Triple
T30142686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volkswagen emissions scandal |
E766169
|
entity |
| Predicate | numberOfVehiclesAffectedUS |
P22510
|
FINISHED |
| Object | approximately 500000 vehicles in the United States |
—
|
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: approximately 500000 vehicles in the United States | Statement: [Volkswagen emissions scandal, numberOfVehiclesAffectedUS, approximately 500000 vehicles in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVehiclesAffectedUS Context triple: [Volkswagen emissions scandal, numberOfVehiclesAffectedUS, approximately 500000 vehicles in the United States]
-
A.
numberOfUSFatalities
Indicates the number of people who died in the United States as a result of the specified event or circumstance.
-
B.
numberOfVictimsInjured
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
C.
numberOfVehicles
chosen
Indicates the total count of vehicles associated with a given entity or context.
-
D.
numberOfCarsDerailed
Indicates the count of cars that have come off the tracks in a derailment incident.
-
E.
estimatedAffectedPeople
Indicates the estimated number of people expected to be impacted by a particular event, condition, or action.
- 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_6a005be4615c8190a710ab704a46c564 |
completed | May 10, 2026, 10:20 a.m. |
| PD | Predicate disambiguation | batch_6a005b8b1cc08190850a392761b84e74 |
completed | May 10, 2026, 10:18 a.m. |
Created at: April 29, 2026, 7:18 p.m.