# V3 Annotation Guidelines

**Schema version:** 1.0.0
**Last updated:** 2025-01-15

## Purpose

These guidelines define how human annotators and automated systems label documents in the Intelligence Pipeline v3 Gold Corpus. Every annotation must be evidence-grounded — no label is valid without a supporting evidence span traceable to the source text.

## Core Principles

1. **Evidence first.** If you cannot point to exact text that supports a label, do not apply the label.
2. **Explicit over inferred.** Mark only what the document explicitly states in primary annotations. Inferred exposure uses a separate, lower-confidence channel.
3. **Precision over recall.** A missed entity is preferable to a fabricated one. The pipeline uses multiple stages — later stages catch omissions.
4. **Reproducibility.** Two annotators given the same document should produce substantially the same labels. Ambiguous cases are marked, not resolved by guess.

---

## Evidence Spans

### Definition

An evidence span is the exact substring of the source document that supports an annotation. It uses zero-based character offsets into the original (pre-chunking) document text.

### Rules

- Every entity, event, relation, numeric fact, and sentiment annotation MUST reference at least one evidence span.
- Spans should be minimal but complete — include enough context for the label to be verifiable without the full document.
- Overlapping spans are permitted (e.g., the same sentence supports both an entity and an event).
- The `text` field MUST exactly match `source_text[start_char:end_char]`.

### Positive example

```
Source: "Apple Inc. reported quarterly earnings of $1.52 per share"
Span:   start_char=0, end_char=10, text="Apple Inc."
```

### Negative example

```
Source: "Apple Inc. reported quarterly earnings of $1.52 per share"
Span:   start_char=0, end_char=5, text="Apple"
```
❌ Truncating "Apple Inc." to "Apple" loses the corporate suffix needed to distinguish from Apple Records or the fruit.

---

## Entity Annotation

### Entity Types

| Type | When to use | Example |
|------|-------------|---------|
| `company` | Legal entity, publicly traded firm, government agency | "Apple Inc.", "The Federal Reserve" |
| `person` | Named individual | "Tim Cook", "Jerome Powell" |
| `product` | Named product or service | "iPhone 16", "Azure OpenAI Service" |
| `event` | Named event instance | "Q1 2025 earnings call" |
| `financial_metric` | Named metric class | "EPS", "revenue", "free cash flow" |
| `date` | Temporal expression | "Q1 2025", "January 15, 2025" |
| `percentage` | Percentage value | "4%", "25 basis points" |
| `currency` | Monetary value | "$1.52", "$10 billion" |
| `relationship` | Explicit relationship mention | "subsidiary", "joint venture partner" |

### Canonical Resolution

- If an entity maps to a company in the symbol registry, set `canonical_id` and `canonical_name` (ticker).
- If an entity is ambiguous (e.g., "Apple" could be AAPL or a fruit company), mark an ambiguity marker and set confidence below 1.0.
- Do NOT invent canonical IDs. If not in the registry, leave `canonical_id` as null.

### Positive example

```json
{
  "entity_type": "company",
  "literal_text": "Alphabet",
  "canonical_id": "googl-uuid",
  "canonical_name": "GOOGL",
  "confidence": 0.97
}
```

### Negative example

```json
{
  "entity_type": "company",
  "literal_text": "the company",
  "canonical_id": "aapl-uuid",
  "canonical_name": "AAPL",
  "confidence": 0.90
}
```
❌ "the company" is a pronoun reference, not an entity mention. Resolve coreference but annotate the actual named mention, not the pronoun.

---

## Event Classification

### Event Classes

| Class | Definition | Distinguishing criteria |
|-------|-----------|------------------------|
| `earnings_beat` | Reported EPS or revenue exceeds consensus | Explicit comparison to estimates |
| `earnings_miss` | Reported EPS or revenue below consensus | Explicit comparison to estimates |
| `guidance_raise` | Forward guidance raised vs prior or consensus | Future-looking, not historical result |
| `guidance_cut` | Forward guidance lowered | Future-looking, not historical result |
| `ma_announcement` | Merger, acquisition, investment, or divestiture | Transaction between entities |
| `legal_regulatory` | Lawsuit, fine, regulatory action, or settlement | Legal or regulatory body involved |
| `product_launch` | New product, service, or major feature announced | Not routine updates |
| `supply_chain` | Disruption, partnership, or change in supply relationships | Affects production/delivery |
| `rating_change` | Analyst upgrade, downgrade, or target change | From research analyst/firm |
| `management_change` | CEO/CFO/board appointment, resignation, or removal | C-suite or board level |
| `macro_event` | Interest rates, policy, trade, geopolitical | Not specific to one company |
| `dividend_change` | Dividend increase, decrease, or special dividend | Shareholder distribution |
| `buyback` | Share repurchase program announcement or completion | Capital return via buyback |

### Adjudication triggers for events

Route to the 9B adjudicator when:
- The same facts could be classified as multiple event types (e.g., guidance_raise during an earnings call could be either earnings_beat or guidance_raise — label the most specific applicable class).
- The event is implied but not explicitly stated.
- The primary company is unclear.

