Triple
T751793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louisiana Purchase Exposition |
E15464
|
entity |
| Predicate | numberOfVisitors |
P427
|
FINISHED |
| Object | approximately 19,000,000 |
—
|
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: approximately 19,000,000 | Statement: [Louisiana Purchase Exposition, numberOfVisitors, approximately 19,000,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVisitors Context triple: [Louisiana Purchase Exposition, numberOfVisitors, approximately 19,000,000]
-
A.
visitorCount
chosen
Indicates the number of visitors associated with a particular entity, context, or time period.
-
B.
frequentlyVisitedBy
Indicates that an entity is regularly or often visited by another entity.
-
C.
touristArrivalsPerYearApprox
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
-
D.
numberOfParticipants
Indicates the total count of entities involved in a particular event, activity, or relationship.
-
E.
visitorCenter
Indicates that a location serves as a visitor center for a place, providing information or services to visitors of that place.
- 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_69a493599a0081908da65f3407af1ef2 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a64c67748190aef3522b4b428563 |
completed | March 1, 2026, 8:49 p.m. |
| PD | Predicate disambiguation | batch_69a4a501c4cc81908de6d63e3d4f60d7 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.