Triple
T11432495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taipei–Hong Kong |
E270918
|
entity |
| Predicate | typicalCabinClasses |
P85721
|
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: [Taipei–Hong Kong, typicalCabinClasses, economy class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCabinClasses Context triple: [Taipei–Hong Kong, typicalCabinClasses, economy class]
-
A.
hasCabinClass
Indicates that an entity (such as a booking, ticket, or seat) is associated with a specific cabin class (e.g., economy, business, first).
-
B.
cabinTypes
chosen
Indicates the types or categories of cabins associated with an entity, such as the different classes or configurations available.
-
C.
cabinClassAbove
Indicates that one cabin class is ranked higher or more premium than another in a class hierarchy.
-
D.
cabinClassHierarchy
Indicates a hierarchical relationship between cabin classes, where one cabin class is ranked above or below another in terms of priority, service level, or status.
-
E.
classesOfSeats
Indicates the different categories or types of seats associated with something, such as a venue, vehicle, or event.
- 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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d806c30d788190b0c939b33de89277 |
completed | April 9, 2026, 8:06 p.m. |
| PD | Predicate disambiguation | batch_69d7e71436f88190ac7e45a04ea5c987 |
completed | April 9, 2026, 5:51 p.m. |
Created at: April 8, 2026, 9:35 p.m.