Triple
T30142685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volkswagen emissions scandal |
E766169
|
entity |
| Predicate | numberOfVehiclesAffected |
P22510
|
FINISHED |
| Object | millions of vehicles worldwide |
—
|
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: millions of vehicles worldwide | Statement: [Volkswagen emissions scandal, numberOfVehiclesAffected, millions of vehicles worldwide]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVehiclesAffected Context triple: [Volkswagen emissions scandal, numberOfVehiclesAffected, millions of vehicles worldwide]
-
A.
numberOfVehicles
chosen
Indicates the total count of vehicles associated with a given entity or context.
-
B.
numberOfCarsDerailed
Indicates the count of cars that have come off the tracks in a derailment incident.
-
C.
affectedCars
Indicates that certain cars are impacted or influenced by a particular event, condition, or action.
-
D.
numberOfTrainsInvolved
Indicates the count of trains that are involved in a particular event, situation, or incident.
-
E.
numberOfPassengerCars
Indicates the total count of passenger cars associated with or contained in a given entity or context.
- 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_69f2247909048190ae86c2160cf8b566 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a005b2e0a9c819081c6f7ccbef49ff8 |
completed | May 10, 2026, 10:17 a.m. |
| PD | Predicate disambiguation | batch_6a005a8bcde88190ace2bc0215e26430 |
completed | May 10, 2026, 10:14 a.m. |
Created at: April 29, 2026, 7:18 p.m.