refactor(ebook): remove spaCy-ner attributes from PhraseCandidate and related functions

python/ebook_search/protected_phrases/models.py

@@ -19,11 +19,8 @@ class PhraseCandidate:
         token_count (int): Number of normalized tokens in the phrase.
         source_raw_ngram (bool): Whether the raw n-gram extractor produced the phrase.
         source_yake (bool): Whether YAKE keyword extraction produced the phrase.
-        source_spacy_ner (bool): Whether spaCy named-entity recognition produced the phrase.
-        source_spacy_noun_chunk (bool): Whether spaCy noun chunking produced the phrase.
         source_capitalized (bool): Whether the capitalized-run extractor produced the phrase.
         source_metadata (bool): Whether book metadata produced the phrase.
-        spacy_label (str | None): spaCy entity label when NER produced the phrase.
         raw_count (int): Occurrences counted across the book text.
         chapter_count (int): Number of chapters containing the phrase.
         yake_score (float | None): Raw YAKE score when available; lower is better.
@@ -36,11 +33,8 @@ class PhraseCandidate:
     token_count: int
     source_raw_ngram: bool = False
     source_yake: bool = False
-    source_spacy_ner: bool = False
-    source_spacy_noun_chunk: bool = False
     source_capitalized: bool = False
     source_metadata: bool = False
-    spacy_label: str | None = None
     raw_count: int = 0
     chapter_count: int = 0
     yake_score: float | None = None