Triple
T18516455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carlo Maserati |
E452476
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Bianchi |
—
|
NE NERFINISHED |
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: Bianchi | Statement: [Carlo Maserati, employer, Bianchi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bianchi Context triple: [Carlo Maserati, employer, Bianchi]
-
A.
Bianchi
chosen
Bianchi is an Italian surname associated with various notable individuals in fields such as sports, politics, and the arts.
-
B.
Bianchi class A
Bianchi class A is a subset of spatially homogeneous cosmological models characterized by structure constants with vanishing trace, often used in general relativity to study anisotropic universes.
-
C.
Gimondi
Gimondi is an Italian surname most famously associated with Felice Gimondi, one of Italy’s greatest professional road cyclists.
-
D.
Bianchi class B
Bianchi class B is a subset of spatially homogeneous but anisotropic cosmological models in general relativity characterized by specific non-unimodular Lie algebra structures of their isometry groups.
-
E.
Canelli
Canelli is a town in Italy’s Piedmont region renowned for its historic wine production and UNESCO-listed underground wine cellars.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d386df84819092355ebb260d848e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5338a628c81909db08ae7dc94f59a |
completed | April 19, 2026, 7:56 p.m. |
Created at: April 10, 2026, 11:36 a.m.