Triple
T1839076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Main |
E41131
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object |
Kitzingen
Kitzingen is a historic town in northern Bavaria, Germany, known for its wine production and location along the Main River.
|
E315601
|
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: Kitzingen | Statement: [Main, flowsThrough, Kitzingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kitzingen Context triple: [Main, flowsThrough, Kitzingen]
-
A.
Günzburg
Günzburg is a small Bavarian town in southern Germany, historically notable as the birthplace of Nazi physician Josef Mengele.
-
B.
Lampoldshausen
Lampoldshausen is a German village best known as a major site for rocket propulsion research and testing facilities of the German Aerospace Center.
-
C.
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.
-
D.
Saalfeld
Saalfeld is a town in the German state of Thuringia, known for its historic old town and former significance as a regional railway and industrial center.
-
E.
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.
- 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: Kitzingen Triple: [Main, flowsThrough, Kitzingen]
Generated description
Kitzingen is a historic town in northern Bavaria, Germany, known for its wine production and location along the Main River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kitzingen Target entity description: Kitzingen is a historic town in northern Bavaria, Germany, known for its wine production and location along the Main River.
-
A.
Günzburg
Günzburg is a small Bavarian town in southern Germany, historically notable as the birthplace of Nazi physician Josef Mengele.
-
B.
Lampoldshausen
Lampoldshausen is a German village best known as a major site for rocket propulsion research and testing facilities of the German Aerospace Center.
-
C.
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.
-
D.
Saalfeld
Saalfeld is a town in the German state of Thuringia, known for its historic old town and former significance as a regional railway and industrial center.
-
E.
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.
- 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_69a88647f9388190909bc36e795bdaec |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb039cb588190b2626245a7f0bd67 |
completed | March 7, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b108b7617c8190938c7ed35e0a791e |
completed | March 11, 2026, 6:16 a.m. |
| NEDg | Description generation | batch_69b109aca8008190aa34902fb63fb1a3 |
completed | March 11, 2026, 6:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b10a5da7d08190967750728135ab68 |
completed | March 11, 2026, 6:23 a.m. |
Created at: March 4, 2026, 7:33 p.m.