Triple
T3574862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laura subcamp |
E75662
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Leutenberg
Leutenberg is a small town in the German state of Thuringia, known for its location in the Thuringian Slate Mountains and its historical sites.
|
E420243
|
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: Leutenberg | Statement: [Laura subcamp, locatedNear, Leutenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leutenberg Context triple: [Laura subcamp, locatedNear, Leutenberg]
-
A.
Lankwitz
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
-
B.
Langendorf
Langendorf is a municipality in the canton of Solothurn in northwestern Switzerland.
-
C.
Oranienburg
Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
-
D.
Friedland
Friedland is a town in present-day Pravdinsk, Russia, historically notable as the site of the decisive 1807 Napoleonic battle between French and Russian forces.
-
E.
Friedland
Friedland is a municipality in Lower Saxony, Germany, known for its historic border location and post-World War II refugee transit camp.
- 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: Leutenberg Triple: [Laura subcamp, locatedNear, Leutenberg]
Generated description
Leutenberg is a small town in the German state of Thuringia, known for its location in the Thuringian Slate Mountains and its historical sites.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Leutenberg Target entity description: Leutenberg is a small town in the German state of Thuringia, known for its location in the Thuringian Slate Mountains and its historical sites.
-
A.
Lankwitz
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
-
B.
Langendorf
Langendorf is a municipality in the canton of Solothurn in northwestern Switzerland.
-
C.
Oranienburg
Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
-
D.
Friedland
Friedland is a town in present-day Pravdinsk, Russia, historically notable as the site of the decisive 1807 Napoleonic battle between French and Russian forces.
-
E.
Friedland
Friedland is a municipality in Lower Saxony, Germany, known for its historic border location and post-World War II refugee transit camp.
- 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_69ad85d5e3008190bdfe0bacdd1f5a1b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0d928f08190830347b3b032178a |
completed | March 8, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589b716648190aeaead138203cbf9 |
completed | March 14, 2026, 4:15 p.m. |
| NEDg | Description generation | batch_69b58a9497b88190a46afd8b1996fed9 |
completed | March 14, 2026, 4:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b58b26e4208190b4ec30a3b635194b |
completed | March 14, 2026, 4:21 p.m. |
Created at: March 8, 2026, 3:21 p.m.