Triple
T7774274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PSG College of Technology |
E179150
|
entity |
| Predicate | hasTrainingModel |
P24513
|
FINISHED |
| Object | practice school and industry internship programs |
—
|
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: practice school and industry internship programs | Statement: [PSG College of Technology, hasTrainingModel, practice school and industry internship programs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrainingModel Context triple: [PSG College of Technology, hasTrainingModel, practice school and industry internship programs]
-
A.
hasTrained
Indicates that one entity has provided training or instruction to another entity.
-
B.
hasTrainingFunction
Indicates that one entity serves as a training function or mechanism for another entity.
-
C.
hasTrainingFor
Indicates that an entity has received or possesses training that prepares it for performing a specific task, role, or function.
-
D.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
E.
hasTrainingType
chosen
Indicates that an entity is associated with or characterized by a specific type or category of training.
- 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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c70461b3e48190bf1e4d4f9e6bb08e |
completed | March 27, 2026, 10:27 p.m. |
| PD | Predicate disambiguation | batch_69c7016f4ce881909c2e9f610255187b |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:11 p.m.