Triple
T4029219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Untempered Schism |
E83665
|
entity |
| Predicate | effectOnViewers |
P53532
|
FINISHED |
| Object | profound psychological impact |
—
|
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: profound psychological impact | Statement: [Untempered Schism, effectOnViewers, profound psychological impact]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnViewers Context triple: [Untempered Schism, effectOnViewers, profound psychological impact]
-
A.
USViewers
Indicates the number or set of viewers located in the United States who watched or were exposed to a particular piece of content or event.
-
B.
effectOnRepresentation
Indicates how one entity influences, alters, or determines the form, quality, or characteristics of another entity’s representation.
-
C.
effectOnMembers
Indicates the impact or influence that something has on the members of a group or organization.
-
D.
effectOnSystem
Indicates the influence, change, or impact that one entity, action, or condition has on the state or behavior of a system.
-
E.
effectOnCampaign
Indicates the influence or impact that one factor has on the outcome or performance of a campaign.
- F. None of above. chosen
Provenance (4 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_69aed92e29ac819080f7a98b594fec05 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaf066b08190afbb4a18ddc8d67e |
completed | March 9, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69aef8fe440c819093a7fa22c4ff3f1a |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefa815f2c8190818c9ffd9d1bf478 |
completed | March 9, 2026, 4:51 p.m. |
Created at: March 9, 2026, 3:36 p.m.