Skill: multi-conditional-entity-localization

[Case 2]

• Initial Environment: A RAG environment containing a series of daily automotive news updates and historical company country-of-origin records.
• Real Question: Which company, headquartered in China, sold exactly 2139 cars in the first quarter of 2023?
• Real Trajectory: 1. Decompose the query into two predicates: 'headquartered in China' and 'sold 2139 cars in 2023'. 2. Search for the specific sales figure '2139' within the database. 3. Retrieve a news update identifying 'FAW' as the company with that specific sales volume. 4. Pivot to verify the headquarters constraint for 'FAW'. 5. Retrieve a company profile document confirming FAW's country of origin is China. 6. Finalize the localize entity as FAW after meeting both conditions.
• Real Answer: FAW
• Why this demonstrates the capability: The question forces an intersection of a quantitative attribute (sales volume) and a qualitative metadata attribute (headquarters), where the entity itself (FAW) must be discovered rather than being provided as a keyword in the prompt. This requires filtering a candidate set through multiple logical gates.

Pipeline Execution Instructions

To synthesize data for this capability, you must strictly follow a 3-phase pipeline. Do not hallucinate steps. Read the corresponding reference file for each phase sequentially:

1. Phase 1: Environment Exploration Read the exploration guidelines to discover raw knowledge seeds: references/EXPLORATION.md

2. Phase 2: Trajectory Selection Once Phase 1 is complete, read the selection criteria to evaluate the trajectory: references/SELECTION.md

3. Phase 3: Data Synthesis Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data: references/SYNTHESIS.md