Triple
T3129858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coffee and Cigarettes |
E65382
|
entity |
| Predicate | hasVignette |
P45542
|
FINISHED |
| Object | Renée |
E175041
|
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: Renée | Statement: [Coffee and Cigarettes, hasVignette, Renée]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Renée Context triple: [Coffee and Cigarettes, hasVignette, Renée]
-
A.
Renée
chosen
Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
-
B.
Françoise
Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
-
C.
Béatrix
Béatrix is a novel by Honoré de Balzac that forms part of his larger La Comédie humaine cycle, depicting the complexities of love and society in 19th-century France.
-
D.
Odile
Odile is the seductive and deceptive Black Swan character in the ballet "Swan Lake," often portrayed as the antagonist and foil to the virtuous Odette.
-
E.
Jeanne
Jeanne was a common French female given name historically borne by notable figures such as queens, saints, and writers.
- 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_69ad8580c72481909672d37acf647893 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada549aaa881908dcf92d20fa6f238 |
completed | March 8, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b261f21620819088b6e7f782efe0b8 |
completed | March 12, 2026, 6:49 a.m. |
Created at: March 8, 2026, 3:04 p.m.