Triple
T1070408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pennsylvania Military College |
E23312
|
entity |
| Predicate | trainingComponent |
P23937
|
FINISHED |
| Object | leadership training |
—
|
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: leadership training | Statement: [Pennsylvania Military College, trainingComponent, leadership training]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingComponent Context triple: [Pennsylvania Military College, trainingComponent, leadership training]
-
A.
trainingSystem
Indicates a system or framework used to train, instruct, or develop skills or knowledge in a target entity.
-
B.
training
Indicates that one entity is teaching, coaching, or otherwise helping another entity acquire or improve a skill, behavior, or capability.
-
C.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
D.
trainingFormat
Indicates the specific method or medium through which training is delivered or conducted.
-
E.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
- 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_69a493ee1f908190992b5f0d1b04459b |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b92848148190ba8795cb8d0a1d0a |
completed | March 1, 2026, 10:09 p.m. |
| PD | Predicate disambiguation | batch_69a4b73844708190a16c9e9824ca2fb6 |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b8d5076481908640a0d873efdf14 |
completed | March 1, 2026, 10:08 p.m. |
Created at: March 1, 2026, 7:42 p.m.