Triple
T1147991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boeing 777 |
E23610
|
entity |
| Predicate | typicalCabinConfiguration |
P16894
|
FINISHED |
| Object | two-class |
—
|
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-class | Statement: [Boeing 777, typicalCabinConfiguration, two-class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCabinConfiguration Context triple: [Boeing 777, typicalCabinConfiguration, two-class]
-
A.
cabinConfiguration
chosen
Indicates how the interior space of a vehicle, vessel, or aircraft is arranged and organized for occupants or cargo.
-
B.
hasCabins
Indicates that an entity possesses or includes one or more cabins as part of its structure or facilities.
-
C.
cabinLocation
Indicates the spatial or geographic location associated with a cabin.
-
D.
hasCabinClass
Indicates that an entity (such as a booking, ticket, or seat) is associated with a specific cabin class (e.g., economy, business, first).
-
E.
climateControlledCabins
Indicates that the subject provides or features cabins whose internal environment (such as temperature and humidity) is actively regulated for comfort or preservation.
- 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_69a493f0d32c8190ac74bad3c87f2641 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bd0bed00819091d71983d787a030 |
completed | March 1, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69a4bb4ee3988190ac89c5ae5b10e316 |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:44 p.m.