Triple
T722035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Werl Prison |
E14637
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Werl
Werl is a town in North Rhine-Westphalia, Germany, known for its historical significance and regional correctional facility.
|
E100762
|
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: Werl | Statement: [Werl Prison, locatedIn, Werl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Werl Context triple: [Werl Prison, locatedIn, Werl]
-
A.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
-
B.
Uithoorn
Uithoorn is a town and municipality in the province of North Holland in the Netherlands, situated along the Amstel River.
-
C.
Heemstede
Heemstede is a town and municipality in the province of North Holland in the Netherlands, known as a leafy residential suburb near Haarlem.
-
D.
Aalsmeer
Aalsmeer is a Dutch town in North Holland best known as a global center for the flower and plant trade, hosting one of the world’s largest flower auctions.
-
E.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
- 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: Werl Triple: [Werl Prison, locatedIn, Werl]
Generated description
Werl is a town in North Rhine-Westphalia, Germany, known for its historical significance and regional correctional facility.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Werl Target entity description: Werl is a town in North Rhine-Westphalia, Germany, known for its historical significance and regional correctional facility.
-
A.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
-
B.
Uithoorn
Uithoorn is a town and municipality in the province of North Holland in the Netherlands, situated along the Amstel River.
-
C.
Heemstede
Heemstede is a town and municipality in the province of North Holland in the Netherlands, known as a leafy residential suburb near Haarlem.
-
D.
Aalsmeer
Aalsmeer is a Dutch town in North Holland best known as a global center for the flower and plant trade, hosting one of the world’s largest flower auctions.
-
E.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
- 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a591124c8190842e7ef18b064198 |
completed | March 1, 2026, 8:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3a8bcc8819091c785ad953ddc54 |
completed | March 4, 2026, 3:14 a.m. |
| NEDg | Description generation | batch_69a7a42e814481908263b19c75f80c51 |
completed | March 4, 2026, 3:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a7a49cd1a88190906b6ab283ba6849 |
completed | March 4, 2026, 3:18 a.m. |
Created at: March 1, 2026, 7:37 p.m.