Triple
T32215715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Computational Learning Theory |
E822917
|
entity |
| Predicate | hasKeyProblem |
P12603
|
FINISHED |
| Object | learnability of Boolean functions |
—
|
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: learnability of Boolean functions | Statement: [Computational Learning Theory, hasKeyProblem, learnability of Boolean functions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKeyProblem Context triple: [Computational Learning Theory, hasKeyProblem, learnability of Boolean functions]
-
A.
hasKeyIssue
chosen
Indicates that an entity is associated with a primary or central problem, concern, or topic of importance.
-
B.
hadKeyIssue
Indicates that an entity experienced a primary or critical problem related to a key aspect, factor, or component.
-
C.
hasIssueWith
Indicates that one entity experiences a problem, conflict, or concern related to another entity.
-
D.
hasKeyQuestion
Indicates that one entity possesses or is associated with a primary or central question relevant to another entity.
-
E.
hasKeyAccord
Indicates that one entity possesses or defines the primary key or governing agreement that authorizes or controls another entity.
- 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_69f3490a3bec819097bc58d4731b9d08 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f79f48acec8190a9d5964581a94f6c |
completed | May 3, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f79e4888248190be2f63cdfb5cd7b7 |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 1, 2026, 12:37 a.m.