Triple
T1731391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Airbus H225 |
E37817
|
entity |
| Predicate | liftClass |
P1984
|
FINISHED |
| Object | heavy-lift |
—
|
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: heavy-lift | Statement: [Airbus H225, liftClass, heavy-lift]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: liftClass Context triple: [Airbus H225, liftClass, heavy-lift]
-
A.
hasLiftType
chosen
Indicates the specific type or category of lift associated with an entity.
-
B.
wearingClass
Indicates that one entity is wearing or dressed in an item belonging to a particular class or category of clothing or accessories.
-
C.
speedClass
Indicates the categorical speed level or range assigned to an entity based on how fast it moves or operates.
-
D.
heightClass
Indicates the categorical height level or range to which an entity is assigned (e.g., short, medium, tall).
-
E.
displacementClass
Indicates the category or type of physical displacement associated with an entity or event, typically grouping similar kinds or magnitudes of movement into a common class.
- 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_69a8861cc6ac8190ac0b2e31ccf62851 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab5c553e508190b0f511b05e07fa20 |
completed | March 6, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69aa61c25a648190892de94c997fb983 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:30 p.m.