- Introduced `llm_extract_archiveslab.py` script for entity and relationship extraction using LLMAnnotator with GLAM-NER v1.7.0.
- Replaced regex-based extraction with generative LLM inference.
- Added functions for loading markdown content, converting annotation sessions to dictionaries, and generating extraction statistics.
- Implemented comprehensive logging of extraction results, including counts of entities, relationships, and specific types like heritage institutions and persons.
- Results and statistics are saved in JSON format for further analysis.
- Implemented `generate_mermaid_with_instances.py` to create ER diagrams that include all classes, relationships, enum values, and instance data.
- Loaded instance data from YAML files and enriched enum definitions with meaningful annotations.
- Configured output paths for generated diagrams in both frontend and schema directories.
- Added support for excluding technical classes and limiting the number of displayed enum and instance values for readability.
- Implemented a Python script to validate KB library YAML files for required fields and data quality.
- Analyzed enrichment coverage from Wikidata and Google Maps, generating statistics.
- Created a comprehensive markdown report summarizing validation results and enrichment quality.
- Included error handling for file loading and validation processes.
- Generated JSON statistics for further analysis.
- Introduced a comprehensive class diagram for the heritage custodian observation reconstruction schema.
- Defined multiple classes including AllocationAgency, ArchiveOrganizationType, AuxiliaryDigitalPlatform, and others, with relevant attributes and relationships.
- Established inheritance and associations among classes to represent complex relationships within the schema.
- Generated on 2025-11-28, version 0.9.0, excluding the Container class.
