Triple
T5160632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2 Fast 2 Furious |
E116425
|
entity |
| Predicate | franchiseNumber |
P42681
|
FINISHED |
| Object | second film in the Fast & Furious series |
—
|
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: second film in the Fast & Furious series | Statement: [2 Fast 2 Furious, franchiseNumber, second film in the Fast & Furious series]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: franchiseNumber Context triple: [2 Fast 2 Furious, franchiseNumber, second film in the Fast & Furious series]
-
A.
franchiseEntryNumber
Indicates the ordinal position or sequence number of an entry within a franchise series.
-
B.
franchiseInstallmentNumber
chosen
Indicates the sequential position or installment number of a work within a larger franchise or series.
-
C.
franchiseName
Indicates that an entity is identified by or associated with a specific franchise’s official name.
-
D.
franchiseElement
Indicates that one entity is a component, installment, or part within a larger franchise or series associated with another entity.
-
E.
franchiseOf
Indicates that one entity operates as a franchise belonging to or licensed 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_69bd445edb3881909b93b34d260717fc |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd79c1354c81908176703b4853c1a4 |
completed | March 20, 2026, 4:45 p.m. |
| PD | Predicate disambiguation | batch_69bd77b0fbb88190851e2d7ae1bdcc09 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:44 p.m.