Triple
T23016488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lancia Fulvia Sport (Zagato) |
E573042
|
entity |
| Predicate | aerodynamicDesign |
P19885
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Lancia Fulvia Sport (Zagato), aerodynamicDesign, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aerodynamicDesign Context triple: [Lancia Fulvia Sport (Zagato), aerodynamicDesign, yes]
-
A.
aerodynamicsFeature
chosen
Indicates that one entity possesses or is characterized by a specific aerodynamic property, component, or design feature affecting airflow and motion through air.
-
B.
aerodynamicsDeveloper
Indicates that an entity (typically a person or organization) is responsible for designing, developing, or improving the aerodynamic properties of another entity.
-
C.
aerodynamicGoal
Indicates a goal or objective specifically related to aerodynamic performance, behavior, or properties in a given context.
-
D.
aircraftDesignFeature
Indicates a relationship where a specific design feature is associated with, or incorporated into, an aircraft.
-
E.
aerodynamicLoad
Indicates the force or stress exerted on an object due to its interaction with surrounding airflow.
- 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_69e245b764cc8190a51be76f1d9611e1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f183e59a1c8190b8048a399a4727cb |
completed | April 29, 2026, 4:07 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9cd5488190bcd23183179f48cd |
completed | April 27, 2026, 10:34 a.m. |
Created at: April 17, 2026, 3:52 p.m.