Triple
T9146456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Münsing |
E219467
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Ammerland
Ammerland is a locality within the Bavarian municipality of Münsing in Germany, situated near Lake Starnberg.
|
E781783
|
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: Ammerland | Statement: [Münsing, hasSubdivision, Ammerland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ammerland Context triple: [Münsing, hasSubdivision, Ammerland]
-
A.
Emsland
Emsland is a rural region in western Germany known for its agriculture, peatlands, and location along the River Ems near the Dutch border.
-
B.
Ammerland (district)
Ammerland is a rural district in Lower Saxony, Germany, known for its peatlands, tree nurseries, and the spa town of Bad Zwischenahn.
-
C.
Pinneberg
Pinneberg is a town in northern Germany that serves as the administrative center of the district of the same name near Hamburg.
-
D.
Havelland
Havelland is a rural district in western Brandenburg, Germany, known for its river landscapes along the Havel, historic towns, and agricultural character.
-
E.
Uckermark
Uckermark is a rural historical region in northeastern Germany, known for its lakes, forests, and low population density, located primarily in the state of Brandenburg.
- 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: Ammerland Triple: [Münsing, hasSubdivision, Ammerland]
Generated description
Ammerland is a locality within the Bavarian municipality of Münsing in Germany, situated near Lake Starnberg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ammerland Target entity description: Ammerland is a locality within the Bavarian municipality of Münsing in Germany, situated near Lake Starnberg.
-
A.
Emsland
Emsland is a rural region in western Germany known for its agriculture, peatlands, and location along the River Ems near the Dutch border.
-
B.
Ammerland (district)
Ammerland is a rural district in Lower Saxony, Germany, known for its peatlands, tree nurseries, and the spa town of Bad Zwischenahn.
-
C.
Pinneberg
Pinneberg is a town in northern Germany that serves as the administrative center of the district of the same name near Hamburg.
-
D.
Havelland
Havelland is a rural district in western Brandenburg, Germany, known for its river landscapes along the Havel, historic towns, and agricultural character.
-
E.
Uckermark
Uckermark is a rural historical region in northeastern Germany, known for its lakes, forests, and low population density, located primarily in the state of Brandenburg.
- 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_69ca83e121dc81909912bd66953081c5 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca917914c8190b97ca9169bbd1e5e |
completed | April 1, 2026, 5:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d05458ac188190acc47fc99f8ee182 |
completed | April 3, 2026, 11:59 p.m. |
| NEDg | Description generation | batch_69d055c6b7f08190ad6ff81adffaeab1 |
completed | April 4, 2026, 12:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d05655a27c8190b0445476c10f57ce |
completed | April 4, 2026, 12:07 a.m. |
Created at: March 30, 2026, 7:20 p.m.