Triple
T13254250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kropotkinskaya |
E315615
|
entity |
| Predicate | hasPassengerFlowPattern |
P35231
|
FINISHED |
| Object | high tourist traffic |
—
|
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: high tourist traffic | Statement: [Kropotkinskaya, hasPassengerFlowPattern, high tourist traffic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerFlowPattern Context triple: [Kropotkinskaya, hasPassengerFlowPattern, high tourist traffic]
-
A.
passengerFlowFeature
Indicates a characteristic or attribute that describes how passengers move or are distributed within a transport system or facility.
-
B.
hasTrafficPattern
Indicates that there is a characteristic or recurring flow of traffic associated with an entity, such as its typical volume, direction, or timing of movement.
-
C.
hasFootfallPattern
Indicates a characteristic pattern or sequence of steps, movements, or impacts made by an entity’s feet during locomotion or activity.
-
D.
hasPassengerTrafficFrom
Indicates that an entity receives or handles passenger traffic originating from another entity.
-
E.
hasHeavyPassengerTraffic
chosen
Indicates that an entity experiences a high volume of passenger movement or usage over a given period.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f60911081909fa346a054f76c9f |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:24 p.m.