The electrification of heating systems is a critical pathway for decarbonizing the building sector; however, the development of sustainable strategies is often hindered by the lack of granular thermal load profiles. The nature of this problem is such that available data are extremely scarce, irregular, and low-frequency, rendering conventional data-hungry machine learning approaches impractical or unreliable. High-resolution gas metering is rarely available, as it falls outside standard utility business requirements. To bridge this gap and enable data-driven AI analytics under severe data limitations, this paper presents the Generalized Proxy-Guided Load and Temporal Disaggregation framework. Validated on a dataset of 11 multi-unit residential buildings over 18 months, the framework achieves a mean squared percentage error (MSPE) of 6.37% for reconstructed total gas consumption. The methodology operates through a four-stage process: (i) Generalized Occupancy Proxy Extraction via weather normalization to isolate behavioral signals from hourly electricity data; (ii) Unified Segmentation and Normalized Pooling to mitigate the statistical limitations of sparse billing data; (iii) Unified Baseload Parameter Estimation enhanced by local calibration to ensure building-specific accuracy; and (iv) Component-Wise Temporal Disaggregation to reconstruct distinct baseload and heating profiles. Overall, the proposed framework effectively bridges the resolution gap, transforming low-frequency utility data into high-fidelity training sets suitable for scalable, data-driven decarbonization modeling.