Triple
T10851131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | B190 |
E256146
|
entity |
| Predicate | passengerCapacityApproximate |
P882
|
FINISHED |
| Object | 19 passengers |
—
|
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: 19 passengers | Statement: [B190, passengerCapacityApproximate, 19 passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerCapacityApproximate Context triple: [B190, passengerCapacityApproximate, 19 passengers]
-
A.
passengersCountApproximate
chosen
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
B.
passengerCapacityCategory
Indicates the classification of an entity based on the number of passengers it is designed or allowed to carry.
-
C.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
D.
seatCount
Indicates the number of seats associated with an entity, such as a venue, vehicle, or room.
-
E.
hasPassengerArea
Indicates that an object or vehicle includes a designated area intended for carrying passengers.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75116af20819084c7f8fa88d18e61 |
completed | April 9, 2026, 7:11 a.m. |
| PD | Predicate disambiguation | batch_69d70d2b51448190bae748ed6c23edde |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:20 p.m.