Triple
T22307791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sophia of Nassau |
E551429
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Biebrich |
—
|
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: Biebrich | Statement: [Sophia of Nassau, placeOfBirth, Biebrich]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Biebrich Context triple: [Sophia of Nassau, placeOfBirth, Biebrich]
-
A.
Biebrich
chosen
Biebrich is a district of Wiesbaden in the German state of Hesse, historically known as an independent town on the Rhine and the site of the Baroque Biebrich Palace.
-
B.
Odershausen
Odershausen is a village and district of the spa town Bad Wildungen in the state of Hesse, Germany.
-
C.
Landsberg an der Warthe
Landsberg an der Warthe is the former German name for the city now known as Gorzów Wielkopolski in western Poland, historically an important urban center in the Neumark region of Brandenburg.
-
D.
Grömitz
Grömitz is a Baltic Sea resort town in northern Germany known for its long sandy beaches and seaside tourism.
-
E.
Gadebusch
Gadebusch is a small historic town in northern Germany known for its medieval architecture and rural surroundings.
- 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_69e11e46c0188190800181a4233f28fe |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1574bccb08190a6236dd14cf0fc5b |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:41 p.m.