Triple
T20101445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anne-Dominique Correa |
E496551
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Anne-Dominique
Anne-Dominique is a person whose given name is Anne-Dominique Correa.
|
E1415457
|
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: Anne-Dominique | Statement: [Anne-Dominique Correa, givenName, Anne-Dominique]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anne-Dominique Context triple: [Anne-Dominique Correa, givenName, Anne-Dominique]
-
A.
Denise Darcel
Denise Darcel was a French-born actress and singer best known for her roles in 1950s Hollywood films and her sultry, glamorous screen presence.
-
B.
Angélique Arnaud
Angélique Arnaud was a 19th-century French feminist writer and activist known for her advocacy of women's rights and social reform.
-
C.
Catherine Delprat
Catherine Delprat is a French local politician who serves as the mayor of the commune of Écouen in northern France.
-
D.
Mireille Soria
Mireille Soria is a film producer best known for her work on major animated features at DreamWorks Animation.
-
E.
Bernadette Lafont
Bernadette Lafont was a pioneering French actress closely associated with the French New Wave, known for her bold, unconventional roles across art-house and popular cinema.
- 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: Anne-Dominique Triple: [Anne-Dominique Correa, givenName, Anne-Dominique]
Generated description
Anne-Dominique is a person whose given name is Anne-Dominique Correa.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anne-Dominique Target entity description: Anne-Dominique is a person whose given name is Anne-Dominique Correa.
-
A.
Denise Darcel
Denise Darcel was a French-born actress and singer best known for her roles in 1950s Hollywood films and her sultry, glamorous screen presence.
-
B.
Angélique Arnaud
Angélique Arnaud was a 19th-century French feminist writer and activist known for her advocacy of women's rights and social reform.
-
C.
Catherine Delprat
Catherine Delprat is a French local politician who serves as the mayor of the commune of Écouen in northern France.
-
D.
Mireille Soria
Mireille Soria is a film producer best known for her work on major animated features at DreamWorks Animation.
-
E.
Bernadette Lafont
Bernadette Lafont was a pioneering French actress closely associated with the French New Wave, known for her bold, unconventional roles across art-house and popular cinema.
- 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6666ff4008190ae1eec907c89bd3b |
completed | April 20, 2026, 5:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08346180dc8190b75740c56016ec12 |
completed | May 16, 2026, 9:09 a.m. |
| NEDg | Description generation | batch_6a0838867cb081908580ab9594653018 |
completed | May 16, 2026, 9:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a083965207c8190babe799e5d45f913 |
completed | May 16, 2026, 9:31 a.m. |
Created at: April 11, 2026, 11:27 p.m.