Triple
T19973375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leon Dabo |
E480133
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Saverne |
—
|
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: Saverne | Statement: [Leon Dabo, placeOfBirth, Saverne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saverne Context triple: [Leon Dabo, placeOfBirth, Saverne]
-
A.
Saverne
chosen
Saverne is a historic town in northeastern France, known for its canal, rose gardens, and the Château des Rohan.
-
B.
Sassenage
Sassenage is a commune in southeastern France near Grenoble, known for its historic château, caves, and scenic setting at the foot of the Vercors massif.
-
C.
Saint-Loup
Saint-Loup is a small French commune located in central France’s Creuse department, within the canton of Évaux-les-Bains.
-
D.
Villetrun
Villetrun is a small commune in the Loir-et-Cher department of central France.
-
E.
Saint-Saphorin
Saint-Saphorin is a picturesque wine-growing village on the shores of Lake Geneva in Switzerland, renowned for its terraced vineyards and historic charm.
- 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_69e65bcb72048190aedb4f085ace0493 |
completed | April 20, 2026, 5 p.m. |
Created at: April 10, 2026, 1:54 p.m.