Triple
T7316911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brenda Leigh Johnson |
E168435
|
entity |
| Predicate | franchiseConnection |
P76267
|
FINISHED |
| Object | predecessor to Major Crimes (TV 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: predecessor to Major Crimes (TV series) | Statement: [Brenda Leigh Johnson, franchiseConnection, predecessor to Major Crimes (TV series)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: franchiseConnection Context triple: [Brenda Leigh Johnson, franchiseConnection, predecessor to Major Crimes (TV series)]
-
A.
franchiseOf
Indicates that one entity operates as a franchise belonging to or licensed by another entity.
-
B.
franchiseName
Indicates that an entity is identified by or associated with a specific franchise’s official name.
-
C.
studioFranchise
Indicates that a studio is associated with, produces, or manages a particular franchise.
-
D.
franchiseElement
Indicates that one entity is a component, installment, or part within a larger franchise or series associated with another entity.
-
E.
franchiseBrand
Indicates that one entity is the brand under which another entity operates as a franchise.
- F. None of above. chosen
Provenance (4 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_69c68a5251508190ad68df4151cfeb04 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6ef162d488190bf1c63b71b20a294 |
completed | March 27, 2026, 8:56 p.m. |
| PD | Predicate disambiguation | batch_69c6e7705f4881909793071dee50c557 |
completed | March 27, 2026, 8:24 p.m. |
| PDg | Predicate description generation | batch_69c6eb88a2648190acc79eeee8733705 |
completed | March 27, 2026, 8:41 p.m. |
Created at: March 27, 2026, 3:02 p.m.