# 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](https://arxiv.org/abs/2406.09639) — 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:

```bash
python build_benchmark.py --smoke_test
```

Then build the benchmark:

```bash
# 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`.