# Entity Extraction of Key Elements in 110 Police Reports Based on Large Language Models
## 3 Materials and Methods
### Dataset Construction and Enhancement
#### 3.2.1 Dataset Construction  
The data used in this study were obtained through collaboration with the Beijing Municipal Public Security Bureau, sourced from their 110 emergency response platform. We selected 3000 high-risk and medium-risk Chinese police incident reports from 2019 for focused attention and annotation by domain experts with extensive practical experience in police incident analysis and relevant operational expertise. The existing Chinese data were initially filtered and cleaned by removing low-quality and duplicate entries, and unrelated content was eliminated using regular expressions optimized for Chinese text processing. These domain experts then manually extracted elements from each police incident report, resulting in an initial high-quality Chinese language training set of approximately 2500 reports. From this set, 500 Chinese reports were selected as the test set. It is important to note that all data processing, annotation, and analysis were conducted entirely in Chinese, ensuring consistency and avoiding any language-related complexities throughout the study. In this study, based on the definitions of police report elements and the existing format of police reports, four key elements were extracted: location, key behaviors, key items, and organizations. * Location: Refers to the specific geographic location where the 110 police incident occurred. * Key behaviors: Encompasses 1-5 critical behaviors in the police report, including but not limited to actions and methods mentioned in the report. Common behaviors include actions like cutting, deceiving, and stealing, usually composed of action verbs and related nouns. These behaviors are fundamental to understanding the nature and severity of the incident. * Key items: Includes important items related to the incident, such as common items in 110 police reports like cars, motorcycles, mobile phones, keys, switchblades, bricks, wallets, and iron rods. Item characteristics are crucial in police reports, especially in cases involving property loss or harm, providing essential information for analysis, statistics, and handling of the incidents. * Organizations: Refers to various groups or organizations involved in the incident, either as perpetrators, victims, or related parties. Characteristics of these organizations include the profession or identity of their members, which may include but are not limited to schools, military units, government departments, legal institutions, enterprises, companies, foreigners, journalists, and petition groups. Extracting organization characteristics is crucial for handling police reports, particularly in cases involving special groups, as it provides essential information for formulating response plans, case linking, and analysis. An example from the training set is as follows:  
Police Report Content:  
\"Mr. [PERSON] reported: In Building X, Unit X, Floor XX of XX Jiayuan, Changping District, a woman is threatening to commit suicide. She is holding the broken bottom of a wine bottle against her neck and appears highly agitated. I am a security guard and am unaware of the reasons behind her suicidal intentions. Currently, she has not sustained any injuries\". Extracted Elements:  
Location: Building X, Unit X, Floor XX of XX Jiayuan, Changping District;  
Key behaviors: Suicide;  
Key items: Broken bottom of a wine bottle;  
Organizations: Security guard. Both the training set and the final output requirements are designed in JSON format. For instance, taking the above police incident as an example, the extracted organizationalelements would be output as [[\"organization\": \"residents\"]]. This output then undergoes further processing to obtain the final extraction results. High-quality instructional data are key to enhancing the specific functions of large language models [52]. There are four main methods for constructing an instruction fine-tuning dataset: (1) manually created, (2) model-generated, (3) collected and improved from existing open-source datasets, and (4) a combination of these methods [53].