Triple
T510218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niagara Falls |
E10589
|
entity |
| Predicate | touristVisitorsPerYear |
P427
|
FINISHED |
| Object | over 20000000 |
—
|
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 20000000 | Statement: [Niagara Falls, touristVisitorsPerYear, over 20000000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: touristVisitorsPerYear Context triple: [Niagara Falls, touristVisitorsPerYear, over 20000000]
-
A.
touristArrivalsPerYearApprox
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
-
B.
visitorCount
chosen
Indicates the number of visitors associated with a particular entity, context, or time period.
-
C.
frequentlyVisitedBy
Indicates that an entity is regularly or often visited by another entity.
-
D.
annualTraffic
Indicates the typical amount or volume of traffic associated with something over the course of a year.
-
E.
numberOfImmigrantsProcessed
Indicates the total count of immigrants that have been processed in a given context or system.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f164a9d48190b525a97b5c06ffe2 |
completed | Feb. 28, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69a2edfe236481909901cc7d4281b33c |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.