Triple
T4940409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kurt Buckman |
E110915
|
entity |
| Predicate | storyThemeInvolvement |
P54524
|
FINISHED |
| Object | workplace abuse |
—
|
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: workplace abuse | Statement: [Kurt Buckman, storyThemeInvolvement, workplace abuse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: storyThemeInvolvement Context triple: [Kurt Buckman, storyThemeInvolvement, workplace abuse]
-
A.
themeInvolvingCharacter
Indicates that a theme, motif, or abstract concept centrally involves or is significantly shaped by a particular character.
-
B.
roleInStories
Indicates the specific function, position, or character part an entity plays within one or more stories.
-
C.
storyElement
Indicates that one entity functions as a narrative component or part within the structure of another entity’s story.
-
D.
storyline
Indicates that one entity serves as the narrative plot or sequence of events associated with another entity.
-
E.
narrativeMotif
chosen
Indicates a recurring thematic element, pattern, or situation that appears across one or more narratives and helps structure or convey their underlying meanings.
- 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_69bd4415eee08190bdce70276e56a5b4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd708a3dcc81908b6628864fe0db0a |
completed | March 20, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69bd6c389b9881908ad7fb1c5393c1b1 |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:31 p.m.