Triple
T24699647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boomerang Coast to Coaster |
E611694
|
entity |
| Predicate | trainLiftSequence |
P157186
|
FINISHED |
| Object | backward lift up first spike |
—
|
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: backward lift up first spike | Statement: [Boomerang Coast to Coaster, trainLiftSequence, backward lift up first spike]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainLiftSequence Context triple: [Boomerang Coast to Coaster, trainLiftSequence, backward lift up first spike]
-
A.
hasApproximateNumberOfLifts
Indicates that an entity is associated with an estimated or non-exact count of lifts.
-
B.
hasNumberOfElevatorBanks
Indicates the relationship specifying how many distinct elevator banks are present in or associated with a given entity.
-
C.
hasNumberOfStepsToObservationPlatform
Indicates the specific count of steps required to reach an observation platform.
-
D.
elevatorTopSpeed_m_per_s
Indicates the maximum speed, in meters per second, that an elevator can travel.
-
E.
hasLift
Indicates that one entity is equipped with, contains, or provides access to a lift (elevator).
- 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_69e2c4d76d148190b58ad612467149a5 |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f422aee0408190899efe7e24ef2b40 |
completed | May 1, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69f420e92cc88190a803aecdae78a051 |
completed | May 1, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f422add8508190a76e56cfa756eeb8 |
completed | May 1, 2026, 3:49 a.m. |
Created at: April 18, 2026, 3:22 a.m.