Triple
T4555889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyra Sedgwick as Brenda Leigh Johnson |
E120475
|
entity |
| Predicate | spouseOccupationInSeries |
P58013
|
FINISHED |
| Object | FBIAgent |
—
|
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: FBIAgent | Statement: [Kyra Sedgwick as Brenda Leigh Johnson, spouseOccupationInSeries, FBIAgent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOccupationInSeries Context triple: [Kyra Sedgwick as Brenda Leigh Johnson, spouseOccupationInSeries, FBIAgent]
-
A.
spouseOccupation
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
B.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
C.
hasSpouseInStory
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
-
D.
spouseFamily
Indicates a family relationship formed through marriage, such as between a person and their spouse’s relatives.
-
E.
spouseOffice
Indicates that one entity holds an office or position that is associated with, or held by, the spouse of another entity.
- 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_69bd4636f1648190a701445c2fcd9c17 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd5814f56c8190a65f61f6148b7e5a |
completed | March 20, 2026, 2:22 p.m. |
| PD | Predicate disambiguation | batch_69bd5223423c81908317351b58cff5f5 |
completed | March 20, 2026, 1:56 p.m. |
| PDg | Predicate description generation | batch_69bd56b4a9508190acdb888eef18f1ee |
completed | March 20, 2026, 2:16 p.m. |
Created at: March 20, 2026, 1:09 p.m.