What I learned

Interpretation of the results. It rests on three models, one task and single-seed runs, so each claim is hedged accordingly.

Fine-tuning appears to help less as the pipeline gets better

On Qwen3-14B the best zero-shot pipeline (0.7999) beats both LoRA and full fine-tuning. On Llama the two are essentially tied. Only on Salamandra, the weakest model without training, does LoRA add on top of the best prompting pipeline. The comparison has a limit: the tuned Qwen and Llama models were not evaluated with the richest pipelines, so it is untested whether training adds anything on top of them. A plausible reading is that fine-tuning teaches the model schema-handling behaviour that the post-processing layers and the retrieved examples already provide at inference time: when those layers are absent, training compensates; when they are present, it has less to add.

Training matters on a different axis: hallucination

Headline F1 hides a consistent effect. With the plain baseline prompt, the null-trap rate dropped on every model under training, strongly on Salamandra and Llama and only slightly on Qwen. This suggests that, compared with an untuned model under the same prompt, training makes the model more willing to abstain. It does not show that training beats the best prompting pipeline on this axis: in the source analysis, a well-designed pipeline stays competitive with LoRA on grounding.

The optimum was stable across model sizes

The same LoRA configuration (rank 64, alpha 128, dropout 0.10) came out best for the 3B, 7B and 14B models, and in-context tuning helped all three. Together with the flat gain from rank 64 to rank 128, this appears to indicate that the difficulty of the task lies in reasoning about the schema, and that the levers acting on that reasoning behave similarly across this size range.

What did not work

Run E229 added a second, targeted LLM pass for the 34 development tasks with nested objects, on top of E226 with Llama-3.2-3B. The target subset improved by 0.0037, but the global F1 fell from 0.7459 to 0.7432 and the estimated cost grew to about 1.23 times that of E226. In addition, 20 of the 115 tasks without nested objects changed between two runs of an unchanged first pass, which points to non-determinism in the serving stack rather than to the method. The branch was dropped.

Limits

Three models, one task, single-seed runs: the interpretation is not a cross-family proof.
The richest pipeline overflows Salamandra's 8k context window on 14 of the 149 development tasks, so its best reported pipeline is a lighter one.
The synthetic corpus is dominated by general-domain, Wikipedia-derived text, so transfer to specialised domains is not tested here.