Pith Number

pith:SSEYKZZH

pith:2026:SSEYKZZHIPFEHOCVMHW6EBTPAN

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Reconstructing temporal multi-relational firm networks at scale using large language models. The case of the semiconductor industry

Christian Diem, Elma Dervic, Georg Heiler, Hernan Picatto, Jan Hurt, Klaus Friesenbichler, Peter Klimek, Seyda K\"ose

Large language models can extract supply-chain, partnership and ownership links from public webpages to build a temporal network of over 1,300 semiconductor firms.

arxiv:2605.15842 v1 · 2026-05-15 · physics.soc-ph · cs.SI

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\usepackage{pith}
\pithnumber{SSEYKZZHIPFEHOCVMHW6EBTPAN}

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

a novel, generalizable methodology combining Large Language Models (LLMs) with open web data can reconstruct this network and its structural dynamics at scale... yielding a temporal network of over 1,300 linked firms. We validate link-extraction quality (Precision: 0.884; F1-score: 0.784), network overlap and complementarity with a proprietary database, and consistency with aggregate economic data.

C2weakest assumption

The assumption that publicly available firm webpages contain sufficiently complete and unbiased information on supply-chain, partnership, and ownership relations, and that LLM extraction can reliably classify these links without systematic errors that would distort network structure or temporal dynamics.

C3one line summary

LLM-based extraction from open web data reconstructs a validated temporal multi-relational network of semiconductor firms and reveals dynamics such as a 9% edge decline during the 2022 chip shortage.

References

51 extracted · 51 resolved · 0 Pith anchors

[1] Anton Pichler, Christian Diem, Alexandra Brintrup, Fran¸ cois Lafond, Glenn Mager- man, Gert Buiten, Thomas Y. Choi, Vasco M. Carvalho, J. Doyne Farmer, and Stefan Thurner. Building an alliance to map 2023

[2] The network origins of aggregate fluctuations.Econometrica, 80(5):1977–2016, 2012 1977

[3] Sys- temic risk analysis on reconstructed economic and financial networks.Scientific reports, 5(1):15758, 2015 2015

[4] Quantifying economic resilience from in- put–output susceptibility to improve predictions of economic growth and recovery 2019

[5] Firm-level propagation of shocks through supply-chain networks.Nature Sustainability, 2(9):841–847, 2019 2019

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:21.309892Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

948985672743ca43b85561ede2066f0362243d24dd5ac48ef3c55841d6e02866

Aliases

arxiv: 2605.15842 · arxiv_version: 2605.15842v1 · doi: 10.48550/arxiv.2605.15842 · pith_short_12: SSEYKZZHIPFE · pith_short_16: SSEYKZZHIPFEHOCV · pith_short_8: SSEYKZZH

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SSEYKZZHIPFEHOCVMHW6EBTPAN \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 948985672743ca43b85561ede2066f0362243d24dd5ac48ef3c55841d6e02866

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "39e0b9d6e05d9cda15512d5d2c1b2bb97bb77a3e3f9d1e28f8f2183e2d146d83",
    "cross_cats_sorted": [
      "cs.SI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "physics.soc-ph",
    "submitted_at": "2026-05-15T10:55:03Z",
    "title_canon_sha256": "1d0180c73a182a259f2bb329e5cc4f2b7d0baed91be1b8e672f6ba06d67eabd7"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15842",
    "kind": "arxiv",
    "version": 1
  }
}