fix(nlp-service): eliminate false-positive person matches from dirty DB records

- Wire _EXTRA_SPAN_STOPS into _extract_persons_and_role so German function
  words (im, seine, ihre, dem, …) terminate name spans — fixes "Clara im"
  and "seine Kinder" leaking into personNames
- Add _NON_NAME_TOKENS filter in PersonMatcher.load() to skip DB records
  whose first_name contains prepositions or possessives — filters 290 bad
  records (annotations like "an seine Eltern", "Eltern in", place references
  like "Enkel Cram aus Mexiko") that were causing exact Pass-2 matches
- Remove spaCy model downloads from Dockerfile (no longer needed after the
  DB-backed matcher rewrite)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nlp-service/extractor.py

@@ -66,6 +66,22 @@ _DATE_BETWEEN: dict[str, frozenset[str]] = {
     "es": frozenset({"entre"}),
 }
 
+# ── Extra span-termination tokens (function words that cannot be in a name) ──
+
+_EXTRA_SPAN_STOPS: dict[str, frozenset[str]] = {
+    # German articles, possessives, and particles that end a name span
+    "de": frozenset({
+        "im", "am", "beim", "zum", "zur",
+        "dem", "den", "des",
+        "sein", "seine", "seinen", "seiner",
+        "ihr", "ihre", "ihrem", "ihren", "ihrer",
+        "unser", "unsere", "unseren",
+        "über", "auch", "oder", "und",
+    }),
+    "en": frozenset(),
+    "es": frozenset({"el", "la", "los", "las", "su", "sus", "mi"}),
+}
+
 # ── Stopword lists ────────────────────────────────────────────────────────────
 
 _STOPWORDS: dict[str, frozenset[str]] = {
@@ -138,7 +154,7 @@ def _extract_persons_and_role(
         return [], "any"
 
     preps = _ALL_PERSON_PREPS[lang]
-    stops = preps | _DATE_BEFORE[lang] | _DATE_AFTER[lang] | _DATE_BETWEEN[lang]
+    stops = preps | _DATE_BEFORE[lang] | _DATE_AFTER[lang] | _DATE_BETWEEN[lang] | _EXTRA_SPAN_STOPS[lang]
     matches = m.find_in_query(query, preps, stop_tokens=stops)
 
     person_names = [text for text, _ in matches]