Triple
T17560449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Address Space Layout Randomization |
E427682
|
entity |
| Predicate | isMoreEffectiveOn |
P84339
|
FINISHED |
| Object | 64-bit architectures |
—
|
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: 64-bit architectures | Statement: [Address Space Layout Randomization, isMoreEffectiveOn, 64-bit architectures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isMoreEffectiveOn Context triple: [Address Space Layout Randomization, isMoreEffectiveOn, 64-bit architectures]
-
A.
effectivenessAgainst
chosen
Indicates how well one entity performs in countering, influencing, or mitigating the impact of another entity.
-
B.
usedAgainst
Indicates that one entity is employed, applied, or deployed in opposition to, or for the purpose of affecting, another entity.
-
C.
isStrongerThan
Indicates that one entity possesses greater physical power, force, or effectiveness than another entity.
-
D.
isResistant
Indicates that an entity can withstand, oppose, or is not significantly affected by a specified force, influence, or agent.
-
E.
hasEffectIn
Indicates that one entity produces, causes, or exerts an effect within a specified context, system, or environment.
- 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e456267e208190a1238fbe1a535bb0 |
completed | April 19, 2026, 4:12 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fb39948190a82a597c5bac5c57 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:50 a.m.