Triple
T21578015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilhelm Groener |
E532449
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Groener |
—
|
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: Groener | Statement: [Wilhelm Groener, familyName, Groener]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Groener Context triple: [Wilhelm Groener, familyName, Groener]
-
A.
Groener
chosen
Groener is a German surname most notably associated with Wilhelm Groener, a prominent German general and politician during the early 20th century.
-
B.
Großer Greiner
Großer Greiner is a prominent mountain peak in the Zillertal Alps on the border between Austria and Italy, known for its alpine climbing and scenic high-altitude terrain.
-
C.
Greiser
Greiser is a German surname most notably borne by Arthur Greiser, a high-ranking Nazi official and war criminal during World War II.
-
D.
Gerzen
Gerzen is a small municipality in the Lower Bavarian region of southeastern Germany.
-
E.
Graubner
Graubner is a German surname most notably associated with the painter Gotthard Graubner, known for his color field and cushion paintings.
- 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_69e0c4618bec8190bcb0feb74568cbb1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeeb59cee08190aae55ad6e2a4e077 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 16, 2026, 6:31 p.m.