Triple
T8607377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Hurricane Express |
E203832
|
entity |
| Predicate | hasRailVehicleType |
P1305
|
FINISHED |
| Object | 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: train | Statement: [The Hurricane Express, hasRailVehicleType, train]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRailVehicleType Context triple: [The Hurricane Express, hasRailVehicleType, train]
-
A.
hasRailMode
Indicates that an entity is associated with or supports transportation via rail-based modes (such as trains, trams, or subways).
-
B.
trainTypeUsed
Indicates that a specific type or category of train is employed or operated in a given context or service.
-
C.
rollingStockType
chosen
Indicates the specific category or type of railway rolling stock associated with an entity (e.g., locomotive, passenger car, freight wagon).
-
D.
usesRollingStock
Indicates that one entity employs or operates specific rolling stock (such as rail vehicles) in its activities or services.
-
E.
usesRollingStockCompatibleWith
Indicates that one entity operates using rolling stock that is technically and operationally compatible with the rolling stock standards or systems associated with another entity.
- 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_69ca832c23e4819095a9f3eea4a21828 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46eabe2c8190a2d13c353055a785 |
completed | March 31, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69cc454eb2908190acf0e4336bc67e7b |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:25 p.m.