Triple
T4018501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delft School |
E91222
|
entity |
| Predicate | notableArtist |
P601
|
FINISHED |
| Object | Emanuel de Witte |
E73044
|
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: Emanuel de Witte | Statement: [Delft School, notableArtist, Emanuel de Witte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emanuel de Witte Context triple: [Delft School, notableArtist, Emanuel de Witte]
-
A.
Emanuel de Witte
chosen
Emanuel de Witte was a Dutch Golden Age painter renowned for his atmospheric church interiors and masterful use of light and perspective.
-
B.
Theodoor Rombouts
Theodoor Rombouts was a prominent Flemish Baroque painter known for his dynamic Caravaggesque genre scenes and dramatic use of light and shadow.
-
C.
Aert van der Neer
Aert van der Neer was a Dutch Golden Age painter renowned for his atmospheric moonlit landscapes and winter scenes.
-
D.
Cornelis Kruseman
Cornelis Kruseman was a 19th-century Dutch painter known for his portraits, historical scenes, and Italian landscapes.
-
E.
Jan van der Heyden
Jan van der Heyden was a 17th-century Dutch painter and inventor renowned for his detailed cityscapes and pioneering improvements in firefighting technology and street lighting.
- 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_69aed9618b04819081750d979d2af098 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaa984948190a252eaeb9dbae454 |
completed | March 9, 2026, 4:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf7fbd0bc881908ba07edb75479b97 |
completed | March 22, 2026, 5:35 a.m. |
Created at: March 9, 2026, 3:35 p.m.