Triple
T23480766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vincent Ludwig |
E570397
|
entity |
| Predicate | isTypeOfVillain |
P152539
|
FINISHED |
| Object | white-collar criminal |
—
|
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: white-collar criminal | Statement: [Vincent Ludwig, isTypeOfVillain, white-collar criminal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isTypeOfVillain Context triple: [Vincent Ludwig, isTypeOfVillain, white-collar criminal]
-
A.
hasVillain
Indicates that one entity is the villain or primary antagonist associated with another entity.
-
B.
isVigilante
Indicates that an entity takes the law into their own hands, acting outside official authority to pursue or punish perceived wrongdoers.
-
C.
hasVillainFactionClasses
Indicates that certain classes or categories are associated with, or belong to, a villainous faction.
-
D.
villainDescription
Indicates that one entity provides a description or characterization of a villainous role or antagonist associated with another entity.
-
E.
isCentralAntagonist
Indicates that an entity serves as the primary opposing force or main villain driving conflict against the protagonist or central characters.
- 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_69e245af8a88819084f2704f6d265a92 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a75002008190b02fbffd94e5e8b1 |
completed | April 29, 2026, 6:38 a.m. |
| PD | Predicate disambiguation | batch_69f0620ac3608190b36916261ea50f54 |
completed | April 28, 2026, 7:30 a.m. |
| PDg | Predicate description generation | batch_69f0bd4a0e408190ad8916faf23562d9 |
completed | April 28, 2026, 1:59 p.m. |
Created at: April 17, 2026, 6:03 p.m.