Triple
T7977728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bob Hoskins as Mr. Fezziwig |
E185486
|
entity |
| Predicate | disneyBrand |
P70907
|
FINISHED |
| Object | Disney Christmas film |
—
|
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: Disney Christmas film | Statement: [Bob Hoskins as Mr. Fezziwig, disneyBrand, Disney Christmas film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: disneyBrand Context triple: [Bob Hoskins as Mr. Fezziwig, disneyBrand, Disney Christmas film]
-
A.
disneyLabel
chosen
Indicates that an entity is labeled, branded, or categorized under the Disney franchise or organization.
-
B.
franchiseTheme
Indicates that one entity has a thematic style, branding, or concept derived from or associated with a particular franchise.
-
C.
franchiseCharacter
Indicates a relationship where a character belongs to, appears in, or is part of a particular media franchise.
-
D.
franchiseUniverse
Indicates that multiple works, characters, or stories exist within and are connected by the same overarching fictional franchise or narrative universe.
-
E.
franchiseBrand
Indicates that one entity is the brand under which another entity operates as a franchise.
- 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_69ca829851908190b4e03829353ee7c3 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3bf716508190b4245bd5d89ae8c4 |
completed | March 31, 2026, 3:13 a.m. |
| PD | Predicate disambiguation | batch_69cb047a8e4c81909b79e0f0bf56440c |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:14 p.m.