Triple
T15951924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TGV V150 |
E386837
|
entity |
| Predicate | distinctionFromMaglev |
P18634
|
FINISHED |
| Object | not a magnetic levitation train |
—
|
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: not a magnetic levitation train | Statement: [TGV V150, distinctionFromMaglev, not a magnetic levitation train]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distinctionFromMaglev Context triple: [TGV V150, distinctionFromMaglev, not a magnetic levitation train]
-
A.
relativeSpeedComparedToConventionalTrains
Indicates how the speed of something compares to that of conventional trains, typically expressing whether it is faster, slower, or similar.
-
B.
comfortLevelComparedToConventionalTrains
Indicates how the comfort level of something compares relative to that of conventional trains.
-
C.
railSystemType
chosen
Indicates the specific category or classification of a rail transportation system that an entity belongs to or operates within.
-
D.
isElectricRailway
Indicates that a given railway system operates using electric power rather than diesel or other forms of propulsion.
-
E.
urbanRailCategory
Indicates the classification of an urban rail system according to its type or category (e.g., metro, tram, light rail).
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4d08f481909f38b75e3f42d9ab |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d37cd88190ab50760f1783e20c |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:53 a.m.