Triple
T13911835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kleinmachnow |
E334515
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object |
Hakeburg
Hakeburg is a historic castle-like manor and former research facility located in Kleinmachnow, Germany.
|
E1067242
|
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: Hakeburg | Statement: [Kleinmachnow, hasLandmark, Hakeburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hakeburg Context triple: [Kleinmachnow, hasLandmark, Hakeburg]
-
A.
Havixbeck
Havixbeck is a municipality in North Rhine-Westphalia, Germany, known for its rural character and proximity to the city of Münster.
-
B.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
C.
Haldenstein
Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
-
D.
Kleeburg
Kleeburg is a historical territory in the Holy Roman Empire that gave its name to the cadet branch of the Wittelsbach dynasty known as the Counts Palatine of Zweibrücken-Kleeburg.
-
E.
Havenstein
Havenstein is a German surname most notably associated with Rudolf Havenstein, the president of the Reichsbank during Germany’s hyperinflation period after World War I.
- 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: Hakeburg Triple: [Kleinmachnow, hasLandmark, Hakeburg]
Generated description
Hakeburg is a historic castle-like manor and former research facility located in Kleinmachnow, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hakeburg Target entity description: Hakeburg is a historic castle-like manor and former research facility located in Kleinmachnow, Germany.
-
A.
Havixbeck
Havixbeck is a municipality in North Rhine-Westphalia, Germany, known for its rural character and proximity to the city of Münster.
-
B.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
C.
Haldenstein
Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
-
D.
Kleeburg
Kleeburg is a historical territory in the Holy Roman Empire that gave its name to the cadet branch of the Wittelsbach dynasty known as the Counts Palatine of Zweibrücken-Kleeburg.
-
E.
Havenstein
Havenstein is a German surname most notably associated with Rudolf Havenstein, the president of the Reichsbank during Germany’s hyperinflation period after World War I.
- 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_69d81c5eaa9c819083b1ff8689179565 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2723461881908376b5509ee0d530 |
completed | April 14, 2026, 11:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c72879e48190ac01d0a2023b098c |
completed | May 3, 2026, 10:07 p.m. |
| NEDg | Description generation | batch_69f7c7b9e4888190822501d439df142a |
completed | May 3, 2026, 10:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7c83e31d4819094209406fc99456a |
completed | May 3, 2026, 10:12 p.m. |
Created at: April 9, 2026, 10:16 p.m.