Triple
T19072972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Box |
E466835
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Michel Sommo
Michel Sommo is a character from the film "Black Box," involved in the story’s central mystery and dramatic tension.
|
E1356681
|
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: Michel Sommo | Statement: [Black Box, hasCharacter, Michel Sommo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michel Sommo Context triple: [Black Box, hasCharacter, Michel Sommo]
-
A.
Michel Tognini
Michel Tognini is a French Air Force test pilot and former CNES/ESA astronaut who flew on two Space Shuttle missions in the 1990s.
-
B.
Michel Vallogia
Michel Vallogia is an archaeologist known for leading excavations at the Pyramid of Djedefre in Egypt.
-
C.
Gérard de Battista
Gérard de Battista is a French cinematographer known for his work on numerous European films, including the acclaimed drama "Monsieur Ibrahim."
-
D.
Michel Bizot
Michel Bizot is a Paris Métro station in the 12th arrondissement, named after the 19th-century French general Michel Brice Bizot.
-
E.
Michel Poletti
Michel Poletti is a French trail-running organizer best known for co-founding and directing the Ultra-Trail du Mont-Blanc, one of the world’s most prestigious ultramarathons.
- 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: Michel Sommo Triple: [Black Box, hasCharacter, Michel Sommo]
Generated description
Michel Sommo is a character from the film "Black Box," involved in the story’s central mystery and dramatic tension.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michel Sommo Target entity description: Michel Sommo is a character from the film "Black Box," involved in the story’s central mystery and dramatic tension.
-
A.
Michel Tognini
Michel Tognini is a French Air Force test pilot and former CNES/ESA astronaut who flew on two Space Shuttle missions in the 1990s.
-
B.
Michel Vallogia
Michel Vallogia is an archaeologist known for leading excavations at the Pyramid of Djedefre in Egypt.
-
C.
Gérard de Battista
Gérard de Battista is a French cinematographer known for his work on numerous European films, including the acclaimed drama "Monsieur Ibrahim."
-
D.
Michel Bizot
Michel Bizot is a Paris Métro station in the 12th arrondissement, named after the 19th-century French general Michel Brice Bizot.
-
E.
Michel Poletti
Michel Poletti is a French trail-running organizer best known for co-founding and directing the Ultra-Trail du Mont-Blanc, one of the world’s most prestigious ultramarathons.
- 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_69d8dd04f4488190b1121cc53ef2bfd6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e2e1ce5881908367424c89d73feb |
completed | April 20, 2026, 8:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a05d35e763c8190b516b9d2bb507524 |
completed | May 14, 2026, 1:51 p.m. |
| NEDg | Description generation | batch_6a05d47474f481909e630db929ce6287 |
completed | May 14, 2026, 1:56 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05d51338a08190b16ac354d2ef1dba |
completed | May 14, 2026, 1:58 p.m. |
Created at: April 10, 2026, 12:04 p.m.