---
title: "Subevent Structure as a Predictor of Entity Identity Change in"
description: "The paper asks how much of the entity change seen in procedural instructions can be… 67.4% precision. 36 references, cited by 1 paper here."
authors: ["Kyeongmin Rim", "James Pustejovsky"]
year: 2026
venue: "Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)"
source: "https://aclanthology.org/2026.slide-1.4/"
doi: "10.63317/3yvk8sp8qkz4"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/rim-2026-subevent-structure-as-predictor-entity/"
---
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# Subevent Structure as a Predictor of Entity Identity Change in Procedural Text

**Authors:** [Kyeongmin Rim](https://md.research.elamur.ai/free/en/authors/kyeongmin-rim/) and [James Pustejovsky](https://md.research.elamur.ai/free/en/authors/james-pustejovsky/) · **Year:** 2026 · **Venue:** [Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)](https://md.research.elamur.ai/free/en/venues/slide/) ([SLiDE 2026](https://md.research.elamur.ai/free/en/venues/slide/2026/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.slide-1.4/) · [PDF](https://aclanthology.org/2026.slide-1.4.pdf) · [DOI 10.63317/3yvk8sp8qkz4](https://doi.org/10.63317/3yvk8sp8qkz4) · [OpenAlex](https://openalex.org/W7166537358)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/rim-2026-subevent-structure-as-predictor-entity/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks how much of the entity change seen in procedural instructions can be predicted from a verb's lexical entry alone, with no information about its arguments. The authors use the subevent decompositions in VerbNet-GL (VN-GL). For each verb class they derive an aspectual type (process vs. transition) and a count of participants entering and leaving the result state. This gives a predicted dynamic event topology (DET) without any training data. Separately, ten LLMs label each entity in about 3100 OpenPI steps with a dynamic object mode (DOM). The labels are merged by majority vote into silver data, and observed DETs are built from them. Comparing the two sources shows that the verb-only predictor is reasonably precise for transformation, the most frequent topology, but performs poorly on the other topologies. The errors cluster where Generative Lexicon theory expects the arguments' qualia to shape the outcome through co-composition. The authors take this as empirical support for the view that event meaning is shared between predicate and arguments rather than stored in the verb.

## Key points
- The VN-only predictor reaches 67.4% precision (F1 = 0.35) on transformation events.
- Of the events VN-GL predicts to lack a result state, just 24% turn out to be no-change in the silver data. The authors attribute the rest to argument-side contributions.
- The DOM labels are created, terminated, transformed and persistent. A fifth label, bystander, marks noisy entities, which are excluded from evaluation. Transformation counts only as a type-level change, e.g. batter → cake.
- The silver DOM resource over OpenPI comes from a ten-LLM ensemble and is checked against manual gold annotation. A theory-based post-processing step overrides the model consensus when an entity's changes involve only location or possession.
- The authors identify three failure patterns:
  - weak emergence precision;
  - state changes in process events that the resource does not model;
  - argument qualia overriding the predicted outcome for destruction-class verbs.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Brandeis University](https://md.research.elamur.ai/free/en/universities/brandeis-university/) — University, Waltham, United States · 34 works · 5,637 citations

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## Cite

