Triple
T13960949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cash for Clunkers program |
E335788
|
entity |
| Predicate | approximateNumberOfNewVehiclesSold |
P22510
|
FINISHED |
| Object | about 700,000 |
—
|
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: about 700,000 | Statement: [Cash for Clunkers program, approximateNumberOfNewVehiclesSold, about 700,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateNumberOfNewVehiclesSold Context triple: [Cash for Clunkers program, approximateNumberOfNewVehiclesSold, about 700,000]
-
A.
numberOfVehicles
chosen
Indicates the total count of vehicles associated with a given entity or context.
-
B.
firstModelYearSales
Indicates the sales figures associated with the first model year of a product or item.
-
C.
numberOfPassengerCars
Indicates the total count of passenger cars associated with or contained in a given entity or context.
-
D.
numberOfCarsPerUnit
Indicates the quantity of cars associated with each single unit of a specified measure (such as time, distance, or entity).
-
E.
producedVehicle
Indicates that one entity manufactured or created a particular vehicle.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e7b2f908190aa32f22298964746 |
completed | April 14, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69de05a3ccf88190b45c742db483fa08 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:17 p.m.