Triple
T21721086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seoul Subway Line 1 |
E536155
|
entity |
| Predicate | isBusiestAmong |
P26423
|
FINISHED |
| Object | Seoul Metropolitan Subway lines |
—
|
NE NERFINISHED |
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: Seoul Metropolitan Subway lines | Statement: [Seoul Subway Line 1, isBusiestAmong, Seoul Metropolitan Subway lines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isBusiestAmong Context triple: [Seoul Subway Line 1, isBusiestAmong, Seoul Metropolitan Subway lines]
-
A.
isBusiestInSystem
chosen
Indicates that an entity has the highest level of activity or load compared to all other entities within the same system.
-
B.
isBusiestStationIn
Indicates that a station has the highest level of activity (e.g., passenger or traffic volume) within a specified area or system.
-
C.
isBusiestTerminalOf
Indicates that one terminal is the busiest (i.e., handles the highest volume of activity) among all terminals associated with a given entity, such as an airport or transportation hub.
-
D.
isBusyDuring
Indicates that an entity is occupied or engaged with some activity throughout a specified time period.
-
E.
isOneOfBusiestStopsOn
Indicates that a stop ranks among the most heavily used or frequently served stops on a given route or line.
- 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_69e0c46c6dd88190a595375fa6ebd701 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efd96f1fbc8190a202f834aec1a319 |
completed | April 27, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69e6969725bc81908e7ad19619ba2688 |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:47 p.m.