entity resolution

Palantir's bread and butter: 'Acme Corp.' and 'ACME Corporation, Inc.' are the same customer; prove it in code. Normalization does more work than the clever algorithm, and blocking is what makes it run before the heat death of the universe.

Everyone spots that the customer records need resolving. The failure that actually sinks this project is quieter: the word fraud means three different things in the three datasets, so the union of their labels trains a model that is confidently wrong.

Palantir's home turf: entity resolution disguised as a dedupe task. The naive O(n²) comparison is the trap, blocking, match scoring, transitive closure, and the golden-record question are where the interview actually happens.

The purest data-modeling prompt Palantir asks, the whole interview lives in the entity-resolution edge cases. Twins, maiden names, shared insurance IDs... and one architectural decision about merging that candidates get fatally wrong.

Per-account scoring catches the lone fraudster and misses the ring whose accounts each look clean. The strong answer reframes the problem as a graph, makes entity resolution the step everything else depends on, and treats a flagged ring as a case for an investigator, not an automatic block.

Every control in the stack validates the shape of the action and none validates its target. A refund of the right amount, to a real customer, through a permitted tool, against the wrong account, is indistinguishable from success at every layer that is watching.