Triple
T13246418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SAM 28000 |
E315416
|
entity |
| Predicate | passengerCapacityClass |
P39872
|
FINISHED |
| Object | high |
—
|
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 | Statement: [SAM 28000, passengerCapacityClass, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerCapacityClass Context triple: [SAM 28000, passengerCapacityClass, high]
-
A.
passengerCapacityCategory
chosen
Indicates the classification of an entity based on the number of passengers it is designed or allowed to carry.
-
B.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
C.
aircraftSeatingCategory
Indicates the classification of an aircraft’s seating arrangement or capacity type associated with an entity.
-
D.
seatClass
Indicates the travel or seating category assigned to a passenger or seat (e.g., economy, business, first class).
-
E.
passengerCount
Indicates the number of passengers associated with a given entity, such as a vehicle or trip.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d5c09f88190bb1566a6d8c073a6 |
completed | April 10, 2026, 11:53 p.m. |
| PD | Predicate disambiguation | batch_69d98bcca7d88190a3e68e99ed3a29e6 |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:23 p.m.