Triple
T12912998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apprentice Part 1 |
E308904
|
entity |
| Predicate | featuresVillainOrganization |
P50207
|
FINISHED |
| Object | Slade’s operations |
—
|
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: Slade’s operations | Statement: [Apprentice Part 1, featuresVillainOrganization, Slade’s operations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresVillainOrganization Context triple: [Apprentice Part 1, featuresVillainOrganization, Slade’s operations]
-
A.
villainOrganization
chosen
Indicates that an entity is an organization characterized as antagonistic, criminal, or evil within a given context or narrative.
-
B.
featuresCharactersFrom
Indicates that one entity (such as a work or production) includes or presents characters originating from another entity.
-
C.
featuresVillainActor
Indicates that the subject includes or presents an actor in the role of a villain.
-
D.
featuresCharacterWith
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
-
E.
featuresFictionalVirus
Indicates that a work includes or depicts a fictional virus as part of its content or storyline.
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9719f96248190b746f9d4a468560c |
completed | April 10, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69d96fa9b7708190a9e9fa30f59ff580 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:41 p.m.