The original sentence texts were encoded through vector embeddings using OpenAI's text-embedding-ada-002 model which generates 1536-dimensional embedding vectors in order to populate a vector datastore. Embeddings are numeric vector representations that capture the content of the text semantically and for instance allow efficient retrieval of sentences resembling target sentences. A Milvus vector store (version 2.3) was implemented with a collection for each split subset (train, validation, and test). Each collection further featured partitions which are subdivisions created for each annotated entity type. Whenever a sentence contained an annotation for an entity type, the sentence was subjoined into the corresponding partition. As the indexing approach, InVerted File Flat (IVF_FLAT) with a number of clusters of 128 (nlist = 128) was used which is a quantization-based indexing approach . The outcome is the original sentence text stored along with potential annotations, the embedding vector, and accompanying metadata in the vector store.