Entity recognizer skill doesn't extracts output.

I have tried using EntityRecognizer skill to extract the entities combined with SplitText, EmbeddingSkill.

On the output I am not getting extracted entities, it is showing null values for people, skills, locations etc.

PFB code:
fields = [
SearchField(name="parent_id", type=SearchFieldDataType.String, sortable=True, filterable=True, facetable=True),
SearchField(name="chunk_id", type=SearchFieldDataType.String, key=True, sortable=True, filterable=True, facetable=True, analyzer_name="keyword"),
SearchField(name="chunk", type=SearchFieldDataType.String, sortable=False, filterable=False, facetable=False),
SearchField(name="vector", type=SearchFieldDataType.Collection(SearchFieldDataType.Single), vector_search_dimensions=1536, vector_search_profile_name="myHnswProfile"),
SearchField(name="title", type=SearchFieldDataType.String),
SearchField(name="url", type=SearchFieldDataType.String),
SearchField(name="last_modified", type=SearchFieldDataType.DateTimeOffset),
SearchField(name="people", type=SearchFieldDataType.String),
SearchField(name="skills", type=SearchFieldDataType.String)
]

entity_skill=EntityRecognitionSkill(
description="Skill Used to detect entities from the document",
context= "/document/content/",
categories=["Person",
"Email",
"Location",
"Organization",
"PhoneNumber",
"Address",
"Skill"
],
inputs=[
InputFieldMappingEntry(name="text", source="/document/content"),
InputFieldMappingEntry(name="languageCode", source="/document/language")
],
outputs=[
OutputFieldMappingEntry(name="persons", target_name="people"),
OutputFieldMappingEntry(name="skills", target_name="skills"),
],
)

index_projections = SearchIndexerIndexProjections(
selectors=[
SearchIndexerIndexProjectionSelector(
target_index_name=index_name,
parent_key_field_name="parent_id",
source_context="/document/pages/*",
mappings=[
InputFieldMappingEntry(name="chunk", source="/document/pages/*"),
InputFieldMappingEntry(name="vector", source="/document/pages/*/vector"),
InputFieldMappingEntry(name="title", source="/document/metadata_storage_name"),
InputFieldMappingEntry(name="people", source="/document/content/people"),
InputFieldMappingEntry(name="skills", source="/document/content/skills")
],
),
],
parameters=SearchIndexerIndexProjectionsParameters(
projection_mode=IndexProjectionMode.SKIP_INDEXING_PARENT_DOCUMENTS
)

indexer = SearchIndexer(
name=indexer_name,
description="Indexer to index documents and generate embeddings",
skillset_name=skillset_name,
target_index_name=index_name,
data_source_name=data_source.name,
# Map the metadata_storage_name field to the title field in the index to display the PDF title in the search results
field_mappings=[FieldMapping(source_field_name="metadata_storage_name", target_field_name="title"),
FieldMapping(source_field_name="metadata_storage_path", target_field_name="url"),
FieldMapping(source_field_name="metadata_storage_last_modified", target_field_name="last_modified")
] ,
output_field_mappings=[FieldMapping(source_field_name="/document/content/people", target_field_name="people"),
FieldMapping(source_field_name="/document/content/skills", target_field_name="skills")]
)

Start by tracing the EntityRecognitionSkill output paths through the index projection selectors and the indexer's output_field_mappings. Verify whether the people and skills outputs are produced under the skill's context and whether those paths match the projected page context. Done means the indexed people and skills fields contain extracted values instead of null.

Written by the indexing model from the issue text.