Triple
T11524838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seoul–Los Angeles |
E273264
|
entity |
| Predicate | passengerSegment |
P87744
|
FINISHED |
| Object | transpacific travelers |
—
|
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: transpacific travelers | Statement: [Seoul–Los Angeles, passengerSegment, transpacific travelers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerSegment Context triple: [Seoul–Los Angeles, passengerSegment, transpacific travelers]
-
A.
passengerSegments
chosen
Indicates a relationship where a journey or trip is divided into distinct legs or segments that a passenger travels through.
-
B.
primaryPassengerGroup
Indicates the main group of passengers that is most directly associated with or served by a given entity or context.
-
C.
passengers
Indicates that one entity is traveling in or being transported by another entity, typically as a non-operating occupant.
-
D.
airlineMarketSegment
Indicates a relationship where an airline is associated with a specific market segment it targets or operates within (e.g., business, leisure, regional).
-
E.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
- 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_69d6aae3fbec8190a14632a5df2538b6 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d87fd26648819083de19bcddf8ad69 |
completed | April 10, 2026, 4:42 a.m. |
| PD | Predicate disambiguation | batch_69d80876e5f0819088cff2e72f773cf6 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:37 p.m.