Triple
T4029750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Quill |
E83677
|
entity |
| Predicate | hasSpinOffTieIn |
P45612
|
FINISHED |
| Object | Class tie-in novels |
—
|
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: Class tie-in novels | Statement: [Miss Quill, hasSpinOffTieIn, Class tie-in novels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpinOffTieIn Context triple: [Miss Quill, hasSpinOffTieIn, Class tie-in novels]
-
A.
hasSpinOff
Indicates that one entity is a derivative or spin-off product, work, or organization that originated from another entity.
-
B.
hasFranchiseOrSpinOff
chosen
Indicates that one work, series, or product is related to another as a franchise entry or a spin-off derived from it.
-
C.
spinoffRelation
Indicates a relationship where one entity originates as a derivative, offshoot, or spin-off from another entity.
-
D.
hasMerchandiseTieIn
Indicates that one entity has a commercial or promotional product or line (merchandise) that is directly tied to, branded with, or derived from another entity.
-
E.
hasSequelOrRelated
Indicates that one work follows, continues, or is otherwise narratively or thematically related to another work.
- 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_69aed92e29ac819080f7a98b594fec05 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaf1d8208190951a20ad7e5ab7bc |
completed | March 9, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69aef8fe440c819093a7fa22c4ff3f1a |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:36 p.m.