Triple
T7251506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | District of Vechta |
E157611
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Lohne
Lohne is a town in Lower Saxony, Germany, known for its industrial economy and location within the Vechta district.
|
E651231
|
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: Lohne | Statement: [District of Vechta, hasMunicipality, Lohne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lohne Context triple: [District of Vechta, hasMunicipality, Lohne]
-
A.
Stadtlohn
Stadtlohn is a small town in western Germany’s Münsterland region, near the Dutch border, known for its rural character and local industry.
-
B.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
-
C.
Langenau
Langenau is a small town in the Alb-Donau district of Baden-Württemberg in southern Germany, known for its historic center and proximity to the Swabian Jura.
-
D.
Ennigerloh
Ennigerloh is a small town in the German state of North Rhine-Westphalia, known as the birthplace of mathematician Karl Weierstrass.
-
E.
Vellinghausen
Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
- 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: Lohne Triple: [District of Vechta, hasMunicipality, Lohne]
Generated description
Lohne is a town in Lower Saxony, Germany, known for its industrial economy and location within the Vechta district.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lohne Target entity description: Lohne is a town in Lower Saxony, Germany, known for its industrial economy and location within the Vechta district.
-
A.
Stadtlohn
Stadtlohn is a small town in western Germany’s Münsterland region, near the Dutch border, known for its rural character and local industry.
-
B.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
-
C.
Langenau
Langenau is a small town in the Alb-Donau district of Baden-Württemberg in southern Germany, known for its historic center and proximity to the Swabian Jura.
-
D.
Ennigerloh
Ennigerloh is a small town in the German state of North Rhine-Westphalia, known as the birthplace of mathematician Karl Weierstrass.
-
E.
Vellinghausen
Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
- 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_69c6882d81d4819085f7ff862951ee4f |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea7ae0e48190bd80c91bad1976c6 |
completed | March 27, 2026, 8:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7d3a7502081909d2a97a60cc445ae |
completed | March 28, 2026, 1:12 p.m. |
| NEDg | Description generation | batch_69c7d4690adc81909abbfb7a756f453d |
completed | March 28, 2026, 1:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7d517a7108190893878cbef7d1a3a |
completed | March 28, 2026, 1:18 p.m. |
Created at: March 27, 2026, 2:56 p.m.