Triple
T16887853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wunstorf Air Base |
E421584
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Wunstorf
Wunstorf is a town in Lower Saxony, Germany, known for its nearby military air base and its location near the Steinhuder Meer.
|
E1244767
|
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: Wunstorf | Statement: [Wunstorf Air Base, locatedIn, Wunstorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wunstorf Context triple: [Wunstorf Air Base, locatedIn, Wunstorf]
-
A.
Westendorf
Westendorf is a popular Austrian alpine village known for its skiing, hiking, and picturesque mountain scenery.
-
B.
Hademstorf
Hademstorf is a small municipality in Lower Saxony, Germany, situated in the Heidekreis district.
-
C.
Rhöndorf
Rhöndorf is a district of Bad Honnef in Germany, best known as the longtime residence and final home of the first Chancellor of the Federal Republic of Germany, Konrad Adenauer.
-
D.
Wolmirstedt
Wolmirstedt is a small town in the German state of Saxony-Anhalt, located near Magdeburg and known for its historical town center and regional administrative role.
-
E.
Wermsdorf
Wermsdorf is a municipality in Saxony, Germany, best known as the site of the large Baroque hunting lodge and former royal residence Hubertusburg Palace.
- 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: Wunstorf Triple: [Wunstorf Air Base, locatedIn, Wunstorf]
Generated description
Wunstorf is a town in Lower Saxony, Germany, known for its nearby military air base and its location near the Steinhuder Meer.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wunstorf Target entity description: Wunstorf is a town in Lower Saxony, Germany, known for its nearby military air base and its location near the Steinhuder Meer.
-
A.
Westendorf
Westendorf is a popular Austrian alpine village known for its skiing, hiking, and picturesque mountain scenery.
-
B.
Hademstorf
Hademstorf is a small municipality in Lower Saxony, Germany, situated in the Heidekreis district.
-
C.
Rhöndorf
Rhöndorf is a district of Bad Honnef in Germany, best known as the longtime residence and final home of the first Chancellor of the Federal Republic of Germany, Konrad Adenauer.
-
D.
Wolmirstedt
Wolmirstedt is a small town in the German state of Saxony-Anhalt, located near Magdeburg and known for its historical town center and regional administrative role.
-
E.
Wermsdorf
Wermsdorf is a municipality in Saxony, Germany, best known as the site of the large Baroque hunting lodge and former royal residence Hubertusburg Palace.
- 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_69d889d470fc8190b4aec199636c0c56 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3bbc1f42481909dcf595358c23497 |
completed | April 18, 2026, 5:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00dbfd6898819083871544c119557c |
completed | May 10, 2026, 7:26 p.m. |
| NEDg | Description generation | batch_6a0114d33cac819083d8e542ea5bc274 |
completed | May 10, 2026, 11:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0115c967b0819088e2335fd45d755b |
completed | May 10, 2026, 11:33 p.m. |
Created at: April 10, 2026, 5:29 a.m.