Triple
T12482642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Picander |
E298349
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Stolpen
Stolpen is a small historic town in Saxony, Germany, best known for its medieval castle and its association with Countess Cosel.
|
E987150
|
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: Stolpen | Statement: [Picander, placeOfBirth, Stolpen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stolpen Context triple: [Picander, placeOfBirth, Stolpen]
-
A.
Riesa
Riesa is a town in the German state of Saxony, situated on the Elbe River and known historically as an important regional railway and industrial center.
-
B.
Prenzlau
Prenzlau is a historic town in northeastern Germany’s Brandenburg region, known for its medieval architecture and role as a regional administrative center.
-
C.
Boltenhagen
Boltenhagen is a Baltic Sea seaside resort town in northern Germany known for its beaches and tourism.
-
D.
Treuenbrietzen
Treuenbrietzen is a historic town in the German state of Brandenburg, known for its medieval architecture and role in Reformation-era history.
-
E.
Oberhof
Oberhof is a German winter sports town in Thuringia renowned for its biathlon, luge, and cross-country skiing facilities and World Cup events.
- 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: Stolpen Triple: [Picander, placeOfBirth, Stolpen]
Generated description
Stolpen is a small historic town in Saxony, Germany, best known for its medieval castle and its association with Countess Cosel.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stolpen Target entity description: Stolpen is a small historic town in Saxony, Germany, best known for its medieval castle and its association with Countess Cosel.
-
A.
Riesa
Riesa is a town in the German state of Saxony, situated on the Elbe River and known historically as an important regional railway and industrial center.
-
B.
Prenzlau
Prenzlau is a historic town in northeastern Germany’s Brandenburg region, known for its medieval architecture and role as a regional administrative center.
-
C.
Boltenhagen
Boltenhagen is a Baltic Sea seaside resort town in northern Germany known for its beaches and tourism.
-
D.
Treuenbrietzen
Treuenbrietzen is a historic town in the German state of Brandenburg, known for its medieval architecture and role in Reformation-era history.
-
E.
Oberhof
Oberhof is a German winter sports town in Thuringia renowned for its biathlon, luge, and cross-country skiing facilities and World Cup events.
- 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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94dcef6548190a6d29375bdabd17d |
completed | April 10, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64ba5efc881909784037b95f7bbe3 |
completed | May 2, 2026, 7:08 p.m. |
| NEDg | Description generation | batch_69f64ce0ca288190bbbcb5459f914c19 |
completed | May 2, 2026, 7:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f64df6488481909dea8387e7000d15 |
completed | May 2, 2026, 7:18 p.m. |
Created at: April 8, 2026, 9:56 p.m.