Triple
T5052049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Opel Vectra |
E113807
|
entity |
| Predicate | vectraBProductionYears |
P44381
|
FINISHED |
| Object | 1995–2002 |
—
|
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: 1995–2002 | Statement: [Opel Vectra, vectraBProductionYears, 1995–2002]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vectraBProductionYears Context triple: [Opel Vectra, vectraBProductionYears, 1995–2002]
-
A.
yearFirstDelivered
Indicates the calendar year in which something (such as a product, service, or item) was first delivered or made available.
-
B.
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.
-
C.
productionYears
chosen
Indicates the span of calendar years during which something was produced or manufactured.
-
D.
lastAppearanceYear
Indicates the calendar year in which an entity made its most recent appearance.
-
E.
modelProduced
Indicates that a particular model has generated or produced a specified output, result, or artifact.
- 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_69bd443aa1f88190abb992d138f2cf42 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7428d7a88190b990aedae390acbe |
completed | March 20, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69bd715479f08190933604aebd34414f |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:37 p.m.