Triple
T9816571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | C.K. Holliday |
E238419
|
entity |
| Predicate | hasPassengerCars |
P7735
|
FINISHED |
| Object | Disneyland Railroad passenger coaches |
—
|
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: Disneyland Railroad passenger coaches | Statement: [C.K. Holliday, hasPassengerCars, Disneyland Railroad passenger coaches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerCars Context triple: [C.K. Holliday, hasPassengerCars, Disneyland Railroad passenger coaches]
-
A.
numberOfPassengerCars
Indicates the total count of passenger cars associated with or contained in a given entity or context.
-
B.
isPassengerOnly
Indicates that the subject entity is restricted to passenger use only and does not accommodate cargo, freight, or mixed-use purposes.
-
C.
hasPassengerArea
Indicates that an object or vehicle includes a designated area intended for carrying passengers.
-
D.
hasPassengerOnlyService
Indicates that the service provided involves only the transportation of passengers, with no freight or cargo component.
-
E.
hasVehicle
chosen
Indicates that one entity possesses, owns, or is assigned a vehicle.
- 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_69ca84dfde1481909f47c286d715f892 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb2f4a1548190a5afc5ee0d7da392 |
completed | April 2, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69cd03e01ea881909a7d93fc3994ace5 |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:30 p.m.