## 5 Results and Discussion  
To assess the quality, completeness, and execution time of the task of extracting data from the text of a web page using ChatGPT 3.5, a series of experiments were conducted, during which the following data extraction results and their representation in a machine-readable format were collected (Table 1):  
1. The total number of data elements found and included in the result (countries; regions; settlements; geographical features; and the relationships region-country, settlement-region, and object-region).  
Figure 8: Fragment of the program code for creating objects in the ontology.  
Figure 9: Created ontology in the Protégé editor with an initialized reasoner.  
The number of irrelevant data elements included in the result, i.e., objects or facts not present in the text and considered \"information noise\" by the neural network.  
The error rate in processing the page (the ratio of the number of irrelevant elements to the total number of elements).  
The time taken to process the page.  
The obtained results indicate that a text analysis of a web page using ChatGPT is, as expected, much faster than an analysis performed by humans. However, the results of text processing can vary significantly even when the query is identical and the input data are the same. This variability is due to the nature of the algorithms used in the LLM model, which are stochastic in character. Additionally, the complexity and volume of the context itself also affect this parameter. In the conducted experiments, this is particularly important since the entire web page is included in the prompt, including markup tags that create \"information noise\".  
During the experiment, it was established that the number of data elements found on the page could vary from 18 to 42 in different experiments. The issue of data completeness can be addressed by making several identical queries and then combining the data.  
A more serious problem is the retrieval of irrelevant elements in the query results--those for which information is completely absent on the processed web page. Such elements are generated by the model based on its own training datasets, and their identification requires additional verification. One way to perform such verification could be to check all found data elements for their presence in the page text. This is precisely how irrelevant data elements were identified during the execution of the above-described experiment.  
The conducted research made it possible to answer the research questions posed.  
Is it feasible to obtain a machine-readable output from processing natural language texts using ChatGPT when the source texts are unadapted web pages? The answer to this research question: Yes, this possibility is confirmed by the experiments conducted and described in this work.  
How consistent is the response from the OpenAI API when the query format and source text remain unchanged? The answer to this research question: The experiments conducted showed that the results obtained using the same query can randomly differ from one another, and there is no reliable way to avoid this.  
Can the result from the OpenAI API query be automatically processed and the derived data be uploaded into an ontological model? The answer to this research question: Yes, it is possible, and such automatic processing was implemented in the program code during the research.