Triple
T13790742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill & Ted's Bogus Journey |
E331387
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Rufus |
E837287
|
NE 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: Rufus | Statement: [Bill & Ted's Bogus Journey, character, Rufus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rufus Context triple: [Bill & Ted's Bogus Journey, character, Rufus]
-
A.
Rufus
Rufus is the given first name of American actor and director Alan Hale Sr., known for his prolific work in early Hollywood cinema.
-
B.
Rufus
Rufus was a 1970s American funk and R&B band best known for launching Chaka Khan’s career and for hits like “Tell Me Something Good” and “Ain’t Nobody.”
-
C.
Rufus
Rufus is a cognomen historically used by members of the Roman gens Octavia, distinguishing a particular family branch within that lineage.
-
D.
Rufus
chosen
Rufus is the time-traveling mentor from the comedy film "Bill & Ted’s Excellent Adventure," who guides the protagonists to ensure their destined future.
-
E.
Rufus
Rufus is a French actor best known internationally for his supporting role in the whimsical film "Amélie."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b07f5cf48190b59827efb5918a95 |
completed | May 3, 2026, 8:30 p.m. |
Created at: April 9, 2026, 10:11 p.m.