Triple
T12458599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madagascar |
E297725
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Mireille Soria |
E868197
|
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: Mireille Soria | Statement: [Madagascar, producer, Mireille Soria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mireille Soria Context triple: [Madagascar, producer, Mireille Soria]
-
A.
Mireille Soria
chosen
Mireille Soria is a film producer best known for her work on major animated features at DreamWorks Animation.
-
B.
Françoise Rosay
Françoise Rosay was a prominent French stage and film actress known for her powerful character roles in European cinema from the 1920s through the 1950s.
-
C.
Suzanne Jolibois
Suzanne Jolibois was the wife of French phenomenologist and philosopher Maurice Merleau-Ponty.
-
D.
Joëlle Léandre
Joëlle Léandre is a renowned French double bassist, composer, and leading figure in contemporary improvised and experimental music.
-
E.
Denise Darcel
Denise Darcel was a French-born actress and singer best known for her roles in 1950s Hollywood films and her sultry, glamorous screen presence.
- 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_69d6ada270808190b1a2b2e7b02bb426 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94da46a588190bc888fafd6d1eb5d |
completed | April 10, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67188f6b481909885e35dce5a5b69 |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 8, 2026, 9:56 p.m.