Cite as: Kyeongmin Rim and James Pustejovsky. 2026. Subevent Structure as a Predictor of Entity Identity Change in Procedural Text. Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{rim2026subevent,
  title = {{Subevent Structure as a Predictor of Entity Identity Change in Procedural Text}},
  author = {Kyeongmin Rim and James Pustejovsky},
  booktitle = {Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)},
  year = {2026},
  doi = {10.63317/3yvk8sp8qkz4},
  url = {https://doi.org/10.63317/3yvk8sp8qkz4}
}
```

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## References

36 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Susan Windisch Brown, Julia Bonn, James Gung, Annie Zaenen, James Pustejovsky, and Martha Palmer. 2019. [VerbNet Representations: Subevent Semantics for Transfer Verbs](https://md.research.elamur.ai/free/en/papers/brown-2019-verbnet-representations-subevent-semantics-transfer/). In Proceedings of the First International Workshop on Designing Meaning Representations, pages 154– 163, Florence, Italy. Associatio…
2. James Gung and Martha Palmer. 2021. Predicate Representations and Polysemy in VerbNet Semantic Parsing. In Proceedings of the 14th International Conference on Computational Semantics (IWCS), pages 51–62, Groningen, The Netherlands (online). Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=James+Gung+and+Martha+Palmer%2E+2021%2E+Predicate+Representations+and+Polysemy+in+VerbNet+Semantic+Parsing%2E+In+Proceedings+of+the+14th+International+Conference+on+Computational+Semantics+%28IWCS%29%2C+pages+51%E2%80%93)
3. Susan Windisch Brown, Julia Bonn, Ghazaleh Kazeminejad, Annie Zaenen, James Pustejovsky, and Martha Palmer. 2022. Semantic Representations for NLP Using VerbNet and the Generative Lexicon. Frontiers in Artificial Intelligence, 5. [find](https://scholar.google.com/scholar?q=Susan+Windisch+Brown%2C+Julia+Bonn%2C+Ghazaleh+Kazeminejad%2C+Annie+Zaenen%2C+James+Pustejovsky%2C+and+Martha+Palmer%2E+2022%2E+Semantic+Representations+for+NLP+Using+VerbNet+and+the+Generative+Lexicon%2E+Frontiers+i)
4. Aditya Gupta and Greg Durrett. 2019a. Effective use of transformer networks for entity tracking. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Aditya+Gupta+and+Greg+Durrett%2E+2019a%2E+Effective+use+of+transformer+networks+for+entity+tracking%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2E+Asso)
5. Susan Windisch Brown, James Pustejovsky, Annie Zaenen, and Martha Palmer. 2018. Integrating Generative Lexicon Event Structures into Verb-Net. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Associ… [find](https://scholar.google.com/scholar?q=Susan+Windisch+Brown%2C+James+Pustejovsky%2C+Annie+Zaenen%2C+and+Martha+Palmer%2E+2018%2E+Integrating+Generative+Lexicon+Event+Structures+into+Verb%2DNet%2E+In+Proceedings+of+the+Eleventh+International+Conference+o)
6. Aditya Gupta and Greg Durrett. 2019b. Tracking discrete and continuous entity state for process understanding. In Proceedings of the Third Workshop on Structured Prediction for NLP. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Aditya+Gupta+and+Greg+Durrett%2E+2019b%2E+Tracking+discrete+and+continuous+entity+state+for+process+understanding%2E+In+Proceedings+of+the+Third+Workshop+on+Structured+Prediction+for+NLP%2E+Association+for+Co)
7. Matthew Honnibal, Ines Montani, Sofie Van Lan-deghem, Adriane Boyd, and Henning Peters. 2023. spaCy: Industrial-strength Natural Language Processing in Python. Zenodo. [find](https://scholar.google.com/scholar?q=Matthew+Honnibal%2C+Ines+Montani%2C+Sofie+Van+Lan%2Ddeghem%2C+Adriane+Boyd%2C+and+Henning+Peters%2E+2023%2E+spaCy%3A+Industrial%2Dstrength+Natural+Language+Processing+in+Python%2E+Zenodo%2E)
8. Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. 2014. Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3):904–911. [find](https://scholar.google.com/scholar?q=Marc+Brysbaert%2C+Amy+Beth+Warriner%2C+and+Victor+Kuperman%2E+2014%2E+Concreteness+ratings+for+40+thousand+generally+known+English+word+lemmas%2E+Behavior+Research+Methods%2C+46%283%29%3A904%E2%80%93911%2E)
9. Elisabetta Jezek. 2017. Dynamic Argument Structure. Linguistic Issues in Language Technology, 15(3). [find](https://scholar.google.com/scholar?q=Elisabetta+Jezek%2E+2017%2E+Dynamic+Argument+Structure%2E+Linguistic+Issues+in+Language+Technology%2C+15%283%29%2E)
10. Peter Clark, Bhavana Dalvi, and Niket Tandon. 2018. What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text. [find](https://scholar.google.com/scholar?q=Peter+Clark%2C+Bhavana+Dalvi%2C+and+Niket+Tandon%2E+2018%2E+What+Happened%3F+Leveraging+VerbNet+to+Predict+the+Effects+of+Actions+in+Procedural+Text%2E)
11. Elisabetta Ježek and Chiara Melloni. 2011. Nominals, polysemy, and co-predication. Journal of Cognitive Science, 12(1):1–31. [find](https://scholar.google.com/scholar?q=Elisabetta+Je%C5%BEek+and+Chiara+Melloni%2E+2011%2E+Nominals%2C+polysemy%2C+and+co%2Dpredication%2E+Journal+of+Cognitive+Science%2C+12%281%29%3A1%E2%80%9331%2E)
12. Bhavana Dalvi, Lifu Huang, Niket Tandon, Wen-tau Yih, and Peter Clark. 2018. Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguis… [find](https://scholar.google.com/scholar?q=Bhavana+Dalvi%2C+Lifu+Huang%2C+Niket+Tandon%2C+Wen%2Dtau+Yih%2C+and+Peter+Clark%2E+2018%2E+Tracking+State+Changes+in+Procedural+Text%3A+A+Challenge+Dataset+and+Models+for+Process+Paragraph+Comprehension%2E+In+Proceedin)
13. Ariel Kamen and Yakov Kamen. 2025. Majority rules: LLM ensemble is a winning approach for content categorization. arXiv preprint arXiv:2511.15714. — [arXiv:2511.15714](https://arxiv.org/abs/2511.15714) [find](https://scholar.google.com/scholar?q=Ariel+Kamen+and+Yakov+Kamen%2E+2025%2E+Majority+rules%3A+LLM+ensemble+is+a+winning+approach+for+content+categorization%2E+arXiv+preprint+arXiv%3A2511%2E15714%2E+%E2%80%94+%5BarXiv%3A2511%2E15714%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2511%2E15714)
14. Ghazaleh Kazeminejad, Martha Palmer, Susan Windisch Brown, and James Pustejovsky. 2022. Componential analysis of English verbs. volume 5. Frontiers. [find](https://scholar.google.com/scholar?q=Ghazaleh+Kazeminejad%2C+Martha+Palmer%2C+Susan+Windisch+Brown%2C+and+James+Pustejovsky%2E+2022%2E+Componential+analysis+of+English+verbs%2E+volume+5%2E+Frontiers%2E)
15. Ghazaleh Kazeminejad, Martha Palmer, Tao Li, and Vivek Srikumar. 2021. Automatic Entity State Annotation using the VerbNet Semantic Parser. In Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop, pages 123–132, Punta Cana, Domin… [find](https://scholar.google.com/scholar?q=Ghazaleh+Kazeminejad%2C+Martha+Palmer%2C+Tao+Li%2C+and+Vivek+Srikumar%2E+2021%2E+Automatic+Entity+State+Annotation+using+the+VerbNet+Semantic+Parser%2E+In+Proceedings+of+the+Joint+15th+Linguistic+Annotation+Works)
16. Hossein Rajaby Faghihi, Parisa Kordjamshidi, Choh Man Teng, and James Allen. 2023. The Role of Semantic Parsing in Understanding Procedural Text. [find](https://scholar.google.com/scholar?q=Hossein+Rajaby+Faghihi%2C+Parisa+Kordjamshidi%2C+Choh+Man+Teng%2C+and+James+Allen%2E+2023%2E+The+Role+of+Semantic+Parsing+in+Understanding+Procedural+Text%2E)
17. Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al. 2024. Gemma 2: Improving open language models at a practical size. arXiv preprint arXiv:2408.00118. [find](https://scholar.google.com/scholar?q=Gemma+Team%2C+Morgane+Riviere%2C+Shreya+Pathak%2C+Pier+Giuseppe+Sessa%2C+Cassidy+Hardin%2C+Surya+Bhupatiraju%2C+L%C3%A9onard+Hussenot%2C+Thomas+Mesnard%2C+Bobak+Shahriari%2C+Alexandre+Ram%C3%A9%2C+et+al%2E+2024%2E+Gemma+2%3A+Improving+o)
