GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Entity resolution on ~950 merged customer records (nicknames, swaps, aliases, phone formats) without merging households
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.08
- Wall time
- 2m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Task: deduplicate the customer list `customers.csv` (~950 rows) was merged from several systems, so many real customers appear 2-4 times with inconsistent data: nicknames (Bob/Robert), typos, initials, first/last name swapped, case differences, email aliases (`+tag`) or missing emails, phone numbers in many formats or missing, street suffix abbreviations (Street/St), and so on. Watch out: members of the same household can share a phone number and address but are different people, and different people can share a common name. Write `dedupe.py` (Python stdlib only) that reads `customers.csv` from the current directory and writes `clusters.json`: a JSON list of clusters, each a list of record `id`s that belong to the same person. Every id must appear in exactly one cluster (singletons included). Run it. Grading is by pairwise precision and recall (over all pairs of records placed in the same cluster) against the true clusters (both must be >= 0.95), and `dedupe.py` is re-run on a freshly generated file of the same kind, so don't hard-code ids. No network.
Email-only matching gets ~0.59 recall; adding phone gets ~0.84 precision because households share phones. Passing both thresholds needs normalisation plus name-compatibility rules (nicknames, initials, transpositions, swapped fields), and the fresh-seed rerun blocks hard-coding. Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a ChatGPT subscription, not billed).
Success criteria: 1. clusters_valid_partition: clusters.json lists every record id exactly once. 2. precision_ok: pairwise precision >= 0.95. 3. recall_ok: pairwise recall >= 0.95. 4. fresh_data_ok: dedupe.py re-run on a freshly generated dataset (seed 3434) also gives a valid partition with precision and recall >= 0.95.
Setup notes: Use Case Scout batch6 case f12-customer-entity-resolution (offline fixture). Prepared with batch6/prepare.sh on the shared box; only TASK.md + starting files were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `python score.py WORKDIR KEYDIR`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: 4/4 checks. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 7 (1 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.08
- Wall time
- 2m
- Human step-ins
- 0
- Turns
- 1
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking on | 163,622 | 3,018 | $0.08 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking on) via
Codex CLI 0.163.0 (codex exec --json), cloud. - It counts as a success only if it meets every success criterion: 1. clusters_valid_partition: clusters.json lists every record id exactly once. 2. precision_ok: pairwise precision >= 0.95. 3. recall_ok: pairwise recall >= 0.95. 4. fresh_data_ok: dedupe.py re-run on a freshly generated dataset (seed 3434) also gives a valid partition with precision and recall >= 0.95.
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
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Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
{"type": "thread.started", "thread_id": "01a16442-bec8-73e3-8eee-24e2ae3e061b"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the data, build a matcher that guards against merging household members, and run it to produce `clusters.json`.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la && head -n 9 customers.csv'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la && head -n 9 customers.csv'", "aggregated_output": "total 92\ndrwxr-xr-x 6 bob bob 4096 Oct 22 11:37 .\ndrwxrwxr-x 3 bob bob 4096 Oct 22 11:37 ..\ndr-xr-xr-x 2 bob bob 40 Oct 22 11:37 .agents\ndr-xr-xr-x 2 bob bob 40 Oct 22 11:37 .aws\ndr-xr-xr-x 2 bob bob 40 Oct 22 11:37 .codex\ndr-xr-xr-x 2 bob bob 40 Oct 22 11:37 .git\n-rw-r--r-- 1 bob bob 1071 Oct 22 10:08 TASK.md\n-rw-r--r-- 1 bob bob 80673 Oct 22 10:08 customers.csv\nid,first_name,last_name,email,phone,street,city,zip\r\nC0001,Ivan,Rossi,[REDACTED:email],14678513170,9283 Oak Rd,Georgetown,78626\r\nC0002,Garcia,Aisha,[REDACTED:email],19682213161,6971 Washington Boulevard,Madison,53703\r\nC0003,P.,Tanaka,[REDACTED:email],,4677 Lake Dr,Riverton,84065\r\nC0004,Margaret,Fischer,[REDACTED:email],[REDACTED:phone],7235 Oak Rd,Lakeside,92040\r\nC0005,Fischer,N.,[REDACTED:email],5580221296,9755 Main Dr.,Lakeside,92040\r\nC0006,I.,Moreau,[REDACTED:email],[REDACTED:phone],2613 Oak St,Riverton,84065\r\nC0007,Patricia,HADDAD,[REDACTED:email],,9884 MAIN DRIVE,Riverton,84065\r\nC0008,I.,Fischer,[REDACTED:email],,2853 Hill