Triple
T18846896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amt Hohe Elbgeest |
E460939
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Escheburg
Escheburg is a small municipality in the district of Herzogtum Lauenburg in Schleswig-Holstein, northern Germany.
|
E1351223
|
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: Escheburg | Statement: [Amt Hohe Elbgeest, hasMunicipality, Escheburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Escheburg Context triple: [Amt Hohe Elbgeest, hasMunicipality, Escheburg]
-
A.
Ebernburg
Ebernburg is a historic castle in Rhineland-Palatinate, Germany, known as a stronghold of the early Reformation and the seat of knight Franz von Sickingen.
-
B.
Eschen
Eschen is a municipality in northern Liechtenstein known for its residential character and role as one of the country’s larger population centers.
-
C.
Schwanenburg
Schwanenburg is a historic hilltop castle in Kleve, Germany, known for its prominent tower and its role in regional medieval and early modern history.
-
D.
Osterburg
Osterburg is a small town in the German state of Saxony-Anhalt, known for its historic architecture and rural surroundings.
-
E.
Eschede
Eschede is a municipality in Lower Saxony, Germany, known for its rural setting and the site of the 1998 ICE high-speed train disaster.
- 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: Escheburg Triple: [Amt Hohe Elbgeest, hasMunicipality, Escheburg]
Generated description
Escheburg is a small municipality in the district of Herzogtum Lauenburg in Schleswig-Holstein, northern Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Escheburg Target entity description: Escheburg is a small municipality in the district of Herzogtum Lauenburg in Schleswig-Holstein, northern Germany.
-
A.
Ebernburg
Ebernburg is a historic castle in Rhineland-Palatinate, Germany, known as a stronghold of the early Reformation and the seat of knight Franz von Sickingen.
-
B.
Eschen
Eschen is a municipality in northern Liechtenstein known for its residential character and role as one of the country’s larger population centers.
-
C.
Schwanenburg
Schwanenburg is a historic hilltop castle in Kleve, Germany, known for its prominent tower and its role in regional medieval and early modern history.
-
D.
Osterburg
Osterburg is a small town in the German state of Saxony-Anhalt, known for its historic architecture and rural surroundings.
-
E.
Eschede
Eschede is a municipality in Lower Saxony, Germany, known for its rural setting and the site of the 1998 ICE high-speed train disaster.
- 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_69d8dcfa11e4819090ab1ef5bdcd2b2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5b8efafdc81909608b8a47deeaa8e |
completed | April 20, 2026, 5:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a059fac441c8190a598c9db941910a5 |
completed | May 14, 2026, 10:10 a.m. |
| NEDg | Description generation | batch_6a05a0bf42a8819084cef3801f489a4a |
completed | May 14, 2026, 10:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05a11135608190a1014cbd3d392d7d |
completed | May 14, 2026, 10:16 a.m. |
Created at: April 10, 2026, 11:56 a.m.