18. Karin Kipper, Anna Korhonen, Neville Ryant, and Martha Palmer. 2008. A large-scale classification of English verbs. Language Resources and Evaluation, 42(1):21–40. [find](https://scholar.google.com/scholar?q=Karin+Kipper%2C+Anna+Korhonen%2C+Neville+Ryant%2C+and+Martha+Palmer%2E+2008%2E+A+large%2Dscale+classification+of+English+verbs%2E+Language+Resources+and+Evaluation%2C+42%281%29%3A21%E2%80%9340%2E)
19. Beth Levin. 1993. English Verb Classes and Alternations: A Preliminary Investigation. University of Chicago Press. [find](https://scholar.google.com/scholar?q=Beth+Levin%2E+1993%2E+English+Verb+Classes+and+Alternations%3A+A+Preliminary+Investigation%2E+University+of+Chicago+Press%2E)
20. Evangelia Spiliopoulou, Artidoro Pagnoni, Yonatan Bisk, and Eduard Hovy. 2022. EvEntS ReaLM: Event Reasoning of Entity States via Language Models. [find](https://scholar.google.com/scholar?q=Evangelia+Spiliopoulou%2C+Artidoro+Pagnoni%2C+Yonatan+Bisk%2C+and+Eduard+Hovy%2E+2022%2E+EvEntS+ReaLM%3A+Event+Reasoning+of+Entity+States+via+Language+Models%2E)
21. Marc Moens and Mark Steedman. 1988. Temporal ontology and temporal reference. Computational Linguistics, 14(2):15–28. [find](https://scholar.google.com/scholar?q=Marc+Moens+and+Mark+Steedman%2E+1988%2E+Temporal+ontology+and+temporal+reference%2E+Computational+Linguistics%2C+14%282%29%3A15%E2%80%9328%2E)
22. Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, and Eduard Hovy. 2020. A Dataset for Tracking Entities in Open Domain Procedural Text. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP),… [find](https://scholar.google.com/scholar?q=Niket+Tandon%2C+Keisuke+Sakaguchi%2C+Bhavana+Dalvi%2C+Dheeraj+Rajagopal%2C+Peter+Clark%2C+Michal+Guerquin%2C+Kyle+Richardson%2C+and+Eduard+Hovy%2E+2020%2E+A+Dataset+for+Tracking+Entities+in+Open+Domain+Procedural+Text%2E)
23. David Oniani, Jordan Hilsman, Hang Dong, Shiven Verma, Fengyi Gao, and Yanshan Wang. 2023. Large language models vote: Prompting for rare disease identification. arXiv preprint arXiv:2308.12890. — [arXiv:2308.12890](https://arxiv.org/abs/2308.12890) [find](https://scholar.google.com/scholar?q=David+Oniani%2C+Jordan+Hilsman%2C+Hang+Dong%2C+Shiven+Verma%2C+Fengyi+Gao%2C+and+Yanshan+Wang%2E+2023%2E+Large+language+models+vote%3A+Prompting+for+rare+disease+identification%2E+arXiv+preprint+arXiv%3A2308%2E12890%2E+%E2%80%94+%5Bar)
24. James Pustejovsky. 1995. The Generative Lexicon. MIT Press. [find](https://scholar.google.com/scholar?q=James+Pustejovsky%2E+1995%2E+The+Generative+Lexicon%2E+MIT+Press%2E)
25. James Pustejovsky. 2013. Dynamic Event Structure and Habitat Theory. In Proceedings of the 6th International Conference on Generative Approaches to the Lexicon (GL2013), pages 1–10, Pisa, Italy. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=James+Pustejovsky%2E+2013%2E+Dynamic+Event+Structure+and+Habitat+Theory%2E+In+Proceedings+of+the+6th+International+Conference+on+Generative+Approaches+to+the+Lexicon+%28GL2013%29%2C+pages+1%E2%80%9310%2C+Pisa%2C+Italy%2E+Assoc)
26. Jizhi Tang, Yanqiang Qu, and Yansong Li. 2020. Understanding procedural text using interactive entity networks. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Jizhi+Tang%2C+Yanqiang+Qu%2C+and+Yansong+Li%2E+2020%2E+Understanding+procedural+text+using+interactive+entity+networks%2E+In+Proceedings+of+the+2020+Conference+on+Empirical+Methods+in+Natural+Language+Processin)
27. James Pustejovsky, Jessica Moszkowicz, Olga Batiukova, and Anna Rumshisky. 2009. GLML: Annotating Argument Selection and Coercion. In Proceedings of the Eight International Conference on Computational Semantics, pages 169– 180, Tilburg, The Netherlands. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=James+Pustejovsky%2C+Jessica+Moszkowicz%2C+Olga+Batiukova%2C+and+Anna+Rumshisky%2E+2009%2E+GLML%3A+Annotating+Argument+Selection+and+Coercion%2E+In+Proceedings+of+the+Eight+International+Conference+on+Computational)
