Triple
T21937327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Middle College |
E541724
|
entity |
| Predicate | learningModel |
P146618
|
FINISHED |
| Object | high school and college integration |
—
|
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: high school and college integration | Statement: [Middle College, learningModel, high school and college integration]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learningModel Context triple: [Middle College, learningModel, high school and college integration]
-
A.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
B.
learningRole
Indicates that one entity has a specific role or function within a learning or educational context in relation to another entity.
-
C.
trainerModel
Indicates that one entity serves as the trainer or training source for a model entity.
-
D.
machineLearningLibrary
Indicates that one entity is a software library or framework specifically designed to support machine learning tasks for another entity.
-
E.
learn
Indicates that an entity acquires knowledge, skills, or understanding from another entity, source, or experience.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1241c909c81908644eb73baa9def1 |
completed | April 28, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69e6f5efc208819091ed2cf6841fa600 |
completed | April 21, 2026, 3:58 a.m. |
| PDg | Predicate description generation | batch_69e6fb6991948190a428c3c3bfd1c3b8 |
completed | April 21, 2026, 4:22 a.m. |
Created at: April 16, 2026, 7:55 p.m.