Triple
T16731477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siku Quanshu |
E406601
|
entity |
| Predicate | copyCount |
P41575
|
FINISHED |
| Object | 7 complete manuscript sets |
—
|
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: 7 complete manuscript sets | Statement: [Siku Quanshu, copyCount, 7 complete manuscript sets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: copyCount Context triple: [Siku Quanshu, copyCount, 7 complete manuscript sets]
-
A.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
B.
count
chosen
Indicates the numerical quantity or total number of instances of a specified entity or event.
-
C.
copyNumberEffect
Indicates that a change in the number of copies of a genomic region (e.g., amplification or deletion) has a functional impact on a biological feature, such as gene expression or cellular behavior.
-
D.
repetitionCount
Indicates the number of times a particular event, action, or pattern is repeated within a given context.
-
E.
copyType
Indicates that one entity is a copy or duplication of another, specifying the type or manner of that copying relationship.
- 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_69d8838f242881908abd8bc138795886 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e39c362bb88190921fab43d76c3ee8 |
completed | April 18, 2026, 2:59 p.m. |
| PD | Predicate disambiguation | batch_69e319c807788190901250ab6e0ca55f |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:20 a.m.