Triple
T4346461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nikola Tesla Memorial Center |
E97914
|
entity |
| Predicate | yearlyNumberOfVisitors |
P427
|
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: [Nikola Tesla Memorial Center, yearlyNumberOfVisitors, tens of thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearlyNumberOfVisitors Context triple: [Nikola Tesla Memorial Center, yearlyNumberOfVisitors, tens of thousands]
-
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.
annualVisitation
Indicates a recurring visit or attendance that takes place once every year between the related entities.
-
D.
visitorFrequency
Indicates how often a visitor comes to or interacts with a particular entity or location.
-
E.
touristArrivalsShareInTerritory
Indicates the proportion of total tourist arrivals that occur within a specific territory relative to a larger reference area or total.
- 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_69b34548402c819085ab68b27c235a87 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3518d6728819084a2f40ae0bd3ac8 |
completed | March 12, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69b34f4fe1c481908d6d66e15697c04b |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:15 p.m.