feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

Extrae al registry funciones del proyecto interno footprint_aurgi:
- core (6): slugify_ascii, normalize_for_join, cp_provincia_es, infer_provincia_from_cp, safe_read_csv_fallback, csv_to_parquet_duckdb
- geo puras (7): haversine_km, point_in_ring, point_in_polygon, point_in_polygons_bbox, polygon_bbox, extent_with_padding, distance_bucket
- geo I/O (4): load_geojson_polygons, load_boundary_gdf, add_basemap_osm, add_basemap_with_timeout
- valhalla client (4): valhalla_route, valhalla_isochrone, valhalla_isochrones_async, valhalla_matrix_1_to_n
- datascience stats (7): trimmed_mean, geometric_mean, detect_distribution_type, best_central_tendency, summary_stats, kde_density_levels, alpha_shape_concave_hull
- datascience fuzzy (3): fuzzy_merge_adaptive (rapidfuzz), words_to_dataset, remove_words_from_column
- datascience viz (2): plot_kde_2d, plot_heatmap_log
- infra (4): compress_pdf_ghostscript, render_table_page_pdfpages, add_header_logo, osm2pgsql_ingest
- pipelines (4): setup_geo_stack_docker, compute_centers_reachability, generate_isochrones_by_zone, count_points_per_zone
- types geo (4): LonLat, BBox, IsochroneRequest, Centro

Incluye:
- apps/footprint_geo_stack/ (PostGIS + Martin + Valhalla via docker-compose)
- 131/132 tests pasan (1 skip esperado: osm2pgsql en PATH)
- Issue tracker dev/issues/0052-footprint-aurgi-extraction.md
- Atribucion uniforme: source_repo internal:footprint_aurgi, source_license internal-aurgi
- Build con 9 agentes en paralelo (8 wave 1 + 1 wave 2 pipelines)

Tambien commitea trabajo previo no commiteado: aggregate_extraction_results, chunk_with_overlap, clean_pdf_text, merge_entity_aliases, extract_graph_gliner2, extract_relations_mrebel, extract_triples_spacy_es, gliner2/mrebel/marianmt/rebel/spacy_es load_model, parse_rebel_output, translate_es_to_en, issue 0050/0051.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

python/functions/datascience/extract_triples_spacy_es.md

@@ -0,0 +1,65 @@
+---
+name: extract_triples_spacy_es
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def extract_triples_spacy_es(text: str, nlp: Any) -> dict"
+description: "Extraccion OpenIE schema-less en castellano via reglas de dependencia spaCy. Detecta patrones sujeto-verbo-objeto con el lemma del verbo como relacion (sin vocabulario fijo). Tambien extrae entidades NER (PER, ORG, LOC, MISC)."
+tags: [spacy, openie, nlp, spanish, triples, dependency, ner, schema-less, datascience, python, mit]
+uses_functions:
+  - spacy_es_load_model_py_datascience
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: [time, typing.Any]
+params:
+  - name: text
+    desc: "Texto en castellano a analizar. Funciona mejor con oraciones completas. Admite multiples oraciones en el mismo texto."
+  - name: nlp
+    desc: "Instancia spaCy Language cargada con spacy_es_load_model. Debe incluir dependencias + POS + NER (es_core_news_md o lg)."
+output: "Dict con 'text' (input), 'triples' (lista de {subject, relation, object, verb_form, object_dep, prep}), 'entities' (lista de {text, label}) y 'elapsed_s'. La relacion es el lemma del verbo, opcionalmente sufijado con preposicion (_en, _con) o modo pasivo ([pass])."
+tested: true
+tests:
+  - "oracion simple produce tripleta con sujeto verbo objeto"
+  - "carlos torres preside bbva produce tripleta president"
+  - "amancio ortega fundo inditex en 1985 produce tripletas con fundar_en"
+  - "texto sin verbos produce tripletas vacias"
+  - "entities NER detecta PER ORG LOC"
+test_file_path: "python/functions/datascience/tests/test_extract_triples_spacy_es.py"
+file_path: "python/functions/datascience/extract_triples_spacy_es.py"
+notes: |
+  LICENSE: spaCy es MIT. Modelo es_core_news_md es CC BY-SA 4.0.
+  Uso comercial permitido con atribucion.
+
+  Validado en notebook 09 del analisis gliner_glirel_tuning.
+  Complementa a extract_graph_gliner2: GLiNER2 usa vocabulario fijo de relaciones
+  pero mayor precision; spaCy OpenIE usa lemmas verbales (sin vocabulario fijo)
+  pero requiere post-filtrado manual.
+
+  impure: invoca inferencia del modelo (side effect computacional).
+  El nlp se inyecta externamente para permitir cache y reutilizacion.
+
+  Relaciones compuestas: 'fundar_en' (fundar + preposicion 'en'),
+  'ser_nombrado[pass]' (pasiva), 'trabajar_con' (trabajar + 'con').
+---
+
+## Ejemplo
+
+```python
+from datascience.spacy_es_load_model import spacy_es_load_model
+from datascience.extract_triples_spacy_es import extract_triples_spacy_es
+
+nlp = spacy_es_load_model()
+
+result = extract_triples_spacy_es(
+    "Amancio Ortega fundo Inditex en 1985 en La Coruna.",
+    nlp=nlp,
+)
+# result["triples"]:
+# [{"subject": "Amancio Ortega", "relation": "fundar", "object": "Inditex", ...},
+#  {"subject": "Amancio Ortega", "relation": "fundar_en", "object": "1985", ...},
+#  {"subject": "Amancio Ortega", "relation": "fundar_en", "object": "La Coruna", ...}]
+```