Triple
T22704558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arthur Sadoun |
E561413
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Anne-Sophie Lapix
Anne-Sophie Lapix is a French journalist and television presenter best known for anchoring major news programs on France 2.
|
E1550873
|
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-Sophie Lapix | Statement: [Arthur Sadoun, spouse, Anne-Sophie Lapix]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anne-Sophie Lapix Context triple: [Arthur Sadoun, spouse, Anne-Sophie Lapix]
-
A.
Karin Viard
Karin Viard is an acclaimed French actress known for her versatile performances in both dramatic and comedic roles in contemporary French cinema.
-
B.
Viviane Le Dissez
Viviane Le Dissez is a French politician known for her role in establishing the centrist political party Union des Démocrates et Indépendants (UDI).
-
C.
Nicole Saunier
Nicole Saunier is a film editor known for her work on the French comedy film "La Totale!".
-
D.
Nathalie Cresson
Nathalie Cresson is the daughter of Édith Cresson, the former Prime Minister of France.
-
E.
Marianne Bertrand
Marianne Bertrand is a prominent economist known for her influential research on labor economics, corporate governance, and behavioral economics, particularly in the areas of discrimination and inequality.
- 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-Sophie Lapix Triple: [Arthur Sadoun, spouse, Anne-Sophie Lapix]
Generated description
Anne-Sophie Lapix is a French journalist and television presenter best known for anchoring major news programs on France 2.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anne-Sophie Lapix Target entity description: Anne-Sophie Lapix is a French journalist and television presenter best known for anchoring major news programs on France 2.
-
A.
Karin Viard
Karin Viard is an acclaimed French actress known for her versatile performances in both dramatic and comedic roles in contemporary French cinema.
-
B.
Viviane Le Dissez
Viviane Le Dissez is a French politician known for her role in establishing the centrist political party Union des Démocrates et Indépendants (UDI).
-
C.
Nicole Saunier
Nicole Saunier is a film editor known for her work on the French comedy film "La Totale!".
-
D.
Nathalie Cresson
Nathalie Cresson is the daughter of Édith Cresson, the former Prime Minister of France.
-
E.
Marianne Bertrand
Marianne Bertrand is a prominent economist known for her influential research on labor economics, corporate governance, and behavioral economics, particularly in the areas of discrimination and inequality.
- 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_69e2454e615481909c177440be559d2c |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f178cdc93481908f85d04560f8c285 |
completed | April 29, 2026, 3:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b7ed5d848819095bd8c0f1954ff2d |
completed | May 18, 2026, 9:04 p.m. |
| NEDg | Description generation | batch_6a0b80dc01a0819087337e44a1cf437e |
completed | May 18, 2026, 9:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b816a956c8190895d87f74c4785f8 |
completed | May 18, 2026, 9:15 p.m. |
Created at: April 17, 2026, 3:16 p.m.