Triple
T1631255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MBTA Type 8 Light Rail Vehicle |
E35259
|
entity |
| Predicate | hasPassengerCapacity |
P11680
|
FINISHED |
| Object | high-capacity light rail vehicle |
—
|
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-capacity light rail vehicle | Statement: [MBTA Type 8 Light Rail Vehicle, hasPassengerCapacity, high-capacity light rail vehicle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerCapacity Context triple: [MBTA Type 8 Light Rail Vehicle, hasPassengerCapacity, high-capacity light rail vehicle]
-
A.
maximumPassengerCapacity
chosen
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
B.
hasCrewCapacity
Indicates that an entity is capable of accommodating a specified number of crew members.
-
C.
designedCargoCapacity
Indicates the maximum amount of cargo an object (such as a vehicle or container) was originally engineered or specified to carry.
-
D.
passengerCount
Indicates the number of passengers associated with a given entity, such as a vehicle or trip.
-
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_69a886036bc081909ff5de16dbe5e8ea |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9431af5ac8190893133f1ae490142 |
completed | March 5, 2026, 8:47 a.m. |
| PD | Predicate disambiguation | batch_69a907c91c888190b6ed295c1a2e0977 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.