Triple
T2443590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bronx Zoo |
E53333
|
entity |
| Predicate | typicalVisitorsPerYear |
P427
|
FINISHED |
| Object | over 2 million visitors per year (approximate) |
—
|
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: over 2 million visitors per year (approximate) | Statement: [Bronx Zoo, typicalVisitorsPerYear, over 2 million visitors per year (approximate)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalVisitorsPerYear Context triple: [Bronx Zoo, typicalVisitorsPerYear, over 2 million visitors per year (approximate)]
-
A.
touristArrivalsPerYearApprox
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
-
B.
visitorFrequency
Indicates how often a visitor comes to or interacts with a particular entity or location.
-
C.
visitorCount
chosen
Indicates the number of visitors associated with a particular entity, context, or time period.
-
D.
primaryVisitors
Indicates that certain entities are the main or most important visitors associated with another entity or context.
-
E.
hasVisitorCenterBy
Indicates that a location or site is served or administered by a specific visitor center.
- 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_69ab495b6dac8190ac82661aa1452222 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abcabf477081908512df63ac1a6414 |
completed | March 7, 2026, 6:50 a.m. |
| PD | Predicate disambiguation | batch_69abc5ad8e588190b97c4cd7cf575043 |
completed | March 7, 2026, 6:29 a.m. |
Created at: March 6, 2026, 9:43 p.m.