Triple
T1935188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gatekeeper |
E41427
|
entity |
| Predicate | policySource |
P33688
|
FINISHED |
| Object | Apple security policies |
—
|
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: Apple security policies | Statement: [Gatekeeper, policySource, Apple security policies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policySource Context triple: [Gatekeeper, policySource, Apple security policies]
-
A.
policyContext
Indicates the situational or regulatory framework within which a policy is defined, interpreted, or applied.
-
B.
policyTool
Indicates that an entity is a tool, mechanism, or instrument used to design, implement, or enforce a policy.
-
C.
policyDomain
Indicates the thematic or subject-matter area to which a given policy, rule, or regulatory action belongs.
-
D.
policyName
Indicates the specific name or title assigned to a policy associated with an entity.
-
E.
policyFramework
Indicates the overarching set of principles, rules, and guidelines that govern how decisions and actions are structured and carried out within a particular domain or context.
- F. None of above. chosen
Provenance (4 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_69a88649b24c819080047f26b6db2ded |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb29c7be08190986df157bf5d7c9e |
completed | March 7, 2026, 5:07 a.m. |
| PD | Predicate disambiguation | batch_69abaff07cf88190b4883c5f17f90abd |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb25ef0ec8190b907963e9db0fe04 |
completed | March 7, 2026, 5:06 a.m. |
Created at: March 4, 2026, 7:35 p.m.