Triple
T3688826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Braunlage |
E78293
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Hohegeiß
Hohegeiß is a mountain village and health resort in the Harz region of central Germany, known for its scenic landscapes and outdoor recreation.
|
E382121
|
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: Hohegeiß | Statement: [Braunlage, hasPart, Hohegeiß]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hohegeiß Context triple: [Braunlage, hasPart, Hohegeiß]
-
A.
Ettersberg
Ettersberg is a hill and surrounding area near Weimar in Thuringia, Germany, historically known as the site of the Buchenwald concentration camp.
-
B.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
-
C.
Weidach
Weidach is a locality or district that forms part of the municipality of Blaustein in the state of Baden-Württemberg, Germany.
-
D.
Luterbach
Luterbach is a municipality in the canton of Solothurn in northwestern Switzerland, known for its residential character and proximity to the Aare River.
-
E.
Erzhausen
Erzhausen is a small municipality in the state of Hesse in central Germany, located near Darmstadt and part of the Rhine-Main metropolitan region.
- 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: Hohegeiß Triple: [Braunlage, hasPart, Hohegeiß]
Generated description
Hohegeiß is a mountain village and health resort in the Harz region of central Germany, known for its scenic landscapes and outdoor recreation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hohegeiß Target entity description: Hohegeiß is a mountain village and health resort in the Harz region of central Germany, known for its scenic landscapes and outdoor recreation.
-
A.
Ettersberg
Ettersberg is a hill and surrounding area near Weimar in Thuringia, Germany, historically known as the site of the Buchenwald concentration camp.
-
B.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
-
C.
Weidach
Weidach is a locality or district that forms part of the municipality of Blaustein in the state of Baden-Württemberg, Germany.
-
D.
Luterbach
Luterbach is a municipality in the canton of Solothurn in northwestern Switzerland, known for its residential character and proximity to the Aare River.
-
E.
Erzhausen
Erzhausen is a small municipality in the state of Hesse in central Germany, located near Darmstadt and part of the Rhine-Main metropolitan region.
- 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_69ad85e285a081908f8cbfa9e2ed9b75 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc4c960788190b73ede08658846aa |
completed | March 8, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4cdeb6c288190a52e81fcbeaeec6d |
completed | March 14, 2026, 2:54 a.m. |
| NEDg | Description generation | batch_69b4d1b7f168819080c88c216c48f83c |
completed | March 14, 2026, 3:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4d2a6d2e48190aa811033129986dd |
completed | March 14, 2026, 3:14 a.m. |
Created at: March 8, 2026, 3:26 p.m.