Triple
T5490109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osnabrück district |
E123679
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Bad Laer
Bad Laer is a small spa town in Lower Saxony, Germany, known for its therapeutic mineral springs and health tourism.
|
E522560
|
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: Bad Laer | Statement: [Osnabrück district, contains, Bad Laer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Laer Context triple: [Osnabrück district, contains, Bad Laer]
-
A.
Bad Lausick
Bad Lausick is a small spa town in the Free State of Saxony in eastern Germany, known for its therapeutic mineral springs and health resorts.
-
B.
Bad Brambach
Bad Brambach is a German spa town in the Vogtland region of Saxony, renowned for its mineral springs and therapeutic health resorts.
-
C.
Bad Ragaz
Bad Ragaz is a Swiss spa and resort town in the canton of St. Gallen, renowned for its thermal baths and alpine setting.
-
D.
Bad Nauheim
Bad Nauheim is a spa town in the German state of Hesse, historically known for its therapeutic mineral springs and health resorts.
-
E.
Bad Elster
Bad Elster is a historic spa town in Saxony, Germany, renowned for its mineral springs and role as a traditional health resort.
- 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: Bad Laer Triple: [Osnabrück district, contains, Bad Laer]
Generated description
Bad Laer is a small spa town in Lower Saxony, Germany, known for its therapeutic mineral springs and health tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bad Laer Target entity description: Bad Laer is a small spa town in Lower Saxony, Germany, known for its therapeutic mineral springs and health tourism.
-
A.
Bad Lausick
Bad Lausick is a small spa town in the Free State of Saxony in eastern Germany, known for its therapeutic mineral springs and health resorts.
-
B.
Bad Brambach
Bad Brambach is a German spa town in the Vogtland region of Saxony, renowned for its mineral springs and therapeutic health resorts.
-
C.
Bad Ragaz
Bad Ragaz is a Swiss spa and resort town in the canton of St. Gallen, renowned for its thermal baths and alpine setting.
-
D.
Bad Nauheim
Bad Nauheim is a spa town in the German state of Hesse, historically known for its therapeutic mineral springs and health resorts.
-
E.
Bad Elster
Bad Elster is a historic spa town in Saxony, Germany, renowned for its mineral springs and role as a traditional health resort.
- 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_69bd464a2d908190869324ce176779c8 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd927dcb848190a9d31e2435f8a755 |
completed | March 20, 2026, 6:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf48ac6e7881908806f88056409b41 |
completed | March 22, 2026, 1:41 a.m. |
| NEDg | Description generation | batch_69bf497a88b48190b87bf175fe224211 |
completed | March 22, 2026, 1:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf4a1f6e1c8190a9ae94e45fb16cf9 |
completed | March 22, 2026, 1:47 a.m. |
Created at: March 20, 2026, 2:10 p.m.