Triple
T9733785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Magic Carpets of Aladdin |
E236007
|
entity |
| Predicate | hasCapacityPerVehicle |
P11680
|
FINISHED |
| Object | 4 riders |
—
|
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: 4 riders | Statement: [The Magic Carpets of Aladdin, hasCapacityPerVehicle, 4 riders]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCapacityPerVehicle Context triple: [The Magic Carpets of Aladdin, hasCapacityPerVehicle, 4 riders]
-
A.
cargoCapacityFeature
Indicates that an entity has a feature specifying how much cargo it can carry or accommodate.
-
B.
transportCapacity
Indicates the maximum quantity of people, goods, or materials that can be transported by an entity or system within a given operation or time frame.
-
C.
maximumPassengerCapacity
chosen
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
D.
designedCargoCapacity
Indicates the maximum amount of cargo an object (such as a vehicle or container) was originally engineered or specified to carry.
-
E.
passengerCapacityCategory
Indicates the classification of an entity based on the number of passengers it is designed or allowed to carry.
- 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_69ca84d313e88190983ee6ffd0ef60d2 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9eb54fe481908b0202f104b75dc1 |
completed | April 1, 2026, 10:39 p.m. |
| PD | Predicate disambiguation | batch_69cd03c6ffc88190a5e9569e19122ad5 |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:22 p.m.