> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/juanceresa/sift-kg/llms.txt
> Use this file to discover all available pages before exploring further.

# FTX Collapse Example

> Knowledge graph from 9 Wikipedia articles about the FTX cryptocurrency exchange collapse

Complete sift-kg pipeline output from 9 Wikipedia articles documenting the FTX cryptocurrency exchange collapse, producing a graph of **373 entities** and **1,184 relations** after deduplication.

## Overview

This example demonstrates knowledge graph extraction from journalistic and encyclopedic content, with emphasis on the entity resolution workflow to merge duplicate entities across multiple documents.

<CardGroup cols={2}>
  <Card title="View Interactive Graph" icon="diagram-project">
    Open `examples/ftx/output/graph.html` in your browser
  </Card>

  <Card title="Source Documents" icon="file-lines">
    9 text files covering FTX, Alameda Research, Binance, and key people
  </Card>
</CardGroup>

## Quick Start

```bash theme={null}
# View the interactive graph (no installation needed)
open examples/ftx/output/graph.html     # macOS
xdg-open examples/ftx/output/graph.html # Linux

# Or use sift's built-in viewer
sift view -o examples/ftx/output
```

## Pipeline Output

The `output/` directory contains the complete pipeline results:

```
ftx/
├── docs/                          # 9 source documents (~148K total)
└── output/
    ├── extractions/               # Per-document entity+relation JSON from LLM
    ├── graph_data.json            # Knowledge graph (373 entities, 1184 relations)
    ├── merge_proposals.yaml       # Entity merge decisions (CONFIRMED/REJECTED)
    ├── entity_descriptions.json   # AI-generated entity descriptions
    ├── narrative.md               # Prose narrative with entity profiles
    └── graph.html                 # Interactive pyvis graph viewer
```

## Pipeline Statistics

| Metric                          | Value                                                                     |
| ------------------------------- | ------------------------------------------------------------------------- |
| **Input Documents**             | 9 text files (\~148K total)                                               |
| **Topics Covered**              | FTX, Alameda Research, Binance, Sam Bankman-Fried, and other key figures  |
| **Raw Entities Extracted**      | \~777 entities from LLM                                                   |
| **After Pre-dedup (semhash)**   | 750 entities (27 deterministic merges)                                    |
| **After Build + Postprocess**   | 432 entities, 1,201 relations                                             |
| **After Resolution (3 passes)** | 373 entities, 1,184 relations (59 entities merged via LLM + human review) |
| **Final Entity Descriptions**   | 100 entity profiles in narrative                                          |
| **Model Used**                  | claude-haiku-4-5-20251001                                                 |
| **Total Cost**                  | \~\$0.28 (extraction was separate)                                        |

## Entity Resolution Workflow

This example showcases the full deduplication pipeline:

### 1. Automatic Semantic Deduplication

During extraction, semantic hashing automatically merges near-identical entities:

* **Before**: 777 raw entities
* **After**: 750 entities (27 deterministic merges)

### 2. Build + Postprocess

Graph construction with normalization and filtering:

* **Result**: 432 entities, 1,201 relations

### 3. LLM-Assisted Resolution

Three passes of `sift resolve` to identify remaining duplicates:

```bash theme={null}
sift resolve -o examples/ftx/output --model claude-haiku-4-5-20251001
```

This generates `merge_proposals.yaml` with candidate merges:

```yaml theme={null}
merges:
  - primary: "Sam Bankman-Fried"
    duplicates:
      - "SBF"
      - "Samuel Bankman-Fried"
    status: CONFIRMED
    reasoning: "Same person, different name variations"
  
  - primary: "FTX Trading Ltd."
    duplicates:
      - "FTX"
      - "FTX.com"
    status: CONFIRMED
    reasoning: "Same exchange entity"
```

### 4. Human Review

```bash theme={null}
sift review -o examples/ftx/output
```

Interactive review to confirm or reject each proposed merge.

### 5. Apply Merges

```bash theme={null}
sift apply-merges -o examples/ftx/output
```

**Final result**: 373 entities, 1,184 relations (59 entities merged)

## Key Insights from the Graph

The generated narrative (`narrative.md`) provides:

* **Overview** — High-level synthesis of the FTX collapse timeline
* **Entity descriptions** — AI-generated profiles for 100 key entities (companies, people, events)
* **Relationship mapping** — How FTX, Alameda, Binance, and key figures are connected

## Re-running the Example

### Option 1: Build from Existing Extractions (Free)

Use the pre-extracted entities — no LLM API calls:

```bash theme={null}
pip install sift-kg
sift build -o examples/ftx/output
sift view -o examples/ftx/output
```

### Option 2: Full Pipeline from Scratch

Re-extract entities from the source documents:

```bash theme={null}
# Extract entities
sift extract examples/ftx/docs \
  --model openai/gpt-4o-mini \
  -o my-output

# Build the knowledge graph
sift build -o my-output

# Resolve duplicate entities (3 passes recommended)
sift resolve -o my-output --model openai/gpt-4o-mini

# Review proposed merges
sift review -o my-output

# Apply confirmed merges
sift apply-merges -o my-output

# Generate narrative
sift narrate -o my-output --model openai/gpt-4o-mini

# View the result
sift view -o my-output
```

<Tip>
  Run `sift resolve` multiple times (2-3 passes) to catch progressively more subtle duplicates. Each pass refines the merge proposals.
</Tip>

## Cost Breakdown

The \~\$0.28 total cost includes:

* **Resolution** — LLM calls across 3 passes to identify duplicates
* **Narration** — LLM calls to generate entity descriptions and overview

(Extraction cost was calculated separately.)

Costs will vary based on:

* Number of entities to resolve
* Model chosen (Haiku vs GPT-4o-mini vs others)
* Number of resolution passes

## Use Cases

This example pattern works well for:

* **Investigative journalism** — Map connections across news articles
* **Business intelligence** — Track companies, people, and events across sources
* **Historical analysis** — Document timelines and relationships in major events
* **Due diligence** — Aggregate information about entities from multiple sources

## Merge Proposals File

The `merge_proposals.yaml` file is a key artifact:

```yaml theme={null}
merges:
  - primary: "Primary Entity Name"
    duplicates:
      - "Duplicate 1"
      - "Duplicate 2"
    status: CONFIRMED  # or REJECTED
    reasoning: "Why these should be merged"
```

This file:

* Is generated by `sift resolve`
* Can be manually edited before applying
* Supports version control and collaboration
* Is applied with `sift apply-merges`

## Next Steps

<CardGroup cols={2}>
  <Card title="Explore Other Examples" icon="folder-open" href="/examples/overview">
    See Transformers and Epstein examples
  </Card>

  <Card title="Resolution Guide" icon="code-merge" href="/guides/resolution-workflow">
    Learn more about entity deduplication
  </Card>
</CardGroup>