Triple
T17367706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2015 Motor Trend Car of the Year |
E422226
|
entity |
| Predicate | awardedBy |
P287
|
FINISHED |
| Object | Motor Trend |
E91043
|
NE 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: Motor Trend | Statement: [2015 Motor Trend Car of the Year, awardedBy, Motor Trend]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Motor Trend Context triple: [2015 Motor Trend Car of the Year, awardedBy, Motor Trend]
-
A.
Motor Trend
chosen
Motor Trend is an American automotive magazine and media brand known for its influential car reviews, comparison tests, and annual Car of the Year awards.
-
B.
Road & Track
Road & Track is an American automotive enthusiast magazine known for its in-depth coverage of performance cars, motorsports, and automotive culture.
-
C.
Autoweek
Autoweek is an American automotive magazine and media brand known for its coverage of car culture, motorsports, and industry news.
-
D.
Car Magazine
Car Magazine is a long-running British automotive publication known for its in-depth car reviews, industry news, and influential motoring journalism.
-
E.
Hearst Autos
Hearst Autos is the automotive-focused division of Hearst Communications that produces car-related media, reviews, and digital content for consumers and industry professionals.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a661fc08190a4c386125bddb16b |
completed | April 19, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a019566da6c819083b59e0911d02bd5 |
completed | May 11, 2026, 8:37 a.m. |
Created at: April 10, 2026, 5:44 a.m.