28. Jingxuan Tu, Timothy Obiso, Bingyang Ye, Kyeongmin Rim, Keer Xu, Liulu Yue, Susan Windisch Brown, Martha Palmer, and James Pustejovsky. 2024. GLAMR: Augmenting AMR with GL-VerbNet Event Structure. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resour… [find](https://scholar.google.com/scholar?q=Jingxuan+Tu%2C+Timothy+Obiso%2C+Bingyang+Ye%2C+Kyeongmin+Rim%2C+Keer+Xu%2C+Liulu+Yue%2C+Susan+Windisch+Brown%2C+Martha+Palmer%2C+and+James+Pustejovsky%2E+2024%2E+GLAMR%3A+Augmenting+AMR+with+GL%2DVerbNet+Event+Structure%2E+In+)
29. Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020. [Stanza: A Python natural language processing toolkit for many human languages](https://md.research.elamur.ai/free/en/papers/qi-2020-stanza-python-natural-language-processing/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 101–108. Associat…
30. Zeno Vendler. 1967. Verbs and times. In Linguistics in Philosophy, pages 97–121. Cornell University Press. [find](https://scholar.google.com/scholar?q=Zeno+Vendler%2E+1967%2E+Verbs+and+times%2E+In+Linguistics+in+Philosophy%2C+pages+97%E2%80%93121%2E+Cornell+University+Press%2E)
31. Veniamin Veselovsky, Manoel Horta Ribeiro, Akhil Arora, Martin Josifoski, Ashton Anderson, and Robert West. 2023. Generating faithful synthetic data with large language models: A case study in computational social science. arXiv preprint arXiv:2305.15041. — [arXiv:2305.15041](https://arxiv.org/abs/2305.15041) [find](https://scholar.google.com/scholar?q=Veniamin+Veselovsky%2C+Manoel+Horta+Ribeiro%2C+Akhil+Arora%2C+Martin+Josifoski%2C+Ashton+Anderson%2C+and+Robert+West%2E+2023%2E+Generating+faithful+synthetic+data+with+large+language+models%3A+A+case+study+in+computa)
32. Qwen Team. 2025. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115. [find](https://scholar.google.com/scholar?q=Qwen+Team%2E+2025%2E+Qwen2%2E5+technical+report%2E+arXiv+preprint+arXiv%3A2412%2E15115%2E)
33. Kyeongmin Rim and James Pustejovsky. 2026. [Missing links: LLM-augmentation of event triggers of state changes in the OpenPI dataset](https://md.research.elamur.ai/free/en/papers/rim-2026-missing-links-llm-augmentation-event/). In Proceedings of the 2026 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2026). European Language Resourc…
34. Xueqing Wu, Sha Li, and Heng Ji. 2023. OpenPI-C: A Better Benchmark and Stronger Baseline for Open-Vocabulary State Tracking. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7213–7222, Toronto, Canada. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Xueqing+Wu%2C+Sha+Li%2C+and+Heng+Ji%2E+2023%2E+OpenPI%2DC%3A+A+Better+Benchmark+and+Stronger+Baseline+for+Open%2DVocabulary+State+Tracking%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+2023%2C+pa)
35. Kyeongmin Rim, Jingxuan Tu, Bingyang Ye, Marc Verhagen, Eben Holderness, and James Pustejovsky. 2023. The Coreference under Transformation Labeling Dataset: Entity Tracking in Procedural Texts Using Event Models. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12448–124… [find](https://scholar.google.com/scholar?q=Kyeongmin+Rim%2C+Jingxuan+Tu%2C+Bingyang+Ye%2C+Marc+Verhagen%2C+Eben+Holderness%2C+and+James+Pustejovsky%2E+2023%2E+The+Coreference+under+Transformation+Labeling+Dataset%3A+Entity+Tracking+in+Procedural+Texts+Using+E)
36. Li Zhang, Hainiu Xu, Yue Yang, Shuyan Zhou, Weiqiu You, Manni Arora, and Chris Callison-Burch. 2024. OpenPI2.0: An improved dataset for entity tracking in texts. In Findings of the Association for Computational Linguistics: EACL 2024, pages 415–431. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Li+Zhang%2C+Hainiu+Xu%2C+Yue+Yang%2C+Shuyan+Zhou%2C+Weiqiu+You%2C+Manni+Arora%2C+and+Chris+Callison%2DBurch%2E+2024%2E+OpenPI2%2E0%3A+An+improved+dataset+for+entity+tracking+in+texts%2E+In+Findings+of+the+Association+for+Com)

*Updated 1 November 2026*