Triple
T8773856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Visakhapatnam railway station |
E208527
|
entity |
| Predicate | hasFreightTrafficLevel |
P28469
|
FINISHED |
| Object | significant |
—
|
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: significant | Statement: [Visakhapatnam railway station, hasFreightTrafficLevel, significant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFreightTrafficLevel Context triple: [Visakhapatnam railway station, hasFreightTrafficLevel, significant]
-
A.
hasCargoTrafficLevel
Indicates the intensity or volume of cargo-related traffic associated with an entity, such as a route, location, or transport facility.
-
B.
freightTraffic
chosen
Indicates the movement or volume of goods and cargo being transported, typically via commercial transport networks such as rail, road, sea, or air.
-
C.
hasCargoTrafficType
Indicates that an entity is associated with a specific type or category of cargo traffic it handles or supports.
-
D.
hasPedestrianTrafficLevel
Indicates the level or intensity of pedestrian traffic associated with a given location or pathway.
-
E.
hasTruckTraffic
Indicates that there is truck-related vehicular movement or flow occurring on or through a specified location or route.
- 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_69ca835edb4481909b4aafb616dc5eb7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f2ef3288190988bd69e8a02e741 |
completed | March 31, 2026, 11:56 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1aff3881908be6a9cbc9f50461 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:41 p.m.