Triple
T1385461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monsieur Ibrahim |
E29833
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Bernard Sasia
Bernard Sasia is a French film editor known for his work on numerous acclaimed European films, including "Monsieur Ibrahim."
|
E162597
|
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: Bernard Sasia | Statement: [Monsieur Ibrahim, editedBy, Bernard Sasia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bernard Sasia Context triple: [Monsieur Ibrahim, editedBy, Bernard Sasia]
-
A.
Daniel Tarschys
Daniel Tarschys is a Swedish political scientist and politician who served as Secretary General of the Council of Europe in the 1990s.
-
B.
Marc Barani
Marc Barani is a French architect renowned for his refined, context-sensitive public projects and recipient of France’s top national architecture honors.
-
C.
Bertrand Fagalde
Bertrand Fagalde was a French admiral best known for his leadership of French naval forces during the Battle of Dunkirk in World War II.
-
D.
Bernard Zehrfuss
Bernard Zehrfuss was a prominent 20th-century French architect known for his modernist public and institutional buildings.
-
E.
Paul Varjak
Paul Varjak is a struggling writer and Holly Golightly’s neighbor and love interest in Truman Capote’s novella and the film adaptation "Breakfast at Tiffany’s."
- 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: Bernard Sasia Triple: [Monsieur Ibrahim, editedBy, Bernard Sasia]
Generated description
Bernard Sasia is a French film editor known for his work on numerous acclaimed European films, including "Monsieur Ibrahim."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bernard Sasia Target entity description: Bernard Sasia is a French film editor known for his work on numerous acclaimed European films, including "Monsieur Ibrahim."
-
A.
Daniel Tarschys
Daniel Tarschys is a Swedish political scientist and politician who served as Secretary General of the Council of Europe in the 1990s.
-
B.
Marc Barani
Marc Barani is a French architect renowned for his refined, context-sensitive public projects and recipient of France’s top national architecture honors.
-
C.
Bertrand Fagalde
Bertrand Fagalde was a French admiral best known for his leadership of French naval forces during the Battle of Dunkirk in World War II.
-
D.
Bernard Zehrfuss
Bernard Zehrfuss was a prominent 20th-century French architect known for his modernist public and institutional buildings.
-
E.
Paul Varjak
Paul Varjak is a struggling writer and Holly Golightly’s neighbor and love interest in Truman Capote’s novella and the film adaptation "Breakfast at Tiffany’s."
- 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_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c339f3d481909c04b14129899945 |
completed | March 1, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ace56bf6c48190839a9d01c935e4bf |
completed | March 8, 2026, 2:56 a.m. |
| NEDg | Description generation | batch_69ace5fb515081908acadef0303b8f6e |
completed | March 8, 2026, 2:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ace9ba4b3481908d03636d8f72d04c |
completed | March 8, 2026, 3:15 a.m. |
Created at: March 1, 2026, 7:59 p.m.