Triple
T20194466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volt Tackle |
E493045
|
entity |
| Predicate | affectedByVoltAbsorb |
P139146
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Volt Tackle, affectedByVoltAbsorb, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedByVoltAbsorb Context triple: [Volt Tackle, affectedByVoltAbsorb, true]
-
A.
vampireAbility
Indicates that one entity possesses a supernatural power or trait specifically associated with being a vampire in relation to another entity or context.
-
B.
notableAbsorbedPowersFrom
Indicates that one entity is known for having taken in or acquired powers or abilities from another entity.
-
C.
usesPowerFor
Indicates that one entity applies or exploits a particular power, energy, or capability for a specific purpose or activity.
-
D.
volta
Indicates a turning or change of direction, such as a shift in movement, course, or focus between entities.
-
E.
effectOnNativeResistance
Indicates the impact that one entity has on the level or strength of another entity’s native resistance.
- 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66ad7ed548190a893110fa2ffb144 |
completed | April 20, 2026, 6:05 p.m. |
| PD | Predicate disambiguation | batch_69e55b14c9d8819095453d0504d9222f |
completed | April 19, 2026, 10:45 p.m. |
| PDg | Predicate description generation | batch_69e56700b1a08190ace53cf95827d72d |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 11, 2026, 11:37 p.m.