### Positive example

```
Source: "Apple beat earnings expectations with EPS of $1.52 vs $1.43 expected"
Event class: earnings_beat
Confidence: 0.98
```

### Negative example

```
Source: "Apple reported EPS of $1.52"
Event class: earnings_beat
```
❌ Without a comparison to consensus/estimates, this is a numeric fact report, not an earnings beat. The document must provide evidence of beating expectations.

---

## Relations

### Relation Types

| Type | Subject | Object | When to use |
|------|---------|--------|-------------|
| `directly_affects` | Event | Company | Event explicitly names or discusses the company |
| `inferred_exposure` | Event | Company | Exposure inferred from sector, supply chain, or competition |
| `competes_with` | Company | Company | Competitive relationship stated or clearly implied |
| `supplies` | Company | Company | Supply chain relationship stated |

### Critical distinction: directly_affects vs inferred_exposure

- `directly_affects`: The document **explicitly states** the company is impacted. Evidence span exists.
- `inferred_exposure`: The impact is **reasoned** from relationships, not stated. May have weak or no direct evidence span.

Only `directly_affects` enters primary company extraction. `inferred_exposure` flows through the separate interpolation/propagation architecture with distinct confidence and provenance.

### Positive example (directly_affects)

```
Source: "Microsoft announced a $10 billion investment in OpenAI"
Relation: directly_affects(event=ma_announcement, company=Microsoft)
Evidence: "Microsoft announced"
```

### Negative example (incorrectly using directly_affects)

```
Source: "Microsoft announced a $10 billion investment in OpenAI"
Relation: directly_affects(event=ma_announcement, company=Google)
```
❌ Google is not mentioned in the event sentence. This should be `inferred_exposure` based on competitive relationship, with appropriate lower confidence.

---

## Numeric Facts

### Annotation rules

1. Always store both `literal_value` (exact text) and `normalized_value` (parsed number).
2. Include `unit` (USD, %, bps, shares, etc.).
3. Link to the subject entity when determinable.
4. Use `predicate` to capture the semantic role: reported, expected, raised_to, cut_to, beat_by, missed_by.
5. Include `period` when the fact references a specific time frame.

### Normalization conventions

| Literal | Normalized | Unit |
|---------|-----------|------|
| "$1.52" | 1.52 | USD |
| "$94.9 billion" | 94900000000 | USD |
| "25 basis points" | 0.25 | percentage_points |
| "4%" | 4.0 | % |
| "$0.26 per share" | 0.26 | USD |

### Positive example

```json
{
  "fact_type": "eps",
  "predicate": "reported",
  "literal_value": "$1.52 per share",
  "normalized_value": 1.52,
  "unit": "USD",
  "period": {"period_type": "fiscal_quarter", "fiscal_year": 2025, "fiscal_quarter": 1}
}
```

### Negative example

```json
{
  "fact_type": "eps",
  "predicate": "reported",
  "literal_value": "$1.52 per share",
  "normalized_value": 152,
  "unit": "cents"
}
```
❌ While $1.52 = 152 cents, always normalize to the unit stated in the source. Conversion to a different unit introduces potential confusion.

---

## Sentiment

### Rules

1. Sentiment is **company-specific**, not document-level. A single article can have positive sentiment for one company and negative for another.
2. Annotate probability distributions (positive, negative, neutral) that sum to 1.0.
3. `mixed` label is used when evidence groups disagree — it is computed from evidence-group-level disagreement, NOT an unconstrained fourth class.
4. The label should reflect the dominant probability.

