Triple
T12580902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rüthen |
E300331
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Kneblinghausen
Kneblinghausen is a village and district (Ortsteil) of the town of Rüthen in the Soest district of North Rhine-Westphalia, Germany.
|
E1070026
|
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: Kneblinghausen | Statement: [Rüthen, hasSubdivision, Kneblinghausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kneblinghausen Context triple: [Rüthen, hasSubdivision, Kneblinghausen]
-
A.
Kühnhausen
Kühnhausen is a locality in Germany known historically as the place where Nazi education minister Bernhard Rust died.
-
B.
Vellinghausen
Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
-
C.
Nennhausen
Nennhausen is a rural municipality in the Havelland district of Brandenburg, Germany, known for its historic manor house and surrounding natural landscapes.
-
D.
Helmarshausen
Helmarshausen is a historic district of the spa town Bad Karlshafen in northern Hesse, Germany, known for its medieval heritage and former Benedictine monastery.
-
E.
Balzhausen
Balzhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
- 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: Kneblinghausen Triple: [Rüthen, hasSubdivision, Kneblinghausen]
Generated description
Kneblinghausen is a village and district (Ortsteil) of the town of Rüthen in the Soest district of North Rhine-Westphalia, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kneblinghausen Target entity description: Kneblinghausen is a village and district (Ortsteil) of the town of Rüthen in the Soest district of North Rhine-Westphalia, Germany.
-
A.
Kühnhausen
Kühnhausen is a locality in Germany known historically as the place where Nazi education minister Bernhard Rust died.
-
B.
Vellinghausen
Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
-
C.
Nennhausen
Nennhausen is a rural municipality in the Havelland district of Brandenburg, Germany, known for its historic manor house and surrounding natural landscapes.
-
D.
Helmarshausen
Helmarshausen is a historic district of the spa town Bad Karlshafen in northern Hesse, Germany, known for its medieval heritage and former Benedictine monastery.
-
E.
Balzhausen
Balzhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954b97a508190b6c901c506441dd0 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce5b2b988190892e14620fb87366 |
completed | May 3, 2026, 10:38 p.m. |
| NEDg | Description generation | batch_69f9fd56da288190b2bd33bc496c3fb9 |
completed | May 5, 2026, 2:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fb039fdb1c8190ad5286d1cfe80a29 |
completed | May 6, 2026, 9:02 a.m. |
Created at: April 9, 2026, 5:02 p.m.