Triple
T25822604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Visa Waiver Program |
E650440
|
entity |
| Predicate | includesTravelTypes |
P39916
|
FINISHED |
| Object | air travel |
—
|
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: air travel | Statement: [Visa Waiver Program, includesTravelTypes, air travel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesTravelTypes Context triple: [Visa Waiver Program, includesTravelTypes, air travel]
-
A.
travelsFor
Indicates that one entity moves from place to place on behalf of, or for the benefit or purpose of, another entity or objective.
-
B.
travelsOn
Indicates that an entity moves or journeys using a particular route, path, or mode of transportation.
-
C.
offersClassOfTravel
Indicates that a service provider makes a particular class or tier of travel (e.g., economy, business, first) available as an option.
-
D.
transportIncludes
chosen
Indicates that a broader transport operation or service encompasses, contains, or makes use of a specific transport segment, mode, or component as part of its overall movement.
-
E.
involvedTravelBetween
Indicates a relationship where an entity participates in or is associated with travel occurring between two specified locations.
- 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_69e7ab367fcc8190a5ff1e7f3da046a4 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f79f48acec8190a9d5964581a94f6c |
completed | May 3, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f79e4888248190be2f63cdfb5cd7b7 |
completed | May 3, 2026, 7:13 p.m. |
Created at: April 22, 2026, 7:30 a.m.