Triple
T577926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Da Vinci Code |
E13796
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Jean Reno
Jean Reno is a French actor known for his roles in films such as Léon: The Professional, The Big Blue, and Mission: Impossible.
|
E72335
|
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: Jean Reno | Statement: [The Da Vinci Code, starring, Jean Reno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean Reno Context triple: [The Da Vinci Code, starring, Jean Reno]
-
A.
Conrad Alexandre Gérard
Conrad Alexandre Gérard was a French diplomat who served as the first French minister to the United States during the American Revolutionary era.
-
B.
George Norton
George Norton was a British colonial-era lawyer and educator best known for establishing Presidency College in Madras, one of India’s earliest and most prestigious institutions of higher learning.
-
C.
Jean-Pierre
Jean-Pierre is a French given name commonly used as a masculine compound first name.
-
D.
Michael Caine
Michael Caine is an acclaimed English actor known for his distinctive voice and versatile performances across decades of film, including frequent roles in Christopher Nolan’s movies.
-
E.
Peter Facinelli
Peter Facinelli is an American actor best known for playing Dr. Carlisle Cullen in the Twilight film series.
- 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: Jean Reno Triple: [The Da Vinci Code, starring, Jean Reno]
Generated description
Jean Reno is a French actor known for his roles in films such as Léon: The Professional, The Big Blue, and Mission: Impossible.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jean Reno Target entity description: Jean Reno is a French actor known for his roles in films such as Léon: The Professional, The Big Blue, and Mission: Impossible.
-
A.
Conrad Alexandre Gérard
Conrad Alexandre Gérard was a French diplomat who served as the first French minister to the United States during the American Revolutionary era.
-
B.
George Norton
George Norton was a British colonial-era lawyer and educator best known for establishing Presidency College in Madras, one of India’s earliest and most prestigious institutions of higher learning.
-
C.
Jean-Pierre
Jean-Pierre is a French given name commonly used as a masculine compound first name.
-
D.
Michael Caine
Michael Caine is an acclaimed English actor known for his distinctive voice and versatile performances across decades of film, including frequent roles in Christopher Nolan’s movies.
-
E.
Peter Facinelli
Peter Facinelli is an American actor best known for playing Dr. Carlisle Cullen in the Twilight film series.
- 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_69a4933fa4d88190a7949cc83c08c5c1 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b69fed88190b5558d4ebd5047a1 |
completed | March 1, 2026, 8:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a501bfb6408190bf7e1f462f39723d |
completed | March 2, 2026, 3:19 a.m. |
| NEDg | Description generation | batch_69a5026c35408190ab22dab86c673e0f |
completed | March 2, 2026, 3:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a5063ad6b481909537a97e6a81eaa4 |
completed | March 2, 2026, 3:38 a.m. |
Created at: March 1, 2026, 7:33 p.m.