Triple
T146086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lifelong Learning Machines program |
E3332
|
entity |
| Predicate | aimsToAddress |
P3847
|
FINISHED |
| Object | catastrophic forgetting in neural networks |
—
|
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: catastrophic forgetting in neural networks | Statement: [Lifelong Learning Machines program, aimsToAddress, catastrophic forgetting in neural networks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aimsToAddress Context triple: [Lifelong Learning Machines program, aimsToAddress, catastrophic forgetting in neural networks]
-
A.
target
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
-
B.
hasPrimaryGoal
Indicates that an entity’s main or most important objective is the specified goal.
-
C.
politicalGoal
Indicates a relationship where an entity aims to achieve, promote, or realize a specific political outcome, policy, or state of affairs.
-
D.
addressesIssue
chosen
Indicates that one entity deals with, responds to, or attempts to resolve a specific issue associated with another entity.
-
E.
secondaryGoal
Indicates that something serves as a subordinate or supporting objective in addition to a primary goal.
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a257ea7eac8190884a53453a9e0dd6 |
completed | Feb. 28, 2026, 2:50 a.m. |
| PD | Predicate disambiguation | batch_69a25656a4fc81908a87678ac3d28f93 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.