Replace rerank with rescore to be consistent

Author: carlosdelest

docs/reference/mapping/types/dense-vector.asciidoc

@@ -122,7 +122,7 @@ The three following quantization strategies are supported:
 * `bbq` - experimental:[] Better binary quantization which reduces each dimension to a single bit precision. This reduces the memory footprint by 96% (or 32x) at a larger cost of accuracy. Generally, oversampling during query time and reranking can help mitigate the accuracy loss.
 
 
-When using a quantized format, you may want to oversample and rescore the results to improve accuracy. See <<dense-vector-knn-search-reranking, oversampling and rescoring>> for more information.
+When using a quantized format, you may want to oversample and rescore the results to improve accuracy. See <<dense-vector-knn-search-rescoring, oversampling and rescoring>> for more information.
 
 To use a quantized index, you can set your index type to `int8_hnsw`, `int4_hnsw`, or `bbq_hnsw`. When indexing `float` vectors, the current default
 index type is `int8_hnsw`.

docs/reference/search/search-your-data/knn-search.asciidoc

@@ -1152,7 +1152,7 @@ POST product-index/_search
 //TEST[continued]
 
 [discrete]
-[[dense-vector-knn-search-reranking]]
+[[dense-vector-knn-search-rescoring]]
 ==== Oversampling and rescoring for quantized vectors
 
 All forms of quantization will result in some accuracy loss and as the quantization level increases the accuracy loss will also increase.
@@ -1163,7 +1163,7 @@ Generally, we have found that:
 
 There are two main ways to oversample and rescore. The first is to utilize the <<rescore, rescore section>> in the `_search` request.
 
-Here is an example using the top level `knn` search with oversampling and using `rescore` to rerank the results:
+Here is an example using the top level `knn` search with oversampling and using `rescore` to rescore the results:
 
 [source,console]
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