Triple
T16484095
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BMW K 1600 GTL |
E400392
|
entity |
| Predicate | passengerAccommodation |
P122955
|
FINISHED |
| Object | full passenger seat with backrest |
—
|
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: full passenger seat with backrest | Statement: [BMW K 1600 GTL, passengerAccommodation, full passenger seat with backrest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerAccommodation Context triple: [BMW K 1600 GTL, passengerAccommodation, full passenger seat with backrest]
-
A.
sleepingAccommodation
Indicates that one entity serves as a place or facility where another entity can sleep or stay overnight.
-
B.
hasAccommodation
Indicates that an entity provides, owns, or is associated with a place for someone to stay or live.
-
C.
accommodationModel
Indicates the specific type or structure of lodging arrangement that characterizes how an accommodation is organized or provided.
-
D.
seeksLodgingIn
Indicates that one entity is actively trying to obtain or arrange a place to stay within a specified location.
-
E.
accommodationStyle
Indicates the manner or type of lodging or housing arrangement provided or used 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_69d883813098819084f5409539723b59 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e0420ac81908f9a3548ddb3b1ff |
completed | April 18, 2026, 7:08 a.m. |
| PD | Predicate disambiguation | batch_69e22706b0588190a48a951c5211a617 |
completed | April 17, 2026, 12:26 p.m. |
| PDg | Predicate description generation | batch_69e24556c1348190902a4d116c3137d9 |
completed | April 17, 2026, 2:36 p.m. |
Created at: April 10, 2026, 5:13 a.m.