Triple
T19966398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corinne Day |
E479945
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Corinne |
—
|
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: Corinne | Statement: [Corinne Day, givenName, Corinne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Corinne Context triple: [Corinne Day, givenName, Corinne]
-
A.
Corinne
chosen
Corinne is a feminine given name of French origin, often considered a variant of Corine and derived from the Greek name Korinna.
-
B.
Sébastienne
Sébastienne is a French feminine given name, used here as part of the full name Louise Sébastienne Gély.
-
C.
Célestine
Célestine is a French feminine given name of Latin origin, derived from "caelestis," meaning "heavenly" or "celestial."
-
D.
Angélique
Angélique is a French feminine given name historically borne by figures such as Angélique Diderot, the daughter of philosopher Denis Diderot.
-
E.
Camille
Camille is a classic 1936 romantic drama film starring Greta Garbo as a tragic Parisian courtesan.
- 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc5e41881908c1e8867820f1c0c |
completed | April 20, 2026, 5 p.m. |
Created at: April 10, 2026, 1:54 p.m.