Triple
T7800742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nickelodeon Streak |
E180422
|
entity |
| Predicate | hasTrainType |
P56947
|
FINISHED |
| Object | traditional wooden coaster trains |
—
|
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: traditional wooden coaster trains | Statement: [Nickelodeon Streak, hasTrainType, traditional wooden coaster trains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrainType Context triple: [Nickelodeon Streak, hasTrainType, traditional wooden coaster trains]
-
A.
trainTypeUsed
chosen
Indicates that a specific type or category of train is employed or operated in a given context or service.
-
B.
hasRailMode
Indicates that an entity is associated with or supports transportation via rail-based modes (such as trains, trams, or subways).
-
C.
usesTrainNumber
Indicates that one entity operates, identifies, or references another entity by a specific train number.
-
D.
railServiceType
Indicates the specific category or type of rail service that applies to the relationship between the involved entities (e.g., local, express, freight).
-
E.
thirdRailType
Indicates the specific design or configuration type of a third rail used in an electrified railway system.
- 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_69ca827e50cc8190a92a733577184938 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78a6d88819093f83528fe88b182 |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae9111b2481909684a2d4aa4831c2 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:33 p.m.