Triple
T1631240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MBTA Type 8 Light Rail Vehicle |
E35259
|
entity |
| Predicate | hasPassengerArea |
P30403
|
FINISHED |
| Object | low-floor center section for mobility devices |
—
|
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: low-floor center section for mobility devices | Statement: [MBTA Type 8 Light Rail Vehicle, hasPassengerArea, low-floor center section for mobility devices]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerArea Context triple: [MBTA Type 8 Light Rail Vehicle, hasPassengerArea, low-floor center section for mobility devices]
-
A.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
B.
hasSeat
Indicates that one entity possesses, provides, or includes a seat for another entity.
-
C.
cargoSpace
Indicates that one entity provides storage capacity or room for carrying goods, equipment, or other items for another entity.
-
D.
passengerCount
Indicates the number of passengers associated with a given entity, such as a vehicle or trip.
-
E.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
- 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_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. |
| PDg | Predicate description generation | batch_69a94319becc819089c2daf45fe08a0c |
completed | March 5, 2026, 8:47 a.m. |
Created at: March 4, 2026, 7:28 p.m.