Triple
T22755659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexandra Mollwo |
E562835
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Alexandra Mollwo |
—
|
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: Alexandra Mollwo | Statement: [Alexandra Mollwo, name, Alexandra Mollwo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alexandra Mollwo Context triple: [Alexandra Mollwo, name, Alexandra Mollwo]
-
A.
Alexandra Mollwo
chosen
Alexandra Mollwo was the wife of English chemist William Henry Perkin, who is renowned for discovering the first synthetic dye, mauveine.
-
B.
Alexandra Scherer
Alexandra Scherer is a German local politician who serves as the mayor of the spa town Bad Wurzach in Baden-Württemberg.
-
C.
Alexandra Christmann
Alexandra Christmann is the former wife of acclaimed British actor Sir Ben Kingsley.
-
D.
Alexandra Henkel
Alexandra Henkel is known as the wife of British actor Adeel Akhtar.
-
E.
Melissa Müller
Melissa Müller is an Austrian author and historian best known for her biographical and historical works on figures and events from the Nazi era, including her research that inspired the film "Downfall."
- 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_69e24551ec7881909a9c924dbea155f6 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f179bd22588190ac724a656194f5b9 |
completed | April 29, 2026, 3:23 a.m. |
Created at: April 17, 2026, 3:25 p.m.