Triple
T29900124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paradox Engine |
E759385
|
entity |
| Predicate | hasFrameEffect |
P175674
|
FINISHED |
| Object | Legendary |
—
|
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: Legendary | Statement: [Paradox Engine, hasFrameEffect, Legendary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrameEffect Context triple: [Paradox Engine, hasFrameEffect, Legendary]
-
A.
hasFrameElement
Indicates that a frame (or structured conceptual scenario) includes or is associated with a specific frame element (a participant, role, or component within that frame).
-
B.
hasEffectIn
Indicates that one entity produces, causes, or exerts an effect within a specified context, system, or environment.
-
C.
usesFrame
Indicates that one entity employs, relies on, or is structured around a particular frame, framework, or reference structure provided by another entity.
-
D.
hasFictionalFrame
Indicates that one entity is presented or interpreted within the context of a fictional narrative, scenario, or imaginative framework provided by another entity.
-
E.
hasPerspectiveEffect
Indicates that one entity visually appears altered in size, shape, or position relative to another due to perspective or viewpoint.
- 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_69f2245f1cf88190978c70d1a1d2cb73 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6d74b20a48190900dda1014cc13a8 |
completed | May 3, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69f6d26ceb08819091c71c001e954936 |
completed | May 3, 2026, 4:43 a.m. |
| PDg | Predicate description generation | batch_69f6d6a482fc8190b526291cd99b8696 |
completed | May 3, 2026, 5:01 a.m. |
Created at: April 29, 2026, 6:06 p.m.