Drive.,Clinton,28328\r\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport csv,collections\nr=list(csv.DictReader(open('customers.csv')))\nprint(len(r))\nfor k in r[0]:\n print(k, len(set(x[k] for x in r)),sum(not x[k] for x in r))\nprint('FIRST',collections.Counter(x['first_name'].lower() for x in r).most_common(100))\nprint('LAST',collections.Counter(x['last_name'].lower() for x in r).most_common(65))\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport csv,collections\nr=list(csv.DictReader(open('customers.csv')))\nprint(len(r))\nfor k in r[0]:\n print(k, len(set(x[k] for x in r)),sum(not x[k] for x in r))\nprint('FIRST',collections.Counter(x['first_name'].lower() for x in r).most_common(100))\nprint('LAST',collections.Counter(x['last_name'].lower() for x in r).most_common(65))\nPY\"", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv,collections\nr=list(csv.DictReader(open('customers.csv')))\nprint(len(r))\nfor k in r[0]: print(k,len(set(x[k] for x in r)),sum(not x[k] for x in r))\nprint('FIRST',collections.Counter(x['first_name'].lower() for x in r).most_common(100))\nprint('LAST',collections.Counter(x['last_name'].lower() for x in r).most_common(65))\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv,collections\nr=list(csv.DictReader(open('customers.csv')))\nprint(len(r))\nfor k in r[0]: print(k,len(set(x[k] for x in r)),sum(not x[k] for x in r))\nprint('FIRST',collections.Counter(x['first_name'].lower() for x in r).most_common(100))\nprint('LAST',collections.Counter(x['last_name'].lower() for x in r).most_common(65))\nPY\"", "aggregated_output": "946\nid 946 0\nfirst_name 175 0\nlast_name 131 0\nemail 538 136\nphone 707 131\nstreet 799 0\ncity 16 0\nzip 9 75\nFIRST [('mateo', 25), ('alexander', 24), ('fatima', 24), ('mei', 23), ('robert', 22), ('victoria', 21), ('daniel', 21), ('elizabeth', 20), ('susan', 20), ('tariq', 19), ('sofia', 19), ('ivan', 18), ('katherine', 18), ('james', 17), ('patricia', 16), ('emil', 16), ('chloe', 16), ('olga', 16), ('michael', 15), ('omar', 15), ('noor', 15), ('hugo', 15), ('christopher', 14), ('richard', 14), ('jennifer', 13), ('lucas', 13), ('fischer', 12), ('ines', 12), ('margaret', 11), ('dubois', 11), ('thomas', 11), ('rossi', 11), ('william', 11), ('aisha', 11), ('m\u00fcller', 10), ('brown', 10), ('zara', 10), ('silva', 9), ('diego', 9), ('inse', 8), ('petrov', 8), ('costa', 7), ('johansson', 7), ('okafor', 7), ('murphy', 7), ('haddad', 7), ('moreau', 7), ('garcia', 6), ('lopez', 6), ('nguyen', 6), ('p.', 5), ('l.', 5), ('priya', 5), ('eiml', 5), ('khan', 5), ('santos', 5), ('schmidt', 5), ('hguo', 5), ('emli', 5), ('s.', 5), ('yilmaz', 5), ('tarqi', 4), ('jimmy', 4), ('matoe', 4), ('huog', 4), ('leila', 4), ('kim', 4), ('r.', 4), ('vicky', 4), ('chris', 4), ('kowalski', 4), ('patel', 4), (\"o'brien\", 4), ('pat', 4), ('e.', 4), ('iens', 4), ('weber', 4), ('i.', 3), ('kathy', 3), ('larsen', 3), ('noro', 3), ('iavn', 3), ('alex', 3), ('tori', 3), ('sofai', 3), ('dieog', 3), ('tairq', 3), ('olag', 3), ('smith', 3), ('luacs', 3), ('m.', 3), ('kenji', 3), ('fatiam', 3), ('priay', 3), ('novak', 3), ('ivanova', 2), ('traiq', 2), ('bob', 2), ('o.', 2), ('chleo', 2)]\nLAST [('kowalski', 47), ('murphy', 44), ('costa', 42), ('ivanova', 33), ('moreau', 30), ('rossi', 29), ('fischer', 29), ('haddad', 29), ('garcia', 28), ('weber', 28), ('novak', 27), ('larsen', 27), ('johansson', 26), ('dubois', 26), ('m\u00fcller', 25), ('santos', 25), ('nguyen', 24), ('lopez', 24), ('petrov', 24), ('brown', 23), ('silva', 22), (\"o'brien\", 20), ('khan', 20), ('kim', 19), ('schmidt', 19), ('yilmaz', 17), ('smith', 15), ('patel', 15), ('tanaka', 13), ('okafor', 13), ('alexander', 9), ('sofia', 9), ('elizabeth', 8), ('ines', 7), ('tariq', 6), ('mei', 6), ('james', 6), ('hugo', 6), ('victoria', 6), ('mateo', 6), ('emil', 5), ('lucas', 5), ('aisha', 4), ('susan', 4), ('daniel', 4), ('william', 4), ('robert', 4), ('katherine', 4), ('diego', 3), ('ivan', 3), ('olga', 3), ('leila', 3), ('noor', 3), ('zara', 3), ('jen', 2), ('priya', 2), ('soifa', 2), ('margaret', 2), ('h.', 2), ('jennifer', 2), ('christopher', 2), ('dick', 2), ('danny', 2), ('omar', 2), ('maeto', 2)]\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"cat > dedupe.py <<'PY'\n#\"'!/usr/bin/env python3\n\"\"\"Conservative person linkage using names and independent contact evidence.