Triple
T3507312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AlexNet |
E74105
|
entity |
| Predicate | optimization |
P27179
|
FINISHED |
| Object | backpropagation |
—
|
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: backpropagation | Statement: [AlexNet, optimization, backpropagation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: optimization Context triple: [AlexNet, optimization, backpropagation]
-
A.
optimize
Indicates improving a process, system, or outcome to achieve the best possible performance or efficiency under given constraints.
-
B.
optimizationType
chosen
Indicates the specific strategy or method used to improve performance or efficiency within a given process or system.
-
C.
optimizationTarget
Indicates that one entity is the goal or objective that another entity is trying to improve, optimize, or make more efficient.
-
D.
typeOfOptimality
Indicates that one entity specifies the particular notion or criterion of optimality that characterizes another entity’s optimal status or solution.
-
E.
regularization
Indicates the application of a constraint or penalty to a model or function to prevent overfitting and encourage simpler, more generalizable behavior.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc0b635c81909bc95ba2562d8f94 |
completed | March 8, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69adae0e770481908528fa35eda53003 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:18 p.m.