Triple
T18685357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Dancer |
E456844
|
entity |
| Predicate | makeupArtist |
P53848
|
FINISHED |
| Object |
Yolande Decarsin
Yolande Decarsin is a makeup artist known for her work on the film "The Dancer."
|
E1356790
|
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: Yolande Decarsin | Statement: [The Dancer, makeupArtist, Yolande Decarsin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yolande Decarsin Context triple: [The Dancer, makeupArtist, Yolande Decarsin]
-
A.
Marceline Loridan-Ivens
Marceline Loridan-Ivens was a French filmmaker, writer, and Holocaust survivor known for her documentary work and autobiographical reflections on memory and exile.
-
B.
Delphine Delaporte
Delphine Delaporte is known as the spouse of French business executive Thierry Delaporte, the CEO of Wipro.
-
C.
Anne Consigny
Anne Consigny is a French actress known for her acclaimed film and television roles, including César-nominated performances.
-
D.
Yolande Moreau
Yolande Moreau is a Belgian actress, comedian, and filmmaker known for her acclaimed performances in French-language cinema and her distinctive blend of humor and poignancy.
-
E.
Michèle Girardon
Michèle Girardon was a French actress known for her roles in European cinema of the 1950s and 1960s.
- 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: Yolande Decarsin Triple: [The Dancer, makeupArtist, Yolande Decarsin]
Generated description
Yolande Decarsin is a makeup artist known for her work on the film "The Dancer."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yolande Decarsin Target entity description: Yolande Decarsin is a makeup artist known for her work on the film "The Dancer."
-
A.
Marceline Loridan-Ivens
Marceline Loridan-Ivens was a French filmmaker, writer, and Holocaust survivor known for her documentary work and autobiographical reflections on memory and exile.
-
B.
Delphine Delaporte
Delphine Delaporte is known as the spouse of French business executive Thierry Delaporte, the CEO of Wipro.
-
C.
Anne Consigny
Anne Consigny is a French actress known for her acclaimed film and television roles, including César-nominated performances.
-
D.
Yolande Moreau
Yolande Moreau is a Belgian actress, comedian, and filmmaker known for her acclaimed performances in French-language cinema and her distinctive blend of humor and poignancy.
-
E.
Michèle Girardon
Michèle Girardon was a French actress known for her roles in European cinema of the 1950s and 1960s.
- 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_69d8d391eb488190ac2e9abf5bf255e4 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e55b2c58188190b906c9ab080a76ff |
completed | April 19, 2026, 10:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a05d341d3c0819083bcdf884aa63953 |
completed | May 14, 2026, 1:50 p.m. |
| NEDg | Description generation | batch_6a05d3fe45c881908622e745b66abfd2 |
completed | May 14, 2026, 1:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05d4b2292c8190bd4cd73969c610d8 |
completed | May 14, 2026, 1:57 p.m. |
Created at: April 10, 2026, 11:49 a.m.