Triple
T12420563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leverkusen |
E296756
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Opladen
Opladen is a district of the German city of Leverkusen, known historically as an independent town and regional rail hub in North Rhine-Westphalia.
|
E983031
|
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: Opladen | Statement: [Leverkusen, hasDistrict, Opladen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Opladen Context triple: [Leverkusen, hasDistrict, Opladen]
-
A.
Plüderhausen
Plüderhausen is a municipality in the German state of Baden-Württemberg, located in the Rems Valley east of Stuttgart.
-
B.
Gardelegen
Gardelegen is a historic town in the German state of Saxony-Anhalt, known for its medieval architecture and its location in the Altmark region.
-
C.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
-
D.
Gescher
Gescher is a small town in western Germany’s Münsterland region, noted for its traditional bell foundries and rural character.
-
E.
Tecklenburg
Tecklenburg is a historic small town in North Rhine-Westphalia, Germany, known for its medieval architecture and open-air theater.
- 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: Opladen Triple: [Leverkusen, hasDistrict, Opladen]
Generated description
Opladen is a district of the German city of Leverkusen, known historically as an independent town and regional rail hub in North Rhine-Westphalia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Opladen Target entity description: Opladen is a district of the German city of Leverkusen, known historically as an independent town and regional rail hub in North Rhine-Westphalia.
-
A.
Plüderhausen
Plüderhausen is a municipality in the German state of Baden-Württemberg, located in the Rems Valley east of Stuttgart.
-
B.
Gardelegen
Gardelegen is a historic town in the German state of Saxony-Anhalt, known for its medieval architecture and its location in the Altmark region.
-
C.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
-
D.
Gescher
Gescher is a small town in western Germany’s Münsterland region, noted for its traditional bell foundries and rural character.
-
E.
Tecklenburg
Tecklenburg is a historic small town in North Rhine-Westphalia, Germany, known for its medieval architecture and open-air theater.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d6efd748190a5d9396a343e41e1 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63f0265fc81909a6288d11b78c2f9 |
completed | May 2, 2026, 6:14 p.m. |
| NEDg | Description generation | batch_69f6403711bc8190b214d4b06792a538 |
completed | May 2, 2026, 6:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f640f543c08190b95b16a8909eebf8 |
completed | May 2, 2026, 6:22 p.m. |
Created at: April 8, 2026, 9:55 p.m.