Triple
T13716537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Innocent Lies |
E328913
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Jean Rochefort |
E546003
|
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: Jean Rochefort | Statement: [Innocent Lies, hasCastMember, Jean Rochefort]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean Rochefort Context triple: [Innocent Lies, hasCastMember, Jean Rochefort]
-
A.
Jean Rochefort
chosen
Jean Rochefort was a distinguished French actor renowned for his wry charm and performances in both comedic and dramatic films from the 1960s onward.
-
B.
René Azaire
René Azaire is a character in Sebastian Faulks's novel "Birdsong," depicted as the wealthy, authoritarian husband of Isabelle Azaire in pre–World War I France.
-
C.
Belmondo
Belmondo is an Italian surname notably borne by Olympic champion cross-country skier Stefania Belmondo.
-
D.
Michel Bouquet
Michel Bouquet was a renowned French actor celebrated for his nuanced performances in theatre and film over a career spanning more than six decades.
-
E.
Georges Benoît
Georges Benoît was a French cinematographer active in early 20th-century cinema, known for his work on both European and American films.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4398f0448190810c840a82228706 |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d5878948190a2aaab2ba31bd1ed |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:54 p.m.