Triple
T10343785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mangfall Mountains |
E243689
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Wendelstein
Wendelstein is a prominent mountain peak in the Bavarian Alps of southern Germany, known for its panoramic views, observatory, and historic cogwheel railway.
|
E858377
|
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: Wendelstein | Statement: [Mangfall Mountains, contains, Wendelstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wendelstein Context triple: [Mangfall Mountains, contains, Wendelstein]
-
A.
Hellenstein
Hellenstein is the historical namesake associated with Hellenstein Castle, a prominent medieval fortress in Heidenheim, Germany.
-
B.
Reichenau
Reichenau is a German municipality best known for its UNESCO-listed monastic island on Lake Constance, renowned for its medieval abbey and cultural heritage.
-
C.
Vohenstrauß
Vohenstrauß is a small town in the Upper Palatinate region of Bavaria, Germany, known for its historic architecture and surrounding forested landscapes.
-
D.
Stechow-Ferchesar
Stechow-Ferchesar is a small rural municipality in the Havelland district of Brandenburg, Germany.
-
E.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
- 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: Wendelstein Triple: [Mangfall Mountains, contains, Wendelstein]
Generated description
Wendelstein is a prominent mountain peak in the Bavarian Alps of southern Germany, known for its panoramic views, observatory, and historic cogwheel railway.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wendelstein Target entity description: Wendelstein is a prominent mountain peak in the Bavarian Alps of southern Germany, known for its panoramic views, observatory, and historic cogwheel railway.
-
A.
Hellenstein
Hellenstein is the historical namesake associated with Hellenstein Castle, a prominent medieval fortress in Heidenheim, Germany.
-
B.
Reichenau
Reichenau is a German municipality best known for its UNESCO-listed monastic island on Lake Constance, renowned for its medieval abbey and cultural heritage.
-
C.
Vohenstrauß
Vohenstrauß is a small town in the Upper Palatinate region of Bavaria, Germany, known for its historic architecture and surrounding forested landscapes.
-
D.
Stechow-Ferchesar
Stechow-Ferchesar is a small rural municipality in the Havelland district of Brandenburg, Germany.
-
E.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
- 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_69d381b22b8c8190aaed476be5f872a9 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e92105888190a08104deb9d0cf1c |
completed | April 7, 2026, 11:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d75077dfbc81908de29aac1a3bb19f |
completed | April 9, 2026, 7:08 a.m. |
| NEDg | Description generation | batch_69d7618c9abc819080c4d6669dfb8320 |
completed | April 9, 2026, 8:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d7702ae24481908b0f5319413e81d4 |
completed | April 9, 2026, 9:23 a.m. |
Created at: April 6, 2026, 11:55 a.m.