Triple
T24168370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maman a tort |
E599056
|
entity |
| Predicate | launchOfCareerFor |
P90207
|
FINISHED |
| Object | Mylène Farmer |
—
|
NE NERFINISHED |
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: [Maman a tort, launchOfCareerFor, Mylène Farmer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: launchOfCareerFor Context triple: [Maman a tort, launchOfCareerFor, Mylène Farmer]
-
A.
launchedCareerOf
Indicates that one entity’s actions, support, or involvement initiated or significantly advanced another entity’s professional career.
-
B.
helpsLaunchCareerOf
chosen
Indicates that one entity plays a significant role in starting, advancing, or establishing the professional career of another entity.
-
C.
careerStart
Indicates the point in time when an entity begins its professional career or main occupational activity.
-
D.
startedCareerWith
Indicates that an entity began its professional career associated with, employed by, or active for another specified entity.
-
E.
careerStartAs
Indicates the role, position, or occupation in which an individual first began their professional career.
- F. None of above.
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_69e288cbd62881909de32ca64a70c17b |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f27c9ddfcc819096697a844b300cce |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c42f942c8190b103ff29a60fef34 |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 17, 2026, 11:33 p.m.