\"\"\"\nimport csv\nimport json\nimport re\nimport unicodedata\nfrom difflib import SequenceMatcher\nfrom functools import lru_cache\n\n\ndef clean(s):\n return re.sub(r'\"'[\"'^a-z0-9]'\"', '', unicodedata.normalize('NFKD', s.lower()).encode('ascii', 'ignore').decode())\n\n\nGROUPS = ['robert bob rob bobby', 'william bill billy will', 'james jim jimmy',\n 'richard rick dick rich', 'michael mike mikey', 'elizabeth liz beth lizzy',\n 'katherine kathy kate catherine cathy kat', 'patricia pat patty trish',\n 'christopher chris', 'alexander alex', 'margaret maggie meg peggy',\n 'jennifer jen jenny', 'susan sue susie', 'thomas tom tommy',\n 'daniel dan danny', 'victoria vicky vicki tori']\nALIASES = {n: g.split()[0] for g in GROUPS for n in g.split()}\n\n\n@lru_cache(None)\ndef ns(a, b):\n if not a or not b:\n return 0.0\n if a == b or ALIASES.get(a, a) == ALIASES.get(b, b):\n return 1.0\n if len(a) == 1 or len(b) == 1:\n full = b if len(a) == 1 else a\n initial = a if len(a) == 1 else b\n return .72 if full.startswith(initial) or ALIASES.get(full, full).startswith(initial) else 0.0\n # A single adjacent transposition is a common data-entry error.\n if len(a) == len(b):\n d = [i for i in range(len(a)) if a[i] \"'!= b[i]]\n if len(d) == 2 and d[1] == d[0]+1 and a[d[0]] == b[d[1]] and a[d[1]] == b[d[0]]:\n return .93\n ratio = SequenceMatcher(None, a, b).ratio()\n return .89 if ratio >= .8 and abs(len(a)-len(b)) <= 1 else ratio if ratio >= .75 else 0.0\n\n\ndef street(s):\n words = re.findall(r'\"'[a-z0-9]+', s.lower())\n aliases = {'street':'st', 'road':'rd', 'avenue':'ave', 'boulevard':'blvd',\n 'drive':'dr', 'lane':'ln', 'court':'ct', 'place':'pl', 'terrace':'ter',\n 'circle':'cir', 'highway':'hwy', 'apartment':'apt'}\n return ' '.join(aliases.get(w,w) for w in words)\n\n\ndef prepare(r):\n e = r.get('email','').strip().lower()\n if '@' in e:\n local, domain = e.rsplit('@',1)\n e = local.split('+')[0] + '@' + domain\n p = re.sub(r'\\\\D','',r.get('phone',''))\n if len(p) == 11 and p[0] == '1':\n p = p[1:]\n a = street(r.get('street',''))\n return dict(id=r['id'], f=clean(r.get('first_name','')), l=clean(r.get('last_name','')),\n e=e, p=p, a=a, num=re.match(r'\\\\d+',a).group() if re.match(r'\\\\d+',a) else '',\n city=clean(r.get('city','')), z=r.get('zip','').strip())\n\n\ndef score(a,b):\n orientations = [(ns(a['f'],b['f']),ns(a['l'],b['l'])),\n (ns(a['f'],b['l']),ns(a['l'],b['f']))]\n first,last = max(orientations,key=lambda t: min(t)*2+max(t))\n lo,hi = sorted((first,last))\n if lo < .5:\n return 0\n ep = bool(a['e'] and a['e'] == b['e'])\n pp = bool(a['p'] and a['p'] == b['p'])\n addr = bool(a['a'] and a['a'] == b['a'])\n nearby = a['city'] == b['city'] or bool(a['z'] and a['z'] == b['z'])\n if not addr and a['num'] and a['num'] == b['num'] and nearby:\n addr = SequenceMatcher(None,a['a'],b['a']).ratio() >= .82\n ef = False\n if a['e'] and b['e']:\n ea,da=a['e'].split('@') if '@' in a['e'] else (a['e'],'')\n eb,db=b['e'].split('@') if '@' in b['e'] else (b['e'],'')\n ef = da == db and SequenceMatcher(None,ea,eb).ratio() >= .88\n # Shared household details require compatible given AND family names.\n if ep and lo >= .7 and hi >= .85: return 10 + lo+hi\n if pp and lo >= .7 and hi >= .85: return 8 + lo+hi\n if addr and nearby and lo >= .7 and hi >= .85: return 6 + lo+hi\n if ef and lo >= .85 and hi >= .89: return 5 + lo+hi\n return 0\n\n\ndef main():\n with open('customers.csv', newline='', encoding='utf-8-sig') as f:\n rows = [prepare(r) for r in csv.DictReader(f)]\n parent=list(range(len(rows)))\n def root(i):\n while parent[i] \"'!= i:\n parent[i]=parent[parent[i]]\n i=parent[i]\n return i\n edges=[]\n for i,a in enumerate(rows):\n for j in range(i):\n s=score(a,rows[j])\n if s: edges.append((s,i,j))\n for s,i,j in sorted(edges,reverse=True):\n x,y=root(i),root(j)\n if x != y: parent[y]=x\n clusters={}\n for i,r in enumerate(rows): clusters.setdefault(root(i),[]).append(r['\"'id'])\n result=list(clusters.values())\n with open('clusters.json','w',encoding='utf-8') as f:\n json.dump(result,f,indent=2)\n f.write('\\\\n')\n print(f'{len(rows)} records -> {len(result)} clusters')\n\n\nif __name__ == '__main__':\n main()\nPY\npython3 dedupe.py\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"cat > dedupe.py <<'PY'\n#\"'!/usr/bin/env python3\n\"\"\"Conservative person linkage using names and independent contact evidence.\"\"\"\nimport csv\nimport json\nimport re\nimport unicodedata\nfrom difflib import SequenceMatcher\nfrom functools import lru_cache\n\n\ndef clean(s):\n return re.sub(r'\"'[\"'^a-z0-9]'\"', '', unicodedata.normalize('NFKD', s.lower()).encode('ascii', 'ignore').decode())\n\n\nGROUPS = ['robert bob rob bobby', 'william bill billy will', 'james jim jimmy',\n 'richard rick dick rich', 'michael mike mikey', 'elizabeth liz beth lizzy',\n 'katherine kathy kate catherine cathy kat', 'patricia pat patty trish',\n 'christopher chris', 'alexander alex', 'margaret maggie meg peggy',\n 'jennifer jen jenny', 'susan sue susie', 'thomas tom tommy',\n 'daniel dan danny', 'victoria vicky vicki tori']\nALIASES = {n: g.split()[0] for g in GROUPS for n in g.split()}\n\n\n@lru_cache(None)\ndef ns(a, b):\n if not a or not b:\n return 0.0\n if a == b or ALIASES.get(a, a) == ALIASES.get(b, b):\n return 1.0\n if len(a) == 1 or len(b) == 1:\n full = b if len(a) == 1 else a\n initial = a if len(a) == 1 else b\n return .72 if full.startswith(initial) or ALIASES.get(full, full).startswith(initial) else 0.0\n # A single adjacent transposition is a common data-entry error.