feat(search): phases in the order a child meets them

The phase filter listed GIAS phases alphabetically, so "16 plus" and
"All-through" came before Nursery. /api/filters (and the result-scoped
list) now order them Nursery, Primary, Middle deemed primary, Middle
deemed secondary, Secondary, 16 plus, then All-through, which spans the
whole path. A phase GIAS adds later follows the known ones, A-Z.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

backend/app.py

@@ -40,7 +40,7 @@ from .data_loader import (
 from .data_loader import get_data_info as get_db_info
 from . import flags
 from .places import build_place_index, build_place_registry, places_for_urn
-from .schemas import METRIC_DEFINITIONS, PHASE_GROUPS, RANKING_COLUMNS, SCHOOL_COLUMNS
+from .schemas import METRIC_DEFINITIONS, PHASE_GROUPS, PHASE_ORDER, RANKING_COLUMNS, SCHOOL_COLUMNS
 from .school_groups import (
     FAITH_GROUPS,
     FAITH_KEYS,
@@ -381,6 +381,12 @@ def clean_filter_values(series: pd.Series) -> list[str]:
     )
 
 
+def order_phases(phases: list[str]) -> list[str]:
+    """Phases in the order a child meets them; any GIAS adds later follow, A-Z."""
+    rank = {p: i for i, p in enumerate(PHASE_ORDER)}
+    return sorted(phases, key=lambda p: (rank.get(p.lower(), len(rank)), p))
+
+
 # =============================================================================
 # SECURITY MIDDLEWARE & HELPERS
 # =============================================================================
@@ -937,7 +943,7 @@ async def get_schools(
     result_filters = {
         "local_authorities": clean_filter_values(schools_df["local_authority"]) if "local_authority" in schools_df.columns else [],
         "school_types": clean_filter_values(schools_df["school_type"]) if "school_type" in schools_df.columns else [],
-        "phases": clean_filter_values(schools_df["phase"]) if "phase" in schools_df.columns else [],
+        "phases": order_phases(clean_filter_values(schools_df["phase"])) if "phase" in schools_df.columns else [],
         "genders": clean_filter_values(schools_df.loc[_sec_mask, "gender"]) if "gender" in schools_df.columns and _sec_mask.any() else [],
         "admissions_policies": clean_filter_values(schools_df.loc[_sec_mask, "admissions_policy"]) if "admissions_policy" in schools_df.columns and _sec_mask.any() else [],
     }
@@ -1207,8 +1213,8 @@ async def get_filter_options(request: Request):
             "faiths": [],
         }
 
-    # Phases: return values from data, ordered sensibly
-    phases = clean_filter_values(df["phase"]) if "phase" in df.columns else []
+    # Phases: the values in the data, in the order a child meets them
+    phases = order_phases(clean_filter_values(df["phase"])) if "phase" in df.columns else []
 
     secondary_df = df[df["attainment_8_score"].notna()] if "attainment_8_score" in df.columns else df.iloc[0:0]
     genders = clean_filter_values(secondary_df["gender"]) if "gender" in secondary_df.columns else []