Triple
T22366711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elser – Er hätte die Welt verändert |
E552922
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Elser |
—
|
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: Elser | Statement: [Elser – Er hätte die Welt verändert, alsoKnownAs, Elser]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elser Context triple: [Elser – Er hätte die Welt verändert, alsoKnownAs, Elser]
-
A.
Elser
chosen
Elser is a German surname most notably associated with Georg Elser, the carpenter who attempted to assassinate Adolf Hitler in 1939.
-
B.
Schultheiss
Schultheiss is a German surname historically derived from a medieval administrative title for a local official or magistrate.
-
C.
Wolf Lieser
Wolf Lieser is a German gallerist and curator best known for founding the Digital Art Museum (DAM) and promoting digital and computer-based art.
-
D.
Wolf Leslau
Wolf Leslau was a prominent linguist and scholar of Semitic and Ethiopian languages, renowned for his extensive fieldwork and documentation of endangered tongues.
-
E.
Bockstael
Bockstael is a railway station in Brussels, Belgium, serving as a local transit hub that connects regional train services with the city’s public transport network.
- 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_69e11e4affcc8190ba7c27d29062558d |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1580074dc819091305ac7017000d3 |
completed | April 29, 2026, 12:59 a.m. |
Created at: April 16, 2026, 8:44 p.m.