Triple
T19098480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matthieu Chedid |
E467467
|
entity |
| Predicate | hasStagePersona |
P61233
|
FINISHED |
| Object | -M- |
—
|
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: -M- | Statement: [Matthieu Chedid, hasStagePersona, -M-]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: -M- Context triple: [Matthieu Chedid, hasStagePersona, -M-]
-
A.
-M-
chosen
-M- is the flamboyant musical alter ego of French singer-songwriter and guitarist Matthieu Chedid, known for his eclectic pop-rock style and inventive live performances.
-
B.
M
M is a functional data mashup and query language used in Microsoft Power BI and related tools for data transformation and preparation.
-
C.
M
M is an experimental musical composition by avant-garde American composer John Cage, reflecting his innovative approaches to sound and structure.
-
D.
M
M is a landmark 1931 German thriller film by Fritz Lang, renowned as an early and influential work in the serial killer and crime genre.
-
E.
M
M is a light rail line in San Francisco’s Muni Metro system that runs between the Embarcadero and the southwestern neighborhoods of the city.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd05ac4c8190b1967d8f97f3fb2f |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e36b279c819091a8d51f044bc644 |
completed | April 20, 2026, 8:27 a.m. |
Created at: April 10, 2026, 12:04 p.m.