Triple
T3287736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dayton Callie |
E69022
|
entity |
| Predicate | genreOfWorkSpecialization |
P21380
|
FINISHED |
| Object | crime drama |
—
|
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: crime drama | Statement: [Dayton Callie, genreOfWorkSpecialization, crime drama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genreOfWorkSpecialization Context triple: [Dayton Callie, genreOfWorkSpecialization, crime drama]
-
A.
genreOfOccupation
Indicates the specific genre or category that characterizes a particular occupation or professional role.
-
B.
genreSpecialty
chosen
Indicates that an entity specializes in or is particularly associated with a specific genre.
-
C.
genreOfWorkHonored
Indicates the specific genre or type of creative work for which an honor, award, or recognition is given.
-
D.
settingOfWorks
Indicates that a place or environment serves as the primary setting where the events or narratives of one or more works take place.
-
E.
fieldOfWork
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
- 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_69ad859d45748190b0742408c954b39f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb058e00881908fdf0a23208860d4 |
completed | March 8, 2026, 5:22 p.m. |
| PD | Predicate disambiguation | batch_69ada421fadc8190b7c7d3c8afd20061 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:10 p.m.