Triple
T9833937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marburg-Biedenkopf |
E239053
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Dautphetal
Dautphetal is a municipality in the Marburg-Biedenkopf district of Hesse, Germany, known for its rural character and location in the Lahn River valley.
|
E877110
|
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: Dautphetal | Statement: [Marburg-Biedenkopf, containsMunicipality, Dautphetal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dautphetal Context triple: [Marburg-Biedenkopf, containsMunicipality, Dautphetal]
-
A.
Petershausen
Petershausen is a Bavarian municipality in southern Germany, located north of Munich and known for its rural character and good rail connections to the city.
-
B.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
-
C.
Lülsfeld
Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
-
D.
Dornstadt
Dornstadt is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, located near the city of Ulm.
-
E.
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.
- 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: Dautphetal Triple: [Marburg-Biedenkopf, containsMunicipality, Dautphetal]
Generated description
Dautphetal is a municipality in the Marburg-Biedenkopf district of Hesse, Germany, known for its rural character and location in the Lahn River valley.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dautphetal Target entity description: Dautphetal is a municipality in the Marburg-Biedenkopf district of Hesse, Germany, known for its rural character and location in the Lahn River valley.
-
A.
Petershausen
Petershausen is a Bavarian municipality in southern Germany, located north of Munich and known for its rural character and good rail connections to the city.
-
B.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
-
C.
Lülsfeld
Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
-
D.
Dornstadt
Dornstadt is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, located near the city of Ulm.
-
E.
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.
- 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_69ca84e314108190978324a4bdb959f8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3385054819094145c96204e3f0d |
completed | April 2, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d979c21f5481908bea7fd2c70d2c0b |
completed | April 10, 2026, 10:29 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: March 30, 2026, 8:32 p.m.