Triple
T5350663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto Zoo |
E124168
|
entity |
| Predicate | cityAttractionRank |
P16499
|
FINISHED |
| Object | one of the largest zoos in Canada |
—
|
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: one of the largest zoos in Canada | Statement: [Toronto Zoo, cityAttractionRank, one of the largest zoos in Canada]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityAttractionRank Context triple: [Toronto Zoo, cityAttractionRank, one of the largest zoos in Canada]
-
A.
visitorAttractionRank
chosen
Indicates the relative ranking or position of a visitor attraction compared to other attractions, typically based on popularity, quality, or importance.
-
B.
areaRank
Indicates the relative ordering or position of an entity based on the size of its area compared to others.
-
C.
areMajorTouristDestinations
Indicates that the referenced places are widely recognized and frequently visited as primary tourist destinations.
-
D.
hasTouristRank
Indicates that an entity is assigned a specific rank or rating based on its attractiveness or importance as a tourist destination.
-
E.
touristArrivalsRank
Indicates the relative position of a place compared to others based on the number of tourists arriving there.
- 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.