Triple
T26408421
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serpent cipher |
E663892
|
entity |
| Predicate | knownAttacks |
P164837
|
FINISHED |
| Object | no practical attacks on full 32-round cipher |
—
|
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: no practical attacks on full 32-round cipher | Statement: [Serpent cipher, knownAttacks, no practical attacks on full 32-round cipher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: knownAttacks Context triple: [Serpent cipher, knownAttacks, no practical attacks on full 32-round cipher]
-
A.
notableAttack
Indicates that an entity carried out, was involved in, or is strongly associated with a particularly significant or well-known attack.
-
B.
numberOfAttacks
Indicates the count of distinct attack events associated with a given entity or interaction.
-
C.
hasAttack
Indicates that one entity performs, possesses, or is associated with an attack directed toward another entity.
-
D.
dataAtaku
Indicates a relationship where one entity performs or carries out an attack on another entity.
-
E.
attackType
Indicates the specific method, style, or category of attack used in an aggressive or hostile action between entities.
- 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_69ee883931888190901be96d75ee23cc |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f650fc44e48190bc0e0a935eac62a6 |
completed | May 2, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69f64cab1f648190a2a9460690d18a37 |
completed | May 2, 2026, 7:12 p.m. |
| PDg | Predicate description generation | batch_69f650c466b881908954e43bfebae8a4 |
completed | May 2, 2026, 7:30 p.m. |
Created at: April 26, 2026, 11:36 p.m.