diff --git a/website/docs/api/architectures.md b/website/docs/api/architectures.md
index cb1f7095e..09c5a5b1c 100644
--- a/website/docs/api/architectures.md
+++ b/website/docs/api/architectures.md
@@ -260,7 +260,7 @@ If the `nO` dimension is not set, the TextCategorizer component will set it when
 
 ## Entity linking architectures {#entitylinker source="spacy/ml/models/entity_linker.py"}
 
-An Entity Linker component disambiguates textual mentions (tagged as named
+An `EntityLinker` component disambiguates textual mentions (tagged as named
 entities) to unique identifiers, grounding the named entities into the "real
 world". This requires 3 main components:
 
@@ -312,7 +312,7 @@ If the `nO` dimension is not set, the Entity Linking component will set it when
 
 ### spacy.EmptyKB.v1 {#EmptyKB}
 
-A function that creates a default, empty Knowledge Base from a
+A function that creates a default, empty `KnowledgeBase` from a
 [`Vocab`](/api/vocab) instance.
 
 | Name                   | Type | Description                                                               |
diff --git a/website/docs/api/entitylinker.md b/website/docs/api/entitylinker.md
index 50ffe5c09..cb5145909 100644
--- a/website/docs/api/entitylinker.md
+++ b/website/docs/api/entitylinker.md
@@ -9,11 +9,12 @@ api_string_name: entity_linker
 api_trainable: true
 ---
 
-An Entity Linker component disambiguates textual mentions (tagged as named
+An `EntityLinker` component disambiguates textual mentions (tagged as named
 entities) to unique identifiers, grounding the named entities into the "real
-world". It requires a Knowledge base, a function to generate plausible
-candidates from that Knowledge base given a certain textual mention, and a ML
-model to pick the right candidate, given the local context of the mention.
+world". It requires a `KnowledgeBase`, as well as a function to generate
+plausible candidates from that `KnowledgeBase` given a certain textual mention,
+and a ML model to pick the right candidate, given the local context of the
+mention.
 
 ## Config and implementation {#config}
 
diff --git a/website/docs/usage/processing-pipelines.md b/website/docs/usage/processing-pipelines.md
index 7c47c0c73..ae1616f8b 100644
--- a/website/docs/usage/processing-pipelines.md
+++ b/website/docs/usage/processing-pipelines.md
@@ -380,8 +380,9 @@ table instead of only returning the structured data.
 
 > #### ✏️ Things to try
 >
-> 1. Add the components `"ner"` and `"sentencizer"` _before_ the entity linker.
->    The analysis should now show no problems, because requirements are met.
+> 1. Add the components `"ner"` and `"sentencizer"` _before_ the
+>    `"entity_linker"`. The analysis should now show no problems, because
+>    requirements are met.
 
 ```python
 ### {executable="true"}
diff --git a/website/docs/usage/spacy-101.md b/website/docs/usage/spacy-101.md
index 19580dc0f..db471b1f0 100644
--- a/website/docs/usage/spacy-101.md
+++ b/website/docs/usage/spacy-101.md
@@ -122,7 +122,7 @@ related to more general machine learning functionality.
 | **Lemmatization**                     | Assigning the base forms of words. For example, the lemma of "was" is "be", and the lemma of "rats" is "rat".      |
 | **Sentence Boundary Detection** (SBD) | Finding and segmenting individual sentences.                                                                       |
 | **Named Entity Recognition** (NER)    | Labelling named "real-world" objects, like persons, companies or locations.                                        |
-| **Entity Linking** (EL)               | Disambiguating textual entities to unique identifiers in a Knowledge Base.                                         |
+| **Entity Linking** (EL)               | Disambiguating textual entities to unique identifiers in a knowledge base.                                         |
 | **Similarity**                        | Comparing words, text spans and documents and how similar they are to each other.                                  |
 | **Text Classification**               | Assigning categories or labels to a whole document, or parts of a document.                                        |
 | **Rule-based Matching**               | Finding sequences of tokens based on their texts and linguistic annotations, similar to regular expressions.       |
@@ -379,7 +379,7 @@ spaCy will also export the `Vocab` when you save a `Doc` or `nlp` object. This
 will give you the object and its encoded annotations, plus the "key" to decode
 it.
 
-## Knowledge Base {#kb}
+## Knowledge base {#kb}
 
 To support the entity linking task, spaCy stores external knowledge in a
 [`KnowledgeBase`](/api/kb). The knowledge base (KB) uses the `Vocab` to store
@@ -426,7 +426,7 @@ print("Number of aliases in KB:", kb.get_size_aliases()) # 2
 
 ### Candidate generation
 
-Given a textual entity, the Knowledge Base can provide a list of plausible
+Given a textual entity, the knowledge base can provide a list of plausible
 candidates or entity identifiers. The [`EntityLinker`](/api/entitylinker) will
 take this list of candidates as input, and disambiguate the mention to the most
 probable identifier, given the document context.