The production of large-scale political datasets typically demands extracting structured facts from vast piles of unstructured documents or web sources, a task that traditionally relies on expensive human experts and remains prohibitively difficult to automate at scale. We leverage Large Language Models (LLMs) to automate the extraction of multi-dimensional elite biographies. We propose a two-stage Synthesis-Coding framework: an upstream synthesis stage that uses recursive agentic LLMs to search, filter, and curate biographies from heterogeneous web sources, followed by a downstream coding stage that maps curated biography into structured dataframes.

Large Reasoning Models (LRMs) embedded in agentic frameworks have transformed information retrieval from static, long-context question answering into open-ended exploration. Yet real-world use requires models to discover and synthesize "long-tail" facts from dispersed sources, a capability that remains under-evaluated. We introduce PolitNuggets, a multilingual benchmark for agentic information synthesis via constructing political biographies for 400 global elites, covering over 10000 political facts. We standardize evaluation with an optimized multi-agent system and propose FactNet, an evidence-conditional protocol that scores discovery, fine-grained accuracy, and efficiency.

Traditional sociological thinking presumes hidden social structure resides within social norms, values, and common sense. We operationalize this idea as a computational social science measurement problem: can we recover population-level perceptions of social hierarchy from text descriptions in a way that is empirically validated by observed behavior? We cast this as an expert coding task: given an occupation description, a coder assigns a perceived-prestige score that captures shared social meaning and status ordering. Building on a consensus-awareness view of shared perceptions, we construct a framework with persona-seeded LLM coders whose generated perceived occupation prestige scores can predict occupational mobility structure.