Triple
T35196696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shannon (TV series) |
E1016280
|
entity |
| Predicate | typicalStoryline |
P24126
|
FINISHED |
| Object | investigation of suspicious insurance claims |
—
|
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: investigation of suspicious insurance claims | Statement: [Shannon (TV series), typicalStoryline, investigation of suspicious insurance claims]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalStoryline Context triple: [Shannon (TV series), typicalStoryline, investigation of suspicious insurance claims]
-
A.
storyline
chosen
Indicates that one entity serves as the narrative plot or sequence of events associated with another entity.
-
B.
storylineTone
Indicates the overall emotional quality or mood conveyed by the storyline in relation to an entity.
-
C.
storylineReason
Indicates that one event, action, or situation occurs as a narrative cause or justification for another within a storyline.
-
D.
genreOfStoryline
Indicates the relationship in which a particular genre characterizes or classifies the storyline of a work.
-
E.
narrativeType
Indicates the specific kind or category of narrative (e.g., genre, structural form, or storytelling mode) associated with an 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_69f76dde814c8190a71f60d514a424a4 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a016960819093ed4990fb4d9d36 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:02 p.m.