Remove redundant object display column

Co-authored-by: Cursor <cursoragent@cursor.com>

README.md

@@ -43,7 +43,7 @@ The release provides two ways to use the same graph:
 The default Dataset Viewer shows each knowledge connection as:
 
 ```text
-subject ── relation ──▶ object
+subject ── relation ──▶ object_node_label
 ```
 
 For example, an edge can also carry a natural-language question, a surface statement, construction-time confidence, and connectivity statistics.
@@ -77,7 +77,7 @@ The default Parquet split contains all 357,205 forward edges. It is not a sample
 - `hf download factprop/FACTPROP --repo-type dataset` downloads the **entire release**, including the canonical graph checkpoint.
 - `load_dataset(..., split="curated")` downloads only the curated entity summary; it is not the complete graph.
 
-If your goal is to obtain all knowledge connections in an easy-to-use format, use `knowledge_edges`. If you also need inverse traversal edges, raw BCE labels, stored evidence, or construction metadata, download `factprop_graph_v1.pkl`.
+If your goal is to obtain all knowledge connections in an easy-to-use format, use `knowledge_edges`. If you also need inverse traversal edges, stored evidence, or construction metadata, download `factprop_graph_v1.pkl`.
 
 ## Data model
 
@@ -96,7 +96,7 @@ The canonical checkpoint stores `qid` and `qid_status` as node attributes.
 Each forward edge represents one stored factual connection:
 
 ```text
-(subject, relation, object)
+(subject, relation, object_node_label)
 ```
 
 The Parquet representation includes:
@@ -107,8 +107,7 @@ The Parquet representation includes:
 | `subject` | string | Source node label |
 | `subject_qid` | string or null | Subject Wikidata QID where available |
 | `relation` | string | FACTPROP relation identifier |
-| `object` | string | Target node label or readable literal |
-| `object_node_label` | string | Canonical checkpoint node label; use this field to reconstruct exact topology |
+| `object_node_label` | string | Canonical target-node label; use this field for display, filtering, and topology reconstruction |
 | `object_qid` | string or null | Object Wikidata QID where available |
 | `object_type` | string | `entity`, `literal`, or `date_literal` |
 | `question` | string or null | Natural-language question associated with the fact |
@@ -118,7 +117,7 @@ The Parquet representation includes:
 | `subject_forward_out_degree` | integer | Number of forward facts originating from the subject |
 | `object_forward_in_degree` | integer | Number of forward facts pointing to the object |
 
-The table is sorted by descending `object_forward_in_degree`, making highly connected objects easy to inspect. For ordinary entities, `object` and `object_node_label` are identical. BCE literals use a readable `object` value while preserving the original negative checkpoint label in `object_node_label`.
+The table is sorted by descending `object_forward_in_degree`, making highly connected objects easy to inspect. `object_node_label` is the single target-node field and exactly matches the canonical checkpoint label.
 
 ### Auxiliary inverse edges
 
@@ -170,7 +169,7 @@ Inspect one connection:
 
 ```python
 row = edges[0]
-print(f'{row["subject"]} --{row["relation"]}--> {row["object"]}')
+print(f'{row["subject"]} --{row["relation"]}--> {row["object_node_label"]}')
 print("Question:", row["question"])
 print("Statement:", row["surface"])
 print("Object connectivity:", row["object_forward_in_degree"])
@@ -189,22 +188,28 @@ edges = load_dataset(
 
 ## Common recipes
 
-### Find all displayed facts involving an entity
+### Find all facts involving an entity
 
 ```python
 entity = "Apple Inc."
 
 connected = edges.filter(
-    lambda row: row["subject"] == entity or row["object"] == entity
+    lambda row: (
+        row["subject"] == entity or row["object_node_label"] == entity
+    )
 )
 
-print(connected.select_columns(["subject", "relation", "object"])[:20])
+print(
+    connected.select_columns(
+        ["subject", "relation", "object_node_label"]
+    )[:20]
+)
 ```
 
 For incoming facts only:
 
 ```python
-incoming = edges.filter(lambda row: row["object"] == entity)
+incoming = edges.filter(lambda row: row["object_node_label"] == entity)
 print("Incoming forward facts:", incoming.num_rows)
 ```
 
@@ -422,9 +427,9 @@ The canonical graph stores BCE year literals as negative internal labels:
 Ancient Egypt --FoundingDate--> -3100
 ```
 
-The edge surface expresses the readable meaning: approximately 3100 BCE. These values are date literals, not negative row IDs, negative connectivity, or corrupted entity indices.
+The edge `surface` expresses the readable meaning: approximately 3100 BCE. These values are date literals, not negative row IDs, negative connectivity, or corrupted entity indices.
 
