Triple
T19444362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seoul Subway Line 2 |
E486432
|
entity |
| Predicate | ridership |
P29684
|
FINISHED |
| Object | one of the busiest lines in the Seoul Metropolitan Subway |
—
|
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: one of the busiest lines in the Seoul Metropolitan Subway | Statement: [Seoul Subway Line 2, ridership, one of the busiest lines in the Seoul Metropolitan Subway]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ridership Context triple: [Seoul Subway Line 2, ridership, one of the busiest lines in the Seoul Metropolitan Subway]
-
A.
ridershipLevel
chosen
Indicates the magnitude or intensity of usage by riders or passengers for a given service, route, or system.
-
B.
riderType
Indicates the category or role of a rider in relation to a ride, transport service, or vehicle (e.g., passenger, driver, courier).
-
C.
numberOfRiders
Indicates the total count of riders associated with a given entity or event.
-
D.
primaryRiders
Indicates that the referenced entities are the main or principal riders associated with a particular vehicle, trip, or ride-related event.
-
E.
rides
Indicates that one entity travels on or is carried by another entity as a passenger or operator.
- 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63387e2048190bfb13fea434ddb46 |
completed | April 20, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4fd6e806081909053f325ba01ab6b |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:38 p.m.