Triple
T22322915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bobo (Python web framework) |
E551829
|
entity |
| Predicate | learningCurve |
P147732
|
FINISHED |
| Object | gentle |
—
|
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: gentle | Statement: [Bobo (Python web framework), learningCurve, gentle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learningCurve Context triple: [Bobo (Python web framework), learningCurve, gentle]
-
A.
learningModel
Indicates that one entity functions as a learning model used to learn from data or examples in relation to another entity.
-
B.
learn
Indicates that an entity acquires knowledge, skills, or understanding from another entity, source, or experience.
-
C.
learningRole
Indicates that one entity has a specific role or function within a learning or educational context in relation to another entity.
-
D.
usesLearningMechanism
Indicates that one entity employs or applies a particular learning mechanism or method in its functioning or behavior.
-
E.
trainingLeadsTo
Indicates that a process of training results in or brings about a particular outcome, state, or effect.
- 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_69e11e482f788190b78d1588fc26d606 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15765a004819081dcdccbfd0efea7 |
completed | April 29, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e73004d9e88190bb862319a5aea06b |
completed | April 21, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e7342ce08c8190bc0a7085f4a952e7 |
completed | April 21, 2026, 8:24 a.m. |
Created at: April 16, 2026, 8:42 p.m.