Triple
T3089964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Swabia (Bavaria) |
E64457
|
entity |
| Predicate | containsDistrict |
P22582
|
FINISHED |
| Object | Günzburg |
E273207
|
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: Günzburg | Statement: [Swabia (Bavaria), containsDistrict, Günzburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Günzburg Context triple: [Swabia (Bavaria), containsDistrict, Günzburg]
-
A.
Günzburg
chosen
Günzburg is a small Bavarian town in southern Germany, historically notable as the birthplace of Nazi physician Josef Mengele.
-
B.
Wolfratshausen
Wolfratshausen is a Bavarian town in southern Germany known for its historic old town, riverside setting on the Loisach and Isar, and proximity to Munich and the Alps.
-
C.
Forchheim
Forchheim is a town in Upper Franconia, Bavaria, Germany, known for its historic old town and location along major regional rail and road routes.
-
D.
Reutlingen
Reutlingen is a city in southwestern Germany known for its location at the foot of the Swabian Jura and its well-preserved medieval old town.
-
E.
Offenburg
Offenburg is a city in southwestern Germany’s Baden-Württemberg state, known as a regional economic and transport hub near the French border in the Upper Rhine region.
- 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_69ad857c97d88190b26f9b1c90839c77 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada20d8f788190b05b8b6b5042bc1a |
completed | March 8, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf779f7d54819083e1cb88e34c6d34 |
completed | March 22, 2026, 5:01 a.m. |
Created at: March 8, 2026, 3:03 p.m.