Triple
T165934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wired Equivalent Privacy |
E3014
|
entity |
| Predicate | recommendation |
P5662
|
FINISHED |
| Object | should not be used for sensitive data |
—
|
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: should not be used for sensitive data | Statement: [Wired Equivalent Privacy, recommendation, should not be used for sensitive data]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recommendation Context triple: [Wired Equivalent Privacy, recommendation, should not be used for sensitive data]
-
A.
canRecommend
Indicates that one entity is able or authorized to suggest or endorse another entity as suitable or preferable.
-
B.
tourismRegion
Indicates that a place or area is designated or recognized as a tourism region associated with another geographic or administrative entity.
-
C.
implementsRecommendationOf
Indicates that one entity carries out or puts into practice a recommendation that was proposed or issued by another entity.
-
D.
relatedPlace
Indicates a relationship where one place is connected or associated with another place in a relevant or meaningful way.
-
E.
explores
Indicates actively investigating, traveling through, or examining something in order to discover or learn more about it.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a25883ac8481909616b2179561bd98 |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a25664ba8081908ac298511a9fc5ba |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a256eb46ec81909c730000e5041d0d |
completed | Feb. 28, 2026, 2:46 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.