Triple
T13503034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nico Rosberg |
E320939
|
entity |
| Predicate | yearsWithMercedes |
P109998
|
FINISHED |
| Object | 2010–2016 |
—
|
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: 2010–2016 | Statement: [Nico Rosberg, yearsWithMercedes, 2010–2016]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearsWithMercedes Context triple: [Nico Rosberg, yearsWithMercedes, 2010–2016]
-
A.
yearModelTDevelopmentBegan
Indicates the calendar year in which the development of model T was initiated.
-
B.
yearFirstDelivered
Indicates the calendar year in which something (such as a product, service, or item) was first delivered or made available.
-
C.
conceptCarYear
Indicates the year in which a concept car was first introduced, presented, or designated.
-
D.
modelYears
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.
-
E.
truckSeriesDebutYear
Indicates the year in which a particular truck series was first introduced or debuted.
- F. None of above. chosen
Provenance (4 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf50f4a48190a44fc537b78c32fd |
completed | April 12, 2026, 2:42 p.m. |
| PD | Predicate disambiguation | batch_69dbae0b63748190b5e207f84b2532ea |
completed | April 12, 2026, 2:36 p.m. |
| PDg | Predicate description generation | batch_69dbaee128d88190b097be17fdd2f92b |
completed | April 12, 2026, 2:40 p.m. |
Created at: April 9, 2026, 9:43 p.m.