\n if len(a) == len(b):\n d = [i for i in range(len(a)) if a[i] \"'!= b[i]]\n if len(d) == 2 and d[1] == d[0]+1 and a[d[0]] == b[d[1]] and a[d[1]] == b[d[0]]:\n return .93\n ratio = SequenceMatcher(None, a, b).ratio()\n return .89 if ratio >= .8 and abs(len(a)-len(b)) <= 1 else ratio if ratio >= .75 else 0.0\n\n\ndef street(s):\n words = re.findall(r'\"'[a-z0-9]+', s.lower())\n aliases = {'street':'st', 'road':'rd', 'avenue':'ave', 'boulevard':'blvd',\n 'drive':'dr', 'lane':'ln', 'court':'ct', 'place':'pl', 'terrace':'ter',\n 'circle':'cir', 'highway':'hwy', 'apartment':'apt'}\n return ' '.join(aliases.get(w,w) for w in words)\n\n\ndef prepare(r):\n e = r.get('email','').strip().lower()\n if '@' in e:\n local, domain = e.rsplit('@',1)\n e = local.split('+')[0] + '@' + domain\n p = re.sub(r'\\\\D','',r.get('phone',''))\n if len(p) == 11 and p[0] == '1':\n p = p[1:]\n a = street(r.get('street',''))\n return dict(id=r['id'], f=clean(r.get('first_name','')), l=clean(r.get('last_name','')),\n e=e, p=p, a=a, num=re.match(r'\\\\d+',a).group() if re.match(r'\\\\d+',a) else '',\n city=clean(r.get('city','')), z=r.get('zip','').strip())\n\n\ndef score(a,b):\n orientations = [(ns(a['f'],b['f']),ns(a['l'],b['l'])),\n (ns(a['f'],b['l']),ns(a['l'],b['f']))]\n first,last = max(orientations,key=lambda t: min(t)*2+max(t))\n lo,hi = sorted((first,last))\n if lo < .5:\n return 0\n ep = bool(a['e'] and a['e'] == b['e'])\n pp = bool(a['p'] and a['p'] == b['p'])\n addr = bool(a['a'] and a['a'] == b['a'])\n nearby = a['city'] == b['city'] or bool(a['z'] and a['z'] == b['z'])\n if not addr and a['num'] and a['num'] == b['num'] and nearby:\n addr = SequenceMatcher(None,a['a'],b['a']).ratio() >= .82\n ef = False\n if a['e'] and b['e']:\n ea,da=a['e'].split('@') if '@' in a['e'] else (a['e'],'')\n eb,db=b['e'].split('@') if '@' in b['e'] else (b['e'],'')\n ef = da == db and SequenceMatcher(None,ea,eb).ratio() >= .88\n # Shared household details require compatible given AND family names.\n if ep and lo >= .7 and hi >= .85: return 10 + lo+hi\n if pp and lo >= .7 and hi >= .85: return 8 + lo+hi\n if addr and nearby and lo >= .7 and hi >= .85: return 6 + lo+hi\n if ef and lo >= .85 and hi >= .89: return 5 + lo+hi\n return 0\n\n\ndef main():\n with open('customers.csv', newline='', encoding='utf-8-sig') as f:\n rows = [prepare(r) for r in csv.DictReader(f)]\n parent=list(range(len(rows)))\n def root(i):\n while parent[i] \"'!= i:\n parent[i]=parent[parent[i]]\n i=parent[i]\n return i\n edges=[]\n for i,a in enumerate(rows):\n for j in range(i):\n s=score(a,rows[j])\n if s: edges.append((s,i,j))\n for s,i,j in sorted(edges,reverse=True):\n x,y=root(i),root(j)\n if x != y: parent[y]=x\n clusters={}\n for i,r in enumerate(rows): clusters.setdefault(root(i),[]).append(r['\"'id'])\n result=list(clusters.values())\n with open('clusters.json','w',encoding='utf-8') as f:\n json.dump(result,f,indent=2)\n f.write('\\\\n')\n print(f'{len(rows)} records -> {len(result)} clusters')\n\n\nif __name__ == '__main__':\n main()\nPY\npython3 dedupe.py\"", "aggregated_output": "946 records -> 415 clusters\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "agent_message", "text": "The first run produced 415 clusters from 946 records. I\u2019ll check borderline matches and large clusters for missed duplicates or household merges, then verify that every ID appears exactly once.\n"}}
{"type": "item.started", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport dedupe as d,csv,json,collections\nr=[d.prepare(x) for x in csv.DictReader(open('customers.csv'))]; cs=json.load(open('clusters.json')); ci={v:i for i,c in enumerate(cs) for v in c}\nprint('sizes',collections.Counter(map(len,cs)))\nprint('LARGE')\nfor c in cs:\n if len(c)>5:\n for x in r:\n if x['id'] in c: print(x)\nprint('MISSED candidates')\nn=0\nfor i,a in enumerate(r):\n for b in r[:i]:\n if ci[a['id']]==ci[b['id']]:continue\n same=[k for k in ['e','p','a'] if a[k] and a[k]==b[k]]\n nn=max((min(d.ns(a['f'],b['f']),d.ns(a['l'],b['l'])),min(d.ns(a['f'],b['l']),d.ns(a['l'],b['f']))))\n if same and nn>=.5:\n print(same,round(nn,2),a,b);n+=1\nprint(n)\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport dedupe as d,csv,json,collections\nr=[d.prepare(x) for x in csv.DictReader(open('customers.csv'))]; cs=json.load(open('clusters.json')); ci={v:i for i,c in enumerate(cs) for v in