Triple
T20007272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La ruleta de la suerte |
E494490
|
entity |
| Predicate | typicalSetFeature |
P5084
|
FINISHED |
| Object | large spinning wheel on stage |
—
|
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: large spinning wheel on stage | Statement: [La ruleta de la suerte, typicalSetFeature, large spinning wheel on stage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSetFeature Context triple: [La ruleta de la suerte, typicalSetFeature, large spinning wheel on stage]
-
A.
typicalFeatures
chosen
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
B.
featureSet
Indicates that one entity is a collection or configuration of features associated with or applied to another entity.
-
C.
targetFeature
Indicates that one entity is the specific feature, attribute, or characteristic that another entity is directed toward, focused on, or intended to affect.
-
D.
featuredSetType
Indicates the specific category or type assigned to a featured set within a collection or system.
-
E.
typicalEnsembleType
Indicates the usual or characteristic type of ensemble associated with or used to perform a given work, piece, or musical 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_69da626b2d748190886981ea90c8b2ea |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e661a648a88190853ee741edcf6ca2 |
completed | April 20, 2026, 5:25 p.m. |
| PD | Predicate disambiguation | batch_69e54cdddbd48190becc8b2aa5ab4ef9 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 3:33 p.m.