Triple
T19383362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Fang (2018 film) |
E484867
|
entity |
| Predicate | voiceCastMember |
P9616
|
FINISHED |
| Object |
Daniel Nicodème
Daniel Nicodème is a French voice actor known for his dubbing work in animated and live-action films.
|
E1373206
|
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: Daniel Nicodème | Statement: [White Fang (2018 film), voiceCastMember, Daniel Nicodème]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Nicodème Context triple: [White Fang (2018 film), voiceCastMember, Daniel Nicodème]
-
A.
Gabriel Le Moyne
Gabriel Le Moyne was a member of the prominent French-Canadian Le Moyne family, known for producing influential colonial figures in New France.
-
B.
Étienne
Étienne is the given first name of the French Symbolist poet Stéphane Mallarmé.
-
C.
Siméon
Siméon is the given name of Siméon Denis Poisson, the influential French mathematician and physicist known for major contributions to probability theory and mathematical physics.
-
D.
Gabriel Le Duc
Gabriel Le Duc was a 17th-century French architect best known for his work on the Val-de-Grâce church and complex in Paris.
-
E.
Abbé Dubois
Abbé Dubois is a central character in the French historical film "Que la fête commence," depicted as a politically influential cleric navigating the intrigues of early 18th-century France.
- 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: Daniel Nicodème Triple: [White Fang (2018 film), voiceCastMember, Daniel Nicodème]
Generated description
Daniel Nicodème is a French voice actor known for his dubbing work in animated and live-action films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daniel Nicodème Target entity description: Daniel Nicodème is a French voice actor known for his dubbing work in animated and live-action films.
-
A.
Gabriel Le Moyne
Gabriel Le Moyne was a member of the prominent French-Canadian Le Moyne family, known for producing influential colonial figures in New France.
-
B.
Étienne
Étienne is the given first name of the French Symbolist poet Stéphane Mallarmé.
-
C.
Siméon
Siméon is the given name of Siméon Denis Poisson, the influential French mathematician and physicist known for major contributions to probability theory and mathematical physics.
-
D.
Gabriel Le Duc
Gabriel Le Duc was a 17th-century French architect best known for his work on the Val-de-Grâce church and complex in Paris.
-
E.
Abbé Dubois
Abbé Dubois is a central character in the French historical film "Que la fête commence," depicted as a politically influential cleric navigating the intrigues of early 18th-century France.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61a614cf88190b561eafaa350ce19 |
completed | April 20, 2026, 12:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a072b7ec8ac8190b9330f239e44f795 |
completed | May 15, 2026, 2:19 p.m. |
| NEDg | Description generation | batch_6a072de198fc8190838ce30942cc46df |
completed | May 15, 2026, 2:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a072e5e62a88190af09d9911a6f1413 |
completed | May 15, 2026, 2:31 p.m. |
Created at: April 10, 2026, 1:35 p.m.