Triple
T13603343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skin (2018 film) |
E324996
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object |
Arnaud Potier
Arnaud Potier is a cinematographer known for his work on the 2018 film "Skin" and other international film projects.
|
E1067415
|
NE FINISHED |
How this triple was built (4 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: Arnaud Potier | Statement: [Skin (2018 film), cinematographyBy, Arnaud Potier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arnaud Potier Context triple: [Skin (2018 film), cinematographyBy, Arnaud Potier]
-
A.
Arnaud Vaillant
Arnaud Vaillant is a French fashion designer and co-founder of the innovative Paris-based label Coperni, known for its tech-inspired, sculptural womenswear.
-
B.
Christophe Pélissier
Christophe Pélissier is a French football coach known for guiding modest clubs to promotion in the French league system.
-
C.
Arnaud Decagny
Arnaud Decagny is a French local politician who serves as the mayor of the northern town of Maubeuge.
-
D.
Matthieu Rougé
Matthieu Rougé is a French Roman Catholic prelate who serves as the bishop of the Diocese of Nanterre.
-
E.
Romain Bouteille
Romain Bouteille was a French actor, playwright, and comedian known for his work in avant-garde theatre and film, and for co-founding the influential Parisian cabaret Café de la Gare.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Arnaud Potier Triple: [Skin (2018 film), cinematographyBy, Arnaud Potier]
Generated description
Arnaud Potier is a cinematographer known for his work on the 2018 film "Skin" and other international film projects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Arnaud Potier Target entity description: Arnaud Potier is a cinematographer known for his work on the 2018 film "Skin" and other international film projects.
-
A.
Arnaud Vaillant
Arnaud Vaillant is a French fashion designer and co-founder of the innovative Paris-based label Coperni, known for its tech-inspired, sculptural womenswear.
-
B.
Christophe Pélissier
Christophe Pélissier is a French football coach known for guiding modest clubs to promotion in the French league system.
-
C.
Arnaud Decagny
Arnaud Decagny is a French local politician who serves as the mayor of the northern town of Maubeuge.
-
D.
Matthieu Rougé
Matthieu Rougé is a French Roman Catholic prelate who serves as the bishop of the Diocese of Nanterre.
-
E.
Romain Bouteille
Romain Bouteille was a French actor, playwright, and comedian known for his work in avant-garde theatre and film, and for co-founding the influential Parisian cabaret Café de la Gare.
- F. None of above. chosen
Provenance (5 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb07ca07481909c45da551ea61ab4 |
completed | April 12, 2026, 2:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c6f931048190ad5182a8c2ebecb6 |
completed | May 3, 2026, 10:06 p.m. |
| NEDg | Description generation | batch_69f7c7957abc8190966528ba353cfbfa |
completed | May 3, 2026, 10:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7c80bc32c8190bd185400bafe00cf |
completed | May 3, 2026, 10:11 p.m. |
Created at: April 9, 2026, 9:49 p.m.