Triple
T36240256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Lee |
E891498
|
entity |
| Predicate | productionUsedCarsCount |
P22510
|
FINISHED |
| Object | dozens of Chargers used during series filming |
—
|
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: dozens of Chargers used during series filming | Statement: [General Lee, productionUsedCarsCount, dozens of Chargers used during series filming]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: productionUsedCarsCount Context triple: [General Lee, productionUsedCarsCount, dozens of Chargers used during series filming]
-
A.
numberOfVehicles
chosen
Indicates the total count of vehicles associated with a given entity or context.
-
B.
usedVehicleModel
Indicates that a vehicle is a pre-owned (used) instance of a particular vehicle model.
-
C.
numberOfPassengerCars
Indicates the total count of passenger cars associated with or contained in a given entity or context.
-
D.
usedUnmarkedVehicles
Indicates that the action or operation was carried out using vehicles that bore no identifying marks, logos, or official insignia.
-
E.
listedVehicle
Indicates that a vehicle has been placed on a list, such as a registry, catalog, or inventory, as part of a tracking or management process.
- 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_69f76e44993481908fa75e4c48d0aab3 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ff0d80c0dc81909fbd12285c7a45c0 |
completed | May 9, 2026, 10:33 a.m. |
| PD | Predicate disambiguation | batch_69ff0cd03e78819094895058f925fbfa |
completed | May 9, 2026, 10:30 a.m. |
Created at: May 3, 2026, 4:09 p.m.