Triple
T31755058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boomerang roller coaster model |
E810534
|
entity |
| Predicate | typicalLiftConfiguration |
P1984
|
FINISHED |
| Object | single lift hill with catch car |
—
|
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: single lift hill with catch car | Statement: [Boomerang roller coaster model, typicalLiftConfiguration, single lift hill with catch car]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalLiftConfiguration Context triple: [Boomerang roller coaster model, typicalLiftConfiguration, single lift hill with catch car]
-
A.
hasLiftType
chosen
Indicates the specific type or category of lift associated with an entity.
-
B.
typicalPickupConfiguration
Indicates the usual or standard arrangement or setup used for picking up or collecting something in a given context.
-
C.
hasVerticalLiftHeight
Indicates the vertical distance through which something can be raised or lifted.
-
D.
hasLift
Indicates that one entity is equipped with, contains, or provides access to a lift (elevator).
-
E.
typicalUnitConfiguration
Indicates the standard or commonly used arrangement, composition, or setup of a unit in a given context.
- 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_69f348e340d48190b780fae618c51464 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e7aa0c8190bdc9ee4d54fc821b |
completed | May 12, 2026, 7:05 p.m. |
Created at: April 30, 2026, 11:29 p.m.