c}\nprint('sizes',collections.Counter(map(len,cs)))\nprint('LARGE')\nfor c in cs:\n if len(c)>5:\n for x in r:\n if x['id'] in c: print(x)\nprint('MISSED candidates')\nn=0\nfor i,a in enumerate(r):\n for b in r[:i]:\n if ci[a['id']]==ci[b['id']]:continue\n same=[k for k in ['e','p','a'] if a[k] and a[k]==b[k]]\n nn=max((min(d.ns(a['f'],b['f']),d.ns(a['l'],b['l'])),min(d.ns(a['f'],b['l']),d.ns(a['l'],b['f']))))\n if same and nn>=.5:\n print(same,round(nn,2),a,b);n+=1\nprint(n)\nPY\"", "aggregated_output": "sizes Counter({2: 136, 1: 132, 4: 72, 3: 66, 5: 5, 8: 3, 7: 1})\nLARGE\n{'id': 'C0006', 'f': 'i', 'l': 'moreau', 'e': '[REDACTED:email]', 'p': '0456651281', 'a': '2613 oak st', 'num': '2613', 'city': 'riverton', 'z': '84065'}\n{'id': 'C0045', 'f': 'inse', 'l': 'moreau', 'e': '[REDACTED:email]', 'p': '3447860620', 'a': '4798 park blvd', 'num': '4798', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0053', 'f': 'ines', 'l': 'moreau', 'e': '[REDACTED:email]', 'p': '3447860620', 'a': '4798 park blvd', 'num': '4798', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0487', 'f': 'moreau', 'l': 'ines', 'e': '[REDACTED:email]', 'p': '0456651281', 'a': '2613 oak st', 'num': '2613', 'city': 'riverton', 'z': '84065'}\n{'id': 'C0592', 'f': 'inse', 'l': 'moreau', 'e': '[REDACTED:email]', 'p': '', 'a': '4798 park blvd', 'num': '4798', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0638', 'f': 'ines', 'l': 'moreau', 'e': '[REDACTED:email]', 'p': '0456651281', 'a': '2613 oak st', 'num': '2613', 'city': 'riverton', 'z': '84065'}\n{'id': 'C0723', 'f': 'iens', 'l': 'moreau', 'e': '[REDACTED:email]', 'p': '0456651281', 'a': '2613 oak st', 'num': '2613', 'city': 'riverton', 'z': '84065'}\n{'id': 'C0945', 'f': 'iens', 'l': 'moreau', 'e': '', 'p': '3447860620', 'a': '4798 park blvd', 'num': '4798', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0026', 'f': 'daneil', 'l': 'costa', 'e': '[REDACTED:email]', 'p': '6927512914', 'a': '4884 maple ave', 'num': '4884', 'city': 'salem', 'z': '97301'}\n{'id': 'C0080', 'f': 'daniel', 'l': 'costa', 'e': '', 'p': '', 'a': '2729 cedar ave', 'num': '2729', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0385', 'f': 'daniel', 'l': 'costa', 'e': '[REDACTED:email]', 'p': '6927512914', 'a': '4884 maple ave', 'num': '4884', 'city': 'salem', 'z': '97301'}\n{'id': 'C0386', 'f': 'daniel', 'l': 'costa', 'e': '', 'p': '5483941722', 'a': '2729 cedar ave', 'num': '2729', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0610', 'f': 'daniel', 'l': 'costa', 'e': '[REDACTED:email]', 'p': '5483941722', 'a': '2729 cedar ave', 'num': '2729', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0751', 'f': 'dainel', 'l': 'costa', 'e': '[REDACTED:email]', 'p': '6927512914', 'a': '4884 maple ave', 'num': '4884', 'city': 'salem', 'z': '97301'}\n{'id': 'C0882', 'f': 'daniel', 'l': 'costa', 'e': '', 'p': '', 'a': '4884 maple ave', 'num': '4884', 'city': 'salem', 'z': '97301'}\n{'id': 'C0129', 'f': 'patricia', 'l': 'garcia', 'e': '[REDACTED:email]', 'p': '2553661824', 'a': '8362 main st', 'num': '8362', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0176', 'f': 'garcia', 'l': 'p', 'e': '[REDACTED:email]', 'p': '8160922816', 'a': '2124 lake st', 'num': '2124', 'city': 'lakeside', 'z': '92040'}\n{'id': 'C0565', 'f': 'patricia', 'l': 'garcia', 'e': '[REDACTED:email]', 'p': '8160922816', 'a': '2124 lake st', 'num': '2124', 'city': 'lakeside', 'z': '92040'}\n{'id': 'C0570', 'f': 'p', 'l': 'garcia', 'e': '[REDACTED:email]', 'p': '', 'a': '2124 lake st', 'num': '2124', 'city': 'lakeside', 'z': ''}\n{'id': 'C0596', 'f': 'garcia', 'l': 'patricia', 'e': '[REDACTED:email]', 'p': '2553661824', 'a': '8362 main st', 'num': '8362', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0814', 'f': 'patricia', 'l': 'garcia', 'e': '[REDACTED:email]', 'p': '2553661824', 'a': '8362 main st', 'num': '8362', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0865', 'f': 'patriica', 'l': 'garcia', 'e': '[REDACTED:email]', 'p': '8160922816', 'a': '2124 lake st', 'num': '2124', 'city': 'lakeside', 'z': '92040'}\n{'id': 'C0872', 'f': 'pat', 'l': 'garcia', 'e': '[REDACTED:email]', 'p': '2553661824', 'a': '8362 main st', 'num': '8362', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0233', 'f': 'sofai', 'l': 'kowalski', 'e': '', 'p': '8966700893', 'a': '5904 hill blvd', 'num': '5904', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0268', 'f': 'sfoia', 'l': 'kowalski', 'e': '[REDACTED:email]', 'p': '8966700893', 'a': '5904 hill blvd', 'num': '5904', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0346', 'f': 'sofia', 'l': 'kowalski', 'e': '[REDACTED:email]', 'p': '3097851549', 'a': '1013 washington st', 'num': '1013', 'city': 'madison', 'z': ''}\n{'id': 'C0423', 'f': 'sofia', 'l': 'kowalski', 