Triple
T28610240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TOMOYO Linux |
E724150
|
entity |
| Predicate | policyGranularity |
P81365
|
FINISHED |
| Object | fine-grained |
—
|
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: fine-grained | Statement: [TOMOYO Linux, policyGranularity, fine-grained]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policyGranularity Context triple: [TOMOYO Linux, policyGranularity, fine-grained]
-
A.
granularityLevel
Indicates the degree of detail or resolution at which something is specified, measured, or analyzed within a given context.
-
B.
securityGranularity
chosen
Indicates the level of detail or specificity at which security controls, permissions, or protections are defined and applied within a system or context.
-
C.
scalingGranularity
Indicates the level of detail or resolution at which a quantity, process, or system is adjusted or scaled.
-
D.
controlGranularity
Indicates the level of detail or fineness with which control or regulation is applied within a given process or system.
-
E.
encryptionGranularity
Indicates the level of detail or scope at which data is encrypted within a system or process.
- 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_69f01d816d7c8190a1fe27e3434041dc |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f69edbb7648190bd89c57e0932eac1 |
completed | May 3, 2026, 1:03 a.m. |
| PD | Predicate disambiguation | batch_69f69d17e8d48190b30bcc2f4bd81eb2 |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 28, 2026, 4:29 a.m.