Triple
T34181859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank T.J. Mackey |
E876841
|
entity |
| Predicate | teachesInFictionalProgram |
P176538
|
FINISHED |
| Object | Seduce and Destroy |
—
|
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: Seduce and Destroy | Statement: [Frank T.J. Mackey, teachesInFictionalProgram, Seduce and Destroy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teachesInFictionalProgram Context triple: [Frank T.J. Mackey, teachesInFictionalProgram, Seduce and Destroy]
-
A.
isTaughtTo
Indicates that some knowledge, skill, or subject is being instructed or conveyed by a teacher or source to a learner or recipient.
-
B.
teacherInScene
chosen
Indicates that an entity is acting in the role of a teacher within a particular scene or context.
-
C.
alsoTeachesIn
Indicates that an individual who teaches in one context or institution additionally teaches in another context or institution.
-
D.
taughtAs
Indicates that one entity served as a teacher or instructor for another entity in an educational or training context.
-
E.
areTaughtIn
Indicates that certain subjects, courses, or topics are instructed or delivered within specific locations, classes, or educational settings.
- 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_69f349ae640c8190b9cd220b5368d8b6 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7100635d481909a201b11a27f181b |
completed | May 3, 2026, 9:06 a.m. |
| PD | Predicate disambiguation | batch_69f70f3c5bfc81908585f52e196dafe5 |
completed | May 3, 2026, 9:02 a.m. |
Created at: May 1, 2026, 1:54 a.m.