How Temporal Grounding Detects Ungrounded Values in Needle

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.

Phase 1: Schema Introspection and Year Collection

The _temporal_grounding function (lines 432-440 of 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) 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:

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:

# 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:

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 (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.

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.
• _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 and 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, specifically within the _temporal_grounding function (lines 432-440) for schema preprocessing and _walk_grounding (lines 401-410) for recursive validation of argument structures.

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