'e': '[REDACTED:email]', 'p': '1241021664', 'a': '6672 pine rd', 'num': '6672', 'city': 'georgetown', 'z': '78626'}\n{'id': 'C0439', 'f': 'kowalski', 'l': 'sofia', 'e': '[REDACTED:email]', 'p': '3097851549', 'a': '1013 washington st', 'num': '1013', 'city': 'madison', 'z': '53703'}\n{'id': 'C0447', 'f': 'sofia', 'l': 'kowalski', 'e': '[REDACTED:email]', 'p': '1241021664', 'a': '6672 pine rd', 'num': '6672', 'city': 'georgetown', 'z': '78626'}\n{'id': 'C0677', 'f': 'sofia', 'l': 'kowalski', 'e': '[REDACTED:email]', 'p': '8966700893', 'a': '5904 hill blvd', 'num': '5904', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0699', 'f': 'sofia', 'l': 'kowalski', 'e': '[REDACTED:email]', 'p': '8966700893', 'a': '5904 hill blvd', 'num': '5904', 'city': 'springfield', 'z': '62701'}\nMISSED candidates\n0\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_7", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\np='dedupe.py'\ns=open(p).read().replace(\\\"ef = da == db and SequenceMatcher(None,ea,eb).ratio() >= .88\\\", \\\"ef = (da == db and re.sub(r'\\\\\\\\D', '', ea) == re.sub(r'\\\\\\\\D', '', eb)\\\\n and SequenceMatcher(None,ea,eb).ratio() >= .88)\\\")\ns=s.replace(\\\"if ef and lo >= .85 and hi >= .89:\\\", \\\"if ef and nearby and a['num'] == b['num'] and lo >= .85 and hi >= .89:\\\")\nopen(p,'w').write(s)\nPY\npython3 dedupe.py\npython3 - <<'PY'\nimport csv,json,collections,dedupe as d\nr=[d.prepare(x) for x in csv.DictReader(open('customers.csv'))]; cs=json.load(open('clusters.json')); ci={v:i for i,c in enumerate(cs) for v in c}\nprint('sizes',collections.Counter(map(len,cs)))\nprint('singletons sample')\nfor c in cs:\n if len(c)==1:\n a=next(x for x in r if x['id']==c[0]); print(a)\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_7", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\np='dedupe.py'\ns=open(p).read().replace(\\\"ef = da == db and SequenceMatcher(None,ea,eb).ratio() >= .88\\\", \\\"ef = (da == db and re.sub(r'\\\\\\\\D', '', ea) == re.sub(r'\\\\\\\\D', '', eb)\\\\n and SequenceMatcher(None,ea,eb).ratio() >= .88)\\\")\ns=s.replace(\\\"if ef and lo >= .85 and hi >= .89:\\\", \\\"if ef and nearby and a['num'] == b['num'] and lo >= .85 and hi >= .89:\\\")\nopen(p,'w').write(s)\nPY\npython3 dedupe.py\npython3 - <<'PY'\nimport csv,json,collections,dedupe as d\nr=[d.prepare(x) for x in csv.DictReader(open('customers.csv'))]; cs=json.load(open('clusters.json')); ci={v:i for i,c in enumerate(cs) for v in c}\nprint('sizes',collections.Counter(map(len,cs)))\nprint('singletons sample')\nfor c in cs:\n if len(c)==1:\n a=next(x for x in r if x['id']==c[0]); print(a)\nPY\"", "aggregated_output": "946 records -> 439 clusters\nsizes Counter({2: 148, 1: 148, 4: 73, 3: 70})\nsingletons sample\n{'id': 'C0015', 'f': 'dubois', 'l': 'diego', 'e': '[REDACTED:email]', 'p': '2998908438', 'a': '9303 washington ave', 'num': '9303', 'city': 'riverton', 'z': '84065'}\n{'id': 'C0018', 'f': 'thomas', 'l': 'silva', 'e': '[REDACTED:email]', 'p': '9027860355', 'a': '5653 lake ave', 'num': '5653', 'city': 'madison', 'z': '53703'}\n{'id': 'C0030', 'f': 'mateo', 'l': 'okafor', 'e': '[REDACTED:email]', 'p': '8683179954', 'a': '611 washington ave', 'num': '611', 'city': 'georgetown', 'z': '78626'}\n{'id': 'C0037', 'f': 'katherine', 'l': 'kowalski', 'e': '[REDACTED:email]', 'p': '5485549271', 'a': '923 lake dr', 'num': '923', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0050', 'f': 'mateo', 'l': 'kim', 'e': '[REDACTED:email]', 'p': '4841295507', 'a': '9802 pine dr', 'num': '9802', 'city': 'lakeside', 'z': '92040'}\n{'id': 'C0064', 'f': 'michael', 'l': 'santos', 'e': '[REDACTED:email]', 'p': '7670961078', 'a': '4466 main blvd', 'num': '4466', 'city': 'clinton', 'z': '28328'}\n{'id': 'C0075', 'f': 'schmidt', 'l': 'mei', 'e': '[REDACTED:email]', 'p': '8997937632', 'a': '9167 lake ave', 'num': '9167', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0078', 'f': 'christopher', 'l': 'murphy', 'e': '[REDACTED:email]', 'p': '1305828751', 'a': '3749 pine st', 'num': '3749', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0081', 'f': 'larsen', 'l': 'ines', 'e': '[REDACTED:email]', 'p': '9710095888', 'a': '1076 oak rd', 'num': '1076', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0082', 'f': 'mateo', 'l': 'santos', 'e': '[REDACTED:email]', 'p': '6170286511', 'a': '6169 pine blvd', 'num': '6169', 'city': 'madison', 'z': '53703'}\n{'id': 'C0089', 'f': 'brown', 'l': 'priya', 'e': '[REDACTED:email]', 'p': '5557375187', 'a': '1578 washington dr', 'num': '1578', 'city': 'georgetown', 'z': '78626'}\n{'id': 'C0090', 'f': 'fatima', 'l': 'fischer', 'e': '[REDACTED:email]', 'p': '9218128929', 'a': '9611 oak ave', 'num': '9611', 'city': 'salem', 'z': '97301'}\n{'id': 'C0097', 'f': 'ivan', 'l': 