diff --git a/entries/tkgl_smallpedia/__init__.py b/entries/tkgl_smallpedia/__init__.py
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+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2026 Tiago de Paula Peixoto <tiago@skewed.de>
+#
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Affero General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
+#
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE.  See the GNU Affero General Public License for more
+# details.
+#
+# You should have received a copy of the GNU Affero General Public License along
+# with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+
+
+import pandas
+
+from .. import *
+
+
+title = "Wikidata temporal knowledge graph, small subset (tkgl-smallpedia, TGB 2.0)"
+description = """A temporal knowledge graph extracted from Wikidata, from the `tkgl-smallpedia` dataset of the Temporal Graph Benchmark (TGB 2.0): the statements between the Wikidata entities with identifiers below one million (i.e. Q1 to Q999999) that are paired with Wikipedia pages. Each node is an entity of Wikidata, and a directed edge from entity A to entity B is a statement (A, relation, B), with a Wikidata property as relation (e.g. `P166`, "award received", or `P54`, "member of sports team"). Pairs of entities may be connected by several edges.
+
+- `temporal`: the statements with a time (a point in time, or the start of a period), as a year from 1900 to 2024.
+- `static`: the statements of the same entities without a time; the statements that are also temporal ones were removed by TGB.
+
+The data do not include the labels of the entities and relations, which can be looked up in Wikidata.
+
+The networks have the following properties:
+
+- `wikidata_id`: the Wikidata identifier of the entity, e.g. `Q648`.
+- Edge `relation`: the Wikidata property of the statement, e.g. `P166`.
+- Edge `year` (`temporal` only): the year of the statement."""
+tags = ['Informational', 'Relatedness', 'Unweighted', 'Multigraph', 'Timestamps', 'Metadata']
+url = ['https://tgb.complexdatalab.com/docs/tkg/',
+       'https://github.com/shenyangHuang/TGB']
+citation = [('J. Gastinger, S. Huang, M. Galkin, E. Loghmani, A. Parviz, F. Poursafaei, J. Danovitch, E. Rossi, I. Koutis, H. Stuckenschmidt, R. Rabbany, G. Rabusseau, "TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs." Advances in Neural Information Processing Systems 37 (Datasets and Benchmarks Track) (2024)', 'https://doi.org/10.48550/arXiv.2406.09639'),
+            ('D. Vrandečić, M. Krötzsch, "Wikidata: a free collaborative knowledgebase." Communications of the ACM 57(10), 78-85 (2014)', 'https://doi.org/10.1145/2629489')]
+icon_hash = None
+upstream_license = ('CC0 1.0 (Wikidata)', 'https://www.wikidata.org/wiki/Wikidata:Licensing')
+upstream_prefix = 'https://object-arbutus.alliancecan.ca/swift/v1/14c95234f6cd4a21a47deafe20cce2a7/tgb'
+files = [('tkgl-smallpedia.zip:tkgl-smallpedia_edgelist.csv', 'temporal', None),
+         ('tkgl-smallpedia.zip:tkgl-smallpedia_static_edgelist.csv', 'static', None)]
+
+def fetch_upstream(force=False):
+    return fetch_upstream_files(__name__.split(".")[-1], upstream_prefix, files,
+                                force)
+
+@cache_network()
+@coerce_props()
+@annotate()
+def parse(alts=None):
+    global files
+    name = __name__.split(".")[-1]
+    for fname, alt, fmt in files:
+        if alts is not None and alt not in alts:
+            continue
+        # "ts,head,tail,relation_type" or "head,tail,relation_type"
+        with open_upstream_file(name, fname, "rb") as f:
+            df = pandas.read_csv(f, dtype=str)
+        cols = ["head", "tail", "relation_type"]
+        eprops = [("relation", "string")]
+        if alt == "temporal":
+            cols.append("ts")
+            eprops.append(("year", "int"))
+            df["ts"] = df["ts"].astype("int64")
+        g = Graph(directed=True)
+        g.vp.wikidata_id = g.add_edge_list(df[cols].itertuples(index=False),
+                                           hashed=True, eprops=list(eprops))
+        for k, t in eprops:
+            g.ep[k] = g.ep[k]
+        yield alt, g