler = QueryHandler(es) result_aggregator = ResultAggregator() cache_manager = CacheManager() documents = ["Document 1", "Document 2", "Document 3…
[12]2abe20aa 42dd 4960 A681 Dd7e973483297 facts
ctx:claims/beam/2abe20aa-42dd-4960-a681-dd7e97348329
- full textbeam-chunk
  text/plain1 KBdoc:beam/2abe20aa-42dd-4960-a681-dd7e97348329
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  - Example: ```python query = { "size": 10, "query": { "match": { "text": "sample" } }, "track_total_hits": False } ``` 3. **Cluster Confi…
[13]Ed2227ce 3ffd 49b1 92b7 C2205349c1463 facts
ctx:claims/beam/ed2227ce-3ffd-49b1-92b7-c2205349c146
[14]0ffdb47f 7355 4044 A040 123b60076c233 facts
ctx:claims/beam/0ffdb47f-7355-4044-a040-123b60076c23
- full textbeam-chunk
  text/plain1 KBdoc:beam/0ffdb47f-7355-4044-a040-123b60076c23
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  #### Step 3: Implement the Main Search Endpoint Combine the results from both services and handle errors appropriately. ```python @app.post("/search", response_model=SearchResponse) async def search(query: SearchQuery): try: s…
[15]F7efd7d0 3d68 4ac6 841d 644f98af804e1 fact
ctx:claims/beam/f7efd7d0-3d68-4ac6-841d-644f98af804e
[16]1a3ec59a C5a8 4cc0 9e26 Ce87ed77ed862 facts
ctx:claims/beam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
- full textbeam-chunk
  text/plain1 KBdoc:beam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
  Show excerpt
  Ensure your queries are optimized for performance. 1. **Use Efficient Query Types**: Prefer `term` and `terms` queries over `match` and `match_phrase` queries when possible. ```json { "query": { "bool": { "mu…
[17]5f26f8c5 Dfd9 40e7 A81f F613a88eead62 facts
ctx:claims/beam/5f26f8c5-dfd9-40e7-a81f-f613a88eead6
- full textbeam-chunk
  text/plain1 KBdoc:beam/5f26f8c5-dfd9-40e7-a81f-f613a88eead6
  Show excerpt
  } }) # Bulk index some data documents = [ {'_index': index_name, '_source': {'text': 'This is some example text'}}, {'_index': index_name, '_source': {'text': 'Another example text'}}, {'_index': index_name, '_source': {'te…
[18]8f0d7477 3a02 46e9 A340 4c293e908ebc6 facts
ctx:claims/beam/8f0d7477-3a02-46e9-a340-4c293e908ebc
[19]63484f14 F077 4119 Aad4 2ec5f59e18013 facts
ctx:claims/beam/63484f14-f077-4119-aad4-2ec5f59e1801
[20]64bee5ce B7c5 4343 9213 164b1fc9c66e4 facts
ctx:claims/beam/64bee5ce-b7c5-4343-9213-164b1fc9c66e
[21]32482dcb F293 412a 8ea0 A9dfc518165e2 facts
ctx:claims/beam/32482dcb-f293-412a-8ea0-a9dfc518165e
- full textbeam-chunk
  text/plain1 KBdoc:beam/32482dcb-f293-412a-8ea0-a9dfc518165e
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  'track_total_hits': True # Enable total hits tracking }) print(response['hits']['total']['value']) # Output: 1 ``` #### 4. Hardware and Resource Allocation - **Ensure Sufficient Resources**: Allocate enough CPU, memory, and disk spa…
[22]9a83a47a E47d 4467 Bbab 2f9a27e7d3bf2 facts
ctx:claims/beam/9a83a47a-e47d-4467-bbab-2f9a27e7d3bf
- full textbeam-chunk
  text/plain1 KBdoc:beam/9a83a47a-e47d-4467-bbab-2f9a27e7d3bf
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  # Get the synonym for the query term synonym = module.get_synonym(query['term']) if synonym: # Rewrite the query using the synonym query['term'] = synonym return query # Example usage: query = {'term': 'hell…
[23]866cc857 Ac06 46bc 8040 C98e5126053f3 facts
ctx:claims/beam/866cc857-ac06-46bc-8040-c98e5126053f
- full textbeam-chunk
  text/plain1 KBdoc:beam/866cc857-ac06-46bc-8040-c98e5126053f
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  self.synonyms[context][term].append(synonym) def get_synonyms(self, term, context): return self.synonyms[context].get(term, []) # Example usage: module = ContextAwareSynonymLookupModule() # Add synonyms with context m…
[24]Dc43e263 Ae12 4ebe Aaee B46ef58b17d02 facts
ctx:claims/beam/dc43e263-ae12-4ebe-aaee-b46ef58b17d0
- full textbeam-chunk
  text/plain1 KBdoc:beam/dc43e263-ae12-4ebe-aaee-b46ef58b17d0
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  'settings': { 'analysis': { 'analyzer': { 'synonym_analyzer': { 'type': 'custom', 'tokenizer': 'standard', 'filter': ['synonym_filter'] …

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query

Rdf:typein disputerdf:type

Inbound mentions (26)

containsContains(5)

isPartOfIs Part of(5)

hasStructureHas Structure(3)

returnsReturns(3)

calledWithCalled With(2)

containsObjectContains Object(1)

createsCreates(1)

definesDefines(1)

exemplifiedByExemplified by(1)

hasValueHas Value(1)

isTypeOfIs Type of(1)

parentQueryParent Query(1)

receivesReceives(1)

Other facts (52)

Timeline

References (24)

See also

Keep researching

Rdf:typein dispute`rdf:type`

`contains`Contains(5)

`isPartOf`Is Part of(5)

`hasStructure`Has Structure(3)

`returns`Returns(3)

`calledWith`Called With(2)

`containsObject`Contains Object(1)

`creates`Creates(1)

`defines`Defines(1)

`exemplifiedBy`Exemplified by(1)

`hasValue`Has Value(1)

`isTypeOf`Is Type of(1)

`parentQuery`Parent Query(1)

`receives`Receives(1)