Triple
T11831575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mahākāśyapa |
E281403
|
entity |
| Predicate | symbolInZen |
P129
|
FINISHED |
| Object | embodiment of direct, wordless transmission |
—
|
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: embodiment of direct, wordless transmission | Statement: [Mahākāśyapa, symbolInZen, embodiment of direct, wordless transmission]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: symbolInZen Context triple: [Mahākāśyapa, symbolInZen, embodiment of direct, wordless transmission]
-
A.
symbolizedIn
Indicates that one entity serves as a symbol or representation of another entity.
-
B.
symbolInBook
Indicates a relationship where a particular symbol appears or is used within a specific book.
-
C.
shapeSymbolism
Indicates how a particular shape is associated with or conveys symbolic meaning within a given context.
-
D.
symbolElement
Indicates that one entity is a constituent element or component that makes up the other entity, which is treated as a symbol.
-
E.
symbolizes
chosen
Indicates that one entity stands for, represents, or is used as a sign for another entity, concept, or idea.
- 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_69d6ab276f8c8190b1966a0ef11349ac |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a62c95988190a45dbaa7001c8846 |
completed | April 10, 2026, 7:26 a.m. |
| PD | Predicate disambiguation | batch_69d8a251fc08819095933f1d13c3b742 |
completed | April 10, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:43 p.m.