Triple
T25584410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shot Caller |
E641337
|
entity |
| Predicate | hasProtagonistOccupationBeforePrison |
P35945
|
FINISHED |
| Object | businessman |
—
|
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: businessman | Statement: [Shot Caller, hasProtagonistOccupationBeforePrison, businessman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProtagonistOccupationBeforePrison Context triple: [Shot Caller, hasProtagonistOccupationBeforePrison, businessman]
-
A.
hasBeenImprisoned
Indicates that an entity has been confined or incarcerated in a prison or similar detention facility at some point in time.
-
B.
hasOccupationDuringStory
Indicates that an entity holds or performs a particular occupation or job role during the time span covered by the story.
-
C.
hasFormerInmate
Indicates that an entity previously housed or supervised an individual who was once an inmate there.
-
D.
characterFormerOccupation
chosen
Indicates that a character previously held a specific occupation but no longer does.
-
E.
hasBeenImprisonedBy
Indicates that one entity has been confined or incarcerated under the authority or control of another entity.
- 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_69e75dc42b588190a98b58e0df359674 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f707f7959881908f037f0d6b1d0c36 |
completed | May 3, 2026, 8:31 a.m. |
| PD | Predicate disambiguation | batch_69f700fc274c8190a128593dc7c7abd0 |
completed | May 3, 2026, 8:02 a.m. |
Created at: April 21, 2026, 4:15 p.m.