Triple
T1003300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auto Train |
E21650
|
entity |
| Predicate | hasBoardingProcess |
P8856
|
FINISHED |
| Object | vehicle check-in and loading prior to departure |
—
|
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: vehicle check-in and loading prior to departure | Statement: [Auto Train, hasBoardingProcess, vehicle check-in and loading prior to departure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBoardingProcess Context triple: [Auto Train, hasBoardingProcess, vehicle check-in and loading prior to departure]
-
A.
hasBoardingType
Indicates the specific manner or method by which an entity is boarded or accessed (e.g., how passengers or items are taken on).
-
B.
hasCustomsAndImmigration
Indicates that customs and immigration control services are present or provided at a given location or facility.
-
C.
boardingProcess
chosen
Indicates the process or sequence of actions by which passengers move from a waiting area onto a vehicle (such as an airplane, train, or bus).
-
D.
hasBorderControlStatus
Indicates the type or condition of border control that applies to a given entity or location.
-
E.
positionOnImmigration
Indicates a stance or viewpoint that an entity holds regarding immigration policies or issues.
- 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_69a493c53e648190ae8cb76c433fd9a7 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b4fe0a548190aee8abf1890e141e |
completed | March 1, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69a4b2b1f4f88190822598cfd2a0fd2b |
completed | March 1, 2026, 9:42 p.m. |
Created at: March 1, 2026, 7:41 p.m.