Triple
T280839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Class (Delta Air Lines) |
E5349
|
entity |
| Predicate | bookingClassType |
P3037
|
FINISHED |
| Object | revenue cabin |
—
|
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: revenue cabin | Statement: [First Class (Delta Air Lines), bookingClassType, revenue cabin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bookingClassType Context triple: [First Class (Delta Air Lines), bookingClassType, revenue cabin]
-
A.
hasCabinClass
chosen
Indicates that an entity (such as a booking, ticket, or seat) is associated with a specific cabin class (e.g., economy, business, first).
-
B.
fareDiscount
Indicates that a reduced price is applied to a standard fare for a product or service.
-
C.
reservationSystem
Indicates a system or process that manages the creation, modification, and tracking of reservations or bookings between parties.
-
D.
customerType
Indicates the classification or category assigned to a customer based on their characteristics, status, or relationship with a business.
-
E.
ticketingCompatibleWith
Indicates that two systems, services, or components can interoperate or be used together within the same ticketing or reservation workflow without conflict.
- 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25e0868708190ad551ca06cc57f4a |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b765f488190b2cbe4b45cd42821 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.