Triple
T17147482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ski Patrol Training Camp |
E416127
|
entity |
| Predicate | waterParkSectionType |
P126295
|
FINISHED |
| Object | kids and family area |
—
|
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: kids and family area | Statement: [Ski Patrol Training Camp, waterParkSectionType, kids and family area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterParkSectionType Context triple: [Ski Patrol Training Camp, waterParkSectionType, kids and family area]
-
A.
waterParkType
Indicates the specific type or category of a water park associated with an entity.
-
B.
partOfAttractionType
Indicates that one attraction type is a component or subset of a broader, more general attraction type.
-
C.
hasWaterRide
Indicates that an entity features or includes at least one water-based ride or attraction.
-
D.
themeParkAttraction
Indicates that something is an attraction or ride located within a theme park.
-
E.
waterParkCount
Indicates the number of water parks associated with a given entity or within a specified area.
- 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_69d886d15af4819092f92f8a129763e6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f404f0e88190b7ac9ac523fdc7da |
completed | April 18, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69e3830d2a90819092386717dc56f0e8 |
completed | April 18, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69e3873f62108190966c4e741ebd548d |
completed | April 18, 2026, 1:29 p.m. |
Created at: April 10, 2026, 5:36 a.m.