Triple
T17573522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Holy Land USA |
E427998
|
entity |
| Predicate | peakAnnualVisitors |
P12597
|
FINISHED |
| Object | tens of thousands |
—
|
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: tens of thousands | Statement: [Holy Land USA, peakAnnualVisitors, tens of thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakAnnualVisitors Context triple: [Holy Land USA, peakAnnualVisitors, tens of thousands]
-
A.
touristArrivalsPerYearApprox
chosen
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
-
B.
typicalVisitorsPerSeason
Indicates the usual number of visitors associated with each season for a given entity or location.
-
C.
hasAnnualPassengerTrafficOver
Indicates that the subject location or transport facility experiences an annual passenger volume exceeding a specified threshold.
-
D.
hasTouristVisits
Indicates that one entity experiences or records visits from tourists to another entity.
-
E.
annualVisitation
Indicates a recurring visit or attendance that takes place once every year between the related 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e459330c788190907a02fc98e0e24b |
completed | April 19, 2026, 4:25 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fd7d048190b54ee4c6155612a5 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:50 a.m.