Triple
T10968509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waldeck-Frankenberg |
E259169
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Lichtenfels
Lichtenfels is a small town in the Waldeck-Frankenberg district of northern Hesse, Germany, known for its rural setting and proximity to the Edersee and Kellerwald-Edersee National Park.
|
E922576
|
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: Lichtenfels | Statement: [Waldeck-Frankenberg, containsTown, Lichtenfels]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lichtenfels Context triple: [Waldeck-Frankenberg, containsTown, Lichtenfels]
-
A.
Lichtenfels
Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
-
B.
Lülsfeld
Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
-
C.
Altenstadt
Altenstadt is a small Bavarian municipality in southern Germany, situated within the rural district of Weilheim-Schongau.
-
D.
Lauterhofen
Lauterhofen is a market town in Bavaria, Germany, known for its rural character and location within the Upper Palatinate region.
-
E.
Maichingen
Maichingen is a district of the city of Sindelfingen in the German state of Baden-Württemberg.
- 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: Lichtenfels Triple: [Waldeck-Frankenberg, containsTown, Lichtenfels]
Generated description
Lichtenfels is a small town in the Waldeck-Frankenberg district of northern Hesse, Germany, known for its rural setting and proximity to the Edersee and Kellerwald-Edersee National Park.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lichtenfels Target entity description: Lichtenfels is a small town in the Waldeck-Frankenberg district of northern Hesse, Germany, known for its rural setting and proximity to the Edersee and Kellerwald-Edersee National Park.
-
A.
Lichtenfels
Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
-
B.
Lülsfeld
Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
-
C.
Altenstadt
Altenstadt is a small Bavarian municipality in southern Germany, situated within the rural district of Weilheim-Schongau.
-
D.
Lauterhofen
Lauterhofen is a market town in Bavaria, Germany, known for its rural character and location within the Upper Palatinate region.
-
E.
Maichingen
Maichingen is a district of the city of Sindelfingen in the German state of Baden-Württemberg.
- 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_69d6aa895f4c8190887a15460ef622f4 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7719800388190943a0bffa48a2731 |
completed | April 9, 2026, 9:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58a8c67248190be284ddd84f9d8b8 |
completed | April 20, 2026, 2:08 a.m. |
| NEDg | Description generation | batch_69e59323c5948190bc2c9512f7b0a54f |
completed | April 20, 2026, 2:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e599c53704819097c0fdbbfbbb1e87 |
completed | April 20, 2026, 3:13 a.m. |
Created at: April 8, 2026, 9:24 p.m.