Triple
T9975502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Göhren |
E196316
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Lars Schwarz
Lars Schwarz is a German local politician who serves as the mayor of the Baltic Sea resort town of Göhren.
|
E839123
|
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: Lars Schwarz | Statement: [Göhren, hasMayor, Lars Schwarz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lars Schwarz Context triple: [Göhren, hasMayor, Lars Schwarz]
-
A.
Lars Schmidt
Lars Schmidt was a Swedish theatrical producer and director known for bringing American and British plays to Scandinavian and European stages and for his marriage to actress Ingrid Bergman.
-
B.
Lars Heikensten
Lars Heikensten is a Swedish economist and former Governor of Sveriges Riksbank who has also held prominent roles in European financial institutions and cultural organizations.
-
C.
Lars Christensen
Lars Christensen was a prominent Norwegian shipowner and whaling magnate known for financing Antarctic expeditions and contributing to polar exploration.
-
D.
Lars Richter
Lars Richter is a person notable enough to be recognized as a bearer of the surname Richter.
-
E.
Lars Bak
Lars Bak is a Danish computer scientist and software engineer best known for designing high-performance virtual machines and just-in-time compilers for languages such as Java and JavaScript.
- 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: Lars Schwarz Triple: [Göhren, hasMayor, Lars Schwarz]
Generated description
Lars Schwarz is a German local politician who serves as the mayor of the Baltic Sea resort town of Göhren.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lars Schwarz Target entity description: Lars Schwarz is a German local politician who serves as the mayor of the Baltic Sea resort town of Göhren.
-
A.
Lars Schmidt
Lars Schmidt was a Swedish theatrical producer and director known for bringing American and British plays to Scandinavian and European stages and for his marriage to actress Ingrid Bergman.
-
B.
Lars Heikensten
Lars Heikensten is a Swedish economist and former Governor of Sveriges Riksbank who has also held prominent roles in European financial institutions and cultural organizations.
-
C.
Lars Christensen
Lars Christensen was a prominent Norwegian shipowner and whaling magnate known for financing Antarctic expeditions and contributing to polar exploration.
-
D.
Lars Richter
Lars Richter is a person notable enough to be recognized as a bearer of the surname Richter.
-
E.
Lars Bak
Lars Bak is a Danish computer scientist and software engineer best known for designing high-performance virtual machines and just-in-time compilers for languages such as Java and JavaScript.
- 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_69ca82eea2b88190a0e511d21a31f386 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb84b47308190aa2f94fa7320cdc3 |
completed | April 2, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d299e3d5fc8190a953be3ebd8250e6 |
completed | April 5, 2026, 5:20 p.m. |
| NEDg | Description generation | batch_69d29b985e308190a6ec3966e02f429c |
completed | April 5, 2026, 5:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d29c5f64c881909aa3d093422fe475 |
completed | April 5, 2026, 5:31 p.m. |
Created at: March 30, 2026, 8:48 p.m.