Triple
T396121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hoyte van Hoytema |
E8986
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | van Hoytema |
E8986
|
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: van Hoytema | Statement: [Hoyte van Hoytema, familyName, van Hoytema]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: van Hoytema Context triple: [Hoyte van Hoytema, familyName, van Hoytema]
-
A.
van Dam
van Dam is a Dutch surname commonly associated with individuals of Dutch origin or ancestry.
-
B.
Van der Koop
Van der Koop is a variant form of the surname "Koop," likely reflecting Dutch or Flemish naming conventions.
-
C.
Hoyte van Hoytema
chosen
Hoyte van Hoytema is a renowned Dutch-Swedish cinematographer known for his visually striking work on major films such as Interstellar, Dunkirk, Tenet, and Oppenheimer.
-
D.
Max Deuring
Max Deuring was a German mathematician known for his influential work in algebraic number theory and the theory of algebraic function fields.
-
E.
Daniël Stalpaert
Daniël Stalpaert was a 17th-century Dutch architect and city planner known for his influential role in shaping Amsterdam’s urban landscape.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec8a941081909a152fda0ce24a98 |
completed | Feb. 28, 2026, 1:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a40ad7ebec8190a0a4ee16b20cea57 |
completed | March 1, 2026, 9:46 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.