Triple
T14354036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Demon (California's Great America) |
E355925
|
entity |
| Predicate | hasInversion |
P68257
|
FINISHED |
| Object | vertical loop |
—
|
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: vertical loop | Statement: [Demon (California's Great America), hasInversion, vertical loop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInversion Context triple: [Demon (California's Great America), hasInversion, vertical loop]
-
A.
hasInversionCapability
Indicates that an entity possesses the ability to reverse or invert another entity, state, or process.
-
B.
hasInversionCount
Indicates that there is a specific number of pairwise order inversions present in a given sequence or arrangement.
-
C.
isInverseOf
Indicates that one relation reverses the direction of another, so that if the original relates A to B, its inverse relates B to A.
-
D.
featuresInversion
chosen
Indicates that one entity exhibits or incorporates an inversion of another entity, such as a reversed, mirrored, or otherwise inverted form or structure.
-
E.
hasReverse
Indicates that one entity serves as the inverse or opposite counterpart of another entity in a given relationship or operation.
- 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_69d82790a7e08190877e2d349b2e8d8e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8f4ff1e48190bd9419d70098cede |
completed | April 14, 2026, 7:02 p.m. |
| PD | Predicate disambiguation | batch_69de2a9958e881909d03ac03f135163e |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:14 a.m.