Triple
T15130749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hermann von Pückler-Muskau |
E361411
|
entity |
| Predicate | honorificTitle |
P2097
|
FINISHED |
| Object |
Fürst
Fürst is a German noble title historically ranking below a duke and above a count, often translated as "prince" in English.
|
E1139104
|
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: Fürst | Statement: [Hermann von Pückler-Muskau, honorificTitle, Fürst]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fürst Context triple: [Hermann von Pückler-Muskau, honorificTitle, Fürst]
-
A.
Prinz
Prinz is a German surname borne by various notable individuals, including figures in politics, religion, and the arts.
-
B.
Prinze
Prinze is the surname of American actor Freddie Prinze Jr., associated with a family of entertainers in film and television.
-
C.
Kœnig
Kœnig is a French surname most notably associated with figures such as General Marie-Pierre Kœnig, a prominent military leader during World War II.
-
D.
Grand Duke
The Grand Duke is the hereditary monarch and ceremonial head of state of the Grand Duchy of Luxembourg.
-
E.
Le Prince
Le Prince is a French surname most notably associated with Jean-Baptiste Le Prince, an 18th-century painter and etcher known for his scenes inspired by travels in Russia.
- 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: Fürst Triple: [Hermann von Pückler-Muskau, honorificTitle, Fürst]
Generated description
Fürst is a German noble title historically ranking below a duke and above a count, often translated as "prince" in English.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fürst Target entity description: Fürst is a German noble title historically ranking below a duke and above a count, often translated as "prince" in English.
-
A.
Prinz
Prinz is a German surname borne by various notable individuals, including figures in politics, religion, and the arts.
-
B.
Prinze
Prinze is the surname of American actor Freddie Prinze Jr., associated with a family of entertainers in film and television.
-
C.
Kœnig
Kœnig is a French surname most notably associated with figures such as General Marie-Pierre Kœnig, a prominent military leader during World War II.
-
D.
Grand Duke
The Grand Duke is the hereditary monarch and ceremonial head of state of the Grand Duchy of Luxembourg.
-
E.
Le Prince
Le Prince is a French surname most notably associated with Jean-Baptiste Le Prince, an 18th-century painter and etcher known for his scenes inspired by travels in Russia.
- 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_69d85a06450081909c5a14ea9851a15e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005b194748190801e3956bf2429d4 |
completed | April 15, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feb7fa449c8190aed8941168b063c3 |
completed | May 9, 2026, 4:28 a.m. |
| NEDg | Description generation | batch_69febbfb58608190830eca7f9a78fc9c |
completed | May 9, 2026, 4:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69febc842a7081908a85ba2833212650 |
completed | May 9, 2026, 4:48 a.m. |
Created at: April 10, 2026, 3:06 a.m.