Triple
T936307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M7 electric multiple unit |
E20201
|
entity |
| Predicate | hasInteriorLayout |
P5253
|
FINISHED |
| Object | longitudinal and transverse seating mix |
—
|
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: longitudinal and transverse seating mix | Statement: [M7 electric multiple unit, hasInteriorLayout, longitudinal and transverse seating mix]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInteriorLayout Context triple: [M7 electric multiple unit, hasInteriorLayout, longitudinal and transverse seating mix]
-
A.
hasInteriorFeature
Indicates that an entity contains or includes a specific feature within its interior space.
-
B.
vehicleLayout
chosen
Indicates how the components or seating within a vehicle are arranged or configured relative to each other.
-
C.
interiorStyle
Indicates that one entity has a particular interior design style or aesthetic characterized by the other entity.
-
D.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
-
E.
cabinConfiguration
Indicates how the interior space of a vehicle, vessel, or aircraft is arranged and organized for occupants or cargo.
- 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_69a493b0270c81909e6c9ce310f6aa55 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b36558588190a2a9c710073624d1 |
completed | March 1, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69a4b29b245c8190b143f28b77fede3c |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.