Triple
T1077190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NAT64 |
E23865
|
entity |
| Predicate | mayAffect |
P12716
|
FINISHED |
| Object | protocols embedding IP addresses in payload |
—
|
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: protocols embedding IP addresses in payload | Statement: [NAT64, mayAffect, protocols embedding IP addresses in payload]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayAffect Context triple: [NAT64, mayAffect, protocols embedding IP addresses in payload]
-
A.
mayResultIn
chosen
Indicates that one entity has the potential to cause, lead to, or bring about another entity or outcome, without guaranteeing that it will occur.
-
B.
affectsProgram
Indicates that one entity produces an influence or change on a program, altering its behavior, state, or outcome.
-
C.
mayReportTo
Indicates that one entity is permitted or allowed to have a reporting relationship to another entity, such as an employee being allowed to report to a particular manager.
-
D.
influenced
Indicates that one entity has affected, shaped, or altered another entity’s state, behavior, or characteristics.
-
E.
affectedArea
Indicates the specific region or extent over which an event, condition, or influence has an impact.
- 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_69a493f1ddf48190a99d54b00e99f8ce |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b94288d88190aae4fb86236c0702 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b73ba8208190be7f3cef8c18689b |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.