Triple
T3347288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siemens Venture coaches |
E70404
|
entity |
| Predicate | interiorConfiguration |
P16894
|
FINISHED |
| Object | 2+2 economy seating (typical) |
—
|
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: 2+2 economy seating (typical) | Statement: [Siemens Venture coaches, interiorConfiguration, 2+2 economy seating (typical)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: interiorConfiguration Context triple: [Siemens Venture coaches, interiorConfiguration, 2+2 economy seating (typical)]
-
A.
interiorStyle
Indicates that one entity has a particular interior design style or aesthetic characterized by the other entity.
-
B.
cabinConfiguration
chosen
Indicates how the interior space of a vehicle, vessel, or aircraft is arranged and organized for occupants or cargo.
-
C.
interiorFloor
Indicates that one entity is the interior floor surface or flooring of another enclosing space or structure.
-
D.
furnishingType
Indicates the type or category of furnishings associated with an entity, such as a property or room.
-
E.
hasInteriorFeature
Indicates that an entity contains or includes a specific feature within its interior space.
- 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_69ad85a4ef7c8190a29e2bbd6fa454e4 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb21c64448190a29631ea8f029868 |
completed | March 8, 2026, 5:30 p.m. |
| PD | Predicate disambiguation | batch_69ada42df1d48190874bb05f95deefde |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:12 p.m.