Triple
T13742671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cambridge University Library MS Nn.2.41 |
E330123
|
entity |
| Predicate | hasFolioCount |
P7664
|
FINISHED |
| Object | approximately 406 folios |
—
|
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 406 folios | Statement: [Cambridge University Library MS Nn.2.41, hasFolioCount, approximately 406 folios]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFolioCount Context triple: [Cambridge University Library MS Nn.2.41, hasFolioCount, approximately 406 folios]
-
A.
hasGradeCount
Indicates a relationship where an entity is associated with the number of grades it has or has received.
-
B.
hasCategoryCount
Indicates the number of distinct categories associated with a given entity.
-
C.
hasSubjectCount
Indicates that an entity is associated with a specific number of subjects.
-
D.
hasTotalNumber
chosen
Indicates that an entity is associated with a specific overall count or sum of items, elements, or units.
-
E.
hasCanonicalNumberOfArticles
Indicates that an entity is associated with a standard, officially recognized count of articles that define or describe it.
- 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_69d80772315881908f980cae40d91664 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de020855ec8190a60fa1cb761f2e68 |
completed | April 14, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69dbbe950b148190ba0df8a749269ec6 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:55 p.m.