Triple
T14951060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weimarer Land |
E372793
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Niederroßla
Niederroßla is a small municipality in the German state of Thuringia, known for its rural character within the Weimarer Land district.
|
E1129108
|
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: Niederroßla | Statement: [Weimarer Land, containsMunicipality, Niederroßla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Niederroßla Context triple: [Weimarer Land, containsMunicipality, Niederroßla]
-
A.
Biebelried
Biebelried is a small municipality in the Kitzingen district of Bavaria, Germany, known for its rural character and proximity to the Franconian wine region.
-
B.
Groß Dölln
Groß Dölln is a small village in Brandenburg, Germany, known for its surrounding forests, lakes, and the nearby former military airfield now used as a driving and testing center.
-
C.
Niederjosbach
Niederjosbach is a village and district of the town of Eppstein in the German state of Hesse.
-
D.
Niederwerrn
Niederwerrn is a municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
-
E.
Stechelberg
Stechelberg is a small Swiss village at the end of the Lauterbrunnen Valley, known for its dramatic alpine scenery and access to hiking and cable cars into the surrounding mountains.
- 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: Niederroßla Triple: [Weimarer Land, containsMunicipality, Niederroßla]
Generated description
Niederroßla is a small municipality in the German state of Thuringia, known for its rural character within the Weimarer Land district.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Niederroßla Target entity description: Niederroßla is a small municipality in the German state of Thuringia, known for its rural character within the Weimarer Land district.
-
A.
Biebelried
Biebelried is a small municipality in the Kitzingen district of Bavaria, Germany, known for its rural character and proximity to the Franconian wine region.
-
B.
Groß Dölln
Groß Dölln is a small village in Brandenburg, Germany, known for its surrounding forests, lakes, and the nearby former military airfield now used as a driving and testing center.
-
C.
Niederjosbach
Niederjosbach is a village and district of the town of Eppstein in the German state of Hesse.
-
D.
Niederwerrn
Niederwerrn is a municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
-
E.
Stechelberg
Stechelberg is a small Swiss village at the end of the Lauterbrunnen Valley, known for its dramatic alpine scenery and access to hiking and cable cars into the surrounding mountains.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded690f2e08190ad9dad6dc05a164a |
completed | April 15, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e9a6098819087b6e81bebcf6805 |
completed | May 9, 2026, 12:23 a.m. |
| NEDg | Description generation | batch_69fe83fdef58819098cda8cca0d810dd |
completed | May 9, 2026, 12:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe8518121c8190b6ffcc14ec5ac8ea |
completed | May 9, 2026, 12:51 a.m. |
Created at: April 10, 2026, 2:39 a.m.