Run step
Step 16: tool results
place_argument_passage
Input
{
"stance": "mentions",
"argument_id": "5098726f-3e4a-4cf9-b001-6250a926741b",
"instance_id": "f60257b6-dfe6-4994-9fce-0c4c67843411"
}{"success":true,"instance_id":"f60257b6-dfe6-4994-9fce-0c4c67843411","message":"Placed the passage on argument \"The argument from METR's attribution estimate\" as a mentions instance."}get_claim_instances
Input
{
"claim_id": "e4cbf66e-fd61-4ae6-8247-19b9b2fa87c9"
}{
"claim_id": "e4cbf66e-fd61-4ae6-8247-19b9b2fa87c9",
"total": 1,
"limit": 20,
"offset": 0,
"has_more": false,
"instances": [
{
"id": "ce0702d7-3316-46a7-ae9d-ca5e74372db0",
"source_id": "f146b243-4913-405e-aaf8-18d4769481df",
"verbatim_text": "We understand that AI is not yet able to make highly significant and fully autonomous contributions to the most important parts of the model training process.",
"context": "Report also says R&D is accelerated primarily via AI assistance rather than autonomous contributions.",
"stance": "affirms",
"confidence": 0.85,
"speaker": null,
"publication": null,
"source_date": null,
"link": null,
"source_title": "Independent Assessment of AI R&D Acceleration at Anthropic in October 2026",
"source_type": "report",
"source_url": "https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/",
"source_published_date": "2026-11-05",
"source_authors": [],
"source_publisher": "METR Blog",
"source_doi": null,
"source_interest": {
"party": "Anthropic",
"stake": "The assessment concerns Anthropic's own research process, and Anthropic held rights to redact non-public information; per the report, it redacted a majority of the evidence METR found most informative.",
"claim_id": null,
"recorded_at": "2026-11-14T02:23:04.711Z"
},
"recorded_by": "extractor",
"display_rank": null,
"reading": null,
"draws_on": [],
"story": {
"standing": "untraced",
"basis": "none",
"depth": 0,
"diverges": false,
"downstream_total": 0
}
}
]
}