Triple
T11759346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Union des Démocrates et Indépendants |
E279610
|
entity |
| Predicate | founder |
P104
|
FINISHED |
| Object |
Michèle Delaunay
Michèle Delaunay is a French politician and former government minister known for her work in public health and social policy.
|
E979999
|
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: Michèle Delaunay | Statement: [Union des Démocrates et Indépendants, founder, Michèle Delaunay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michèle Delaunay Context triple: [Union des Démocrates et Indépendants, founder, Michèle Delaunay]
-
A.
Delphine Delaporte
Delphine Delaporte is known as the spouse of French business executive Thierry Delaporte, the CEO of Wipro.
-
B.
Françoise Noguès
Françoise Noguès is a French physician best known as the mother of Brigitte Macron, the First Lady of France.
-
C.
Sylviane Alaux
Sylviane Alaux is a French politician known for her involvement in centrist and independent political movements.
-
D.
Michèle Méritz
Michèle Méritz was a French actress known for her role in Claude Chabrol’s influential New Wave film "Le Beau Serge."
-
E.
Françoise Schein
Françoise Schein is a Belgian-born artist and architect known for large-scale public artworks, often in metro stations, that integrate human rights themes and typographic design.
- 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: Michèle Delaunay Triple: [Union des Démocrates et Indépendants, founder, Michèle Delaunay]
Generated description
Michèle Delaunay is a French politician and former government minister known for her work in public health and social policy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michèle Delaunay Target entity description: Michèle Delaunay is a French politician and former government minister known for her work in public health and social policy.
-
A.
Delphine Delaporte
Delphine Delaporte is known as the spouse of French business executive Thierry Delaporte, the CEO of Wipro.
-
B.
Françoise Noguès
Françoise Noguès is a French physician best known as the mother of Brigitte Macron, the First Lady of France.
-
C.
Sylviane Alaux
Sylviane Alaux is a French politician known for her involvement in centrist and independent political movements.
-
D.
Michèle Méritz
Michèle Méritz was a French actress known for her role in Claude Chabrol’s influential New Wave film "Le Beau Serge."
-
E.
Françoise Schein
Françoise Schein is a Belgian-born artist and architect known for large-scale public artworks, often in metro stations, that integrate human rights themes and typographic design.
- 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_69d6ab01038c819080714901502c84fc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a5220f148190ae60d1941a579ab6 |
completed | April 10, 2026, 7:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6344d86648190ad270517b36c8815 |
completed | May 2, 2026, 5:28 p.m. |
| NEDg | Description generation | batch_69f6356b545c819089a5f5b901afc5f2 |
completed | May 2, 2026, 5:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f636382ffc8190becfae41757a45d8 |
completed | May 2, 2026, 5:36 p.m. |
Created at: April 8, 2026, 9:41 p.m.