Triple
T13278620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silver Preferred |
E316257
|
entity |
| Predicate | travelContext |
P100062
|
FINISHED |
| Object | commercial 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: commercial air travel | Statement: [Silver Preferred, travelContext, commercial air travel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelContext Context triple: [Silver Preferred, travelContext, commercial air travel]
-
A.
travelMechanic
chosen
Indicates the method or system by which movement or travel between locations is carried out.
-
B.
travelScope
Indicates the extent or range within which travel is allowed, intended, or applicable for an entity or activity.
-
C.
coTraveler
Indicates that two or more entities are traveling together along (part of) the same journey or route.
-
D.
journeyDestination
Indicates that one entity serves as the endpoint or intended destination of another entity’s journey or travel.
-
E.
travelRouteOf
Indicates the path or itinerary that an entity follows or uses when traveling from one location to another.
- 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_69d806b349908190a9a61dd9323bf153 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6535688190a5a4549b7be2d611 |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:26 p.m.