Triple
T1743832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lech River |
E38291
|
entity |
| Predicate | mouthLocation |
P417
|
FINISHED |
| Object |
Donauwörth
Donauwörth is a historic Bavarian town in southern Germany situated at the confluence of the Danube and Lech rivers.
|
E368758
|
NE FINISHED |
How this triple was built (4 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: Donauwörth | Statement: [Lech River, mouthLocation, Donauwörth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donauwörth Context triple: [Lech River, mouthLocation, Donauwörth]
-
A.
Straubing
Straubing is a Bavarian town on the Danube River known for its historic city center and role as a regional economic and educational hub.
-
B.
Markranstädt
Markranstädt is a small town in the German state of Saxony, located near Leipzig and known for its local industry and proximity to the Kulkwitzer See recreation area.
-
C.
Amberg
Amberg is a historic town in Bavaria, Germany, known for its well-preserved medieval old town and former role as a regional administrative and trading center.
-
D.
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.
-
E.
Rosenheim
Rosenheim is a town in Upper Bavaria, Germany, known as a regional economic and transportation hub near the Alps.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Donauwörth Triple: [Lech River, mouthLocation, Donauwörth]
Generated description
Donauwörth is a historic Bavarian town in southern Germany situated at the confluence of the Danube and Lech rivers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Donauwörth Target entity description: Donauwörth is a historic Bavarian town in southern Germany situated at the confluence of the Danube and Lech rivers.
-
A.
Straubing
Straubing is a Bavarian town on the Danube River known for its historic city center and role as a regional economic and educational hub.
-
B.
Markranstädt
Markranstädt is a small town in the German state of Saxony, located near Leipzig and known for its local industry and proximity to the Kulkwitzer See recreation area.
-
C.
Amberg
Amberg is a historic town in Bavaria, Germany, known for its well-preserved medieval old town and former role as a regional administrative and trading center.
-
D.
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.
-
E.
Rosenheim
Rosenheim is a town in Upper Bavaria, Germany, known as a regional economic and transportation hub near the Alps.
- F. None of above. chosen
Provenance (5 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_69a8862b01a48190ab47209063af82d9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa63e804848190a19c10f4e609e900 |
completed | March 6, 2026, 5:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bb4c726c81909bc916e76bb0f96f |
completed | March 13, 2026, 7:22 a.m. |
| NEDg | Description generation | batch_69b3bbf649088190813e1418004b64d3 |
completed | March 13, 2026, 7:25 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3f1915dc08190aa01f1e2f9d1a315 |
completed | March 13, 2026, 11:14 a.m. |
Created at: March 4, 2026, 7:31 p.m.