Triple
T16034519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wayne the werewolf |
E388933
|
entity |
| Predicate | hasSpinoffAppearances |
P29864
|
FINISHED |
| Object | Hotel Transylvania short films |
—
|
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: Hotel Transylvania short films | Statement: [Wayne the werewolf, hasSpinoffAppearances, Hotel Transylvania short films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpinoffAppearances Context triple: [Wayne the werewolf, hasSpinoffAppearances, Hotel Transylvania short films]
-
A.
hasSpinOff
Indicates that one entity is a derivative or spin-off product, work, or organization that originated from another entity.
-
B.
spinoffAppearance
chosen
Indicates that an entity appears in a derivative or spinoff work related to another original work or series.
-
C.
hasFranchiseOrSpinOff
Indicates that one work, series, or product is related to another as a franchise entry or a spin-off derived from it.
-
D.
hasSpinOffPotential
Indicates that an entity (such as a project, product, or idea) has the capacity to be developed into a separate, independent venture or derivative offering.
-
E.
hasFictionalShowWithinShow
Indicates that one show contains or features another fictional show within its narrative.
- 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_69d86dada3808190825d5f80d72fbe88 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1ff63edb0819092cbb671967bbdcd |
completed | April 17, 2026, 9:37 a.m. |
| PD | Predicate disambiguation | batch_69e1826f34c081908005bb736f1c485d |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:56 a.m.