Triple
T17849114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johann Gottlieb Benjamin Siegert |
E445747
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Siegert
Siegert is a German-language surname most notably associated with Johann Gottlieb Benjamin Siegert, the 19th-century creator of Angostura bitters.
|
E1292155
|
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: Siegert | Statement: [Johann Gottlieb Benjamin Siegert, familyName, Siegert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siegert Context triple: [Johann Gottlieb Benjamin Siegert, familyName, Siegert]
-
A.
Sieg
The Sieg is a river in western Germany that flows through North Rhine-Westphalia and Rhineland-Palatinate before joining the Rhine.
-
B.
Sigel
Sigel is a surname most notably associated with American cinematographer Newton Thomas Sigel, known for his work on major Hollywood films.
-
C.
Siebert
Siebert is a surname most notably associated with Sonny Siebert, an American Major League Baseball pitcher active in the 1960s and 1970s.
-
D.
Siegl
Siegl is the surname of Zev Siegl, an American entrepreneur best known as one of the co-founders of Starbucks.
-
E.
Sperrle
Sperrle is a German surname most notably borne by Hugo Sperrle, a senior Luftwaffe field marshal during World War II.
- 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: Siegert Triple: [Johann Gottlieb Benjamin Siegert, familyName, Siegert]
Generated description
Siegert is a German-language surname most notably associated with Johann Gottlieb Benjamin Siegert, the 19th-century creator of Angostura bitters.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Siegert Target entity description: Siegert is a German-language surname most notably associated with Johann Gottlieb Benjamin Siegert, the 19th-century creator of Angostura bitters.
-
A.
Sieg
The Sieg is a river in western Germany that flows through North Rhine-Westphalia and Rhineland-Palatinate before joining the Rhine.
-
B.
Sigel
Sigel is a surname most notably associated with American cinematographer Newton Thomas Sigel, known for his work on major Hollywood films.
-
C.
Siebert
Siebert is a surname most notably associated with Sonny Siebert, an American Major League Baseball pitcher active in the 1960s and 1970s.
-
D.
Siegl
Siegl is the surname of Zev Siegl, an American entrepreneur best known as one of the co-founders of Starbucks.
-
E.
Sperrle
Sperrle is a German surname most notably borne by Hugo Sperrle, a senior Luftwaffe field marshal during World War II.
- 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_69d8b9f26f18819089c9e43250bee6ae |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48ffd7e2c81909a42cc7ab64e7db9 |
completed | April 19, 2026, 8:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a030c5c53b88190bd641a310ad29923 |
completed | May 12, 2026, 11:17 a.m. |
| NEDg | Description generation | batch_6a030d22013c8190801475da925e0ab9 |
completed | May 12, 2026, 11:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a030dd233208190b7af976e68e4296c |
completed | May 12, 2026, 11:24 a.m. |
Created at: April 10, 2026, 10:16 a.m.