Triple
T16471177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calw |
E400063
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Alzenberg
Alzenberg is a small district or locality that forms part of the town of Calw in the state of Baden-Württemberg, Germany.
|
E1246766
|
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: Alzenberg | Statement: [Calw, hasSubdivision, Alzenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alzenberg Context triple: [Calw, hasSubdivision, Alzenberg]
-
A.
Altenburg
Altenburg is a historic town in eastern Thuringia, Germany, known for its playing-card tradition and as the birthplace of the card game Skat.
-
B.
Dornstadt
Dornstadt is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, located near the city of Ulm.
-
C.
Altenstadt
Altenstadt is a small Bavarian municipality in southern Germany, situated within the rural district of Weilheim-Schongau.
-
D.
Amberg
Amberg is a historic town in Bavaria, Germany, known for its well-preserved medieval old town and former role as a regional administrative and trading center.
-
E.
Lankwitz
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
- 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: Alzenberg Triple: [Calw, hasSubdivision, Alzenberg]
Generated description
Alzenberg is a small district or locality that forms part of the town of Calw in the state of Baden-Württemberg, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Alzenberg Target entity description: Alzenberg is a small district or locality that forms part of the town of Calw in the state of Baden-Württemberg, Germany.
-
A.
Altenburg
Altenburg is a historic town in eastern Thuringia, Germany, known for its playing-card tradition and as the birthplace of the card game Skat.
-
B.
Dornstadt
Dornstadt is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, located near the city of Ulm.
-
C.
Altenstadt
Altenstadt is a small Bavarian municipality in southern Germany, situated within the rural district of Weilheim-Schongau.
-
D.
Amberg
Amberg is a historic town in Bavaria, Germany, known for its well-preserved medieval old town and former role as a regional administrative and trading center.
-
E.
Lankwitz
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32dd0d2fc81909b68b5afb00f192f |
completed | April 18, 2026, 7:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a011b33bc7081908170fbcde9a65fd6 |
completed | May 10, 2026, 11:56 p.m. |
| NEDg | Description generation | batch_6a011bb52af0819098d70c50b86255e1 |
completed | May 10, 2026, 11:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a011c25d6988190a00dc7b00abb628d |
completed | May 11, 2026, midnight |
Created at: April 10, 2026, 5:11 a.m.