### When to label "mixed"

Label `mixed` when:
- Different paragraphs contain opposing sentiment for the same company
- The same fact has both positive and negative implications (e.g., restructuring = cost cuts but also layoffs)
- Analyst opinions explicitly disagree within the document

Do NOT label `mixed` when:
- Sentiment is merely uncertain or mild — that's `neutral` with lower confidence
- The document discusses multiple companies with different sentiments — annotate separately per company

### Positive example

```json
{
  "label": "mixed",
  "positive_probability": 0.40,
  "negative_probability": 0.45,
  "neutral_probability": 0.15,
  "evidence_ids": ["ev-pressure", "ev-validation"]
}
```
(Article says AI investment pressures cloud revenue but validates the broader thesis)

### Negative example

```json
{
  "label": "mixed",
  "positive_probability": 0.85,
  "negative_probability": 0.05,
  "neutral_probability": 0.10
}
```
❌ When positive_probability dominates at 0.85, the label should be `positive`, not `mixed`. Mixed requires genuine disagreement in evidence.

---

## Direct Effects vs Inferred Exposure

### Direct Effects

A direct effect means the document **explicitly states or clearly demonstrates** that an event impacts a specific company.

**Criteria:**
- The company is named in the same sentence or paragraph as the event
- The causal link is stated, not inferred
- Evidence span directly connects event to company

### Inferred Exposure

Inferred exposure captures **reasoned but unstated** impacts on companies.

**Criteria:**
- The company is NOT explicitly linked to the event in the source text
- The connection comes from known relationships (competitor, supplier, sector peer)
- Confidence should be lower than direct effects (typically 0.5–0.8)
- Requires `reasoning` field explaining the inference chain

### Adjudication routing

When it's unclear whether an effect is direct or inferred, mark an ambiguity marker with type `implied_causal_impact` and route to the 9B adjudicator.

---

## Ambiguity Markers

### When to flag

Flag ambiguity when:
- An alias resolves to multiple candidate companies (`unresolved_alias`)
- Multiple companies could be the primary subject (`multiple_primary_companies`)
- Numeric facts within the same document contradict each other (`contradictory_numeric_facts`)
- Sentiment evidence points in opposing directions for the same company (`conflicting_sentiment`)
- Impact is implied through causal chain, not stated (`implied_causal_impact`)
- Guidance must be compared to consensus to determine direction (`guidance_vs_consensus_requires_reasoning`)
- A required field cannot be determined from available evidence (`material_field_missing`)
- Evidence covers less than the minimum threshold for confident extraction (`evidence_coverage_below_threshold`)
- Calibrated confidence falls below the routing threshold (`calibrated_confidence_below_threshold`)
- A relation spans multiple document chunks (`long_document_cross_chunk_relation`)

### Severity levels

- **low**: The annotation is likely correct but has reduced certainty. Fast path may proceed with a confidence penalty.
- **medium**: The annotation requires review. Routes to adjudication by default.
- **high**: The annotation cannot be reliably made without semantic reasoning. Always routes to adjudication.

---

## Safety-Critical Fields

The following fields are **safety-critical** for promotion gates. Errors in these fields can directly cause incorrect trading decisions:

| Field | Why it's critical | Minimum promotion gate |
|-------|-------------------|----------------------|
| Company identity (ticker) | Wrong ticker = trade on wrong security | Precision ≥ 0.95, Recall ≥ 0.90 |
| Event class | Misclassifying beat/miss inverts signal direction | Macro-F1 ≥ 0.85 |
| Sentiment direction | Wrong sentiment → wrong position direction | Direction accuracy ≥ 0.90 |
| Numeric fact values | Wrong magnitude affects impact estimation | Tolerance match ≥ 0.92 |
| Direct effect attribution | Wrong company attribution creates false signals | Precision ≥ 0.93 |
| Evidence support | Unsupported claims are unverifiable | Support rate ≥ 0.95 |
| Confidence calibration | Overconfidence bypasses review | ECE ≤ 0.05 |

Annotators must pay special attention to these fields. During review, any error in a safety-critical field requires correction before the annotation can receive "gold" status.

---

## Annotation Workflow

1. **First pass:** Identify all entities and evidence spans
2. **Second pass:** Classify events and link to companies
3. **Third pass:** Extract numeric facts with periods
4. **Fourth pass:** Assess per-company sentiment
5. **Fifth pass:** Identify relations, direct effects, and inferred exposures
6. **Sixth pass:** Flag ambiguities and set confidence levels
7. **Review:** Senior annotator validates safety-critical fields

### Inter-annotator agreement

Hard cases (flagged with ambiguity markers) receive double annotation. Inter-annotator agreement is measured per field type using Cohen's kappa. Target: κ ≥ 0.80 for entity and event labels, κ ≥ 0.70 for relations and sentiment.

---

## Version History

| Version | Date | Changes |
|---------|------|---------|
| 1.0.0 | 2025-01-15 | Initial schema and guidelines |