Problem. There’s broad consensus today: LLMs are phenomenal personal productivity tools — they draft, summarize, and assist effortlessly.
But there’s also growing recognition that they’re still not ready for enterprise-grade deployment.

Rationale. NER tools are at the heart of how the scientific community is solving LLM issues using GraphRAG and NodeRAG architectures.

LLMs need knowledge graphs to control hallucinations and make them more solid for enterprise-level use.

And knowledge graphs are built using automatic data extraction tools: not only entity extraction but also concept extraction and relationships among entities or concepts.

As described in our previous post “Using Public Corpora to Build Your NER systems”, we are going to highlight areas where public datasets like OntoNotes or CoNLL can be improved. We will provide some tips on how to avoid these issues, whenever possible, using (semi-)automatic techniques.

Tagging consistency is essential to ensure that training is smooth. Contradictions and inconsistencies not only decrease accuracy but also generate hidden costs in MLOps when trying to debug and fix errors. We often take this consistency for granted, but that is rarely the case, not only in these datasets but also in any other manual tagging work.

Consistency starts with having a solid and clear definition of what an entity is. Typically, if not always, that’s not the case.

And knowledge graphs are built using automatic data extraction tools: not only entity extraction but also concept extraction and relationships among entities or concepts.

The new Forrester Wave?: Data Governance Solutions, Q3 2025 makes one thing clear: governance is no longer about static catalogs. Vendors are moving fast into Active Metadata and Agentic AI, with features like lineage, observability, policy enforcement, and marketplaces for data assets.

The process of building Knowledge Graphs is essential for organizations seeking to organize, structure, and extract actionable insights from their data. However, traditional methods of constructing Knowledge Graphs are often slow, expensive, and complex, requiring significant expertise and manual effort. Bitext NAMER changes the game by automating key steps in the Knowledge Graph creation process, making it faster, more cost-effective, and accessible for businesses of all sizes.

In the era of data-driven decision-making, Knowledge Graphs (KGs) have emerged as pivotal tools for structuring, organizing, and interconnecting vast amounts of information. From enhancing search engine capabilities to powering AI-driven insights, KGs rely heavily on extracting, interpreting, and linking data elements with precision. At the core of this process lies Named Entity Recognition (NER), event extraction, and relationship mapping, foundational technologies for enabling robust knowledge management. Bitext’s NER solution, NAMER, is uniquely positioned to support the growing needs of KG companies, offering unparalleled features that address common industry challenges.

Verticalizing AI21’s Jamba 1.5 with Bitext Synthetic Text

Efficiency and Benefits of Verticalizing LLMs – The Case of Jamba 1.5 Mini.

A robust discussion persists within the technical and academic communities about the suitability of LLMs for tasks like Named Entity Recognition (NER). While LLMs have demonstrated extraordinary capabilities across a wide range of language-related tasks, several concerns remain, including:
LLMs were designed primarily for generation tasks, rather than classification tasks.
LLMs are not the most efficient approach, given the significant computational resources they require.
LLMs may pose privacy concerns, as they typically involve sending data to the cloud.
In practical terms, these factors mean that in some cases, integrating a classical NLP solution with an LLM is the optimal approach—especially when computational resources and privacy are critical considerations. Classical NLP solutions, such as SDK-based tools that can be installed locally for enhanced privacy and require minimal hardware resources, offer an attractive alternative. Fortunately, such solutions can be integrated with any LLM, combining the power of LLMs with efficient NER functionality.

Chinese, Southeast Asian, and Arabic names require transliteration, often resulting in inconsistent spellings in Roman alphabets. These inconsistencies can hinder analysts from tracking targets effectively. Bitext NER mitigates this by using semantic technologies to normalize transliterated entities, ensuring continuity in the detection process.

GPT and other generative models tend to provide disparate answers for the same question. Having control is called Fine-tuning.