Triple
T15196333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Utrecht Caravaggism |
E363145
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | Gerrit Dou |
E127620
|
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: Gerrit Dou | Statement: [Utrecht Caravaggism, influenced, Gerrit Dou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gerrit Dou Context triple: [Utrecht Caravaggism, influenced, Gerrit Dou]
-
A.
Gerrit Dou
chosen
Gerrit Dou was a Dutch Golden Age painter renowned for his meticulously detailed genre scenes and mastery of light as a leading figure of the Leiden fijnschilders.
-
B.
Hendrik de Wit
Hendrik de Wit is a notable individual distinguished enough to be specifically recognized as a prominent bearer of the surname De Wit.
-
C.
Daniël Vosmaer
Daniël Vosmaer was a 17th-century Dutch painter associated with the Delft School, known for his detailed cityscapes and architectural views.
-
D.
Willem Claesz Heda
Willem Claesz Heda was a Dutch Golden Age painter renowned for his monochromatic banquet and breakfast still lifes distinguished by subtle lighting and meticulous realism.
-
E.
Aelbert Cuyp
Aelbert Cuyp was a prominent 17th-century Dutch Golden Age painter renowned for his luminous, atmospheric landscapes, especially river and pastoral scenes bathed in warm light.
- 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_69d85a09a39c81908759f23268e2d408 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0067fcc788190abdc083d4eadeb36 |
completed | April 15, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf0a12e081908d7d2ae7c9774f94 |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 3:10 a.m.