Triple
T37464329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Overkill |
E930996
|
entity |
| Predicate | effectTrigger |
P196067
|
FINISHED |
| Object | after dealing excess damage to a target that is destroyed |
—
|
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: after dealing excess damage to a target that is destroyed | Statement: [Overkill, effectTrigger, after dealing excess damage to a target that is destroyed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectTrigger Context triple: [Overkill, effectTrigger, after dealing excess damage to a target that is destroyed]
-
A.
eventEffect
Indicates the resulting change, outcome, or consequence that one event has on another state, entity, or event.
-
B.
magicTrigger
Indicates that one entity initiates or activates a magical effect, condition, or event upon another entity or context.
-
C.
effectCategory
Indicates the general type or classification of an effect that one entity has on another or on a system.
-
D.
effectCount
Indicates the number of distinct effects or outcomes associated with a given action, event, or entity.
-
E.
specialEffect
Indicates that one entity produces, applies, or is associated with a distinctive, often non-standard effect on another entity or on an event.
- 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_69f76ec1a1148190b0a961f188d621b0 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fe031bc6208190860099aef72d8dcb |
completed | May 8, 2026, 3:36 p.m. |
| PD | Predicate disambiguation | batch_69fe014c8b388190b5d4e0cb95ee2be5 |
completed | May 8, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69fe031af3248190816da6829aef7bab |
completed | May 8, 2026, 3:36 p.m. |
Created at: May 3, 2026, 4:17 p.m.