Triple
T10541601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kronach district |
E248706
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Wallenfels
Wallenfels is a small town in northern Bavaria, Germany, known for its scenic location in the Franconian Forest region.
|
E952072
|
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: Wallenfels | Statement: [Kronach district, contains, Wallenfels]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wallenfels Context triple: [Kronach district, contains, Wallenfels]
-
A.
Wilhelmsruh
Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
-
B.
Lülsfeld
Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
-
C.
Fürstenzell
Fürstenzell is a market town and municipality in Lower Bavaria, Germany, known for its historic monastery and rural setting near the city of Passau.
-
D.
Zusenhofen
Zusenhofen is a village and district within the town of Oberkirch in the Ortenau region of Baden-Württemberg, Germany.
-
E.
Pfeffenhausen
Pfeffenhausen is a market town in Lower Bavaria, Germany, known for its rural character and location within the Landshut district.
- 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: Wallenfels Triple: [Kronach district, contains, Wallenfels]
Generated description
Wallenfels is a small town in northern Bavaria, Germany, known for its scenic location in the Franconian Forest region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wallenfels Target entity description: Wallenfels is a small town in northern Bavaria, Germany, known for its scenic location in the Franconian Forest region.
-
A.
Wilhelmsruh
Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
-
B.
Lülsfeld
Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
-
C.
Fürstenzell
Fürstenzell is a market town and municipality in Lower Bavaria, Germany, known for its historic monastery and rural setting near the city of Passau.
-
D.
Zusenhofen
Zusenhofen is a village and district within the town of Oberkirch in the Ortenau region of Baden-Württemberg, Germany.
-
E.
Pfeffenhausen
Pfeffenhausen is a market town in Lower Bavaria, Germany, known for its rural character and location within the Landshut district.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50a5918648190b16c2d1bc1bf015f |
completed | April 7, 2026, 1:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f2802d74f081909c7af34bf266ae01 |
completed | April 29, 2026, 10:03 p.m. |
| NEDg | Description generation | batch_69f28b7db84c8190b4c2b22a7465cf97 |
completed | April 29, 2026, 10:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f2a0e8861c8190a9ac8e0f488f4a95 |
completed | April 30, 2026, 12:23 a.m. |
Created at: April 6, 2026, 12:32 p.m.