Triple
T22570274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chinese characters |
E558057
|
entity |
| Predicate | hasCommonSubsetSize |
P148770
|
FINISHED |
| Object | a few thousand characters for literacy |
—
|
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: a few thousand characters for literacy | Statement: [Chinese characters, hasCommonSubsetSize, a few thousand characters for literacy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommonSubsetSize Context triple: [Chinese characters, hasCommonSubsetSize, a few thousand characters for literacy]
-
A.
hasCommonElement
Indicates that two sets, collections, or groups share at least one element in common.
-
B.
hasSubset
Indicates that one set is entirely contained within another set, with all elements of the first set also belonging to the second.
-
C.
hasCommonRepresentative
Indicates that two or more entities share the same person or organization acting as their representative.
-
D.
hasCommonShape
Indicates that two or more entities share the same or a very similar geometric or visual shape.
-
E.
hasCommonValue
Indicates that two or more entities share at least one identical value or attribute in common.
- F. None of above. chosen
Provenance (4 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_69e11e5ae4ac8190b1f503457603d969 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15fad35448190b51a3dd639ca8568 |
completed | April 29, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69ee626e6bb08190ada4dd8b48cc0c43 |
completed | April 26, 2026, 7:07 p.m. |
| PDg | Predicate description generation | batch_69ee8841e9cc81908d23b34215e3be71 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 16, 2026, 8:52 p.m.