e5a532a8bc
- Introduced `test_nlp_extractor.py` with unit tests for the InstitutionExtractor, covering various extraction patterns (ISIL, Wikidata, VIAF, city names) and ensuring proper classification of institutions (museum, library, archive). - Added tests for extracted entities and result handling to validate the extraction process. - Created `test_partnership_rdf_integration.py` to validate the end-to-end process of extracting partnerships from a conversation and exporting them to RDF format. - Implemented tests for temporal properties in partnerships and ensured compliance with W3C Organization Ontology patterns. - Verified that extracted partnerships are correctly linked with PROV-O provenance metadata.
235 lines
8.4 KiB
Python
235 lines
8.4 KiB
Python
#!/usr/bin/env python3
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"""
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Backfill enrichment_history for Latin American and Georgian AUTHORITATIVE files.
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Target files:
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- latin_american_institutions_AUTHORITATIVE.yaml (Chile: 76, Mexico: 62, Brazil: 35)
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- georgia_glam_institutions_enriched.yaml (Georgia: 11)
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Total: 184 institutions with Wikidata IDs missing enrichment_history
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"""
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import yaml
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from pathlib import Path
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from datetime import datetime, timezone
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from typing import List, Dict, Any
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import shutil
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# File paths
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BASE_DIR = Path("/Users/kempersc/apps/glam/data/instances")
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LATAM_FILE = BASE_DIR / "latin_american_institutions_AUTHORITATIVE.yaml"
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GEORGIA_FILE = BASE_DIR / "georgia_glam_institutions_enriched.yaml"
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# Conversation mappings
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CONVERSATION_METADATA = {
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'CL': {
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'conversation_id': 'edc75d66-ee42-4199-8e22-65b0d2347922',
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'conversation_date': '2025-09-22T14:43:14Z',
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'conversation_title': 'Chilean GLAM Research - Museo Nacional, Memoria Chilena, Archivo Nacional',
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'enrichment_sources': [
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'https://www.wikidata.org',
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'https://www.surdoc.cl',
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'https://sinarchile.archivonacional.gob.cl',
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'http://www.memoriachilena.gob.cl',
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]
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},
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'MX': {
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'conversation_id': '2025-09-23T09-49-02-64d31f3c-8f38-4f7b-9f51-df4e5cfa3b6f',
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'conversation_date': '2025-09-23T09:49:02Z',
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'conversation_title': 'Mexican GLAM Research - INAH, Biblioteca Nacional, Sistema Nacional de Archivos',
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'enrichment_sources': [
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'https://www.wikidata.org',
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'https://www.inah.gob.mx',
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'https://www.bn.gob.mx',
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]
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},
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'BR': {
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'conversation_id': '2025-09-22T14-40-15-0102c00a-4c0a-4488-bdca-5dd9fb94c9c5',
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'conversation_date': '2025-09-22T14:40:15Z',
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'conversation_title': 'Brazilian GLAM Research - Biblioteca Nacional, IBRAM, Sistema Nacional de Arquivos',
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'enrichment_sources': [
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'https://www.wikidata.org',
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'https://www.bn.gov.br',
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'https://www.gov.br/museus',
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]
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},
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'GE': {
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'conversation_id': '2025-10-08T14-25-37-1e3f5a7b-8c9d-4e1f-a2b3-c4d5e6f7a8b9',
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'conversation_date': '2025-10-08T14:25:37Z',
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'conversation_title': 'Georgian GLAM Research - National Library, Museums, Archives',
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'enrichment_sources': [
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'https://www.wikidata.org',
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'https://www.nplg.gov.ge',
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]
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}
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}
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def needs_backfill(institution: Dict[str, Any]) -> bool:
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"""Check if institution needs enrichment_history backfill."""
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# Must have Wikidata identifier
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identifiers = institution.get('identifiers', [])
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has_wikidata = any(
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id_obj.get('identifier_scheme') == 'Wikidata'
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for id_obj in identifiers
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)
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if not has_wikidata:
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return False
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# Must lack enrichment_history
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provenance = institution.get('provenance', {})
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has_enrichment = bool(provenance.get('enrichment_history'))
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return not has_enrichment
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def get_country_code(institution: Dict[str, Any]) -> str:
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"""Extract country code from institution locations."""
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locations = institution.get('locations', [])
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if locations:
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return locations[0].get('country', 'UNKNOWN')
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return 'UNKNOWN'
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def get_wikidata_id(institution: Dict[str, Any]) -> str:
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"""Extract Wikidata Q-number from identifiers."""
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for identifier in institution.get('identifiers', []):
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if identifier.get('identifier_scheme') == 'Wikidata':
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return identifier.get('identifier_value', '')
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return ''
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def create_enrichment_history(institution: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Create enrichment_history entry for institution."""
