Triple
T2049914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schleswig-Holstein |
E45540
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Bad Segeberg
Bad Segeberg is a small spa town in northern Germany best known for its limestone caves and annual Karl May Festival.
|
E228982
|
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: Bad Segeberg | Statement: [Schleswig-Holstein, hasCity, Bad Segeberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Segeberg Context triple: [Schleswig-Holstein, hasCity, Bad Segeberg]
-
A.
Bad Salzdetfurth
Bad Salzdetfurth is a spa town in Lower Saxony, Germany, known for its historic saltworks and therapeutic health resorts.
-
B.
Bad Kleinen
Bad Kleinen is a small municipality in northern Germany, known historically as the place where philosopher and logician Gottlob Frege died.
-
C.
Bad Honnef
Bad Honnef is a spa town on the Rhine in North Rhine-Westphalia, Germany, known for its scenic setting near the Siebengebirge hills and its historical associations with prominent political figures.
-
D.
Lauenburg
Lauenburg is a historic town in northern Germany situated on the banks of the Elbe River.
-
E.
Havelberg
Havelberg is a small historic town in Saxony-Anhalt, Germany, known for its medieval cathedral and location at the confluence of the Havel and Elbe rivers.
- 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: Bad Segeberg Triple: [Schleswig-Holstein, hasCity, Bad Segeberg]
Generated description
Bad Segeberg is a small spa town in northern Germany best known for its limestone caves and annual Karl May Festival.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bad Segeberg Target entity description: Bad Segeberg is a small spa town in northern Germany best known for its limestone caves and annual Karl May Festival.
-
A.
Bad Salzdetfurth
Bad Salzdetfurth is a spa town in Lower Saxony, Germany, known for its historic saltworks and therapeutic health resorts.
-
B.
Bad Kleinen
Bad Kleinen is a small municipality in northern Germany, known historically as the place where philosopher and logician Gottlob Frege died.
-
C.
Bad Honnef
Bad Honnef is a spa town on the Rhine in North Rhine-Westphalia, Germany, known for its scenic setting near the Siebengebirge hills and its historical associations with prominent political figures.
-
D.
Lauenburg
Lauenburg is a historic town in northern Germany situated on the banks of the Elbe River.
-
E.
Havelberg
Havelberg is a small historic town in Saxony-Anhalt, Germany, known for its medieval cathedral and location at the confluence of the Havel and Elbe rivers.
- 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_69ae2007386481908b46c7bc2db8e4dd |
completed | March 9, 2026, 1:19 a.m. |
| NEDg | Description generation | batch_69ae20fbcd30819099499853d7dc2cc4 |
completed | March 9, 2026, 1:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae216d450c8190ad2ecfdafa2354c7 |
completed | March 9, 2026, 1:25 a.m. |
Created at: March 4, 2026, 7:39 p.m.