Triple
T3671709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Praise (film) |
E77894
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Dion Beebe |
E170277
|
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: Dion Beebe | Statement: [Praise (film), cinematographyBy, Dion Beebe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dion Beebe Context triple: [Praise (film), cinematographyBy, Dion Beebe]
-
A.
Dion Beebe
chosen
Dion Beebe is an Academy Award–winning Australian–South African cinematographer known for his visually distinctive work on films such as "Memoirs of a Geisha" and "Collateral."
-
B.
Don Beyer
Don Beyer is an American Democratic politician and former Lieutenant Governor of Virginia who serves in the U.S. House of Representatives.
-
C.
Ron Feemster
Ron Feemster is a music producer known for his work on the album "Afrodisiac."
-
D.
Ian Megibben
Ian Megibben is a cinematographer best known for his work on the animated film "Finding Dory."
-
E.
Phil Wenneck
Phil Wenneck is a charismatic, fast-talking schoolteacher and member of the "Wolfpack" whose misadventures drive much of the comedy in The Hangover film series.
- 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_69ad85e083008190b2e1b7085fe500bd |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc42de1bc819090e19dd0805f13a1 |
completed | March 8, 2026, 6:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5120849188190bea912ed14f90bf3 |
completed | March 14, 2026, 7:45 a.m. |
Created at: March 8, 2026, 3:25 p.m.