Triple
T6640858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mylène Farmer |
E150582
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mylène Farmer |
E150582
|
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: Mylène Farmer | Statement: [Mylène Farmer, name, Mylène Farmer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mylène Farmer Context triple: [Mylène Farmer, name, Mylène Farmer]
-
A.
Mylène Farmer
chosen
Mylène Farmer is a French singer-songwriter and producer renowned for her melancholic pop music, poetic lyrics, and visually striking, often controversial music videos.
-
B.
Mireille Mathieu
Mireille Mathieu is a French chanteuse renowned for her powerful voice, classic chanson repertoire, and international success since the 1960s.
-
C.
Nelly Roussel
Nelly Roussel was a pioneering French feminist, neo-Malthusian activist, and orator known for her advocacy of birth control, women’s rights, and social reform in the early 20th century.
-
D.
Alizée
Alizée is a French pop singer and dancer who rose to international fame in the early 2000s with her hit single "Moi... Lolita."
-
E.
Nancy Marchand
Nancy Marchand was an acclaimed American actress best known for her roles on the television series "Lou Grant" and "The Sopranos."
- 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_69c687f1a3048190828b7342f7125d5c |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6aff42c748190b818cf55f83647cb |
completed | March 27, 2026, 4:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6eeed01608190bbc6461ac8c3cadb |
completed | March 27, 2026, 8:56 p.m. |
Created at: March 27, 2026, 2 p.m.