Triple
T6551926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ottenstein |
E151148
|
entity |
| Predicate | hasMunicipalAuthority |
P3379
|
FINISHED |
| Object |
Stadt Ahaus
Stadt Ahaus is a town in the district of Borken in North Rhine-Westphalia, Germany, known for its historic castle and proximity to the Dutch border.
|
E691013
|
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: Stadt Ahaus | Statement: [Ottenstein, hasMunicipalAuthority, Stadt Ahaus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stadt Ahaus Context triple: [Ottenstein, hasMunicipalAuthority, Stadt Ahaus]
-
A.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
-
B.
Bentheim
Bentheim is a historical county in Lower Saxony, Germany, known for its Reformed Protestant heritage and the former County of Bentheim.
-
C.
Lippstadt
Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
-
D.
Iserlohn
Iserlohn is a city in the Märkischer Kreis district of North Rhine-Westphalia, Germany, known historically for its role in World War II and its metalworking and industrial heritage.
-
E.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
- 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: Stadt Ahaus Triple: [Ottenstein, hasMunicipalAuthority, Stadt Ahaus]
Generated description
Stadt Ahaus is a town in the district of Borken in North Rhine-Westphalia, Germany, known for its historic castle and proximity to the Dutch border.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stadt Ahaus Target entity description: Stadt Ahaus is a town in the district of Borken in North Rhine-Westphalia, Germany, known for its historic castle and proximity to the Dutch border.
-
A.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
-
B.
Bentheim
Bentheim is a historical county in Lower Saxony, Germany, known for its Reformed Protestant heritage and the former County of Bentheim.
-
C.
Lippstadt
Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
-
D.
Iserlohn
Iserlohn is a city in the Märkischer Kreis district of North Rhine-Westphalia, Germany, known historically for its role in World War II and its metalworking and industrial heritage.
-
E.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
- 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_69c687f3fd60819083bfa583e5bcfa71 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ae05cd988190a013226b14cd98f0 |
completed | March 27, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c963dc62f88190b2aff49f5cb5fe27 |
completed | March 29, 2026, 5:39 p.m. |
| NEDg | Description generation | batch_69c9647e648c8190ad309a8a9fdd0b42 |
completed | March 29, 2026, 5:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c964ce10708190a7c475395b1c2854 |
completed | March 29, 2026, 5:43 p.m. |
Created at: March 27, 2026, 1:51 p.m.