Triple
T27089864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Cooper Freedman |
E686134
|
entity |
| Predicate | marriedInSeries |
P140690
|
FINISHED |
| Object | Private Practice |
—
|
NE NERFINISHED |
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: Private Practice | Statement: [Dr. Cooper Freedman, marriedInSeries, Private Practice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedInSeries Context triple: [Dr. Cooper Freedman, marriedInSeries, Private Practice]
-
A.
marriedIn
Indicates that two entities entered into a marital relationship at a specific place or within a particular jurisdiction.
-
B.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
-
C.
hasSpouseInTVSeries
chosen
Indicates that one person is the spouse of another person within the context of a specific TV series.
-
D.
resultedInMarriageTo
Indicates that one event, action, or circumstance led to or caused a marriage to occur between the related entities.
-
E.
hasSpouseInStory
Indicates that one entity is depicted as the spouse of another within the context of a particular story or 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_69ef148940ec819097b5c20fbfbf7c81 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f6978fe97081908fe568091ad9b159 |
completed | May 3, 2026, 12:32 a.m. |
| PD | Predicate disambiguation | batch_69f69661e6ec8190948251c7516a32ad |
completed | May 3, 2026, 12:27 a.m. |
Created at: April 27, 2026, 8:40 a.m.