Triple
T37225771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yogi’s Gang |
E923000
|
entity |
| Predicate | featuresTypeOfAntagonist |
P119796
|
FINISHED |
| Object | villains representing negative traits |
—
|
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: villains representing negative traits | Statement: [Yogi’s Gang, featuresTypeOfAntagonist, villains representing negative traits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresTypeOfAntagonist Context triple: [Yogi’s Gang, featuresTypeOfAntagonist, villains representing negative traits]
-
A.
featuresAntagonistEntity
Indicates that the subject includes or involves an entity serving as an antagonist in the context of a narrative, interaction, or scenario.
-
B.
facesAntagonistType
Indicates that an entity confronts or opposes an antagonist of a specified type.
-
C.
featuresAntagonistNationality
Indicates that the work includes an antagonist whose nationality matches the specified country.
-
D.
featuresVillainActor
Indicates that the subject includes or presents an actor in the role of a villain.
-
E.
antagonistAttribute
chosen
Indicates that an entity possesses a characteristic or role specifically associated with being an antagonist in a narrative or conflict.
- 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_69f76ea7f0008190b31b8e30f3d05a71 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c8efcd4819088c2aeead65d93df |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a11efc08190bb7cacc1325b4dc6 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:15 p.m.