Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 9,000 referrals (medical and dental) was manually annotated with ten types of entities, six attributes, and pairs of relations with clinical relevance. A trained medical doctor or dentist annotated these referrals and then, together with three other researchers, consolidated each of the annotations. The annotated corpus has more than 48% of entities embedded in other entities or containing another. We use this corpus to build Named Entity Recognition (NER) models. The best results were achieved using Multiple Single-entity architectures with clinical word embeddings stacked with character and Flair contextual embeddings (refer to this paper: https://aclanthology.org/2022.coling-1.184/). The entity with the best performance is abbreviation, and the hardest to recognize is finding. NER models applied to this corpus can leverage statistics of diseases and pending procedures. This work constitutes the first annotated corpus using clinical narratives from Chile and one of the few in Spanish. The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community.This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/. We are releasing the dataset in 3 formats: cwlc.zip: Contains the raw text files for each document along with its annotation file in Standoff format cwlc_conll-format: CoNLL format for training NER models. In addition, the dataset has been released in hugging face (https://huggingface.co/plncmm) to facilitate experiments with transformer-based architectures.