Triple
T30946874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Dark Tower (novel) |
E788416
|
entity |
| Predicate | featuresAuthorAsCharacter |
P194037
|
FINISHED |
| Object | Stephen King |
—
|
NE NERFINISHED |
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: Stephen King | Statement: [The Dark Tower (novel), featuresAuthorAsCharacter, Stephen King]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresAuthorAsCharacter Context triple: [The Dark Tower (novel), featuresAuthorAsCharacter, Stephen King]
-
A.
hasAuthorAsCharacter
chosen
Indicates that an author appears as a character within a work (such as a book, film, or other narrative).
-
B.
creatorOfCharacter
Indicates that one entity is the originator or author who created or conceived the other entity as a character.
-
C.
semiAutobiographicalCharacter
Indicates that a character is based partly on the real-life experiences, personality, or identity of its creator or author, but is not a fully direct self-portrayal.
-
D.
leadActorPlaysAuthorCharacter
Indicates that the film’s lead actor portrays a character who is an author.
-
E.
featuresAuthor
Indicates that something includes or highlights a particular author as a primary associated contributor.
- 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_69f224c180f88190ad177372ee02b7e2 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a0049e40d60819081e3899d8d9fca51 |
completed | May 10, 2026, 9:03 a.m. |
| PD | Predicate disambiguation | batch_6a0048c47b548190ad31b2901cc3784a |
completed | May 10, 2026, 8:58 a.m. |
Created at: April 29, 2026, 8:53 p.m.