Triple
T3835593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Union Pacific lounge cars |
E91122
|
entity |
| Predicate | carTypeVariant |
P52295
|
FINISHED |
| Object | observation-lounge car |
—
|
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: observation-lounge car | Statement: [Union Pacific lounge cars, carTypeVariant, observation-lounge car]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carTypeVariant Context triple: [Union Pacific lounge cars, carTypeVariant, observation-lounge car]
-
A.
vehicleType
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
-
B.
wheelbaseVariantOf
Indicates a relationship where one vehicle’s wheelbase configuration is a variant or modified version of another vehicle’s wheelbase.
-
C.
carModel
Indicates the specific model designation of a car within a particular make or brand.
-
D.
featuresVehicle
Indicates that one entity includes, presents, or prominently incorporates a particular vehicle as part of its content, composition, or offering.
-
E.
intendedVehicle
Indicates that one entity is the vehicle that another entity plans or is meant to use.
- 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_69aed960b538819096561c8ed448dec9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeeb9a27508190b05e5312cc7c8033 |
completed | March 9, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69aee74dcecc819098285483ec721b40 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aeeb828fb08190901d51edbe8bd304 |
completed | March 9, 2026, 3:47 p.m. |
Created at: March 9, 2026, 3:18 p.m.