Triple
T32536489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Device Guard |
E831602
|
entity |
| Predicate | policyMode |
P4331
|
FINISHED |
| Object | whitelisting |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: whitelisting | Statement: [Device Guard, policyMode, whitelisting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policyMode Context triple: [Device Guard, policyMode, whitelisting]
-
A.
policyModel
Indicates a relationship where an entity serves as, or is governed by, a particular policy model that defines rules, strategies, or decision-making behavior.
-
B.
policyLevel
Indicates the degree or tier of strictness, scope, or priority associated with a given policy.
-
C.
policySetBy
Indicates that a particular policy is established, defined, or determined by a specific entity or authority.
-
D.
policyShift
Indicates a change or adjustment in an existing policy, typically reflecting a new direction, priority, or approach.
-
E.
policyApproach
chosen
Indicates the strategy, method, or overall course of action adopted in creating, implementing, or managing a policy.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f34924b1cc8190ad3aca0c0f012a7e |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f757898fe48190b124dc7301672623 |
completed | May 3, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69f754c484348190948d2a04ff228fb1 |
completed | May 3, 2026, 1:59 p.m. |
Created at: May 1, 2026, 1:01 a.m.