Triple
T25011281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hercules Segers |
E625991
|
entity |
| Predicate | numberOfKnownPrintPlates |
P33015
|
FINISHED |
| Object | approximately 54 |
—
|
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: approximately 54 | Statement: [Hercules Segers, numberOfKnownPrintPlates, approximately 54]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfKnownPrintPlates Context triple: [Hercules Segers, numberOfKnownPrintPlates, approximately 54]
-
A.
numberOfPlates
chosen
Indicates the quantity of plates associated with or involved in a particular entity, event, or context.
-
B.
estimatedCopiesPrinted
Indicates the approximate number of copies of an item that have been printed.
-
C.
numberOfSheets
Indicates the quantity of individual sheets associated with or contained in an entity.
-
D.
plateNumberOf
Indicates the license plate number that is assigned to or associated with a particular vehicle.
-
E.
hasApproximateVellumCopies
Indicates that one entity possesses or is associated with a number of vellum copies that is approximate rather than exact.
- 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_69e2ff27755881908490178e83701160 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f69edbb7648190bd89c57e0932eac1 |
completed | May 3, 2026, 1:03 a.m. |
| PD | Predicate disambiguation | batch_69f69d17e8d48190b30bcc2f4bd81eb2 |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 18, 2026, 6:05 a.m.