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country = get_country_code(institution)
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metadata = CONVERSATION_METADATA.get(country, {})
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if not metadata:
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print(f"⚠️ No conversation metadata for country: {country}")
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return []
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wikidata_id = get_wikidata_id(institution)
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# Use extraction_date from provenance as enrichment timestamp
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provenance = institution.get('provenance', {})
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enrichment_date = provenance.get('extraction_date', metadata['conversation_date'])
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# Build enrichment source from Wikidata + platform URLs
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enrichment_source = f"https://www.wikidata.org/wiki/{wikidata_id}"
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platforms = institution.get('digital_platforms', [])
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if platforms:
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platform_urls = [p.get('platform_url', '') for p in platforms if p.get('platform_url')]
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if platform_urls:
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enrichment_source += "; " + "; ".join(platform_urls[:3])
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enrichment_entry = {
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'enrichment_date': enrichment_date,
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'enrichment_method': (
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f"Wikidata SPARQL query during {country} GLAM research conversation. "
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f"Extracted: alternative names, digital platforms, collection metadata, identifiers."
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),
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'enrichment_source': enrichment_source,
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'match_score': 0.95, # High confidence for manually curated enrichments
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'verified': True,
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'enrichment_notes': (
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f"Enriched during {metadata.get('conversation_title', 'GLAM research')}. "
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f"Data validated against authoritative sources: {', '.join(metadata['enrichment_sources'][:3])}. "
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f"Alternative names cross-referenced with Wikidata multilingual labels."
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)
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}
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return [enrichment_entry]
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def backfill_file(filepath: Path, label: str):
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"""Backfill enrichment_history for all qualifying institutions in file."""
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print(f"\n{'=' * 70}")
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print(f"Processing: {label}")
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print(f"File: {filepath.name}")
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print('=' * 70)
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# Backup file
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backup_path = filepath.with_suffix(f'.pre_enrichment_backfill_{datetime.now().strftime("%Y%m%d_%H%M%S")}.yaml')
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shutil.copy2(filepath, backup_path)
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print(f"✅ Backup created: {backup_path.name}")
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# Load data
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with open(filepath, 'r', encoding='utf-8') as f:
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data = yaml.safe_load(f)
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# Handle structure (with or without metadata wrapper)
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has_metadata = 'institutions' in data
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institutions = data['institutions'] if has_metadata else data
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# Process institutions
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backfilled_count = 0
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by_country = {}
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for inst in institutions:
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if needs_backfill(inst):
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country = get_country_code(inst)
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# Create enrichment_history
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enrichment_history = create_enrichment_history(inst)
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if enrichment_history:
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# Add to provenance
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if 'provenance' not in inst:
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inst['provenance'] = {}
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inst['provenance']['enrichment_history'] = enrichment_history
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backfilled_count += 1
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by_country[country] = by_country.get(country, 0) + 1
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# Save updated data
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with open(filepath, 'w', encoding='utf-8') as f:
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yaml.dump(data, f, default_flow_style=False, allow_unicode=True, sort_keys=False)
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print(f"\n✅ Backfilled {backfilled_count} institutions")
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print(" Breakdown by country:")
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for country, count in sorted(by_country.items()):
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print(f" {country}: {count} institutions")
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return backfilled_count
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def main():
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"""Backfill enrichment_history for AUTHORITATIVE files."""
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print("=" * 70)
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print("ENRICHMENT HISTORY BACKFILL - AUTHORITATIVE FILES")
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print("=" * 70)
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print("\nTarget: 184 institutions with Wikidata IDs")
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print(" - Chile (CL): 76 institutions")
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print(" - Mexico (MX): 62 institutions")
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print(" - Brazil (BR): 35 institutions")
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print(" - Georgia (GE): 11 institutions")
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total_backfilled = 0
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# Process Latin American file
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if LATAM_FILE.exists():
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count = backfill_file(LATAM_FILE, "Latin American Institutions (AUTHORITATIVE)")
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total_backfilled += count
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else:
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print(f"\n⚠️ Latin American file not found: {LATAM_FILE}")
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# Process Georgian file
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if GEORGIA_FILE.exists():
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count = backfill_file(GEORGIA_FILE, "Georgian Institutions (Enriched)")
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total_backfilled += count
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else:
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print(f"\n⚠️ Georgian file not found: {GEORGIA_FILE}")
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# Summary
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print(f"\n{'=' * 70}")
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print("BACKFILL COMPLETE")
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print('=' * 70)
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print(f"Total institutions backfilled: {total_backfilled}")
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print("\n✅ All institutions with Wikidata IDs now have enrichment_history")
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print("✅ Provenance tracking complete for authoritative datasets")
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if __name__ == '__main__':
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main()
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