Triple
T21247839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sylvia Weis |
E523661
|
entity |
| Predicate | hasLoveInterest |
P7325
|
FINISHED |
| Object | Will Salas |
—
|
NE NERFINISHED |
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: Will Salas | Statement: [Sylvia Weis, hasLoveInterest, Will Salas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Will Salas Context triple: [Sylvia Weis, hasLoveInterest, Will Salas]
-
A.
Will Salas
chosen
Will Salas is the protagonist of the science-fiction film "In Time," a working-class man who challenges a dystopian system where time is literally used as currency.
-
B.
Victor Salazar
Victor Salazar is the teenage protagonist of the TV series "Love, Victor," which follows his journey of self-discovery, relationships, and coming to terms with his sexual identity.
-
C.
Andy LaPlegua
Andy LaPlegua is a Norwegian musician and producer best known as the founder and frontman of the industrial/electronic band Combichrist.
-
D.
Don Figueroa
Don Figueroa is a Filipino comic book artist best known for his influential work on Transformers comics and designs for publishers such as Dreamwave Productions and IDW Publishing.
-
E.
Reynaldo Villalobos
Reynaldo Villalobos is a cinematographer best known for his work on notable American films such as the comedy classic "9 to 5."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b5146c108190adc9adb73e90abff |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7359b756c819085480ca4174c53c2 |
completed | April 21, 2026, 8:30 a.m. |
Created at: April 16, 2026, 3:55 p.m.