Triple
T7104845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Verena Huber-Dyson |
E165555
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Verena |
E165554
|
NE FINISHED |
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: Verena | Statement: [Verena Huber-Dyson, givenName, Verena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verena Context triple: [Verena Huber-Dyson, givenName, Verena]
-
A.
Verena
chosen
Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
-
B.
Franziska
Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
-
C.
Odile
Odile is the seductive and deceptive Black Swan character in the ballet "Swan Lake," often portrayed as the antagonist and foil to the virtuous Odette.
-
D.
Ricarda
Ricarda is a feminine given name, primarily used in German- and Spanish-speaking countries, derived from the male name Richard.
-
E.
Dorothee
Dorothee is a feminine given name, commonly used in German- and French-speaking countries, that is a variant of the name Dorothea.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c6887fcddc8190a5d58908f6dee590 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e58b3f708190bebca7d4c4db40f2 |
completed | March 27, 2026, 8:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7ad83aa288190b06262ac9898f5c1 |
completed | March 28, 2026, 10:29 a.m. |
Created at: March 27, 2026, 2:42 p.m.