## 3 Proposed Method
### Schema Definition and Annotation Guidelines  
We propose a zero-shot-based information extraction method. We provide the model with the required schema and annotation guidelines required to extract the correct information at the document level without any pretrained relevant information [26]. Thus, we aim to achieve successful zero-shot extraction by providing guidelines based on the performance results of the LLM model. The schema template we utilized is designed to be universally applicable for triple extraction in zero-shot scenarios. This template ensures flexibility and robustness across various contexts. However, it is important to note that different scenarios might benefit from using tailored templates, depending on the specific requirements and characteristics of the data involved. Schema Definition: The schema definition is an essential initial step in setting the standards for the information extraction process. It clearly defines the types of entities and relationships to be extracted from the documents, thereby enabling structured and consistent data processing and establishing the semantic structure of the data through entities and relationships [27]. By defining the schema, we ensure that the extracted data are relevant and accurately categorized, which is essential for maintaining data integrity and reliability. The schema acts as a blueprint that guides the model in identifying and classifying entities and relationships within the text, thereby enhancing the overall accuracy of the information extraction process. Entity Definition: Each entity represents a specific concept or object, and entity types and attributes are structured using Python's@dataclass. These entities function as key components in the data extraction and analysis. ```
1@dataclass
2 classEntity:
3\"\"\"
4 Representsanentiyinadocument,whichcouldbeaperson,location,organization,oranyothernamedobject. 5 The'name'attributeholdsthecanonicalnameoftheentityasidentifiedinthedocument'text. 6
7 Attributes:
8 name(str):Thecanonicalnameoftheentity. 9\"\"\"
10 name:str
```  
Relationship Definition: Relationships specify the interactions between entities and provide connections among them. The relationship types and examples are defined precisely to ensure consistency in the annotation guidelines. ```
1@dataclass
2 classTriple:
3\"\"\"
4 Representsatripleconsistingoftwentitiesandarelationasemanticgraphderivedfromtextualdata. * 5 Atriplesistypicallyusedinkowledgerepresentationandinformationextractiontaskstomodelfactual\"statements. * 6
* 7 Attributes:
* 8 Subject(Entity):Thesubjectentityofttheriple,
* 9 usuallythemoepperformingorcausingamaction. * 10 Predicate(str):Therelationshipthatconnects
* 11 thesubjectandobjectentities. * 12 Object(Entity):Theobjectentityofttheriple,
* 13 usuallythemoep experiencingoraffectedbytheaction. * 14\"\"\"
* 15
* 16 Subject:Entity
* 17 Predicate:str
* 18 Object:Entity

Annotation Guidelines and Model Utilization: The annotation guidelines are based on the schema definition to ensure that the data are processed consistently and accurately within the documents. To enable the LLM to perform a specific task, we provide natural language descriptions of that task, known as prompts, along with the input data. This allows the model to recognize the in-context information within the guidelines and generate the appropriate output based on the format [28; 29]. The use of annotation guidelines as prompts within the schema framework is essential for several reasons. Firstly, prompts help the model understand the context and generate appropriate outputs by leveraging natural language descriptions. This ensures consistency in the data annotation process and enhances the accuracy of data analysis. Secondly, the GoLLIE model, which we employed in our research, has demonstrated exceptional performance as a guideline-based model. The GoLLIE model outperforms other zero-shot state-of-the-art methods, largely due to its use of guidelines.