Triple
T3467794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MUCEM |
E73178
|
entity |
| Predicate | coArchitect |
P6475
|
FINISHED |
| Object |
Roland Carta
Roland Carta is a French architect known for co-designing Marseille’s Museum of European and Mediterranean Civilisations (MUCEM).
|
E361799
|
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: Roland Carta | Statement: [MUCEM, coArchitect, Roland Carta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roland Carta Context triple: [MUCEM, coArchitect, Roland Carta]
-
A.
Anthony Veiller
Anthony Veiller was an American screenwriter known for his work on notable mid-20th-century films, including several acclaimed Hollywood dramas and thrillers.
-
B.
Raoul La Roche
Raoul La Roche was a Swiss banker and prominent art collector known for his patronage of modern architecture and the arts, including commissioning Le Corbusier’s Villa La Roche in Paris.
-
C.
Thierry Cruanes
Thierry Cruanes is a computer scientist and entrepreneur best known as a co-founder of the cloud data warehousing company Snowflake.
-
D.
Patrick de Carolis
Patrick de Carolis is a French journalist and television executive who became the mayor of Arles.
-
E.
Thierry Burkhard
Thierry Burkhard is a French Army general who has served as France’s top military officer and a key figure in shaping the country’s contemporary defense policy and armed forces.
- 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: Roland Carta Triple: [MUCEM, coArchitect, Roland Carta]
Generated description
Roland Carta is a French architect known for co-designing Marseille’s Museum of European and Mediterranean Civilisations (MUCEM).
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Roland Carta Target entity description: Roland Carta is a French architect known for co-designing Marseille’s Museum of European and Mediterranean Civilisations (MUCEM).
-
A.
Anthony Veiller
Anthony Veiller was an American screenwriter known for his work on notable mid-20th-century films, including several acclaimed Hollywood dramas and thrillers.
-
B.
Raoul La Roche
Raoul La Roche was a Swiss banker and prominent art collector known for his patronage of modern architecture and the arts, including commissioning Le Corbusier’s Villa La Roche in Paris.
-
C.
Thierry Cruanes
Thierry Cruanes is a computer scientist and entrepreneur best known as a co-founder of the cloud data warehousing company Snowflake.
-
D.
Patrick de Carolis
Patrick de Carolis is a French journalist and television executive who became the mayor of Arles.
-
E.
Thierry Burkhard
Thierry Burkhard is a French Army general who has served as France’s top military officer and a key figure in shaping the country’s contemporary defense policy and armed forces.
- 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_69ad85b224d481908ff8be51338d24ff |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb11ec5881908347bf92883a25ee |
completed | March 8, 2026, 6:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3680763608190acdd146dc7c0b239 |
completed | March 13, 2026, 1:27 a.m. |
| NEDg | Description generation | batch_69b36c4d77448190abe198ec9d48597d |
completed | March 13, 2026, 1:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b36cca06d48190bc72ad2e9bd9bdb5 |
completed | March 13, 2026, 1:47 a.m. |
Created at: March 8, 2026, 3:17 p.m.