*Introduction* DEFT Spanish Light and Rich ERE Annotation, Linguistic Data Consortium (LDC) Catalog Number LDC2025T04, was developed by LDC and consists of 158 Spanish discussion forum and newswire documents annotated for entities, relations and events (ERE). DARPA's Deep Exploration and Filtering of Text (DEFT) program aimed to address remaining capability gaps in state-of-the-art natural language processing technologies related to inference, causal relationships and anomaly detection. LDC supported the DEFT program by collecting, creating and annotating a variety of data sources. Light ERE annotation labels entity mentions for the target set of entity, relation and event types between and among those entities including coreference. Rich ERE annotation expands types and tagging in the entities, relations, and events annotation tasks and replaces strict event coreference with a more loosely defined event hopper annotation. Further information about the annotation methodology is contained in the documentation accompanying this release. *Data* The source data consists of Spanish newswire text and Latin American discussion forum data from DEFT Spanish Treebank LDC2018T01. 128 documents were annotated following Light ERE annotation guidelines. 154 files were labeled with Rich ERE annotation, 124 of which were also labeled with Light ERE annotation. Below is a data summary: Light ERE Files: 128 Words: 72,896 Entities (mentions): 4,309(11,003) Fillers: N/A Relations: 2,382 Event Mentions: 1,193 Event Hoppers: 725 Rich ERE Files: 154 Words: 104,833 Entities (mentions): 5,895(15,537) Fillers: 1,913 Relations: 3,005 Event Mentions: 4,423 Event Hoppers: 3,093 Source documents are in plain text format, annotation is in XML format, and both are UTF-8 encoded. *Acknowledgement* This material is based on research sponsored by Air Force Research Laboratory and Defense Advance Research Projects Agency under agreement number FA8750-13-2-0045. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory and Defense Advanced Research Projects Agency or the U.S. Government.

Creator(s)

Song Chen

Justin Mott

Stephanie Strassel

Distributor(s)

Linguistic Data Consortium

Right Holder(s)

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