Triple
T1413996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anita Gütermann |
E31869
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Gütermann
Gütermann is a German surname most notably associated with the Gütermann family involved in industry and manufacturing, particularly in the production of sewing threads.
|
E162141
|
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: Gütermann | Statement: [Anita Gütermann, familyName, Gütermann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gütermann Context triple: [Anita Gütermann, familyName, Gütermann]
-
A.
Hammann
Hammann is a German-origin surname borne by various notable individuals in fields such as aviation, music, and academia.
-
B.
Edelmann
Edelmann is a surname of German origin borne by various individuals across fields such as music, sports, and academia.
-
C.
Kretschmann
Kretschmann is a German surname most prominently associated with actor Thomas Kretschmann, known for his roles in international film and television.
-
D.
Hartmann
Hartmann is a German surname borne by numerous notable individuals across fields such as music, philosophy, and aviation.
-
E.
Wolthusen
Wolthusen is a district of the seaport city of Emden in Lower Saxony, Germany, known for its residential character and proximity to the Ems estuary.
- 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: Gütermann Triple: [Anita Gütermann, familyName, Gütermann]
Generated description
Gütermann is a German surname most notably associated with the Gütermann family involved in industry and manufacturing, particularly in the production of sewing threads.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gütermann Target entity description: Gütermann is a German surname most notably associated with the Gütermann family involved in industry and manufacturing, particularly in the production of sewing threads.
-
A.
Hammann
Hammann is a German-origin surname borne by various notable individuals in fields such as aviation, music, and academia.
-
B.
Edelmann
Edelmann is a surname of German origin borne by various individuals across fields such as music, sports, and academia.
-
C.
Kretschmann
Kretschmann is a German surname most prominently associated with actor Thomas Kretschmann, known for his roles in international film and television.
-
D.
Hartmann
Hartmann is a German surname borne by numerous notable individuals across fields such as music, philosophy, and aviation.
-
E.
Wolthusen
Wolthusen is a district of the seaport city of Emden in Lower Saxony, Germany, known for its residential character and proximity to the Ems estuary.
- 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_69a49919a994819086528951bc224775 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3e476f08190aed1576805c62462 |
completed | March 1, 2026, 10:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ace57ec2d88190b06d0f20e3b52462 |
completed | March 8, 2026, 2:57 a.m. |
| NEDg | Description generation | batch_69ace5fc91d081909b33009d06a38616 |
completed | March 8, 2026, 2:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ace6c10f54819089a4afcf49ef894f |
completed | March 8, 2026, 3:02 a.m. |
Created at: March 1, 2026, 7:59 p.m.