Triple
T20101136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GranTurismo MC Stradale |
E496539
|
entity |
| Predicate | weightSavingFeatures |
P138704
|
FINISHED |
| Object | reduced sound insulation |
—
|
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: reduced sound insulation | Statement: [GranTurismo MC Stradale, weightSavingFeatures, reduced sound insulation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: weightSavingFeatures Context triple: [GranTurismo MC Stradale, weightSavingFeatures, reduced sound insulation]
-
A.
emptyWeight
Indicates the weight of an object or vehicle when it is empty, excluding any load, cargo, or passengers.
-
B.
energyEfficiencyFeature
Indicates that an entity has a design, technology, or characteristic specifically intended to reduce energy consumption or improve energy performance.
-
C.
weight
Indicates a relationship where a numerical value quantifies how heavy an entity is, often used to measure or compare mass or load.
-
D.
featuresIn
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
-
E.
usesWeightFreezing
Indicates that an entity applies weight freezing, keeping certain model parameters fixed and untrainable during learning or optimization.
- 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6666ff4008190ae1eec907c89bd3b |
completed | April 20, 2026, 5:46 p.m. |
| PD | Predicate disambiguation | batch_69e54cf788188190a46cc49c9ce7617f |
completed | April 19, 2026, 9:45 p.m. |
| PDg | Predicate description generation | batch_69e54fc2bc3c819088c33cd263303433 |
completed | April 19, 2026, 9:57 p.m. |
Created at: April 11, 2026, 11:26 p.m.