Triple
T2572370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Intermediate Capacity Transit System |
E57692
|
entity |
| Predicate | passengerCapacityCategory |
P39872
|
FINISHED |
| Object | medium-capacity transit |
—
|
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: medium-capacity transit | Statement: [Intermediate Capacity Transit System, passengerCapacityCategory, medium-capacity transit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerCapacityCategory Context triple: [Intermediate Capacity Transit System, passengerCapacityCategory, medium-capacity transit]
-
A.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
B.
hasPassengerArea
Indicates that an object or vehicle includes a designated area intended for carrying passengers.
-
C.
passengerCount
Indicates the number of passengers associated with a given entity, such as a vehicle or trip.
-
D.
seatingCapacity
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
E.
designedCargoCapacity
Indicates the maximum amount of cargo an object (such as a vehicle or container) was originally engineered or specified to carry.
- F. None of above. chosen
Provenance (4 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_69ab4a51410081908501dcf8bad9adc4 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd3853c848190970e8a2da16d726d |
completed | March 7, 2026, 7:28 a.m. |
| PD | Predicate disambiguation | batch_69abd0ce4dcc8190b17a65abf9bd1bb0 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd251b48c8190862c7b39ea1bf8ea |
completed | March 7, 2026, 7:22 a.m. |
Created at: March 6, 2026, 9:48 p.m.