Triple
T34239600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skyways Coach-Air |
E878425
|
entity |
| Predicate | travelModel |
P174965
|
FINISHED |
| Object | coach-and-air combination |
—
|
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: coach-and-air combination | Statement: [Skyways Coach-Air, travelModel, coach-and-air combination]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelModel Context triple: [Skyways Coach-Air, travelModel, coach-and-air combination]
-
A.
travelMechanic
Indicates the method or system by which movement or travel between locations is carried out.
-
B.
travelRouteContext
Indicates the contextual details (such as purpose, conditions, or circumstances) under which a particular travel route is taken or defined.
-
C.
travelScope
Indicates the extent or range within which travel is allowed, intended, or applicable for an entity or activity.
-
D.
travelDescriptor
chosen
Indicates how an instance of travel is characterized, such as by its mode, conditions, style, or other descriptive attributes of the journey.
-
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_69f349b22d8c819096b22df268382aa9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f728a345488190bd7e6751b09ac591 |
completed | May 3, 2026, 10:51 a.m. |
| PD | Predicate disambiguation | batch_69f7283ef2608190a7a85d7e7f5332c0 |
completed | May 3, 2026, 10:49 a.m. |
Created at: May 1, 2026, 1:56 a.m.