Triple
T20585708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carl Schurz |
E505780
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Schurz
Schurz is a German surname most notably associated with Carl Schurz, a 19th-century German-American statesman, reformer, and Union Army general.
|
E1438948
|
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: Schurz | Statement: [Carl Schurz, familyName, Schurz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schurz Context triple: [Carl Schurz, familyName, Schurz]
-
A.
Hillenbrand
Hillenbrand is a surname of German origin borne by various notable individuals in fields such as diplomacy, literature, and sports.
-
B.
Petit & Fritsen
Petit & Fritsen is a historic Dutch bell foundry renowned for casting church bells and carillons used in notable towers and monuments worldwide.
-
C.
Tönnies
Tönnies is a German surname most notably associated with the sociologist Ferdinand Tönnies, a founding figure of modern sociology.
-
D.
Fleischmann
Fleischmann is a German-language surname borne by various notable individuals across fields such as music, science, and the arts.
-
E.
Dr. Oetker
Dr. Oetker is a German multinational food company best known for its baking products, desserts, frozen pizzas, and other convenience foods.
- 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: Schurz Triple: [Carl Schurz, familyName, Schurz]
Generated description
Schurz is a German surname most notably associated with Carl Schurz, a 19th-century German-American statesman, reformer, and Union Army general.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schurz Target entity description: Schurz is a German surname most notably associated with Carl Schurz, a 19th-century German-American statesman, reformer, and Union Army general.
-
A.
Hillenbrand
Hillenbrand is a surname of German origin borne by various notable individuals in fields such as diplomacy, literature, and sports.
-
B.
Petit & Fritsen
Petit & Fritsen is a historic Dutch bell foundry renowned for casting church bells and carillons used in notable towers and monuments worldwide.
-
C.
Tönnies
Tönnies is a German surname most notably associated with the sociologist Ferdinand Tönnies, a founding figure of modern sociology.
-
D.
Fleischmann
Fleischmann is a German-language surname borne by various notable individuals across fields such as music, science, and the arts.
-
E.
Dr. Oetker
Dr. Oetker is a German multinational food company best known for its baking products, desserts, frozen pizzas, and other convenience foods.
- 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_69e0b4b9669c8190b8e81fc72817d42c |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a976bca4819086a4949e299159b5 |
completed | April 20, 2026, 10:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08aceef59881908b63e33baa53b35f |
completed | May 16, 2026, 5:44 p.m. |
| NEDg | Description generation | batch_6a08ae401ed481908b6222d572d57b35 |
completed | May 16, 2026, 5:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08aec256988190bc43a1f784a458ec |
completed | May 16, 2026, 5:52 p.m. |
Created at: April 16, 2026, 11:40 a.m.