Triple
T2465003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karl von Rundstedt |
E55225
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Aschersleben |
E116349
|
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: Aschersleben | Statement: [Karl von Rundstedt, placeOfBirth, Aschersleben]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aschersleben Context triple: [Karl von Rundstedt, placeOfBirth, Aschersleben]
-
A.
Aschersleben
chosen
Aschersleben is a historic town in the German state of Saxony-Anhalt, known as one of the oldest documented cities in central Germany.
-
B.
Wurzen
Wurzen is a historic town in the German state of Saxony, known for its medieval architecture and location on the river Mulde east of Leipzig.
-
C.
Degendorf
Degendorf is a locality within the Bavarian town and district of Lichtenfels in Germany.
-
D.
Gauting
Gauting is a municipality in the district of Starnberg in Bavaria, Germany, known for its residential character and proximity to Munich.
-
E.
Oranienburg
Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
- 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_69ab49e3622c8190ad22afa2c4fbb807 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd1216f44819094c46ae7c2c1e394 |
completed | March 7, 2026, 7:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b53fc6f4b48190aebcf7457b6d670d |
completed | March 14, 2026, 11 a.m. |
Created at: March 6, 2026, 9:44 p.m.