Triple
T1071456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canada's Wonderland |
E23337
|
entity |
| Predicate | numberOfRollerCoasters |
P23947
|
FINISHED |
| Object | more than 15 |
—
|
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: more than 15 | Statement: [Canada's Wonderland, numberOfRollerCoasters, more than 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRollerCoasters Context triple: [Canada's Wonderland, numberOfRollerCoasters, more than 15]
-
A.
numberOfGondolas
Indicates the quantity of gondolas associated with a given entity or system.
-
B.
numberOfTowers
Indicates the quantity of towers associated with or contained by a given entity.
-
C.
themeParkConnection
Indicates a relationship where two or more entities are connected through a theme park, such as by location, operation, partnership, or shared attractions.
-
D.
numberOfRailwayTracks
Indicates the quantity of railway tracks associated with or present at a given entity or location.
-
E.
numberOfStations
Indicates the total count of stations associated with or contained by a given entity.
- 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_69a493ee1f908190992b5f0d1b04459b |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b92848148190ba8795cb8d0a1d0a |
completed | March 1, 2026, 10:09 p.m. |
| PD | Predicate disambiguation | batch_69a4b73844708190a16c9e9824ca2fb6 |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b8d5076481908640a0d873efdf14 |
completed | March 1, 2026, 10:08 p.m. |
Created at: March 1, 2026, 7:42 p.m.