Triple
T38407676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BGA |
E901380
|
entity |
| Predicate | relatedToAircraftType |
P67179
|
FINISHED |
| Object | Airbus Beluga |
E40417
|
NE 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: Airbus Beluga | Statement: [BGA, relatedToAircraftType, Airbus Beluga]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedToAircraftType Context triple: [BGA, relatedToAircraftType, Airbus Beluga]
-
A.
relatesToAircraft
Indicates a general relationship or association between an entity and an aircraft, without specifying the exact nature of that connection.
-
B.
associatedWithAircraftModel
chosen
Indicates that something has a specified relationship or connection to a particular aircraft model.
-
C.
relatedAircraft
Indicates that there is an association or connection between two aircraft, such as operational, functional, or contextual relatedness.
-
D.
associatedWithCarrierAircraft
Indicates a relationship where something is linked or connected to a carrier-based aircraft, such as being used by, transported on, or otherwise functionally related to it.
-
E.
usedByAircraftType
Indicates that something (such as equipment, infrastructure, or a procedure) is employed or operated by a specific type or category of aircraft.
- F. None of above.
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_69f76e61e79c81908b787d83b46ab92b |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41c283e5948190b8b9cfdd3633b21d |
completed | June 29, 2026, 12:55 a.m. |
| PD | Predicate disambiguation | batch_6a037a1c850c819088795a7ae59bdeb8 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:31 p.m.