Triple
T1867649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zion National Park |
E34957
|
entity |
| Predicate | receivesVisitorsPerYear |
P427
|
FINISHED |
| Object | millions of visitors |
—
|
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: millions of visitors | Statement: [Zion National Park, receivesVisitorsPerYear, millions of visitors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: receivesVisitorsPerYear Context triple: [Zion National Park, receivesVisitorsPerYear, millions of visitors]
-
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.
visitorFrequency
Indicates how often a visitor comes to or interacts with a particular entity or location.
-
D.
frequentlyVisitedBy
Indicates that an entity is regularly or often visited by another entity.
-
E.
typicalNumberOfRecipientsPerYear
Indicates the usual or average count of recipients involved in or affected by something within a one-year period.
- 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_69a88600b2f88190bc09303e68ab517e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abb16c09e48190a345c95eab59fd87 |
completed | March 7, 2026, 5:02 a.m. |
| PD | Predicate disambiguation | batch_69abafe02c3c819093a4744b476106ca |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:34 p.m.