code/README.md

TempBench construction pipeline

The deterministic pipeline that builds TempBench from a temporal knowledge graph. This is the code behind the paper's claim that construction is algorithmic and reproducible, not LLM-generated: given the same source KG and the same seed, it reproduces the released benchmark.

Contents

file	what it does
indexer.py	The foundation. Triple, ValidityWindow, and TemporalKGIndexer (entity index + interval tree). Every other file imports it. Loads the TGB 2.0 tkgl-smallpedia CSV format.
build_benchmark.py	The 6-stage construction pipeline: chain sampling → operator expansion → composability filter → answer-uniqueness filter → MinHash dedup → stratified split, then gold/distractor/stale subgraph construction.
resolve_labels.py	Rewrites Wikidata QIDs/PIDs into human-readable labels. Two modes: API (rate-limited) and offline dump.
benchmark-design-decisions.md	The formal operator semantics and the design choices the paper does not have room to state. Read this before extending the generator.

What is not here

This directory is the construction pipeline only. The reference-baseline systems evaluated in the paper are not included; their evaluation outputs ship in ../baselines/ and every number in the paper is re-derivable from those.

The source KG is not redistributed. tkgl-smallpedia comes from TGB 2.0 — download tkgl-smallpedia_edgelist.csv from TGB and point --kg_path at it. (550,376 quadruples, 47,433 entities, 283 relations, 1900–2024.)

Running it

No dependencies beyond the Python standard library. Tested on Python 3.11+.

Verify the install with the built-in smoke test, which runs on a synthetic 5-triple graph and needs no data:

python build_benchmark.py --smoke_test

Then build the benchmark:

# 1. Construct. target_n is the pre-filter target; the composability,
#    uniqueness and dedup stages reduce 10,000 -> the released 8,710.
python build_benchmark.py \
    --kg_path /path/to/tkgl-smallpedia_edgelist.csv \
    --output_dir ./out/ \
    --target_n 10000 --seed 42

# 2. Resolve QIDs/PIDs to labels. Dump mode is offline and preferred;
#    the fetch step is the only part that needs network access.
python resolve_labels.py --collect_ids ./out/benchmark.jsonl \
    --id_output ./out/ids.txt
python resolve_labels.py --fetch_dump ./out/ids.txt \
    --dump_output ./out/labels.tsv
python resolve_labels.py --input ./out/benchmark.jsonl \
    --label_dump ./out/labels.tsv \
    --output ./out/benchmark_labelled.jsonl

Step 2 is optional — the benchmark is usable on raw QIDs, which is what you want if you are running air-gapped.

--seed 42 is the released configuration. The pipeline is deterministic: the only nondeterminism is the seed, and label resolution depends on the Wikidata dump you fetch (we ship ours as ../benchmark/labels.tsv, so use that for an exact match).

Licence

CC BY 4.0, same as the rest of TempBench. See the top-level README.md.