Triple
T34033094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cessna 206 |
E872711
|
entity |
| Predicate | cabinArrangement |
P16894
|
FINISHED |
| Object | two front seats plus rear bench or individual seats |
—
|
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: two front seats plus rear bench or individual seats | Statement: [Cessna 206, cabinArrangement, two front seats plus rear bench or individual seats]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cabinArrangement Context triple: [Cessna 206, cabinArrangement, two front seats plus rear bench or individual seats]
-
A.
cabinConfiguration
chosen
Indicates how the interior space of a vehicle, vessel, or aircraft is arranged and organized for occupants or cargo.
-
B.
cabinFeatures
Indicates that a cabin possesses or includes specific features, amenities, or characteristics.
-
C.
cabinFloor
Indicates that one entity is the floor or floor level within a cabin associated with another entity.
-
D.
roomConfiguration
Indicates how elements within a room are arranged or organized relative to each other.
-
E.
cabinTypes
Indicates the types or categories of cabins associated with an entity, such as the different classes or configurations available.
- 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_69f349a2527c81909a7cd4bda94d70ad |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f70b966860819089cf92927f47c5f1 |
completed | May 3, 2026, 8:47 a.m. |
| PD | Predicate disambiguation | batch_69f70abe43e08190b2a30930d96247c1 |
completed | May 3, 2026, 8:43 a.m. |
Created at: May 1, 2026, 1:51 a.m.