Triple
T32960591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marine Military Academy |
E843227
|
entity |
| Predicate | modelsTrainingOn |
P153131
|
FINISHED |
| Object | United States Marine Corps structure |
—
|
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: United States Marine Corps structure | Statement: [Marine Military Academy, modelsTrainingOn, United States Marine Corps structure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modelsTrainingOn Context triple: [Marine Military Academy, modelsTrainingOn, United States Marine Corps structure]
-
A.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
B.
trainerModel
Indicates that one entity serves as the trainer or training source for a model entity.
-
C.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
D.
equipmentTypeTrainedOn
Indicates the type of equipment on which an entity has received training or is qualified to operate.
-
E.
trainingIn
chosen
Indicates that one entity is undergoing or receiving training within the context, program, or domain specified by 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_69f3494af2808190ad98cec2f1bc0fe6 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d17a0d88819082ad29b5ec8b2aec |
completed | May 3, 2026, 4:39 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe5f93c8190995c53dbbe380a32 |
completed | May 3, 2026, 4:32 a.m. |
Created at: May 1, 2026, 1:21 a.m.