Triple
T10870131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Union of Auhausen |
E256626
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Auhausen |
E256627
|
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: Auhausen | Statement: [Union of Auhausen, namedAfter, Auhausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Auhausen Context triple: [Union of Auhausen, namedAfter, Auhausen]
-
A.
Auhausen
chosen
Auhausen is a small Bavarian locality historically notable as the site where the Protestant Union of German states was founded in 1608.
-
B.
Anhausen
Anhausen is a small German village best known as the birthplace of professional golfer Bernhard Langer.
-
C.
Kaufering
Kaufering is a municipality in Bavaria, Germany, known historically for its World War II subcamps of Dachau and its location near the town of Landsberg am Lech.
-
D.
Geisenhausen
Geisenhausen is a market town in Lower Bavaria, Germany, known for its rural character and proximity to the city of Landshut.
-
E.
Bad Lauchstädt
Bad Lauchstädt is a historic spa town in the German state of Saxony-Anhalt, known for its classical Kurpark and Goethe-Theater.
- 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7518610e48190bee50db71ae0ca3e |
completed | April 9, 2026, 7:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e154cc97f88190aef41d18b5ebe836 |
completed | April 16, 2026, 9:29 p.m. |
Created at: April 8, 2026, 9:20 p.m.