diff --git a/src/backend_data_retrieval/data_retrieval_app/data_deposit/item_base_type/item_base_type_data/Jewels.csv b/src/backend_data_retrieval/data_retrieval_app/data_deposit/item_base_type/item_base_type_data/Jewels.csv
index b1d801377..71d2b9bf7 100644
--- a/src/backend_data_retrieval/data_retrieval_app/data_deposit/item_base_type/item_base_type_data/Jewels.csv
+++ b/src/backend_data_retrieval/data_retrieval_app/data_deposit/item_base_type/item_base_type_data/Jewels.csv
@@ -4,7 +4,7 @@ baseType,category,subCategory,relatedUniques
 "Cobalt Jewel","any jewel","base jewel","Grand Spectrum|Forbidden Flesh|The Balance of Terror"
 "Viridian Jewel","any jewel","base jewel","Impossible Escape|Grand Spectrum"
 "Prismatic Jewel","any jewel","base jewel","Watcher's Eye|Sublime Vision|The Light of Meaning|Bound By Destiny"
-"Timeless Jewel","any jewel","base jewel","Glorious Vanity|Lethal Pride|Brutal Restraint|Militant Faith|Elegant Hubris"
+"Timeless Jewel","any jewel","base jewel","Glorious Vanity|Lethal Pride|Brutal Restraint|Militant Faith|Elegant Hubris|Heroic Tragedy"
 "Large Cluster Jewel","any jewel","cluster jewel","Voices"
 "Medium Cluster Jewel","any jewel","cluster jewel",
 "Small Cluster Jewel","any jewel","cluster jewel",
diff --git a/src/backend_data_retrieval/data_retrieval_app/data_deposit/item_base_type/item_base_type_data_depositor.py b/src/backend_data_retrieval/data_retrieval_app/data_deposit/item_base_type/item_base_type_data_depositor.py
index a0fe56046..82c5ceedd 100644
--- a/src/backend_data_retrieval/data_retrieval_app/data_deposit/item_base_type/item_base_type_data_depositor.py
+++ b/src/backend_data_retrieval/data_retrieval_app/data_deposit/item_base_type/item_base_type_data_depositor.py
@@ -1,13 +1,118 @@
-from pandas import DataFrame
+from io import StringIO
+
+import pandas as pd
+import requests
 
 from data_retrieval_app.data_deposit.data_depositor_base import DataDepositorBase
+from data_retrieval_app.logs.logger import data_deposit_logger as logger
 
 
 class ItemBaseTypeDataDepositor(DataDepositorBase):
     def __init__(self) -> None:
         super().__init__(data_type="item_base_type")
 
+        self.update_url = str(self.data_url)
         self.data_url += "?on_duplicate_pkey_do_nothing=true"
+        self.current_basetypes_df = self._get_current_base_types()
+
+    def _get_current_base_types(self) -> pd.DataFrame:
+        logger.info("Retrieving previously deposited data.")
+
+        response = requests.get(self.data_url, headers=self.pom_auth_headers)
+
+        df = pd.DataFrame()
+        # Check if the request was successful
+        if response.status_code == 200:
+            # Load the JSON data into a pandas DataFrame
+            json_io = StringIO(response.content.decode("utf-8"))
+            df = pd.read_json(json_io, dtype=str)
+
+        if df.empty:
+            logger.info("Found no previously deposited data.")
+            return pd.DataFrame(
+                columns=[
+                    "itemBaseTypeId",
+                    "baseType",
+                    "category",
+                    "subCategory",
+                    "relatedUniques",
+                ]
+            )
+        else:
+            logger.info("Successfully retrieved previously deposited data.")
+            return df
+
+    def _update_duplicates(self, duplicate_df: pd.DataFrame):
+        """
+        Note that this method does not remove uniques which are not present in the file.
+        """
+        changed_rows = duplicate_df[
+            (duplicate_df["relatedUniques"] != duplicate_df["relatedUniques_y"])
+            & (~duplicate_df["relatedUniques"].isna())
+        ]
+
+        if not changed_rows.empty:
+            logger.info(
+                "Found changes in related uniques for some item base types. Updating these changes."
+            )
+            changed_rows["new_related_uniques"] = changed_rows.apply(
+                lambda row: (
+                    "|".join(
+                        set(
+                            row["relatedUniques"].split("|")
+                            + row["relatedUniques_y"].split("|")
+                        )
+                    )
+                    if row["relatedUniques_y"]
+                    else row["relatedUniques"]
+                ),
+                axis=1,
+            )
+            headers = {
+                "accept": "application/json",
+                "Content-Type": "application/json",
+            }
+            headers.update(self.pom_auth_headers)
+
+            for _, row in changed_rows.iterrows():
+                item_base_type_id = row["itemBaseTypeId"]
+                data = {
+                    "baseType": row["baseType"],
+                    "category": row["category"],
+                    "subCategory": row["subCategory"],
+                    "relatedUniques": row["new_related_uniques"],
+                }
+
+                try:
+                    response = requests.put(
+                        self.update_url + str(item_base_type_id),
+                        json=data,
+                        headers=headers,
+                        # add HTTP Basic Auth
+                    )
+                    response.raise_for_status()
+                except Exception as e:
+                    logger.error(
+                        f"The following error occurred while making request during _update_duplicates modifiers: {e}"
+                    )
+                    raise e
+
+    def _process_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        merged_df = pd.merge(
+            df,
+            self.current_basetypes_df,
+            how="left",
+            on="baseType",
+            suffixes=("", "_y"),
+        )
+        non_duplicate_mask = merged_df["itemBaseTypeId"].isna()
+        non_duplicate_df = merged_df[non_duplicate_mask]
+        non_duplicate_df = non_duplicate_df.drop(
+            columns=[
+                column for column in non_duplicate_df.columns if column.endswith("_y")
+            ]
+        )
+
+        self._update_duplicates(merged_df[~non_duplicate_mask])
 
-    def _process_data(self, df: DataFrame) -> DataFrame:
-        return df
+        return non_duplicate_df