Triple
T28193764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seoul–Busan |
E716382
|
entity |
| Predicate | typicalHighSpeedRailTravelTime |
P194919
|
FINISHED |
| Object | about 2 hours 15 minutes |
—
|
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: about 2 hours 15 minutes | Statement: [Seoul–Busan, typicalHighSpeedRailTravelTime, about 2 hours 15 minutes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalHighSpeedRailTravelTime Context triple: [Seoul–Busan, typicalHighSpeedRailTravelTime, about 2 hours 15 minutes]
-
A.
speedOfHighSpeedTrain
Indicates the velocity at which a high-speed train is traveling.
-
B.
hasApproximateFastRailJourneyTimeHours
chosen
Indicates that there is an estimated duration, measured in hours, for a fast rail journey between the related entities.
-
C.
hasApproximateFastRailJourneyTimeMinutes
Indicates that there is an estimated duration, measured in minutes, for a fast rail journey between two locations.
-
D.
hasHighSpeedRailStation
Indicates that a location is served by a high-speed rail station where high-speed trains regularly stop.
-
E.
hasHighSpeedLine
Indicates that there exists a high-speed rail line connection between the related entities.
- 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_69efd6b612f48190a72012b520afbd10 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_6a00717f539c8190b70dd0e72c6f7d24 |
completed | May 10, 2026, 11:52 a.m. |
| PD | Predicate disambiguation | batch_6a00710278fc81908889afdcf459ff01 |
completed | May 10, 2026, 11:50 a.m. |
Created at: April 27, 2026, 10:26 p.m.