Triple
T5350777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cedar Fair |
E124171
|
entity |
| Predicate | hasKeyAttractionType |
P8648
|
FINISHED |
| Object | roller coasters |
—
|
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: roller coasters | Statement: [Cedar Fair, hasKeyAttractionType, roller coasters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKeyAttractionType Context triple: [Cedar Fair, hasKeyAttractionType, roller coasters]
-
A.
hasAttractionType
chosen
Indicates that one entity is associated with a specific kind or category of attraction (e.g., tourist, cultural, natural).
-
B.
containsAttraction
Indicates that one entity includes or encompasses an attraction (such as a point of interest, feature, or draw) within its bounds or scope.
-
C.
partOfAttractionType
Indicates that one attraction type is a component or subset of a broader, more general attraction type.
-
D.
hasAttractionTheme
Indicates that something (such as a place, event, or attraction) is characterized by or associated with a particular theme or motif.
-
E.
isAttractionFor
Indicates that one entity serves as an attraction or point of interest specifically intended for another entity (such as a person, group, or audience).
- 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_69bd464be27081908807b40b75c1bbae |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd860fe4048190846a933d0e1b9386 |
completed | March 20, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69bd845c6f108190832a8d14b356368a |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:01 p.m.