Triple
T24042675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 446 series EMU |
E595426
|
entity |
| Predicate | hasPassengerCompartmentType |
P30403
|
FINISHED |
| Object | enclosed coaches |
—
|
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: enclosed coaches | Statement: [446 series EMU, hasPassengerCompartmentType, enclosed coaches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerCompartmentType Context triple: [446 series EMU, hasPassengerCompartmentType, enclosed coaches]
-
A.
hasPassengerArea
chosen
Indicates that an object or vehicle includes a designated area intended for carrying passengers.
-
B.
hasTrunk
Indicates that one entity possesses or is equipped with a trunk as a physical feature or component.
-
C.
hasCargoAccess
Indicates that an entity has the ability or permission to access a designated cargo area or its contents.
-
D.
hasFrunk
Indicates that an entity possesses a front trunk or front storage compartment.
-
E.
canCarryPassengersInCargoArea
Indicates that an entity is capable of transporting passengers specifically within its designated cargo area.
- 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_69e288c06a908190899cad4531f32c9a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d8db3b4c81908a36eace8ec136cc |
completed | April 29, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69f1764345388190a3102b62ddb729b4 |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 9:58 p.m.