Triple
T8486010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trance |
E200833
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Franck |
E172426
|
NE FINISHED |
How this triple was built (2 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: Franck | Statement: [Trance, mainCharacter, Franck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Franck Context triple: [Trance, mainCharacter, Franck]
-
A.
Franck
chosen
Franck is a surname most notably associated with James Franck, the German physicist and Nobel laureate recognized for the Franck–Hertz experiment.
-
B.
Marcel Fournier
Marcel Fournier was a French businessman best known as a co-founder of the multinational retail corporation Carrefour, a pioneer of the modern hypermarket concept.
-
C.
Royer
Royer was a costume designer known for his work on classic Hollywood films, including the 1939 drama "The Rains Came."
-
D.
Guillaumin
Guillaumin is the surname of Armand Guillaumin, a French Impressionist painter known for his vibrant landscapes and use of color.
-
E.
Fernand
Fernand is a given name, primarily used in French and other Romance-language contexts, that corresponds to the name Ferdinand.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca831d7b148190a6e32c1de43ab13b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe53b359c81908174addcd6e12785 |
completed | March 31, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3a45e30c8190838ac499bbc66fbd |
completed | April 2, 2026, 9:43 a.m. |
Created at: March 30, 2026, 6:12 p.m.