Triple
T9859673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soarin' Over California |
E239676
|
entity |
| Predicate | numberOfRideVehiclesPerTheater |
P90930
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Soarin' Over California, numberOfRideVehiclesPerTheater, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRideVehiclesPerTheater Context triple: [Soarin' Over California, numberOfRideVehiclesPerTheater, 3]
-
A.
numberOfAttractions
Indicates the total count of attractions associated with a given entity or context.
-
B.
numberOfRollerCoasters
Indicates the quantity of roller coasters associated with a given entity.
-
C.
numberOfRidersPerVehicle
Indicates the quantity of riders associated with each individual vehicle in the relationship.
-
D.
hasRideSystem
Indicates that one entity (typically an attraction or ride) uses or is associated with a particular ride system or ride mechanism.
-
E.
hasNumberOfTheatres
Indicates the quantity of theatres associated with or present in a given 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_69ca84e6493081909cf58c8d42ea856b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb39b06b48190ab53ff00ff0513ca |
completed | April 2, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69cd1d7621d48190aa6a6f34399514b0 |
completed | April 1, 2026, 1:28 p.m. |
| PDg | Predicate description generation | batch_69cd3581a9688190a00cef4c3eebb0ae |
completed | April 1, 2026, 3:10 p.m. |
Created at: March 30, 2026, 8:35 p.m.