Triple
T11713474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princesse Tam-Tam |
E278429
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Andrée Danis
Andrée Danis was a film editor known for her work on French cinema, including the 1935 film "Princesse Tam-Tam."
|
E942074
|
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: Andrée Danis | Statement: [Princesse Tam-Tam, editedBy, Andrée Danis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andrée Danis Context triple: [Princesse Tam-Tam, editedBy, Andrée Danis]
-
A.
Regine Crespin
Régine Crespin was a renowned French soprano and later mezzo-soprano celebrated for her luminous tone and interpretations of French opera and song.
-
B.
Odile Decq
Odile Decq is a renowned French architect and urban planner known for her bold, avant-garde designs and influential role in contemporary architecture.
-
C.
Dorothée Blanck
Dorothée Blanck was a French actress best known for her role in Agnès Varda’s influential New Wave film "Cléo from 5 to 7."
-
D.
Jeannine Roussel
Jeannine Roussel is a film producer best known for her work on Disney’s animated sequel "The Lion King II: Simba’s Pride."
-
E.
Pascale Helleboid
Pascale Helleboid is a French local politician serving as the mayor of the suburban Paris commune of Villeneuve-la-Garenne.
- 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: Andrée Danis Triple: [Princesse Tam-Tam, editedBy, Andrée Danis]
Generated description
Andrée Danis was a film editor known for her work on French cinema, including the 1935 film "Princesse Tam-Tam."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Andrée Danis Target entity description: Andrée Danis was a film editor known for her work on French cinema, including the 1935 film "Princesse Tam-Tam."
-
A.
Regine Crespin
Régine Crespin was a renowned French soprano and later mezzo-soprano celebrated for her luminous tone and interpretations of French opera and song.
-
B.
Odile Decq
Odile Decq is a renowned French architect and urban planner known for her bold, avant-garde designs and influential role in contemporary architecture.
-
C.
Dorothée Blanck
Dorothée Blanck was a French actress best known for her role in Agnès Varda’s influential New Wave film "Cléo from 5 to 7."
-
D.
Jeannine Roussel
Jeannine Roussel is a film producer best known for her work on Disney’s animated sequel "The Lion King II: Simba’s Pride."
-
E.
Pascale Helleboid
Pascale Helleboid is a French local politician serving as the mayor of the suburban Paris commune of Villeneuve-la-Garenne.
- 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_69d6aaff2ce88190b4a1e4b341ad5377 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4be10088190854699385d1f6a95 |
completed | April 10, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef838562d08190b9a764e88c50d423 |
completed | April 27, 2026, 3:40 p.m. |
| NEDg | Description generation | batch_69ef9b68309081909f3f614efeeb2ab1 |
completed | April 27, 2026, 5:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69efd6aba82c81909ff22e6b26db3cfe |
completed | April 27, 2026, 9:35 p.m. |
Created at: April 8, 2026, 9:40 p.m.