Triple
T20103635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maserati MC12 |
E496611
|
entity |
| Predicate | totalUnitsProduced |
P19248
|
FINISHED |
| Object | 50 road cars |
—
|
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: 50 road cars | Statement: [Maserati MC12, totalUnitsProduced, 50 road cars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalUnitsProduced Context triple: [Maserati MC12, totalUnitsProduced, 50 road cars]
-
A.
totalProduction
Indicates the overall quantity of goods, services, or output produced by an entity or system over a specified period or within a defined scope.
-
B.
numberOfUnits
chosen
Indicates the quantity or count of discrete units associated with an entity or relationship.
-
C.
productionCapacity
Indicates the maximum amount of output an entity can produce within a given time or resource constraint.
-
D.
massProduced
Indicates that an item is manufactured in large quantities, typically using standardized, industrial production processes.
-
E.
approximateProductionNumber
Indicates that one entity specifies an estimated or non-exact quantity associated with the production output of another entity.
- 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6667170a4819085d07a4188ded541 |
completed | April 20, 2026, 5:46 p.m. |
| PD | Predicate disambiguation | batch_69e54cf788188190a46cc49c9ce7617f |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 11:27 p.m.