Triple
T21727874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louis Guglielmi |
E536320
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Louis Guglielmi |
—
|
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: Louis Guglielmi | Statement: [Louis Guglielmi, name, Louis Guglielmi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Louis Guglielmi Context triple: [Louis Guglielmi, name, Louis Guglielmi]
-
A.
Louis Guglielmi
chosen
Louis Guglielmi, better known by his pseudonym Louiguy, was a French composer famed for writing popular songs such as "La Vie en rose."
-
B.
René Donnio
René Donnio was an actor known for appearing in early 20th-century French cinema, including the 1935 film "Princesse Tam-Tam."
-
C.
Jacques Cruppi
Jacques Cruppi was a French lawyer, politician, and art patron active in the late 19th and early 20th centuries.
-
D.
Raymond Pellegrin
Raymond Pellegrin was a French film and television actor known for his prolific career in European cinema from the 1940s through the late 20th century.
-
E.
Louis Cioffi
Louis Cioffi is a film editor known for his work on feature films, including the crime drama "Wonderland" (2003).
- 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_69e0c46d3284819099a4f9d5a704eb95 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69effd03c6ec8190a2f0445c1f3a45b4 |
completed | April 28, 2026, 12:19 a.m. |
Created at: April 16, 2026, 6:48 p.m.