Triple
T25822607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Visa Waiver Program |
E650440
|
entity |
| Predicate | typicalTravelCategory |
P195302
|
FINISHED |
| Object | B-1 business visitor equivalent |
—
|
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: B-1 business visitor equivalent | Statement: [Visa Waiver Program, typicalTravelCategory, B-1 business visitor equivalent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTravelCategory Context triple: [Visa Waiver Program, typicalTravelCategory, B-1 business visitor equivalent]
-
A.
tourismType
Indicates the specific category or kind of tourism activity or experience associated with an entity.
-
B.
travelClassRelevance
Indicates the degree to which a particular travel class (e.g., economy, business) is pertinent or applicable within a given travel context or scenario.
-
C.
travelStyle
Indicates the manner or characteristic way in which an entity typically travels or undertakes journeys.
-
D.
roadTripType
Indicates the specific category or style of a road trip associated with an entity (e.g., scenic, business, long-distance).
-
E.
travelsFor
Indicates that one entity moves from place to place on behalf of, or for the benefit or purpose of, another entity or objective.
- F. None of above. chosen
Provenance (4 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_69e7ab367fcc8190a5ff1e7f3da046a4 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69fdbaa226708190b8ed96e93aad38de |
completed | May 8, 2026, 10:27 a.m. |
| PD | Predicate disambiguation | batch_69fdb58b07e48190837e00966de050d4 |
completed | May 8, 2026, 10:06 a.m. |
| PDg | Predicate description generation | batch_69fdbaa1313081908beea28a5597ae40 |
completed | May 8, 2026, 10:27 a.m. |
Created at: April 22, 2026, 7:30 a.m.