Triple
T3052825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peanuts |
E60410
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Schroeder
Schroeder is a character from the Peanuts comic strip known for his serious devotion to playing the piano and his admiration for Beethoven.
|
E322341
|
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: Schroeder | Statement: [Peanuts, hasCharacter, Schroeder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schroeder Context triple: [Peanuts, hasCharacter, Schroeder]
-
A.
Sanders
Sanders is a common English-language surname borne by numerous notable individuals across politics, sports, entertainment, and other fields.
-
B.
Michael Schroeder
Michael Schroeder is a software developer best known for his work on the GNU Screen terminal multiplexer.
-
C.
Weiner
Weiner is a surname of Germanic origin borne by various notable individuals across fields such as business, politics, and entertainment.
-
D.
Paul Wallot
Paul Wallot was a German architect best known for designing Berlin’s iconic Reichstag building, the historic seat of the German parliament.
-
E.
Schröder
Schröder is a common German surname borne by numerous notable figures in politics, sports, and the arts.
- 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: Schroeder Triple: [Peanuts, hasCharacter, Schroeder]
Generated description
Schroeder is a character from the Peanuts comic strip known for his serious devotion to playing the piano and his admiration for Beethoven.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schroeder Target entity description: Schroeder is a character from the Peanuts comic strip known for his serious devotion to playing the piano and his admiration for Beethoven.
-
A.
Sanders
Sanders is a common English-language surname borne by numerous notable individuals across politics, sports, entertainment, and other fields.
-
B.
Michael Schroeder
Michael Schroeder is a software developer best known for his work on the GNU Screen terminal multiplexer.
-
C.
Weiner
Weiner is a surname of Germanic origin borne by various notable individuals across fields such as business, politics, and entertainment.
-
D.
Paul Wallot
Paul Wallot was a German architect best known for designing Berlin’s iconic Reichstag building, the historic seat of the German parliament.
-
E.
Schröder
Schröder is a common German surname borne by numerous notable figures in politics, sports, and the arts.
- 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_69ad8578137c81908259dcb27c7d6d7c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ad9bf3c52c8190bbe8e5cb98c21715 |
completed | March 8, 2026, 3:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1eefd3860819085cefea633b5dca9 |
completed | March 11, 2026, 10:38 p.m. |
| NEDg | Description generation | batch_69b1efa8c11081908661b33e465e11bc |
completed | March 11, 2026, 10:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1f05e44e08190be8b194938b6c1c7 |
completed | March 11, 2026, 10:44 p.m. |
Created at: March 8, 2026, 3:01 p.m.