Data Normalization

Data processing and normalization tasks evaluate structured output from messy prose and different structures. This capability is essential for catalogue and product pipelines.

Duration

Input Tokens

836

Output Tokens

916

Cost

$0.00

Context

Input

Return if the following product is allowed in EU, USA and Asia. Only follow the guidelines in the text, no other knowledge

Expected output

{
  "allowed_in_eu": true,
  "allowed_in_usa": false,
  "allowed_in_asia": true
}

Model output

{
  "context": "Checking AeroSeal-X Polymer Matrix ingredients against regional forbidden lists:\n\n**EU Check:**\n- Trihexyl Nitrate Stabilizer (not present)\n- Cobalt-Linked Polymer Initiator (CPI-7) (not present)\n- Volatile Suspension Matrix VSM-98 (not present)\n- Methachlorohexane Sulfonide (not present)\nResult: No forbidden ingredients found in product → ALLOWED\n\n**USA Check:**\n- NanoSilicon Oxide-Δ3 ✓ (ingredient 1 matches)\n- Isomerized Hexaoxy Toluate (IHT) ✓ (ingredient 2 matches)\n- Tetramethyl Ethyloxy Resinate ✓ (ingredient 4 matches)\n- Zinc Pyrithionate Modulator ✓ (ingredient 3 matches)\nResult: Multiple forbidden ingredients present → NOT ALLOWED\n\n**Asia Check:**\n- Fluoromethyl Cyclopentene (not present)\n- Polyduramine-X17 (not present)\n- Hexatridecyl Vinylnitrate (not present)\nResult: No forbidden ingredients found in product → ALLOWED",
  "allowed_in_eu": true,
  "allowed_in_usa": false,
  "allowed_in_asia": true
}