Triple
T19768507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gangnam Station |
E474819
|
entity |
| Predicate | rankInPassengerTraffic |
P25678
|
FINISHED |
| Object | among busiest stations in Seoul |
—
|
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: among busiest stations in Seoul | Statement: [Gangnam Station, rankInPassengerTraffic, among busiest stations in Seoul]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInPassengerTraffic Context triple: [Gangnam Station, rankInPassengerTraffic, among busiest stations in Seoul]
-
A.
peakPassengerTrafficRank
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
-
B.
hasPassengerTrafficRank
chosen
Indicates the relative position or ranking of an entity based on the volume of passenger traffic it handles compared to others.
-
C.
passengerTrafficRankingWorld
Indicates the relative position of an entity in a global ranking based on the volume of passenger traffic it handles.
-
D.
passengerTraffic
Indicates the flow or volume of passengers moving through or using a particular transport service, route, or facility.
-
E.
servedPassengerTraffic
Indicates that an entity has provided transportation services to a certain volume or set of passengers.
- 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_69d8e51a43a08190956bc6df13c91a77 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65359bb9881908f48282b63a83f2f |
completed | April 20, 2026, 4:24 p.m. |
| PD | Predicate disambiguation | batch_69e5305016e08190b9561a96baecb0b8 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:48 p.m.