Triple
T14306627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marbach am Neckar |
E354713
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Jan Trost
Jan Trost is a German local politician who serves as the mayor of the town of Marbach am Neckar in Baden-Württemberg.
|
E1146745
|
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: Jan Trost | Statement: [Marbach am Neckar, hasMayor, Jan Trost]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jan Trost Context triple: [Marbach am Neckar, hasMayor, Jan Trost]
-
A.
Charles Bergstresser
Charles Bergstresser was an American journalist and financier best known as one of the co-founders of The Wall Street Journal.
-
B.
Karl Grobben
Karl Grobben was an Austrian zoologist known for his influential work in animal classification, including helping to establish major groups such as the Deuterostomia.
-
C.
Albert Schickedanz
Albert Schickedanz was a Hungarian architect and designer best known for his monumental historicist works in Budapest, including key buildings and ensembles on Andrássy Avenue.
-
D.
Emil Sieg
Emil Sieg was a German linguist and philologist known for his pioneering work on Tocharian and other Indo-European languages.
-
E.
Thomas Tuschl
Thomas Tuschl is a German biochemist and pioneering RNA interference researcher whose work helped lay the scientific foundation for RNA-based therapeutics.
- 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: Jan Trost Triple: [Marbach am Neckar, hasMayor, Jan Trost]
Generated description
Jan Trost is a German local politician who serves as the mayor of the town of Marbach am Neckar in Baden-Württemberg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jan Trost Target entity description: Jan Trost is a German local politician who serves as the mayor of the town of Marbach am Neckar in Baden-Württemberg.
-
A.
Charles Bergstresser
Charles Bergstresser was an American journalist and financier best known as one of the co-founders of The Wall Street Journal.
-
B.
Karl Grobben
Karl Grobben was an Austrian zoologist known for his influential work in animal classification, including helping to establish major groups such as the Deuterostomia.
-
C.
Albert Schickedanz
Albert Schickedanz was a Hungarian architect and designer best known for his monumental historicist works in Budapest, including key buildings and ensembles on Andrássy Avenue.
-
D.
Emil Sieg
Emil Sieg was a German linguist and philologist known for his pioneering work on Tocharian and other Indo-European languages.
-
E.
Thomas Tuschl
Thomas Tuschl is a German biochemist and pioneering RNA interference researcher whose work helped lay the scientific foundation for RNA-based therapeutics.
- 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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de85b156b0819083f2bd319deed1b6 |
completed | April 14, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5dc9b908190b1d7583810dc9c41 |
completed | May 9, 2026, 7:44 a.m. |
| NEDg | Description generation | batch_69fee6ea4fec81908d770df705e0512b |
completed | May 9, 2026, 7:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fee897a7248190b3d81ea1da1d49e1 |
completed | May 9, 2026, 7:56 a.m. |
Created at: April 10, 2026, 1:12 a.m.