Triple
T280952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Main Cabin |
E5352
|
entity |
| Predicate | targetPassengerSegment |
P481
|
FINISHED |
| Object | most passengers |
—
|
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: most passengers | Statement: [Main Cabin, targetPassengerSegment, most passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetPassengerSegment Context triple: [Main Cabin, targetPassengerSegment, most passengers]
-
A.
passengers
Indicates that one entity is traveling in or being transported by another entity, typically as a non-operating occupant.
-
B.
formerPassengerService
Indicates that an entity previously provided passenger transportation services but no longer does so.
-
C.
targetMarket
chosen
Indicates the group of consumers or organizations that a product, service, or campaign is specifically intended and designed to reach.
-
D.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
E.
target
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
- 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_69a25e0a23c0819083abee28b2dea49c |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b77e028819087e606fc321219f7 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.