Triple
T16731463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siku Quanshu |
E406601
|
entity |
| Predicate | approximateCharacters |
P7444
|
FINISHED |
| Object | about 800 million characters |
—
|
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: about 800 million characters | Statement: [Siku Quanshu, approximateCharacters, about 800 million characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateCharacters Context triple: [Siku Quanshu, approximateCharacters, about 800 million characters]
-
A.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
B.
hasApproximateNumberOfLetters
chosen
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
C.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
D.
hasCharacters
Indicates that an entity (such as a work or story) includes or features certain characters as part of its content.
-
E.
usesCharactersAs
Indicates that one entity employs or incorporates specific characters (such as letters, symbols, or glyphs) from another entity for its representation or functioning.
- 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.