Triple
T9833274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guangzhou BRT |
E239038
|
entity |
| Predicate | peakHourlyThroughput |
P22949
|
FINISHED |
| Object | over 27000 passengers per hour per direction |
—
|
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: over 27000 passengers per hour per direction | Statement: [Guangzhou BRT, peakHourlyThroughput, over 27000 passengers per hour per direction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakHourlyThroughput Context triple: [Guangzhou BRT, peakHourlyThroughput, over 27000 passengers per hour per direction]
-
A.
peakHours
Indicates that an action, event, or condition occurs during the busiest or most heavily trafficked time period.
-
B.
hasPeakHourFunction
Indicates that something performs a specific role or behavior during peak hours of activity or usage.
-
C.
capacityPerHour
chosen
Indicates the maximum amount of output or throughput an entity can handle or produce in one hour.
-
D.
hasPeakHourService
Indicates that a service operates or is available during designated peak or high-demand hours.
-
E.
operationalPeak
Indicates the highest level or period of performance, capacity, or activity that a system, process, or entity reaches during its operation.
- 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_69ca84e314108190978324a4bdb959f8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb336bfc4819084f0d4d6d1867484 |
completed | April 2, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69cd03e30bc08190816c0a6d29c21b0f |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:32 p.m.