Triple
T1815073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Airbus Beluga |
E40417
|
entity |
| Predicate | cargoLoadingMethod |
P17499
|
FINISHED |
| Object | nose-mounted cargo door |
—
|
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: nose-mounted cargo door | Statement: [Airbus Beluga, cargoLoadingMethod, nose-mounted cargo door]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cargoLoadingMethod Context triple: [Airbus Beluga, cargoLoadingMethod, nose-mounted cargo door]
-
A.
cargoLoading
chosen
Indicates the action or process of placing cargo onto a vehicle, vessel, or other transport medium for shipment or movement.
-
B.
mainCargo
Indicates that one entity serves as the primary or principal cargo carried or transported by another entity.
-
C.
cargoSpace
Indicates that one entity provides storage capacity or room for carrying goods, equipment, or other items for another entity.
-
D.
cargoHoldWidth
Indicates the width dimension of a cargo hold in a vehicle, vessel, or storage structure.
-
E.
designedCargoCapacity
Indicates the maximum amount of cargo an object (such as a vehicle or container) was originally engineered or specified to carry.
- 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_69a8864526c081908a3a4d74f689e2c5 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aba67721788190951beae25e885457 |
completed | March 7, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69aa61d884548190a19cf3a6b5ae9d48 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:32 p.m.