Triple
T792261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jürgen Habermas |
E16940
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Jürgen
Jürgen is a masculine given name of German origin, commonly used in German-speaking countries.
|
E140183
|
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: Jürgen | Statement: [Jürgen Habermas, givenName, Jürgen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jürgen Context triple: [Jürgen Habermas, givenName, Jürgen]
-
A.
Helmut
Helmut is a masculine given name of German origin, historically common in German-speaking countries.
-
B.
Olaf Kölzig
Olaf Kölzig is a former German-Canadian NHL goaltender best known for his long, standout career with the Washington Capitals, including winning the Vezina Trophy in 2000.
-
C.
Gerhard
Gerhard is a masculine given name of German origin, historically common in German-speaking countries.
-
D.
Nico Habermann
Nico Habermann was a German-American computer scientist known for his contributions to programming languages, operating systems, and software engineering, and for his influential academic leadership at Carnegie Mellon University.
-
E.
Hannes Nikel
Hannes Nikel was a German film editor known for his work on major German and international productions, including the war drama "Stalingrad" (1993).
- 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: Jürgen Triple: [Jürgen Habermas, givenName, Jürgen]
Generated description
Jürgen is a masculine given name of German origin, commonly used in German-speaking countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jürgen Target entity description: Jürgen is a masculine given name of German origin, commonly used in German-speaking countries.
-
A.
Helmut
Helmut is a masculine given name of German origin, historically common in German-speaking countries.
-
B.
Olaf Kölzig
Olaf Kölzig is a former German-Canadian NHL goaltender best known for his long, standout career with the Washington Capitals, including winning the Vezina Trophy in 2000.
-
C.
Gerhard
Gerhard is a masculine given name of German origin, historically common in German-speaking countries.
-
D.
Nico Habermann
Nico Habermann was a German-American computer scientist known for his contributions to programming languages, operating systems, and software engineering, and for his influential academic leadership at Carnegie Mellon University.
-
E.
Hannes Nikel
Hannes Nikel was a German film editor known for his work on major German and international productions, including the war drama "Stalingrad" (1993).
- 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_69a4936cb7448190914f5fe4b8d81607 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a798c7608190b9c79c52a1fe0859 |
completed | March 1, 2026, 8:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac82e8bd788190a20a580bae9bd94e |
completed | March 7, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69ac870e762881909b8fb892a1f0c338 |
completed | March 7, 2026, 8:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac8761ff0481908eeabcc1b10d7492 |
completed | March 7, 2026, 8:15 p.m. |
Created at: March 1, 2026, 7:38 p.m.