Triple
T30152862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miko Hughes |
E766438
|
entity |
| Predicate | roleDynamicsDifferFromNovel |
P92866
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Miko Hughes, roleDynamicsDifferFromNovel, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleDynamicsDifferFromNovel Context triple: [Miko Hughes, roleDynamicsDifferFromNovel, yes]
-
A.
portrayalDiffersFrom
chosen
Indicates that one portrayal of an entity differs in some respect from another portrayal of the same entity.
-
B.
worksForInNovel
Indicates that one entity is employed by or serves another entity within the fictional context of a specific novel.
-
C.
novelCounterpart
Indicates a relationship where one entity serves as a new or innovative counterpart or alternative to another.
-
D.
characterContrastWithWork
Indicates a relationship where a character’s traits, behavior, or role are intentionally contrasted with the themes, style, or overall nature of a work.
-
E.
associatedWithAuthorOfSourceNovel
Indicates a relationship where one entity is connected or linked in some way to the author of the original source novel.
- 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_69f22479cd088190ab4c6f3fce39d1c5 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67ed4df5081908d1431e3a1a1fb0e |
completed | May 2, 2026, 10:46 p.m. |
| PD | Predicate disambiguation | batch_69f673c7a4588190837854f3ef61e6bf |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 29, 2026, 7:20 p.m.