Triple
T14643728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orange Line Metro Train |
E343789
|
entity |
| Predicate | designedCapacityDailyPassengers |
P30663
|
FINISHED |
| Object | 250000 |
—
|
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: 250000 | Statement: [Orange Line Metro Train, designedCapacityDailyPassengers, 250000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: designedCapacityDailyPassengers Context triple: [Orange Line Metro Train, designedCapacityDailyPassengers, 250000]
-
A.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
B.
hasAnnualPassengerTrafficOver
Indicates that the subject location or transport facility experiences an annual passenger volume exceeding a specified threshold.
-
C.
transportCapacity
Indicates the maximum quantity of people, goods, or materials that can be transported by an entity or system within a given operation or time frame.
-
D.
hasDailyPassengerTraffic
chosen
Indicates the number of passengers that regularly use or pass through something (such as a station or route) each day.
-
E.
seatingCapacity
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4ea6d8481908e6331ca173c646b |
completed | April 14, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69de657359c88190b082e3e9f86fc1d7 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:26 a.m.