Triple
T13674183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | district of Roth |
E327828
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Allersberg
Allersberg is a market town in the Roth district of Bavaria, Germany, known for its historic center and proximity to the city of Nuremberg.
|
E1052741
|
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: Allersberg | Statement: [district of Roth, contains, Allersberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allersberg Context triple: [district of Roth, contains, Allersberg]
-
A.
Wackersberg
Wackersberg is a rural Bavarian municipality in southern Germany, known for its scenic Alpine foothills and traditional village character.
-
B.
Landensberg
Landensberg is a small municipality in the Bavarian region of southern Germany.
-
C.
Ettersberg
Ettersberg is a hill and surrounding area near Weimar in Thuringia, Germany, historically known as the site of the Buchenwald concentration camp.
-
D.
Deutschlandsberg
Deutschlandsberg is a small Austrian town in the southwest of the state of Styria, known for its surrounding vineyards, castle, and scenic hilly landscape.
-
E.
Witzmannsberg
Witzmannsberg is a small rural municipality in the Bavarian region of Lower Bavaria, Germany, known for its scenic countryside and traditional village character.
- 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: Allersberg Triple: [district of Roth, contains, Allersberg]
Generated description
Allersberg is a market town in the Roth district of Bavaria, Germany, known for its historic center and proximity to the city of Nuremberg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Allersberg Target entity description: Allersberg is a market town in the Roth district of Bavaria, Germany, known for its historic center and proximity to the city of Nuremberg.
-
A.
Wackersberg
Wackersberg is a rural Bavarian municipality in southern Germany, known for its scenic Alpine foothills and traditional village character.
-
B.
Landensberg
Landensberg is a small municipality in the Bavarian region of southern Germany.
-
C.
Ettersberg
Ettersberg is a hill and surrounding area near Weimar in Thuringia, Germany, historically known as the site of the Buchenwald concentration camp.
-
D.
Deutschlandsberg
Deutschlandsberg is a small Austrian town in the southwest of the state of Styria, known for its surrounding vineyards, castle, and scenic hilly landscape.
-
E.
Witzmannsberg
Witzmannsberg is a small rural municipality in the Bavarian region of Lower Bavaria, Germany, known for its scenic countryside and traditional village character.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc65aab348190a6611f5765f8392d |
completed | April 12, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78b145fa081908521c103201f3afe |
completed | May 3, 2026, 5:51 p.m. |
| NEDg | Description generation | batch_69f78bd727048190a57a75294a9ab53d |
completed | May 3, 2026, 5:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f78c94da6c8190b9bc1d04cee19c3c |
completed | May 3, 2026, 5:57 p.m. |
Created at: April 9, 2026, 9:53 p.m.