Triple
T26629973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beast (2017 film character) |
E668463
|
entity |
| Predicate | curseTrigger |
P161070
|
FINISHED |
| Object | cruelty |
—
|
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: cruelty | Statement: [Beast (2017 film character), curseTrigger, cruelty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: curseTrigger Context triple: [Beast (2017 film character), curseTrigger, cruelty]
-
A.
curseTrigger
chosen
Indicates that one entity causes, activates, or is responsible for initiating a curse that affects another entity.
-
B.
curseCondition
Indicates a condition or state in which an entity is affected by a curse or cursed effect.
-
C.
associatedCurse
Indicates that one entity is linked to, affected by, or bears responsibility for a particular curse related to another entity.
-
D.
scripturalCurse
Indicates that one entity pronounces or embodies a curse upon another as recorded or prescribed in a religious or scriptural context.
-
E.
attemptedCurseReversal
Indicates an action where one entity tried, but did not necessarily succeed, to reverse or undo a curse affecting 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_69ee9cff507c819092b95bf7219a702e |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f61a17a7788190946f7e32d63cd43f |
completed | May 2, 2026, 3:36 p.m. |
| PD | Predicate disambiguation | batch_69f611ab768c8190b1849c15a3e59dda |
completed | May 2, 2026, 3 p.m. |
Created at: April 27, 2026, 2:24 a.m.