Triple
T17748065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cloak & Dagger |
E443037
|
entity |
| Predicate | featuresPowerType |
P118971
|
FINISHED |
| Object | light manipulation |
—
|
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: light manipulation | Statement: [Cloak & Dagger, featuresPowerType, light manipulation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresPowerType Context triple: [Cloak & Dagger, featuresPowerType, light manipulation]
-
A.
powersType
chosen
Indicates that one entity has a specific type or category of power or ability in relation to another entity.
-
B.
hadPowerType
Indicates that an entity possessed or was associated with a specific type or category of power.
-
C.
involvesPowerType
Indicates that the relationship or action is associated with, characterized by, or dependent on a specific type or category of power.
-
D.
shaftPowerType
Indicates the type or category of power transmitted through a shaft in a mechanical or propulsion system.
-
E.
winnerPowertrainType
Indicates the type of powertrain used by the entity that is identified as the winner in a given context or competition.
- 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47ad46a50819089c87f74efe3c7ca |
completed | April 19, 2026, 6:48 a.m. |
| PD | Predicate disambiguation | batch_69e3cde9dc288190af0e2198487f2051 |
completed | April 18, 2026, 6:31 p.m. |
Created at: April 10, 2026, 10:10 a.m.