diff --git a/libs/experimental/langchain_experimental/graph_transformers/llm.py b/libs/experimental/langchain_experimental/graph_transformers/llm.py
index 16023ab452d..8886a550684 100644
--- a/libs/experimental/langchain_experimental/graph_transformers/llm.py
+++ b/libs/experimental/langchain_experimental/graph_transformers/llm.py
@@ -242,22 +242,31 @@ def create_unstructured_prompt(
     system_message = SystemMessage(content=system_prompt)
     parser = JsonOutputParser(pydantic_object=UnstructuredRelation)
 
+    human_string_parts = [
+        "Based on the following example, extract entities and "
+        "relations from the provided text.\n\n",
+        "Use the following entity types, don't use other entity "
+        "that is not defined below:"
+        "# ENTITY TYPES:"
+        "{node_labels}"
+        if node_labels
+        else "",
+        "Use the following relation types, don't use other relation "
+        "that is not defined below:"
+        "# RELATION TYPES:"
+        "{rel_types}"
+        if rel_types
+        else "",
+        "Below are a number of examples of text and their extracted "
+        "entities and relationships."
+        "{examples}\n"
+        "For the following text, extract entities and relations as "
+        "in the provided example."
+        "{format_instructions}\nText: {input}",
+    ]
+    human_prompt_string = "\n".join(filter(None, human_string_parts))
     human_prompt = PromptTemplate(
-        template="""Based on the following example, extract entities and 
-relations from the provided text.\n\n
-Use the following entity types, don't use other entity that is not defined below:
-# ENTITY TYPES:
-{node_labels}
-
-Use the following relation types, don't use other relation that is not defined below:
-# RELATION TYPES:
-{rel_types}
-
-Below are a number of examples of text and their extracted entities and relationships.
-{examples}
-
-For the following text, extract entities and relations as in the provided example.
-{format_instructions}\nText: {input}""",
+        template=human_prompt_string,
         input_variables=["input"],
         partial_variables={
             "format_instructions": parser.get_format_instructions(),