Triple
T35406182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FedRAMP High impact level |
E1023367
|
entity |
| Predicate | hasImpactLevel |
P97046
|
FINISHED |
| Object | High |
—
|
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: High | Statement: [FedRAMP High impact level, hasImpactLevel, High]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasImpactLevel Context triple: [FedRAMP High impact level, hasImpactLevel, High]
-
A.
hasImpactScale
chosen
Indicates the degree or magnitude of impact that one entity or action has on another, typically expressed along a defined scale.
-
B.
impactLevel
Indicates the degree or intensity of effect that one entity, action, or event has on another.
-
C.
canImpact
Indicates that one entity has the potential or ability to affect, influence, or cause a change in another entity.
-
D.
hasImpactFocus
Indicates that an entity is primarily concerned with or directed toward a particular type or area of impact.
-
E.
affectedLevel
Indicates the degree or extent to which one entity is impacted or influenced by another entity or event.
- 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_69f76df43ca4819098711ca4370f1bb9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fb2e940d5c8190bceae77daf4ef512 |
completed | May 6, 2026, 12:05 p.m. |
| PD | Predicate disambiguation | batch_69f9fec70bd881909c658a3c5020318b |
completed | May 5, 2026, 2:29 p.m. |
Created at: May 3, 2026, 4:03 p.m.