| # OTel 2.5 sample training rows (10 per category, for review before scaling) |
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|
| Format follows `OTel_data_label.md`: one JSON object per line with `source_repo`, `source_id` (chunk id), |
| `source_spec_id: null`, `source_version: null`, and exactly two `messages` (user = document chunk + question, |
| assistant = answer grounded in that chunk). |
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| | file | category | source collection | data origin | |
| |---|---|---|---| |
| | lte-signalling.jsonl | LTE S1AP/NAS control-plane events at the EPC | powder-lte-v1 | measured, POWDER real radios (srsRAN), benign vs fake-base-station runs | |
| | rf-signal-quality.jsonl | LTE measurement reports (RSRP/RSRQ/SINR/PHR/TA, neighbours) | powder-lte-v1 | measured, POWDER real radios | |
| | 5g-core-signalling.jsonl | NGAP, PFCP and SBI request flows in Open5GS | zmq-5g-core-sim-v1, powder-5g-core-sim-v1 | measured core signalling; radio simulated (ZMQ) or UERANSIM | |
| | ran-telemetry.jsonl | srsUE per-second radio metrics + ocudu gNB scheduler metrics | zmq-5g-core-sim-v1 | simulated radio link (ZMQ) | |
| | network-performance.jsonl | iperf3/ping/HTTP results and procedure timings per run | zmq-5g-core-sim-v1 | measured application results over a simulated link | |
| | faults-ground-truth.jsonl | injected NF restarts/stops/config caps (ground-truth timeline) with NF warning/error logs | zmq-5g-core-sim-v1, powder-5g-core-sim-v1 | measured logs, ground truth from orchestrator | |
| | aaa-radius.jsonl | FreeRADIUS Access/Accounting exchanges | powder-5g-core-sim-v1 | measured | |
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| How they were made (`pipeline/trainrows/` in the collection repo): |
| 1. `render.py` turns processed, pseudonymised records of one run into a plain-text document with a header |
| (run id, scenario label, provenance statement, ground-truth timeline for fault runs) and a record table. |
| 2. Documents are chunked on line boundaries (header repeated), sized so the largest row stays under ~3.5k tokens in the generator tokenizer (tables tokenize at ~2 chars per token), under the 4,096-token row limit. |
| 3. `generate.py` asks `google/gemma-4-31b-it` (via OpenRouter) for one question an operations engineer would ask |
| about that chunk and an answer using only the chunk; a second judge call checks every claim is supported and |
| that provenance is not over-claimed (simulated radio never presented as over-the-air). Rejected rows are dropped. |
| 4. Rows are scanned for raw identifiers (15-digit numbers, test IMSIs); subscriber ids are keyed pseudonyms. |
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| Each `*.log.json` lists, per generated row, the run, scenario, judge verdict and token usage. |
| Rows are sampled round-robin over scenarios so each category covers several conditions. |
| Known limits of this first batch: questions are single-chunk and mostly descriptive (counts, sequences, |
| before/after comparisons); the judge model is the same model as the generator; chunks are tables, not prose. |
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