-For usability, `knowledge_edges` renders such objects as `3100 BCE` and marks them as `object_type="date_literal"`. The same row preserves `-3100` in `object_node_label`, allowing exact topology reconstruction. The canonical checkpoint and complete raw entity index also retain the original labels.
+To avoid two nearly identical target columns, `knowledge_edges` retains only the canonical `object_node_label`, such as `-3100`, and marks it as `object_type="date_literal"`. Use the `surface` field when a human-readable BCE expression is needed.
 
 ## Recommended uses
 
@@ -517,7 +522,7 @@ sha256sum -c SHA256SUMS
 
 The released construction code uses `gpt-4o-mini` to generate candidate triplets and natural-language fields, then validates supported relations through Wikidata API and SPARQL queries. Project materials describe these candidates as Wikipedia-derived; the released builder does not directly ingest Wikipedia article text. The entity popularity index was exported from the unchanged paper graph, and its source checksum is retained in `metadata/graph.json`.
 
-`knowledge_edges` contains every forward edge from the checkpoint. It changes only the user-facing display of negative BCE literals and preserves each canonical object label separately. Topology and connectivity metrics are derived directly from the canonical graph. No graph nodes, mappings, edge topology, or checkpoint metadata were changed for this release.
+`knowledge_edges` contains every forward edge from the checkpoint and uses the canonical target label in `object_node_label`. Topology and connectivity metrics are derived directly from the canonical graph. No graph nodes, mappings, edge topology, or checkpoint metadata were changed for this release.
 
 Use a Hub revision to freeze data. Proposed mapping corrections or enhanced versions should use a new versioned artifact rather than overwrite `factprop_graph_v1.pkl`.
 

SHA256SUMS

@@ -1,12 +1,12 @@
 8eebe4a8b875d1061c7b55947883b455fa1565fb6a6e39b8056c7160381d7cfc  GRAPH.md
 06d1d7c1b3d72eb9ba4efc0d3a14cb0a52049b36883d6b5b666a24babf132777  LICENSE_DATA
-5863c0d8320fe0fd3e5e2dfabc4a2de13927e342c9ff5eafde112d85f31afb34  README.md
+8050ea1ee01238cce56ee08ee92229be211afcfd5ee3fc41eaf7ba349bba84b8  README.md
 33fb70fa85c957c1d702cd57b81a4ed0d3cde6d558394409f96046fbee9f9093  THIRD_PARTY_NOTICES.md
 fa7d9aec01b9c98c09cebc941265e4351693b362ffb3048d2c86585e84d7800d  browser-index.json
 3a9211368ac52af6cd0647cb026c05cb62743804011b68624682e11df964c038  data/entities_curated.parquet
 010beb905ef89b491159a8d8b2b013ea55219d26c8545bed07f64010bfa9ffce  data/entities_curated.jsonl
 2c21f2aefb9cbcc749cd813412dd90fea440050eeb8efb3356de786ebeb501e6  data/entities.jsonl
-58c0aadc93626731bac8ee26002e7b82fbc4919f0dc9ea6bf2b2c0b112c8d0a4  data/knowledge_edges.parquet
+95c003701c343fb7dfa49b3dd324154e31f1da66ec5cfda7f1868c9a0a86bcb5  data/knowledge_edges.parquet
 437a434260edbb019c85575d65b4775cb2461f145e17966ee3acc8e4625ce7c8  factprop_graph_v1.pkl
 c73d0a4b3f6df2166ba960a64cd5d3522728f46c2597f4f6604cc65533b46888  load_graph.py
 29e825c9838eb795a18672eb6a569cc2b666cf6971b5bd911655113a138e94ad  metadata/graph.json

data/knowledge_edges.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:58c0aadc93626731bac8ee26002e7b82fbc4919f0dc9ea6bf2b2c0b112c8d0a4
-size 18599971
+oid sha256:95c003701c343fb7dfa49b3dd324154e31f1da66ec5cfda7f1868c9a0a86bcb5
+size 17291119