Triple
T3925269
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2017 Motor Trend SUV of the Year |
E93259
|
entity |
| Predicate | modelYearEvaluated |
P4161
|
FINISHED |
| Object | 2017 |
—
|
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: 2017 | Statement: [2017 Motor Trend SUV of the Year, modelYearEvaluated, 2017]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modelYearEvaluated Context triple: [2017 Motor Trend SUV of the Year, modelYearEvaluated, 2017]
-
A.
yearOfManufacture
Indicates the specific calendar year in which an item was produced or manufactured.
-
B.
modelYears
chosen
Indicates the association between a product (often a vehicle or device) and the specific calendar years in which that model version was produced or marketed.
-
C.
demonstrationYear
Indicates the year in which a demonstration, display, or public showing of something took place.
-
D.
modelProduced
Indicates that a particular model has generated or produced a specified output, result, or artifact.
-
E.
purchaseYear
Indicates the calendar year in which a purchase or acquisition of something took place.
- 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeed7f3cc881909464db1970ba39ae |
completed | March 9, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69aee7609c4081908000ce12ae827c3f |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:23 p.m.