Triple
T10215700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Havelland |
E242434
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Stechow-Ferchesar
Stechow-Ferchesar is a small rural municipality in the Havelland district of Brandenburg, Germany.
|
E850655
|
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: Stechow-Ferchesar | Statement: [Havelland, containsMunicipality, Stechow-Ferchesar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stechow-Ferchesar Context triple: [Havelland, containsMunicipality, Stechow-Ferchesar]
-
A.
Luterbach
Luterbach is a municipality in the canton of Solothurn in northwestern Switzerland, known for its residential character and proximity to the Aare River.
-
B.
Reuss-Greiz
Reuss-Greiz was a small historical German principality ruled by a branch of the House of Reuss, located in what is now the state of Thuringia.
-
C.
Vohenstrauß
Vohenstrauß is a small town in the Upper Palatinate region of Bavaria, Germany, known for its historic architecture and surrounding forested landscapes.
-
D.
Gaisbach
Gaisbach is a village and district of the town of Oberkirch in the state of Baden-Württemberg in southwestern Germany.
-
E.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
- 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: Stechow-Ferchesar Triple: [Havelland, containsMunicipality, Stechow-Ferchesar]
Generated description
Stechow-Ferchesar is a small rural municipality in the Havelland district of Brandenburg, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stechow-Ferchesar Target entity description: Stechow-Ferchesar is a small rural municipality in the Havelland district of Brandenburg, Germany.
-
A.
Luterbach
Luterbach is a municipality in the canton of Solothurn in northwestern Switzerland, known for its residential character and proximity to the Aare River.
-
B.
Reuss-Greiz
Reuss-Greiz was a small historical German principality ruled by a branch of the House of Reuss, located in what is now the state of Thuringia.
-
C.
Vohenstrauß
Vohenstrauß is a small town in the Upper Palatinate region of Bavaria, Germany, known for its historic architecture and surrounding forested landscapes.
-
D.
Gaisbach
Gaisbach is a village and district of the town of Oberkirch in the state of Baden-Württemberg in southwestern Germany.
-
E.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa2894d0819095704449ecc2db6c |
completed | April 6, 2026, 12:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6a804e8748190b4ebcfa9a0bb889f |
completed | April 8, 2026, 7:09 p.m. |
| NEDg | Description generation | batch_69d6d0003434819093e3f82a556db79c |
completed | April 8, 2026, 10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d6df3c8f748190923db41ef1a9a03a |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 6, 2026, 11:05 a.m.