Triple
T19406061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marie-Luce Penchard |
E485463
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Marie-Luce Penchard |
—
|
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: Marie-Luce Penchard | Statement: [Marie-Luce Penchard, name, Marie-Luce Penchard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marie-Luce Penchard Context triple: [Marie-Luce Penchard, name, Marie-Luce Penchard]
-
A.
Marie-Luce Penchard
chosen
Marie-Luce Penchard is a French politician from Guadeloupe who has served in national government and local leadership roles.
-
B.
Louise Pignon
Louise Pignon is a fictional character from the 1973 French comedy film "L'Emmerdeur," which centers on the chaotic entanglements between a hitman and a suicidal salesman.
-
C.
Michèle Girardon
Michèle Girardon was a French actress known for her roles in European cinema of the 1950s and 1960s.
-
D.
Michèle Delaunay
Michèle Delaunay is a French politician and former government minister known for her work in public health and social policy.
-
E.
Gisèle Préville
Gisèle Préville was a French actress known for her roles in mid-20th-century French cinema.
- 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_69d8e8d5162481909db12435d9535c1a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6257af68881908147beedc29ff64c |
completed | April 20, 2026, 1:09 p.m. |
Created at: April 10, 2026, 1:36 p.m.