Triple
T2049916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schleswig-Holstein |
E45540
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Plön
Plön is a small lakeside town in northern Germany known for its historic castle and scenic location in the Holstein Switzerland region.
|
E246173
|
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: Plön | Statement: [Schleswig-Holstein, hasCity, Plön]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Plön Context triple: [Schleswig-Holstein, hasCity, Plön]
-
A.
Soest
Soest is a historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and former significance as a Hanseatic trading center.
-
B.
Schwarmstedt
Schwarmstedt is a municipality in Lower Saxony, Germany, situated in the Heidekreis district along the River Aller.
-
C.
Lingen
Lingen is a town in Lower Saxony, Germany, known for its location on the River Ems and its role as a regional economic and cultural center.
-
D.
Biebrich
Biebrich is a district of Wiesbaden in the German state of Hesse, historically known as an independent town on the Rhine and the site of the Baroque Biebrich Palace.
-
E.
Gifhorn
Gifhorn is a town in Lower Saxony, Germany, known for its location near the confluence of several rivers and its historic windmill museum.
- 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: Plön Triple: [Schleswig-Holstein, hasCity, Plön]
Generated description
Plön is a small lakeside town in northern Germany known for its historic castle and scenic location in the Holstein Switzerland region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Plön Target entity description: Plön is a small lakeside town in northern Germany known for its historic castle and scenic location in the Holstein Switzerland region.
-
A.
Soest
Soest is a historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and former significance as a Hanseatic trading center.
-
B.
Schwarmstedt
Schwarmstedt is a municipality in Lower Saxony, Germany, situated in the Heidekreis district along the River Aller.
-
C.
Lingen
Lingen is a town in Lower Saxony, Germany, known for its location on the River Ems and its role as a regional economic and cultural center.
-
D.
Biebrich
Biebrich is a district of Wiesbaden in the German state of Hesse, historically known as an independent town on the Rhine and the site of the Baroque Biebrich Palace.
-
E.
Gifhorn
Gifhorn is a town in Lower Saxony, Germany, known for its location near the confluence of several rivers and its historic windmill museum.
- 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_69a8891948208190ab7898da21824c77 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb98e10d48190bb96cd1f8ea3c08b |
completed | March 7, 2026, 5:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae652a87748190b9e18356ffb13bed |
completed | March 9, 2026, 6:14 a.m. |
| NEDg | Description generation | batch_69ae669aa29c81909770cd69d27c274c |
completed | March 9, 2026, 6:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae66fb7f1c8190b2bc306f06c423f1 |
completed | March 9, 2026, 6:21 a.m. |
Created at: March 4, 2026, 7:39 p.m.