Triple
T11126795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | System Integrity Protection |
E263158
|
entity |
| Predicate | enforcementLevel |
P75822
|
FINISHED |
| Object | kernel level |
—
|
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: kernel level | Statement: [System Integrity Protection, enforcementLevel, kernel level]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: enforcementLevel Context triple: [System Integrity Protection, enforcementLevel, kernel level]
-
A.
enforcementStrength
chosen
Indicates the degree or intensity with which rules, laws, or policies are applied and enforced in a given context.
-
B.
lawEnforcementLevel
Indicates the degree or intensity of law enforcement presence, activity, or strictness applied in a given context.
-
C.
sanctioningLevel
Indicates the degree or severity of formal penalties, restrictions, or disciplinary measures imposed in response to a violation or noncompliant behavior.
-
D.
enforcement
Indicates the act of compelling compliance with rules, laws, or agreements through monitoring, pressure, or sanctions.
-
E.
enforcementModel
Indicates the method or framework by which rules, policies, or constraints are applied, monitored, and enforced within a system or interaction.
- 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_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7e82fc5f88190b34c55de3d50f6a7 |
completed | April 9, 2026, 5:55 p.m. |
| PD | Predicate disambiguation | batch_69d7441efe948190ae4ab020121e169d |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:28 p.m.