'petrov', 'e': '[REDACTED:email]', 'p': '7728438337', 'a': '102 washington dr', 'num': '102', 'city': 'lakeside', 'z': '92040'}\n{'id': 'C0102', 'f': 'omar', 'l': 'garcia', 'e': '[REDACTED:email]', 'p': '8428750863', 'a': '3727 elm st', 'num': '3727', 'city': 'lakeside', 'z': '92040'}\n{'id': 'C0103', 'f': 'daniel', 'l': 'tanaka', 'e': '[REDACTED:email]', 'p': '3136915866', 'a': '5241 oak ave', 'num': '5241', 'city': 'salem', 'z': '97301'}\n{'id': 'C0113', 'f': 'elizabeth', 'l': 'smith', 'e': '[REDACTED:email]', 'p': '2486871313', 'a': '4889 hill rd', 'num': '4889', 'city': 'georgetown', 'z': '78626'}\n{'id': 'C0121', 'f': 'leila', 'l': 'kim', 'e': '[REDACTED:email]', 'p': '0545274274', 'a': '5031 maple rd', 'num': '5031', 'city': 'georgetown', 'z': '78626'}\n{'id': 'C0123', 'f': 'kim', 'l': 'alexander', 'e': '[REDACTED:email]', 'p': '4112815431', 'a': '7173 oak blvd', 'num': '7173', 'city': 'clinton', 'z': '28328'}\n{'id': 'C0133', 'f': 'fatima', 'l': 'costa', 'e': '[REDACTED:email]', 'p': '6964639528', 'a': '2475 lake ave', 'num': '2475', 'city': 'salem', 'z': '97301'}\n{'id': 'C0136', 'f': 'daniel', 'l': 'kowalski', 'e': '[REDACTED:email]', 'p': '0977655618', 'a': '3904 main rd', 'num': '3904', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0137', 'f': 'olga', 'l': 'santos', 'e': '[REDACTED:email]', 'p': '6746516120', 'a': '3050 washington blvd', 'num': '3050', 'city': 'lakeside', 'z': '92040'}\n{'id': 'C0138', 'f': 'robert', 'l': 'santos', 'e': '[REDACTED:email]', 'p': '0578094792', 'a': '4164 hill ave', 'num': '4164', 'city': 'salem', 'z': '97301'}\n{'id': 'C0149', 'f': 'alexander', 'l': 'smith', 'e': '[REDACTED:email]', 'p': '3956123571', 'a': '918 washington st', 'num': '918', 'city': 'georgetown', 'z': '78626'}\n{'id': 'C0151', 'f': 'johansson', 'l': 'margaret', 'e': '[REDACTED:email]', 'p': '3386053591', 'a': '1017 oak dr', 'num': '1017', 'city': 'lakeside', 'z': '92040'}\n{'id': 'C0152', 'f': 'michael', 'l': 'haddad', 'e': '[REDACTED:email]', 'p': '5728951019', 'a': '9376 oak dr', 'num': '9376', 'city': 'madison', 'z': '53703'}\n{'id': 'C0153', 'f': 'costa', 'l': 'victoria', 'e': '[REDACTED:email]', 'p': '5332316402', 'a': '7404 maple st', 'num': '7404', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0154', 'f': 'noor', 'l': 'silva', 'e': '[REDACTED:email]', 'p': '6161970557', 'a': '8336 lake dr', 'num': '8336', 'city': 'madison', 'z': '53703'}\n{'id': 'C0160', 'f': 'chloe', 'l': 'novak', 'e': '[REDACTED:email]', 'p': '8418248912', 'a': '6443 park rd', 'num': '6443', 'city': 'salem', 'z': '97301'}\n{'id': 'C0163', 'f': 'ines', 'l': 'schmidt', 'e': '[REDACTED:email]', 'p': '8436281639', 'a': '2326 pine st', 'num': '2326', 'city': 'riverton', 'z': '84065'}\n{'id': 'C0165', 'f': 'fatima', 'l': 'costa', 'e': '[REDACTED:email]', 'p': '1803975604', 'a': '7132 lake blvd', 'num': '7132', 'city': 'salem', 'z': '97301'}\n{'id': 'C0167', 'f': 'brown', 'l': 'aisha', 'e': '[REDACTED:email]', 'p': 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'michael', 'l': 'rossi', 'e': '[REDACTED:email]', 'p': '8047440518', 'a': '9860 maple blvd', 'num': '9860', 'city': 'lakeside', 'z': '92040'}\n{'id': 'C0754', 'f': 'christopher', 'l': 'patel', 'e': '[REDACTED:email]', 'p': '1327859265', 'a': '7044 hill st', 'num': '7044', 'city': 'salem', 'z': '97301'}\n{'id': 'C0763', 'f': 'elizabeth', 'l': 'weber', 'e': '[REDACTED:email]', 'p': '7082629955', 'a': '4110 cedar ave', 'num': '4110', 'city': 'georgetown', 'z': '78626'}\n{'id': 'C0766', 'f': 'lucas', 'l': 'dubois', 'e': '[REDACTED:email]', 'p': '1622770010', 'a': '7935 main blvd', 'num': '7935', 'city': 'salem', 'z': '97301'}\n{'id': 'C0771', 'f': 'james', 'l': 'santos', 'e': '[REDACTED:email]', 'p': '0645155408', 'a': '1461 pine rd', 'num': '1461', 'city': 'lakeside', 'z': '92040'}\n{'id': 'C0779', 'f': 'michael', 'l': 'khan', 'e': '[REDACTED:email]', 'p': '7280638928', 'a': '601 pine blvd', 'num': '601', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0781', 'f': 'victoria', 'l': 'yilmaz', 'e': '[REDACTED:email]', 'p': '9255145463', 'a': '9235 washington st', 'num': '9235', 'city': 'salem', 'z': '97301'}\n{'id': 'C0785', 'f': 'patricia', 'l': 'smith', 'e': '[REDACTED:email]', 'p': '3325470915', 'a': '720 hill rd', 'num': '720', 'city': 'madison', 'z': '53703'}\n{'id': 'C0786', 'f': 'margaret', 'l': 'murphy', 'e': '[REDACTED:email]', 'p': '9118028689', 'a': '7923 hill dr', 'num': '7923', 'city': 'riverton', 'z': '84065'}\n{'id': 'C0792', 'f': 'jennifer', 'l': 'fischer', 'e': '[REDACTED:email]', 'p': '6256192226', 'a': '860 lake rd', 'num': '860', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0799', 'f': 'ines', 'l': 'yilmaz', 'e': '[REDACTED:email]', 'p': '6645026707', 'a': '8502 elm rd', 'num': '8502', 'city': 'georgetown', 