Triple
T12620600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank |
E301367
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Franck |
E172426
|
NE 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: Franck | Statement: [Frank, hasVariant, Franck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Franck Context triple: [Frank, hasVariant, Franck]
-
A.
Franck
chosen
Franck is a surname most notably associated with James Franck, the German physicist and Nobel laureate recognized for the Franck–Hertz experiment.
-
B.
Guy-Blaché
Guy-Blaché is the surname of pioneering French filmmaker Alice Guy-Blaché, one of the first female directors and early innovators in narrative cinema.
-
C.
Marcel Fournier
Marcel Fournier was a French businessman best known as a co-founder of the multinational retail corporation Carrefour, a pioneer of the modern hypermarket concept.
-
D.
Royer
Royer was a costume designer known for his work on classic Hollywood films, including the 1939 drama "The Rains Came."
-
E.
Franck Leroy
Franck Leroy is a French politician known for serving as the mayor of Épernay, a commune in the Marne department of northeastern France.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d7bdeaf49c8190b13800111fa77ea3 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d960c75c9c819092265ebc2b39f21d |
completed | April 10, 2026, 8:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ed6f79881908872c644a9789f04 |
completed | May 2, 2026, 8:30 p.m. |
Created at: April 9, 2026, 5:13 p.m.