Triple
T22461319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexandre Giros |
E555234
|
entity |
| Predicate | notableCompany |
P4120
|
FINISHED |
| Object | Vinci |
—
|
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: Vinci | Statement: [Alexandre Giros, notableCompany, Vinci]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vinci Context triple: [Alexandre Giros, notableCompany, Vinci]
-
A.
Vinci
Vinci is a small Tuscan town in Italy best known as the birthplace of Renaissance polymath Leonardo da Vinci.
-
B.
Vinci
chosen
Vinci is a major French concessions and construction company and one of the largest infrastructure and engineering groups in the world.
-
C.
Vinci Da
Vinci Da is a Bengali psychological thriller film directed by Srijit Mukherji, centered on a make-up artist drawn into a series of morally complex crimes.
-
D.
Leonardi
Leonardi is an Italian surname borne by various notable individuals in fields such as film, sports, and the arts.
-
E.
Viollet
Viollet is a surname most notably associated with Dennis Viollet, an English footballer who starred for Manchester United in the 1950s.
- 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_69e11e51fdec8190adfdf9f8a6362221 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b7f74948190beaf6ea24ba29276 |
completed | April 29, 2026, 1:14 a.m. |
Created at: April 16, 2026, 8:48 p.m.