Triple
T6407454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gerrit Dou |
E127620
|
entity |
| Predicate | apprenticeshipWith |
P33629
|
FINISHED |
| Object | Rembrandt in Leiden |
—
|
LITERAL 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: Rembrandt in Leiden | Statement: [Gerrit Dou, apprenticeshipWith, Rembrandt in Leiden]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: apprenticeshipWith Context triple: [Gerrit Dou, apprenticeshipWith, Rembrandt in Leiden]
-
A.
offersApprenticeshipTraining
chosen
Indicates that one entity provides apprenticeship-based training opportunities or programs to another entity.
-
B.
trainedAs
Indicates that one entity has received education or instruction to perform the role, profession, or function represented by another entity.
-
C.
occupationBegan
Indicates the point in time when an entity started holding a particular occupation or job.
-
D.
providesTrainingFor
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
-
E.
mayWorkIn
Indicates that an entity is allowed or has the possibility to work in a particular place, organization, or context.
- F. None of above.
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_69c0083723d88190b1e37b19df162c08 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c068ccd804819097b106604372c14a |
completed | March 22, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69c060f40ecc8190b1df17b96767675c |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:41 p.m.