Triple
T478873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Machu Picchu |
E9121
|
entity |
| Predicate | touristArrivalsPerYear |
P12597
|
FINISHED |
| Object | over one million visitors in many years |
—
|
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 one million visitors in many years | Statement: [Machu Picchu, touristArrivalsPerYear, over one million visitors in many years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: touristArrivalsPerYear Context triple: [Machu Picchu, touristArrivalsPerYear, over one million visitors in many years]
-
A.
touristArrivalsPerYearApprox
chosen
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
-
B.
peakPassengerTrafficRank
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
-
C.
tourismRegion
Indicates that a place or area is designated or recognized as a tourism region associated with another geographic or administrative entity.
-
D.
passengerTrafficRankUS
Indicates the relative ranking of a location or facility within the United States based on the volume of passenger traffic it handles.
-
E.
hasApproxAnnualPassengerUsageRank
Indicates the approximate position or ranking of an entity based on its annual passenger usage compared to similar entities.
- 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_69a2e7ff81708190b0507a24a997232c |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f056459881909749764cc4a7f9e8 |
completed | Feb. 28, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69a2edf1d5848190a7da27e2fddc136f |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.