Triple
T13716519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Innocent Lies |
E328913
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Sophie Aubry
Sophie Aubry is an actress known for her role in the 1995 British mystery drama film "Innocent Lies."
|
E1058231
|
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: Sophie Aubry | Statement: [Innocent Lies, hasCastMember, Sophie Aubry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sophie Aubry Context triple: [Innocent Lies, hasCastMember, Sophie Aubry]
-
A.
Sophie Labbé
Sophie Labbé is a renowned French perfumer known for creating numerous successful fragrances for major international luxury brands.
-
B.
Sophie Vavasseur
Sophie Vavasseur is an Irish actress best known for her role in the film "Evelyn" and appearances in various horror and drama productions.
-
C.
Tiphaine Auzière
Tiphaine Auzière is a French lawyer and political figure known both for her legal career and as the daughter of France’s First Lady, Brigitte Macron.
-
D.
Sophie Meunier
Sophie Meunier is a scholar known for her work on international political economy, particularly French and European Union trade policy and globalization.
-
E.
Annick Castiaux
Annick Castiaux is a Belgian academic and university leader who serves as rector of the University of Namur.
- 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: Sophie Aubry Triple: [Innocent Lies, hasCastMember, Sophie Aubry]
Generated description
Sophie Aubry is an actress known for her role in the 1995 British mystery drama film "Innocent Lies."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sophie Aubry Target entity description: Sophie Aubry is an actress known for her role in the 1995 British mystery drama film "Innocent Lies."
-
A.
Sophie Labbé
Sophie Labbé is a renowned French perfumer known for creating numerous successful fragrances for major international luxury brands.
-
B.
Sophie Vavasseur
Sophie Vavasseur is an Irish actress best known for her role in the film "Evelyn" and appearances in various horror and drama productions.
-
C.
Tiphaine Auzière
Tiphaine Auzière is a French lawyer and political figure known both for her legal career and as the daughter of France’s First Lady, Brigitte Macron.
-
D.
Sophie Meunier
Sophie Meunier is a scholar known for her work on international political economy, particularly French and European Union trade policy and globalization.
-
E.
Annick Castiaux
Annick Castiaux is a Belgian academic and university leader who serves as rector of the University of Namur.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4398f0448190810c840a82228706 |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d5878948190a2aaab2ba31bd1ed |
completed | May 3, 2026, 7:09 p.m. |
| NEDg | Description generation | batch_69f79e9e6ff88190b031fb1403cacabc |
completed | May 3, 2026, 7:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7a2d6e7ec81908a4cbc324e793c24 |
completed | May 3, 2026, 7:32 p.m. |
Created at: April 9, 2026, 9:54 p.m.