Triple
T30242049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volkswagen California |
E768942
|
entity |
| Predicate | hasSleepingCapacity |
P160198
|
FINISHED |
| Object | up to four people |
—
|
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: up to four people | Statement: [Volkswagen California, hasSleepingCapacity, up to four people]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSleepingCapacity Context triple: [Volkswagen California, hasSleepingCapacity, up to four people]
-
A.
sleepingCapacity
chosen
Indicates the maximum number of people or occupants that can sleep in or be accommodated for sleeping by something.
-
B.
hasSleepCoverAccessory
Indicates that an entity is equipped with or includes an accessory specifically intended for use during sleep.
-
C.
hasSleeperBerths
Indicates that an object (typically a vehicle) is equipped with one or more sleeper berths for occupants to rest or sleep.
-
D.
hasStandingCapacity
Indicates that an entity is capable of maintaining or supporting a specified condition, function, or load on an ongoing basis.
-
E.
hasTemporarySeatingCapacity
Indicates the number of seats that can be added or arranged temporarily for an entity, beyond its permanent seating capacity.
- 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_69f224820c048190b1435c4cc145acf1 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6805072f88190a05c0467cdeffb8a |
completed | May 2, 2026, 10:53 p.m. |
| PD | Predicate disambiguation | batch_69f6760216108190bbb708d53a6c2c25 |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 29, 2026, 7:39 p.m.