Triple
T1759297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Schlemihl |
E38618
|
entity |
| Predicate | hasNationalityInFiction |
P15237
|
FINISHED |
| Object | German |
—
|
LITERAL FINISHED |
How this triple was built (2 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: German | Statement: [Peter Schlemihl, hasNationalityInFiction, German]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNationalityInFiction Context triple: [Peter Schlemihl, hasNationalityInFiction, German]
-
A.
nationalityInStory
chosen
Indicates that a character or entity in a narrative is associated with a particular nationality within the context of that story.
-
B.
locatedInFictionalCountry
Indicates that an entity exists or is situated within a country that is fictional rather than real.
-
C.
nationalityInText
Indicates that a person's nationality is mentioned or specified within a given text.
-
D.
authorNationality
Indicates the relationship between an author and the country or nationality with which that author is identified.
-
E.
hasNotableFictionalBearer
Indicates that an entity is associated with at least one well-known fictional character that bears its name or designation.
- F. None of above.
Provenance (3 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_69a8862d562481908d7025a1c1f67c0d |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab173936b4819097332ee185996bbd |
completed | March 6, 2026, 6:04 p.m. |
| PD | Predicate disambiguation | batch_69aa61c9e06c819085489e00cfe72153 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.