Triple
T31645914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chinese Sanlun school |
E807581
|
entity |
| Predicate | textualCorpusName |
P95315
|
FINISHED |
| Object | Sanlun (Three Treatises) |
—
|
NE NERFINISHED |
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: Sanlun (Three Treatises) | Statement: [Chinese Sanlun school, textualCorpusName, Sanlun (Three Treatises)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textualCorpusName Context triple: [Chinese Sanlun school, textualCorpusName, Sanlun (Three Treatises)]
-
A.
corpus
Indicates that an entity is a collection or body of texts, documents, or linguistic data used as a unified set for analysis or reference.
-
B.
hasTextualCorpus
chosen
Indicates that an entity is associated with or possesses a collection of written or textual materials.
-
C.
primaryCorpusType
Indicates the main or dominant type or category of corpus associated with an entity.
-
D.
hasPartOfCorpus
Indicates that one entity constitutes a component or segment of the overall corpus associated with another entity.
-
E.
numberInCorpus
Indicates the numerical count or frequency with which a given item appears within a specified corpus.
- 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_69f348d9ce58819093ea2da83cbeeec1 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6abaa1f648190b77073771df3bf3b |
completed | May 3, 2026, 1:58 a.m. |
| PD | Predicate disambiguation | batch_69f6aa1e84b88190b025f6ca40f17a8a |
completed | May 3, 2026, 1:51 a.m. |
Created at: April 30, 2026, 10:51 p.m.