# PET: An Annotated Dataset for Process Extraction from Natural Language Text

arXiv: https://arxiv.org/abs/2203.04860 · HTML: https://mlsanity.com/paper/2203.04860/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Built on by (most cited first, 15 of 15)

- [Large Language Models can accomplish Business Process Management Tasks](https://mlsanity.com/paper/2307.09923/) (2023)
- [ProMoAI: Process Modeling with Generative AI](https://mlsanity.com/paper/2403.04327/) (2024)
- [Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text](https://mlsanity.com/paper/2305.03960/) (2023)
- [Towards a Benchmark for Causal Business Process Reasoning with LLMs](https://mlsanity.com/paper/2406.05506/) (2024)
- [Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?](https://mlsanity.com/paper/2304.11065/) (2023)
- [PAGED: A Benchmark for Procedural Graphs Extraction from Documents](https://mlsanity.com/paper/2408.03630/) (2024)
- [NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods](https://mlsanity.com/paper/2409.13738/) (2024)
- [Recent Advances in Data-Driven Business Process Management](https://mlsanity.com/paper/2406.01786/) (2024)
- [Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts](https://mlsanity.com/paper/2309.01812/) (2023)
- [Evaluating the Process Modeling Abilities of Large Language Models -- Preliminary Foundations and Results](https://mlsanity.com/paper/2503.13520/) (2025)
- [Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text](https://mlsanity.com/paper/2507.08362/) (2025)
- [What is the Best Process Model Representation? A Comparative Analysis for Process Modeling with Large Language Models](https://mlsanity.com/paper/2507.11356/) (2025)
- [COVENANT: Natural-Language Workflow Compilation for Aligned Agent Execution](https://mlsanity.com/paper/2607.25400/) (2026)
- [Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement](https://mlsanity.com/paper/2601.19170/) (2026)
- [Pragmos: A Process Agentic Modeling System](https://mlsanity.com/paper/2604.27311/) (2026)

## Abstract (arXiv)

Process extraction from text is an important task of process discovery, for which various approaches have been developed in recent years. However, in contrast to other information extraction tasks, there is a lack of gold-standard corpora of business process descriptions that are carefully annotated with all the entities and relationships of interest. Due to this, it is currently hard to compare the results obtained by extraction approaches in an objective manner, whereas the lack of annotated texts also prevents the application of data-driven information extraction methodologies, typical of the natural language processing field. Therefore, to bridge this gap, we present the PET dataset, a first corpus of business process descriptions annotated with activities, gateways, actors, and flow information. We present our new resource, including a variety of baselines to benchmark the difficulty and challenges of business process extraction from text. PET can be accessed via huggingface.co/datasets/patriziobellan/PET