Triple
T9380012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Central Hall (Kelvingrove) |
E225757
|
entity |
| Predicate | hasVisitorTrafficLevel |
P80470
|
FINISHED |
| Object | high |
—
|
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 | Statement: [Grand Central Hall (Kelvingrove), hasVisitorTrafficLevel, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVisitorTrafficLevel Context triple: [Grand Central Hall (Kelvingrove), hasVisitorTrafficLevel, high]
-
A.
hasCargoTrafficLevel
Indicates the intensity or volume of cargo-related traffic associated with an entity, such as a route, location, or transport facility.
-
B.
hasDailyPassengerTraffic
Indicates the number of passengers that regularly use or pass through something (such as a station or route) each day.
-
C.
hasPedestrianTrafficLevel
Indicates the level or intensity of pedestrian traffic associated with a given location or pathway.
-
D.
touristTraffic
chosen
Indicates the level, flow, or intensity of tourists visiting or moving through a particular place or area.
-
E.
trafficLevel
Indicates the degree of congestion or flow intensity present in a transportation network or route at a given time.
- 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_69ca842d8ee88190aaaa639aa953185d |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd50bc94108190a1b8146a78166009 |
completed | April 1, 2026, 5:07 p.m. |
| PD | Predicate disambiguation | batch_69cca53bd6ec81909bf403ce304e5c08 |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:44 p.m.