Triple
T10311524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katherine Sarafian |
E241900
|
entity |
| Predicate | workedOn |
P3
|
FINISHED |
| Object | Lifted |
E712894
|
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: Lifted | Statement: [Katherine Sarafian, workedOn, Lifted]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lifted Context triple: [Katherine Sarafian, workedOn, Lifted]
-
A.
Lifted
Lifted is a music producer known for working on the hit song "Cruel Summer."
-
B.
Lifted
"Lifted" is a work authored by Mercy, likely a literary or creative piece such as a book, story, or script.
-
C.
Lifted
chosen
Lifted is a 2006 Pixar animated short film that humorously depicts a novice alien’s clumsy attempt to abduct a sleeping human under the watchful eye of his stern instructor.
-
D.
LIFT
LIFT is a paratransit and demand-response transportation service provided by the North County Transit District in San Diego County for riders with disabilities and mobility limitations.
-
E.
The Big Lift
The Big Lift is a 1950 American drama film about the Berlin Airlift, notable for its semi-documentary style and on-location shooting in postwar Germany.
- 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_69d381ac38808190a8ca7457c85b625b |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d32ac6c08190b23eb042b3ec284a |
completed | April 7, 2026, 9:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71d78ece88190885768c979b038df |
completed | April 9, 2026, 3:31 a.m. |
Created at: April 6, 2026, 11:47 a.m.