Triple
T2668482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Armed Forces of Peru |
E55692
|
entity |
| Predicate | hasTrainingRole |
P41566
|
FINISHED |
| Object | military academies of Peru |
—
|
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: military academies of Peru | Statement: [Armed Forces of Peru, hasTrainingRole, military academies of Peru]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrainingRole Context triple: [Armed Forces of Peru, hasTrainingRole, military academies of Peru]
-
A.
hasTrainingType
Indicates that an entity is associated with or characterized by a specific type or category of training.
-
B.
hasTrained
Indicates that one entity has provided training or instruction to another entity.
-
C.
providesTrainingFor
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
-
D.
trainedAs
Indicates that one entity has received education or instruction to perform the role, profession, or function represented by another entity.
-
E.
requiresTraining
Indicates that one entity can only be properly or legitimately used, performed, or engaged with if the other entity has first received appropriate training.
- 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_69ab49e54de48190be708cd1cf8be073 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd98bacf48190b5633f1a3ec8f0cf |
completed | March 7, 2026, 7:53 a.m. |
| PD | Predicate disambiguation | batch_69abd8190ad481908f3e14ac84d0940a |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd8f98c348190a68064c565589459 |
completed | March 7, 2026, 7:51 a.m. |
Created at: March 6, 2026, 9:54 p.m.