Triple
T4875488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johannes Stark |
E109192
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Schickenhof
Schickenhof is a small locality in Germany best known as the birthplace of Nobel Prize–winning physicist Johannes Stark.
|
E476366
|
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: Schickenhof | Statement: [Johannes Stark, placeOfBirth, Schickenhof]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schickenhof Context triple: [Johannes Stark, placeOfBirth, Schickenhof]
-
A.
Hartmannshof
Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
-
B.
Erbdrostenhof
Erbdrostenhof is an 18th-century Baroque palace in Münster, Germany, renowned for its ornate architecture and historical significance.
-
C.
Helmscherode
Helmscherode is a small locality in Germany known as the birthplace of Wilhelm Keitel, a senior military leader of Nazi Germany.
-
D.
Saalhof
Saalhof is a historic medieval building complex in Frankfurt am Main that forms part of the city’s museum landscape and reflects its architectural and urban history.
-
E.
Teutschenthal
Teutschenthal is a municipality in the Saalekreis district of Saxony-Anhalt in central Germany.
- 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: Schickenhof Triple: [Johannes Stark, placeOfBirth, Schickenhof]
Generated description
Schickenhof is a small locality in Germany best known as the birthplace of Nobel Prize–winning physicist Johannes Stark.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schickenhof Target entity description: Schickenhof is a small locality in Germany best known as the birthplace of Nobel Prize–winning physicist Johannes Stark.
-
A.
Hartmannshof
Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
-
B.
Erbdrostenhof
Erbdrostenhof is an 18th-century Baroque palace in Münster, Germany, renowned for its ornate architecture and historical significance.
-
C.
Helmscherode
Helmscherode is a small locality in Germany known as the birthplace of Wilhelm Keitel, a senior military leader of Nazi Germany.
-
D.
Saalhof
Saalhof is a historic medieval building complex in Frankfurt am Main that forms part of the city’s museum landscape and reflects its architectural and urban history.
-
E.
Teutschenthal
Teutschenthal is a municipality in the Saalekreis district of Saxony-Anhalt in central Germany.
- 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_69bd440e9d64819083e82cf33b4d9570 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6dba3efc8190adcf8b30490b4984 |
completed | March 20, 2026, 3:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be67f90e848190a36eee1e670657e4 |
completed | March 21, 2026, 9:42 a.m. |
| NEDg | Description generation | batch_69be6892c02481908dc64c7e84aac3b2 |
completed | March 21, 2026, 9:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be695116788190903fbd5e375bd31d |
completed | March 21, 2026, 9:48 a.m. |
Created at: March 20, 2026, 1:27 p.m.