Triple
T14455737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delicacy |
E358454
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Mélanie Bernier
Mélanie Bernier is a French actress known for her roles in film and television, including the romantic comedy "Delicacy."
|
E1100715
|
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: Mélanie Bernier | Statement: [Delicacy, castMember, Mélanie Bernier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mélanie Bernier Context triple: [Delicacy, castMember, Mélanie Bernier]
-
A.
Marcheline Bertrand
Marcheline Bertrand was an American actress and humanitarian best known as the mother of Angelina Jolie and for her advocacy work, particularly in cancer awareness.
-
B.
Aurélie Vachon
Aurélie Vachon is a person notable enough to be recognized as a significant bearer of the surname Vachon.
-
C.
Emma Tremblay
Emma Tremblay is a Canadian child actress known for roles in films and television series such as Elysium and The Giver.
-
D.
Nathalie Perron
Nathalie Perron is a person notable enough to be specifically cited as a bearer of the surname Perron, though detailed public information about her is limited.
-
E.
Sandrine Doucet
Sandrine Doucet is a French politician known for her involvement in centrist and liberal political movements.
- 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: Mélanie Bernier Triple: [Delicacy, castMember, Mélanie Bernier]
Generated description
Mélanie Bernier is a French actress known for her roles in film and television, including the romantic comedy "Delicacy."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mélanie Bernier Target entity description: Mélanie Bernier is a French actress known for her roles in film and television, including the romantic comedy "Delicacy."
-
A.
Marcheline Bertrand
Marcheline Bertrand was an American actress and humanitarian best known as the mother of Angelina Jolie and for her advocacy work, particularly in cancer awareness.
-
B.
Aurélie Vachon
Aurélie Vachon is a person notable enough to be recognized as a significant bearer of the surname Vachon.
-
C.
Emma Tremblay
Emma Tremblay is a Canadian child actress known for roles in films and television series such as Elysium and The Giver.
-
D.
Nathalie Perron
Nathalie Perron is a person notable enough to be specifically cited as a bearer of the surname Perron, though detailed public information about her is limited.
-
E.
Sandrine Doucet
Sandrine Doucet is a French politician known for her involvement in centrist and liberal political movements.
- 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_69d82794dfa081909b9134ad2e32244b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91a9c0d48190ae015e5e0db806ca |
completed | April 14, 2026, 7:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd649177108190be32af72dcae04ee |
completed | May 8, 2026, 4:20 a.m. |
| NEDg | Description generation | batch_69fd664347c48190a411141398794b88 |
completed | May 8, 2026, 4:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd66e5d6b08190ad94a6a5f1809c2a |
completed | May 8, 2026, 4:30 a.m. |
Created at: April 10, 2026, 1:19 a.m.