Triple
T26673919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Navigator Training Wings |
E672406
|
entity |
| Predicate | trainsPersonnelType |
P14268
|
FINISHED |
| Object | student navigators |
—
|
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: student navigators | Statement: [Navigator Training Wings, trainsPersonnelType, student navigators]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainsPersonnelType Context triple: [Navigator Training Wings, trainsPersonnelType, student navigators]
-
A.
personnelType
Indicates the classification or role category assigned to a person within an organization or system.
-
B.
crewType
Indicates the specific role or category of crew associated with an entity, such as the type of personnel assigned to operate or support it.
-
C.
railwayTrafficRole
Indicates the specific function or responsibility an entity has within the operation, management, or control of railway traffic.
-
D.
trainsForOccupation
chosen
Indicates that an entity undergoes training or preparation aimed at qualifying for or performing a specific occupation.
-
E.
trainsCategory
Indicates that one entity is a category or type under which the other entity is trained or classified.
- 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_69eecda00a9c8190b2691f4d89db03b6 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f61a17a7788190946f7e32d63cd43f |
completed | May 2, 2026, 3:36 p.m. |
| PD | Predicate disambiguation | batch_69f611ab768c8190b1849c15a3e59dda |
completed | May 2, 2026, 3 p.m. |
Created at: April 27, 2026, 3:15 a.m.