Triple
T13790743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill & Ted's Bogus Journey |
E331387
|
entity |
| Predicate | hasEvilDoppelgangers |
P84501
|
FINISHED |
| Object | evil robot Bill and Ted |
—
|
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: evil robot Bill and Ted | Statement: [Bill & Ted's Bogus Journey, hasEvilDoppelgangers, evil robot Bill and Ted]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEvilDoppelgangers Context triple: [Bill & Ted's Bogus Journey, hasEvilDoppelgangers, evil robot Bill and Ted]
-
A.
hasDoppelgangerTheme
chosen
Indicates that something features or involves a doppelganger-related theme, such as doubles, look-alikes, or mirrored identities.
-
B.
hasVillain
Indicates that one entity is the villain or primary antagonist associated with another entity.
-
C.
associatedDemon
Indicates that there exists a relationship in which one entity is linked or connected to a particular demon.
-
D.
hasFictionalAlterEgoOf
Indicates that one entity is the fictional alter ego, persona, or alternate identity of another entity.
-
E.
hasAntagonisticProtagonist
Indicates that the work features a main character who opposes or undermines the typical heroic or moral expectations of a traditional protagonist.
- 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_69d81c58feb08190a77bca8bf7d6d20f |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de024af32c8190a9bd1278e09564ba |
completed | April 14, 2026, 9 a.m. |
| PD | Predicate disambiguation | batch_69dbc85fb600819098a2aab48169be96 |
completed | April 12, 2026, 4:29 p.m. |
Created at: April 9, 2026, 10:11 p.m.