multi-conditional-entity-localization

name

multi-conditional-entity-localization

description

Use this skill when the user provides multiple specific clues and wants to find the one person, place, or thing that fits all of them. Trigger it for requests like 'find someone who did A and also did B', 'who is the person mentioned in both these articles', 'locate an entity satisfying several conditions', or 'which item fits this set of descriptions'. This skill is specifically for identifying a unique subject through the intersection of independent factual constraints found across different parts of the corpus.

Skill: multi-conditional-entity-localization

1. Capability Definition & Real Case

• Professional Definition: The ability to pinpoint a unique entity or subject by logically intersecting multiple semantic constraints (triplets) distributed throughout a retrieved document pool. Unlike simple attribute integration—which aggregates data for a known entity—this capability requires the agent to perform 'subject identification' by uncovering an entity that simultaneously satisfies multiple independent predicates (e.g., Entity X has Relation 1 with A, and Entity X has Relation 2 with B) found across disjointed documents.
• Dimension Hierarchy: Multi-Source Evidence Composition->Cross-Document Synthesis->multi-conditional-entity-localization

Real Case

[Case 1]

• Initial Environment: A bounded news corpus containing factual snippets about Russian public figures, performances, and social events. The documents are split into small, non-overlapping chunks.
• Real Question: Who performed at M-bar and met with Dmitry Dibrov?
• Real Trajectory: 1. Analyze the two distinct constraints: 'performed at M-bar' and 'met with Dmitry Dibrov'. 2. Execute a search for 'Dmitry Dibrov' to find meeting records. 3. Retrieve a snippet identifying a meeting with 'Roman Miroshnichenko'. 4. Execute a secondary search for 'M-bar' performances. 5. Retrieve an independent snippet stating that 'Roman Miroshnichenko' performed at M-bar. 6. Intersect the two candidate sets: identify that 'Roman Miroshnichenko' is the only entity satisfying both the location constraint (M-bar) and the social constraint (Dmitry Dibrov).
• Real Answer: Roman Miroshnichenko
• Why this demonstrates the capability: This case demonstrates the 'Conditional' reasoning pattern where the answer is not a composite list of facts, but the identification of a specific entity that acts as the intersection of two distinct factual relations. The agent must verify the shared identity across disjointed contexts to localize the target.

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[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