Triple
T5965117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dinkelsbühl |
E132731
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Nördlingen |
E660035
|
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: Nördlingen | Statement: [Dinkelsbühl, hasNearbyCity, Nördlingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nördlingen Context triple: [Dinkelsbühl, hasNearbyCity, Nördlingen]
-
A.
Nördlingen
chosen
Nördlingen is a historic Bavarian town in southern Germany, notable for its well-preserved medieval walls and location within a large meteorite impact crater.
-
B.
Burghausen
Burghausen is a historic Bavarian town in southeastern Germany, renowned for its remarkably well-preserved medieval old town and one of the longest castle complexes in the world.
-
C.
Kempten
Kempten is a historic town in Bavaria, Germany, considered one of the country’s oldest urban settlements and known for its location in the Allgäu region.
-
D.
Kaufbeuren
Kaufbeuren is a historic Bavarian town in southern Germany known for its well-preserved medieval old town and traditional Swabian culture.
-
E.
Günzburg
Günzburg is a small Bavarian town in southern Germany, historically notable as the birthplace of Nazi physician Josef Mengele.
- 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_69c0086c2364819091e9fe2f58fa2517 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c03a3ca1dc819098cde8ae5ec1d845 |
completed | March 22, 2026, 6:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9061044ac8190b204f5002955df72 |
completed | March 29, 2026, 10:59 a.m. |
Created at: March 22, 2026, 4:03 p.m.