Triple
T2991659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lahn |
E80767
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object |
Bad Ems
Bad Ems is a historic spa town in western Germany, renowned for its mineral springs and picturesque location along the Lahn River.
|
E317369
|
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 Ems | Statement: [Lahn, flowsThrough, Bad Ems]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Ems Context triple: [Lahn, flowsThrough, Bad Ems]
-
A.
Bad Mergentheim
Bad Mergentheim is a historic spa town in the German state of Baden-Württemberg, renowned for its mineral springs and picturesque setting in the Tauber Valley.
-
B.
Bad Tölz
Bad Tölz is a Bavarian spa town in southern Germany known for its historic old town, alpine scenery, and traditional German architecture.
-
C.
Bad Harzburg
Bad Harzburg is a German spa and resort town on the northern edge of the Harz Mountains, known for its thermal baths, hiking trails, and historic castle ruins.
-
D.
Bad Pyrmont
Bad Pyrmont is a German spa town in Lower Saxony renowned for its mineral springs, historic Kurpark, and long tradition as a health resort.
-
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 Ems Triple: [Lahn, flowsThrough, Bad Ems]
Generated description
Bad Ems is a historic spa town in western Germany, renowned for its mineral springs and picturesque location along the Lahn River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bad Ems Target entity description: Bad Ems is a historic spa town in western Germany, renowned for its mineral springs and picturesque location along the Lahn River.
-
A.
Bad Mergentheim
Bad Mergentheim is a historic spa town in the German state of Baden-Württemberg, renowned for its mineral springs and picturesque setting in the Tauber Valley.
-
B.
Bad Tölz
Bad Tölz is a Bavarian spa town in southern Germany known for its historic old town, alpine scenery, and traditional German architecture.
-
C.
Bad Harzburg
Bad Harzburg is a German spa and resort town on the northern edge of the Harz Mountains, known for its thermal baths, hiking trails, and historic castle ruins.
-
D.
Bad Pyrmont
Bad Pyrmont is a German spa town in Lower Saxony renowned for its mineral springs, historic Kurpark, and long tradition as a health resort.
-
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_69ad8b16c3488190b47b6aa7a59a335b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99df69d08190a0e25efb0dc8d653 |
completed | March 8, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b109039cfc8190a286c83df752967e |
completed | March 11, 2026, 6:17 a.m. |
| NEDg | Description generation | batch_69b10bc71c708190b1e620d41278c3e0 |
completed | March 11, 2026, 6:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b10c43a7c48190b63a7b3f0f180d44 |
completed | March 11, 2026, 6:31 a.m. |
Created at: March 8, 2026, 2:59 p.m.