Triple
T30442466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo (Narita) – Hong Kong |
E774486
|
entity |
| Predicate | typicalPassengerClass |
P50616
|
FINISHED |
| Object | economy 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: economy class | Statement: [Tokyo (Narita) – Hong Kong, typicalPassengerClass, economy class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalPassengerClass Context triple: [Tokyo (Narita) – Hong Kong, typicalPassengerClass, economy class]
-
A.
airlineClass
Indicates the specific travel class or service level assigned to a passenger or ticket on an airline flight.
-
B.
passengerLevel
Indicates the relative status, class, or priority assigned to a passenger within a transportation or service context.
-
C.
seatClass
chosen
Indicates the travel or seating category assigned to a passenger or seat (e.g., economy, business, first class).
-
D.
amenityLevelComparedToBusinessClass
Indicates how the level of amenities provided compares to those offered in business class.
-
E.
cabinClassAbove
Indicates that one cabin class is ranked higher or more premium than another in a class hierarchy.
- 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_69f22493ef9c8190ae8c2afcb7f994c8 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a037c876524819098545e6037d3107d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e2fac0819089b522db3260028c |
completed | May 12, 2026, 7:05 p.m. |
Created at: April 29, 2026, 8:08 p.m.