Triple
T18423051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sinematic |
E442067
|
entity |
| Predicate | hasProducer |
P30366
|
FINISHED |
| Object | Marius de Vries |
—
|
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: Marius de Vries | Statement: [Sinematic, hasProducer, Marius de Vries]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marius de Vries Context triple: [Sinematic, hasProducer, Marius de Vries]
-
A.
Marius de Vries
chosen
Marius de Vries is a British composer, producer, and arranger known for his innovative work on film soundtracks and collaborations with prominent pop and electronic artists.
-
B.
Marius de Jonge
Marius de Jonge is a Dutch biblical scholar known for his influential work on New Testament studies and early Christianity.
-
C.
Johan de Jonge
Johan de Jonge is a person notable enough to be recognized as a distinguished bearer of the surname "de Jonge."
-
D.
Hendrik de Vries
Hendrik de Vries was a mathematician who supervised and mentored the influential algebraist Bartel Leendert van der Waerden.
-
E.
Sidney van den Bergh
Sidney van den Bergh is a Canadian astronomer renowned for his work on galaxies and galaxy clusters, including the discovery of several dwarf galaxies.
- 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_69d8b9eb8a508190a942fd75ebd8b1dc |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e51b102dd88190af474902fe58ead5 |
completed | April 19, 2026, 6:12 p.m. |
Created at: April 10, 2026, 10:47 a.m.