Triple
T598511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tesla Semi |
E11440
|
entity |
| Predicate | seatingConfiguration |
P16826
|
FINISHED |
| Object | driver centered with passenger jump seat |
—
|
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: driver centered with passenger jump seat | Statement: [Tesla Semi, seatingConfiguration, driver centered with passenger jump seat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seatingConfiguration Context triple: [Tesla Semi, seatingConfiguration, driver centered with passenger jump seat]
-
A.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
-
B.
seatingCapacity
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
C.
seatCategory
Indicates the classification or type of a seat (e.g., by comfort level, price tier, or section) assigned to an entity.
-
D.
otherSeat
Indicates that one entity is the alternative or different seat relative to another seat in a given context.
-
E.
seatSelectionPolicy
Indicates the rules or constraints governing how seats are chosen or assigned in a given context.
- F. None of above. chosen
Provenance (4 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_69a4932779b881908688590d59c71900 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49dc4f7d08190990f70b9b3af6ce5 |
completed | March 1, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69a49cf59cd0819084e67981cb371e25 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49dc0e6a08190b81d82a6f2571c41 |
completed | March 1, 2026, 8:12 p.m. |
Created at: March 1, 2026, 7:35 p.m.