Triple
T23437309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frederuna |
E563498
|
entity |
| Predicate | otherName |
P39
|
FINISHED |
| Object | Frederonne |
—
|
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: Frederonne | Statement: [Frederuna, otherName, Frederonne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frederonne Context triple: [Frederuna, otherName, Frederonne]
-
A.
Frederuna
chosen
Frederuna was a 10th-century Frankish queen consort of West Francia as the first wife of King Charles the Simple.
-
B.
Faventia
Faventia is the ancient Roman name for the Italian city of Faenza, historically known as an important settlement in northern Italy.
-
C.
Freren
Freren is a small town in Lower Saxony, Germany, known for its rural character and historical roots in the Emsland region.
-
D.
Samoreau
Samoreau is a small commune in the Seine-et-Marne department in north-central France, situated near the Seine River and known for its quiet residential character.
-
E.
Ifremer
Ifremer is the French national institute for ocean science, conducting marine research and operating major oceanographic infrastructures and data centers.
- 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_69e24553980c8190bb66a2ae0bdab125 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a5dcd4608190a543cc747e0daab8 |
completed | April 29, 2026, 6:31 a.m. |
Created at: April 17, 2026, 5:50 p.m.