Triple
T7858032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Outlook Express |
E182424
|
entity |
| Predicate | notableVulnerability |
P67454
|
FINISHED |
| Object | email-borne malware exposure |
—
|
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: email-borne malware exposure | Statement: [Outlook Express, notableVulnerability, email-borne malware exposure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableVulnerability Context triple: [Outlook Express, notableVulnerability, email-borne malware exposure]
-
A.
vulnerabilityType
Indicates the specific kind or category of vulnerability associated with an entity or situation.
-
B.
notableMalware
Indicates that the entity is recognized as a significant or well-known piece of malware, often due to its impact, prevalence, or technical characteristics.
-
C.
notableSafety
Indicates that an entity is recognized for having significant safety characteristics, performance, or impact relative to others.
-
D.
associatedWithVulnerability
chosen
Indicates a relationship where an entity is linked to, affected by, or relevant to a specific vulnerability or security weakness.
-
E.
majorSecurityIssue
Indicates that the subject faces or causes a significant security vulnerability or threat.
- 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_69ca82887fd48190975896bf38c4596b |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb1a76f8648190976b488d0d8658ef |
completed | March 31, 2026, 12:51 a.m. |
| PD | Predicate disambiguation | batch_69cae925ca388190ae4a01fa76e957e8 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:52 p.m.