'z': '78626'}\n{'id': 'C0806', 'f': 'tariq', 'l': 'silva', 'e': '[REDACTED:email]', 'p': '0163263980', 'a': '141 lake blvd', 'num': '141', 'city': 'riverton', 'z': '84065'}\n{'id': 'C0807', 'f': 'elizabeth', 'l': 'obrien', 'e': '[REDACTED:email]', 'p': '5282614879', 'a': '5026 pine ave', 'num': '5026', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0809', 'f': 'mei', 'l': 'novak', 'e': '[REDACTED:email]', 'p': '5345088863', 'a': '9728 washington st', 'num': '9728', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0845', 'f': 'olga', 'l': 'lopez', 'e': '[REDACTED:email]', 'p': '7094636466', 'a': '30 elm dr', 'num': '30', 'city': 'salem', 'z': '97301'}\n{'id': 'C0855', 'f': 'mateo', 'l': 'okafor', 'e': '[REDACTED:email]', 'p': '5821284218', 'a': '2413 oak blvd', 'num': '2413', 'city': 'salem', 'z': '97301'}\n{'id': 'C0858', 'f': 'lucas', 'l': 'costa', 'e': '[REDACTED:email]', 'p': '1912453009', 'a': '4013 oak st', 'num': '4013', 'city': 'salem', 'z': '97301'}\n{'id': 'C0860', 'f': 'robert', 'l': 'dubois', 'e': '[REDACTED:email]', 'p': '5902736375', 'a': '5018 oak st', 'num': '5018', 'city': 'springfield', 'z': '62701'}\n{'id': 'C0867', 'f': 'priya', 'l': 'silva', 'e': '[REDACTED:email]', 'p': '3349483866', 'a': '773 main ave', 'num': '773', 'city': 'salem', 'z': '97301'}\n{'id': 'C0869', 'f': 'aisha', 'l': 'obrien', 'e': '[REDACTED:email]', 'p': '9853555607', 'a': '8910 cedar blvd', 'num': '8910', 'city': 'riverton', 'z': '84065'}\n{'id': 'C0870', 'f': 'kim', 'l': 'ines', 'e': '[REDACTED:email]', 'p': '1505103469', 'a': '4082 maple rd', 'num': '4082', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0871', 'f': 'moreau', 'l': 'diego', 'e': '[REDACTED:email]', 'p': '6175593094', 'a': '1605 cedar ave', 'num': '1605', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0886', 'f': 'william', 'l': 'novak', 'e': '[REDACTED:email]', 'p': '3451945254', 'a': '6428 hill st', 'num': '6428', 'city': 'fairview', 'z': '37062'}\n{'id': 'C0895', 'f': 'daniel', 'l': 'silva', 'e': '[REDACTED:email]', 'p': '4794426012', 'a': '8256 main dr', 'num': '8256', 'city': 'georgetown', 'z': '78626'}\n{'id': 'C0907', 'f': 'mateo', 'l': 'muller', 'e': '[REDACTED:email]', 'p': '5824768921', 'a': '1353 cedar dr', 'num': '1353', 'city': 'lakeside', 'z': '92040'}\n{'id': 'C0915', 'f': 'robert', 'l': 'petrov', 'e': '[REDACTED:email]', 'p': '6366198297', 'a': '7363 hill blvd', 'num': '7363', 'city': 'salem', 'z': '97301'}\n{'id': 'C0917', 'f': 'elizabeth', 'l': 'nguyen', 'e': '[REDACTED:email]', 'p': '0693927466', 'a': '4492 hill st', 'num': '4492', 'city': 'riverton', 'z': '84065'}\n{'id': 'C0919', 'f': 'khan', 'l': 'ivan', 'e': '[REDACTED:email]', 'p': '3877569863', 'a': '5959 park rd', 'num': '5959', 'city': 'madison', 'z': '53703'}\n{'id': 'C0930', 'f': 'kenji', 'l': 'khan', 'e': '[REDACTED:email]', 'p': '9994205706', 'a': '8659 elm rd', 'num': '8659', 'city': 'salem', 'z': '97301'}\n{'id': 'C0942', 'f': 'victoria', 'l': 'larsen', 'e': '[REDACTED:email]', 'p': '4605411059', 'a': '7535 cedar st', 'num': '7535', 'city': 'salem', 'z': '97301'}\n{'id': 'C0944', 'f': 'margaret', 'l': 'murphy', 'e': '[REDACTED:email]', 'p': '8350058844', 'a': '6671 main dr', 'num': '6671', 'city': 'riverton', 'z': '84065'}\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_8", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport dedupe as d,csv,json,collections\nr={x['id']:d.prepare(x) for x in csv.DictReader(open('customers.csv'))};cs=json.load(open('clusters.json'))\nfor c in cs:\n es=set(r[x]['e'] for x in c)-{''}\n if len(es)>1:\n print([r[x] for x in c])\nids=[i for c in cs for i in c]\nassert len(ids)==len(set(ids))==len(r) and set(ids)==set(r)\nprint('Verified exact ID coverage')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_8", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport dedupe as d,csv,json,collections\nr={x['id']:d.prepare(x) for x in csv.DictReader(open('customers.csv'))};cs=json.load(open('clusters.json'))\nfor c in cs:\n es=set(r[x]['e'] for x in c)-{''}\n if len(es)>1:\n print([r[x] for x in c])\nids=[i for c in cs for i in c]\nassert len(ids)==len(set(ids))==len(r) and set(ids)==set(r)\nprint('Verified exact ID coverage')\nPY\"", "aggregated_output": "Verified exact ID coverage\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_9", "type": "agent_message", "text": "Created [dedupe.py](dedupe.py) and ran it, producing [clusters.json](clusters.json): 946 records grouped into 439 clusters.\n\nVerified every ID appears exactly once. Uses Python stdlib only, with no network access or hard-coded IDs."}}
{"type": "turn.completed", "usage": {"input_tokens": 163622, "cached_input_tokens": 146304, "cache_write_input_tokens": 0, "output_tokens": 3018, "reasoning_output_tokens": 163}}