Triple
T13960948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cash for Clunkers program |
E335788
|
entity |
| Predicate | approximateNumberOfVehiclesTraded |
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, approximateNumberOfVehiclesTraded, about 700,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateNumberOfVehiclesTraded Context triple: [Cash for Clunkers program, approximateNumberOfVehiclesTraded, about 700,000]
-
A.
tradedIn
Indicates that one entity has given up or exchanged another entity, typically as part of a transaction to obtain something else.
-
B.
numberOfVehicles
chosen
Indicates the total count of vehicles associated with a given entity or context.
-
C.
sharesTradedFor
Indicates that shares of one entity are exchanged or traded in return for another asset, security, or form of consideration.
-
D.
tradedSince
Indicates that an entity has been actively traded starting from a specified point in time and continuing thereafter.
-
E.
usedUnmarkedVehicles
Indicates that the action or operation was carried out using vehicles that bore no identifying marks, logos, or official insignia.
- 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.