Triple
T10587224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kulmbach district |
E249884
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Wirsberg
Wirsberg is a small market town in the Upper Franconia region of Bavaria, Germany, known for its scenic location in the Franconian Forest and its historic architecture.
|
E876952
|
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: Wirsberg | Statement: [Kulmbach district, hasMunicipality, Wirsberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wirsberg Context triple: [Kulmbach district, hasMunicipality, Wirsberg]
-
A.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
-
B.
Wielenbach
Wielenbach is a small municipality in the Upper Bavarian region of Germany, situated within the district of Weilheim-Schongau.
-
C.
Eschbach
Eschbach is a village and district of the town of Usingen in the Hochtaunus region of Hesse, Germany.
-
D.
Voitsberg
Voitsberg is a small town in southeastern Austria known for its industrial heritage and location within the federal state of Styria.
-
E.
Wackersberg
Wackersberg is a rural Bavarian municipality in southern Germany, known for its scenic Alpine foothills and traditional village character.
- 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: Wirsberg Triple: [Kulmbach district, hasMunicipality, Wirsberg]
Generated description
Wirsberg is a small market town in the Upper Franconia region of Bavaria, Germany, known for its scenic location in the Franconian Forest and its historic architecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wirsberg Target entity description: Wirsberg is a small market town in the Upper Franconia region of Bavaria, Germany, known for its scenic location in the Franconian Forest and its historic architecture.
-
A.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
-
B.
Wielenbach
Wielenbach is a small municipality in the Upper Bavarian region of Germany, situated within the district of Weilheim-Schongau.
-
C.
Eschbach
Eschbach is a village and district of the town of Usingen in the Hochtaunus region of Hesse, Germany.
-
D.
Voitsberg
Voitsberg is a small town in southeastern Austria known for its industrial heritage and location within the federal state of Styria.
-
E.
Wackersberg
Wackersberg is a rural Bavarian municipality in southern Germany, known for its scenic Alpine foothills and traditional village character.
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5276b0ae48190b2935230363239e0 |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d97a1bba1c8190af5a078f40f3bc0a |
completed | April 10, 2026, 10:30 p.m. |
| NEDg | Description generation | batch_69d97c7bc87481908d50eb6f294170eb |
completed | April 10, 2026, 10:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d97e015b088190a97822675eecaa5a |
completed | April 10, 2026, 10:47 p.m. |
Created at: April 6, 2026, 12:39 p.m.