Triple
T1762934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volkswagen Beetle |
E38697
|
entity |
| Predicate | massProductionStart |
P31468
|
FINISHED |
| Object | 1945 |
—
|
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: 1945 | Statement: [Volkswagen Beetle, massProductionStart, 1945]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: massProductionStart Context triple: [Volkswagen Beetle, massProductionStart, 1945]
-
A.
massProduced
Indicates that an item is manufactured in large quantities, typically using standardized, industrial production processes.
-
B.
enteredAutomobileProduction
Indicates that an entity began manufacturing automobiles as a commercial or industrial activity.
-
C.
firstProducedAt
Indicates the location or context where something was originally created, manufactured, or brought into existence for the first time.
-
D.
timePeriodOfProduction
Indicates the span of time during which something was produced or created.
-
E.
wasFirstBuiltInYear
Indicates that the initial construction of an entity was completed in a specified year.
- 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_69a8862d562481908d7025a1c1f67c0d |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab173936b4819097332ee185996bbd |
completed | March 6, 2026, 6:04 p.m. |
| PD | Predicate disambiguation | batch_69aa61c9e06c819085489e00cfe72153 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab173830a481908b67928f16f5d999 |
completed | March 6, 2026, 6:04 p.m. |
Created at: March 4, 2026, 7:31 p.m.