Triple
T2214353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A King’s Story: The Memoirs of the Duke of Windsor |
E50988
|
entity |
| Predicate | literarySubject |
P36841
|
FINISHED |
| Object | royalty |
—
|
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: royalty | Statement: [A King’s Story: The Memoirs of the Duke of Windsor, literarySubject, royalty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literarySubject Context triple: [A King’s Story: The Memoirs of the Duke of Windsor, literarySubject, royalty]
-
A.
inLiterature
Indicates that a work, concept, or entity is mentioned, discussed, or represented within a piece of literature.
-
B.
literaryUniverse
Indicates that two or more works of literature exist within the same fictional universe or continuity, sharing settings, characters, or canonical events.
-
C.
literaryLanguage
Indicates that an entity is expressed, written, or communicated using a particular literary or standardized written language.
-
D.
literaryGenreOfWork
Indicates that a work belongs to or is classified under a particular literary genre.
-
E.
literarySource
Indicates that one entity serves as the written or literary origin, reference, or basis for another entity.
- F. None of above. chosen
Provenance (4 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfef875c8190b642736b4cc11d4c |
completed | March 7, 2026, 6:04 a.m. |
| PD | Predicate disambiguation | batch_69abbdaa26d48190860c33fd464c4845 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbf0c2b8881908553eed5be17a9c2 |
completed | March 7, 2026, 6 a.m. |
Created at: March 4, 2026, 7:46 p.m.