Triple
T35556206
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Now You See Me 3 |
E1027501
|
entity |
| Predicate | hasFranchiseGenre |
P39173
|
FINISHED |
| Object | heist-thriller |
—
|
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: heist-thriller | Statement: [Now You See Me 3, hasFranchiseGenre, heist-thriller]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFranchiseGenre Context triple: [Now You See Me 3, hasFranchiseGenre, heist-thriller]
-
A.
franchiseGenre
chosen
Indicates that a franchise belongs to or is categorized under a particular genre.
-
B.
supportsFranchiseType
Indicates that one entity is compatible with, or provides the necessary capabilities for, a particular type of franchise.
-
C.
hasFranchiseSlot
Indicates that an entity holds or is assigned a specific franchise position, license, or allocation within a larger franchising structure.
-
D.
hasFranchiseRelation
Indicates a relationship where one entity holds franchise rights or operates under the brand, business model, or authorization of another entity.
-
E.
hasFranchiseModel
Indicates that one entity operates under, offers, or is associated with a business franchise system or structure defined by another entity.
- 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_69f76e014fd481909e9f04ac603a2aa9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 3, 2026, 4:04 p.m.