Triple
T16875989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Les Noces rouges |
E421299
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Eliana De Santis
Eliana De Santis is an actress known for her role in the French television film "Les Noces rouges."
|
E1244203
|
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: Eliana De Santis | Statement: [Les Noces rouges, hasCastMember, Eliana De Santis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eliana De Santis Context triple: [Les Noces rouges, hasCastMember, Eliana De Santis]
-
A.
Tharita Cesaroni
Tharita Cesaroni is an Italian film producer and cinematographer known for her work behind the camera and for being married to actor Dermot Mulroney.
-
B.
Eliana Miglio
Eliana Miglio is an Italian actress known for her work in film and television.
-
C.
Alixandra Fazzina
Alixandra Fazzina is a British photojournalist renowned for her powerful documentation of refugees, conflict, and humanitarian crises around the world.
-
D.
Elena Conti
Elena Conti is an Italian structural biologist renowned for her pioneering work on RNA metabolism and macromolecular complexes, recognized by major scientific honors in molecular biology.
-
E.
Gabriella Cristiani
Gabriella Cristiani is an Italian film editor best known for her Academy Award–winning work on Bernardo Bertolucci’s epic film "The Last Emperor."
- 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: Eliana De Santis Triple: [Les Noces rouges, hasCastMember, Eliana De Santis]
Generated description
Eliana De Santis is an actress known for her role in the French television film "Les Noces rouges."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Eliana De Santis Target entity description: Eliana De Santis is an actress known for her role in the French television film "Les Noces rouges."
-
A.
Tharita Cesaroni
Tharita Cesaroni is an Italian film producer and cinematographer known for her work behind the camera and for being married to actor Dermot Mulroney.
-
B.
Eliana Miglio
Eliana Miglio is an Italian actress known for her work in film and television.
-
C.
Alixandra Fazzina
Alixandra Fazzina is a British photojournalist renowned for her powerful documentation of refugees, conflict, and humanitarian crises around the world.
-
D.
Elena Conti
Elena Conti is an Italian structural biologist renowned for her pioneering work on RNA metabolism and macromolecular complexes, recognized by major scientific honors in molecular biology.
-
E.
Gabriella Cristiani
Gabriella Cristiani is an Italian film editor best known for her Academy Award–winning work on Bernardo Bertolucci’s epic film "The Last Emperor."
- 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_69d889d470fc8190b4aec199636c0c56 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3b7f646308190b5e277b5f51cd315 |
completed | April 18, 2026, 4:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00dbfd6898819083871544c119557c |
completed | May 10, 2026, 7:26 p.m. |
| NEDg | Description generation | batch_6a0114d33cac819083d8e542ea5bc274 |
completed | May 10, 2026, 11:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0115c583608190bf07ac205399f253 |
completed | May 10, 2026, 11:33 p.m. |
Created at: April 10, 2026, 5:29 a.m.