Triple
T664880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ore Mountains |
E12837
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Marienberg
Marienberg is a historic mining town in Saxony, Germany, known for its Renaissance-era planned layout and location in the central Ore Mountains.
|
E84126
|
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: Marienberg | Statement: [Ore Mountains, contains, Marienberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marienberg Context triple: [Ore Mountains, contains, Marienberg]
-
A.
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.
-
B.
Mohrungen
Mohrungen is a historic town in former East Prussia (now Morąg in northern Poland), known as the birthplace of philosopher and theologian Johann Gottfried Herder.
-
C.
Boblingen
Böblingen is a town in the German state of Baden-Württemberg, known for its automotive industry presence and proximity to Stuttgart.
-
D.
Gerswalde
Gerswalde is a small rural municipality in the Uckermark district of Brandenburg, northeastern Germany.
-
E.
Breselenz
Breselenz is a small village in Lower Saxony, Germany, best known as the birthplace of the mathematician Bernhard Riemann.
- 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: Marienberg Triple: [Ore Mountains, contains, Marienberg]
Generated description
Marienberg is a historic mining town in Saxony, Germany, known for its Renaissance-era planned layout and location in the central Ore Mountains.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marienberg Target entity description: Marienberg is a historic mining town in Saxony, Germany, known for its Renaissance-era planned layout and location in the central Ore Mountains.
-
A.
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.
-
B.
Mohrungen
Mohrungen is a historic town in former East Prussia (now Morąg in northern Poland), known as the birthplace of philosopher and theologian Johann Gottfried Herder.
-
C.
Boblingen
Böblingen is a town in the German state of Baden-Württemberg, known for its automotive industry presence and proximity to Stuttgart.
-
D.
Gerswalde
Gerswalde is a small rural municipality in the Uckermark district of Brandenburg, northeastern Germany.
-
E.
Breselenz
Breselenz is a small village in Lower Saxony, Germany, best known as the birthplace of the mathematician Bernhard Riemann.
- 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_69a493355dec819098d4244b2fa34885 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49fd3d8fc8190866af5c76c08f486 |
completed | March 1, 2026, 8:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5dc9b645881908c7d2d69aa2f44aa |
completed | March 2, 2026, 6:53 p.m. |
| NEDg | Description generation | batch_69a5e63dbd488190a2cd3c241cc76465 |
completed | March 2, 2026, 7:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a601c3b8f081908e821092ca9cfc82 |
completed | March 2, 2026, 9:31 p.m. |
Created at: March 1, 2026, 7:36 p.m.