Triple
T1301164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boeing KC-46 Pegasus |
E27765
|
entity |
| Predicate | medicalEvacuationCapacity |
P26779
|
FINISHED |
| Object | up to 58 patients |
—
|
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: up to 58 patients | Statement: [Boeing KC-46 Pegasus, medicalEvacuationCapacity, up to 58 patients]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalEvacuationCapacity Context triple: [Boeing KC-46 Pegasus, medicalEvacuationCapacity, up to 58 patients]
-
A.
paratroopCapacity
Indicates the maximum number of paratroopers or amount of airborne troops that something (typically a vehicle or vessel) is capable of carrying or deploying.
-
B.
numberOfEvacuated
Indicates the total count of individuals who have been evacuated from a location or situation.
-
C.
designedCargoCapacity
Indicates the maximum amount of cargo an object (such as a vehicle or container) was originally engineered or specified to carry.
-
D.
approximateNumberOfRefugeesTransported
Indicates an estimated count of refugees who were transported in the described event or context.
-
E.
annualCapacity
Indicates the maximum amount of output or throughput an entity can produce or handle within a one-year period.
- 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_69a496d6682881909ba658f1c1e0e2b0 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c1145c2481908adf22dfd6ce349b |
completed | March 1, 2026, 10:43 p.m. |
| PD | Predicate disambiguation | batch_69a4bee8544c8190874efd9bae9bccf9 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4bf60545c8190901ccfb2cb7c4b41 |
completed | March 1, 2026, 10:36 p.m. |
Created at: March 1, 2026, 7:51 p.m.