Find Entity Linking
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
Find Entity Linking has 50 facts recorded in Dontopedia across 4 references, with 6 live disagreements.
Mostly:api parameter(4), api parameter(4), rdf:type(3)
Maturity scale
raw canonical shape-checked rule-derived certifiedRdf:typein disputerdf:type
Has Parameterin disputehasParameter
Api Parameterin disputeapiParameter
Return Valuein disputereturn-value
Api Parameterin disputeapi-parameter
Conditional Returnin disputeconditionalReturn
Rdfs:labelrdfs:label
Has Return TypehasReturnType
- string or term[2]all time · 7af96457 2865 458d 89c2 Afec41b8e7ec
Called bycalledBy
- Disambiguate Terms[2]sourceall time · 7af96457 2865 458d 89c2 Afec41b8e7ec
Returns Original TermreturnsOriginalTerm
- term[2]sourceall time · 7af96457 2865 458d 89c2 Afec41b8e7ec
Returns LabelreturnsLabel
- data['search'][0]['label'][2]sourceall time · 7af96457 2865 458d 89c2 Afec41b8e7ec
Checks Search ResultchecksSearchResult
- data['search'][2]sourceall time · 7af96457 2865 458d 89c2 Afec41b8e7ec
Inbound mentions (10)
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.
callsCalls(2)
- Disambiguate Terms
ex:disambiguate_terms - Replace Oov Terms
ex:replace_oov_terms
used-byUsed by(2)
- Requests
ex:requests - Wikidata Api
ex:Wikidata-API
usedInUsed in(2)
- Context Parameter
ex:context parameter - Term Parameter
ex:term parameter
decoratesDecorates(1)
- Lru Cache
ex:lru_cache
importedInImported in(1)
- Requests
ex:requests
providesEntityLinkingProvides Entity Linking(1)
- Wikidata
ex:Wikidata
uses-asyncio-to-threadUses Asyncio to Thread(1)
- Replace Oov Terms
ex:replace_oov_terms
Other facts (25)
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.
| Predicate | Value | Ref |
|---|---|---|
| Parses Response | response.json() | [2] |
| Sends Http Request | requests.get | [2] |
| Constructs Url | f-string URL | [2] |
| Defined in | Python Code Block | [2] |
| Uses Api Endpoint | Api.php | [2] |
| Returns on Failure | term | [2] |
| Returns on Success | label | [2] |
| Cached With | Lru Cache | [3] |
| String Formatting | f-string-interpolation | [1] |
| Called by | Replace Oov Terms | [1] |
| Api Call | Wikidata-search-entities | [1] |
| Is Synchronous | true | [1] |
| Return Condition | if search results exist returns label else returns term | [1] |
| Returns | Label or Original Term | [1] |
| Parses | Json Response | [1] |
| Performs | Http Get Request | [1] |
| Uses Library | Requests | [1] |
| Base Url | Api.php | [1] |
| Constructs | Wikidata Api Url | [1] |
| Extracts From | data['search'][0]['label'] | [4] |
| Http Method | GET | [4] |
| External Dependency | Wikidata API | [4] |
| Handles No Result | returns original term | [4] |
| Inverse Returns | term when no search results | [4] |
| Part of | entity resolution system | [4] |
Timeline
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.
References (4)
- custom
ctx:claims/beam/93399bbc-ebe1-4c6b-be2c-c95de6e77fa8 - custom
ctx:claims/beam/7af96457-2865-458d-89c2-afec41b8e7ec- full textbeam-chunktext/plain1 KB
doc:beam/7af96457-2865-458d-89c2-afec41b8e7ecShow excerpt
Here's an example of how you can use a knowledge graph to disambiguate terms: ```python import requests def find_entity_linking(term, context): url = f"https://www.wikidata.org/w/api.php?action=wbsearchentities&search={term}&language=…
- custom
ctx:claims/beam/38f157db-8944-4f1f-85bb-a43db2d03fa9 - custom
ctx:claims/beam/34094d4f-c249-4e79-922e-dfb9f6ea172a- full textbeam-chunktext/plain1 KB
doc:beam/34094d4f-c249-4e79-922e-dfb9f6ea172aShow excerpt
word_embeddings = KeyedVectors.load_word2vec_format('path/to/word2vec.txt', binary=False) def find_nearest_neighbor(embedding, word_embeddings): min_distance = float('inf') nearest_neighbor = None for word in word_embeddings.in…
See also
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