# How Temporal Grounding Detects Ungrounded Values in Needle

> Discover how Temporal Grounding in Needle validates extracted year values against schema constraints, ensuring data integrity by rejecting ungrounded temporal data.

- Repository: [Cactus Compute, Inc./needle](https://github.com/cactus-compute/needle)
- Tags: deep-dive
- Published: 2026-09-10

---

**Needle's temporal grounding validates extracted year values against schema-defined allowed years through recursive argument walking, rejecting or omitting any temporal data that falls outside the permitted range depending on the strictness configuration.**

Temporal grounding is a critical safety mechanism in the cactus-compute/needle library that ensures time-related values extracted from prompts conform to predefined schema constraints. According to the Needle source code, this validation system prevents hallucinated or out-of-scope dates from reaching downstream language model calls by systematically checking arguments against temporal boundaries defined in the schema.

## The Two-Phase Detection Mechanism

The detection of ungrounded temporal values operates through two distinct phases implemented in [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py).

### Phase 1: Schema Introspection and Year Collection

The **`_temporal_grounding`** function (lines 432-440 of [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py)) first inspects the schema for temporal field definitions. When encountering fields marked as `temporal`, Needle extracts the `allowed_years` parameter from the schema definition and compiles a reference list of valid temporal boundaries. This establishes the authoritative dataset against which all extracted values are compared.

### Phase 2: Recursive Argument Validation

Following schema analysis, the **`_walk_grounding`** helper function (lines 401-410 of [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py)) recursively traverses the extracted argument structure. For each candidate value matching a temporal pattern—specifically four-digit year strings—the function checks membership in the previously compiled allowed list. Values matching the schema are preserved, while ungrounded values are flagged for removal from the final result.

## Strict vs. Non-Strict Grounding Modes

Needle provides configurable behavior through the **`strict`** parameter in the `ground()` method. When `strict=True` (the default), detection of any ungrounded temporal value triggers a `ValueError`, immediately halting processing and indicating the offending field. When `strict=False`, ungrounded values are silently omitted from the final grounding result while processing continues with valid arguments.

## Implementation in the Source Code

The core validation logic resides in Needle's main module. The `_walk_grounding` function implements the recursive traversal and pattern matching logic, while `_temporal_grounding` handles the schema preprocessing at lines 432-440. These functions work in concert to strip ungrounded temporal data before it reaches the model invocation layer.

## Practical Usage Examples

The following example demonstrates temporal grounding with schema-defined allowed years:

```python
from needle import Needle, Schema

# Define a schema that only allows years 2020-2023

my_schema = Schema(
    name="event",
    fields=[
        {"name": "year", "type": "temporal", "allowed_years": [2020, 2021, 2022, 2023]},
        {"name": "description", "type": "string"},
    ],
)

needle = Needle(schema=my_schema)

# Prompt containing a valid year – grounding succeeds

result = needle.ground(
    "The conference will be held in 2022 and will focus on AI."
)
print(result["year"])  # → 2022

```

When encountering an invalid year with strict mode enabled, Needle raises an exception:

```python

# Prompt containing an invalid year – grounding raises an error (strict=True)

try:
    needle.ground(
        "The conference will be held in 2099 and will focus on AI."
    )
except ValueError as e:
    print(e)  # → "Temporal value 2099 is not grounded to schema"

```

To handle ungrounded values gracefully without exceptions, disable strict mode:

```python
result = needle.ground(
    "The conference will be held in 2099 and will focus on AI.",
    strict=False,
)
print(result.get("year"))  # → None (the invalid year is omitted)

```

## Test Coverage and Validation

The temporal grounding behavior is verified in the Needle test suite. The **`test_extraction_rejects_fabricated_temporal_year`** case in [`tests/test_weights.py`](https://github.com/cactus-compute/needle/blob/main/tests/test_weights.py) (lines 199-206) demonstrates rejection of fabricated years like `2099` when they violate schema constraints. Additional scenarios covering both successful grounding and proper rejection of out-of-scope values are implemented in [`tests/test_grounding.py`](https://github.com/cactus-compute/needle/blob/main/tests/test_grounding.py).

## Summary

- **Temporal grounding** validates four-digit year strings against schema-defined allowed years to prevent hallucinated dates.
- The **`_temporal_grounding`** function extracts permissible years from schema definitions at lines 432-440 of [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py).
- **`_walk_grounding`** recursively checks extracted arguments against the allowed year list at lines 401-410.
- **Strict mode** (`strict=True`) raises `ValueError` for ungrounded values, while non-strict mode omits them silently.
- Test suites in [`tests/test_weights.py`](https://github.com/cactus-compute/needle/blob/main/tests/test_weights.py) and [`tests/test_grounding.py`](https://github.com/cactus-compute/needle/blob/main/tests/test_grounding.py) verify the rejection logic against fabricated temporal data.

## Frequently Asked Questions

### What happens when Needle detects an ungrounded temporal value?

When strict mode is enabled (the default), Needle raises a `ValueError` indicating the specific ungrounded field and value. In non-strict mode, the invalid value is simply excluded from the grounding result while processing continues with valid arguments.

### How do I configure the allowed years for temporal grounding in Needle?

Define the `allowed_years` parameter in your Schema's temporal field definitions. The `_temporal_grounding` function extracts these values from the schema to build the validation reference list against which all extracted years are compared.

### Can I disable strict temporal checking in Needle?

Yes, pass `strict=False` to the `ground()` method. This allows the extraction to proceed by omitting ungrounded temporal values rather than raising an exception, which is useful for extracting partial data from ambiguous prompts.

### Where is the temporal grounding implementation located in the Needle codebase?

The core logic resides in [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py), specifically within the `_temporal_grounding` function (lines 432-440) for schema preprocessing and `_walk_grounding` (lines 401-410) for recursive validation of argument structures.