Triple
T26725790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yasastilaka Champu |
E673831
|
entity |
| Predicate | didacticAudience |
P10804
|
FINISHED |
| Object | courtly and lay Jain readership |
—
|
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: courtly and lay Jain readership | Statement: [Yasastilaka Champu, didacticAudience, courtly and lay Jain readership]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: didacticAudience Context triple: [Yasastilaka Champu, didacticAudience, courtly and lay Jain readership]
-
A.
didacticPurpose
Indicates that something is intended to teach, instruct, or convey educational content or guidance.
-
B.
didacticCharacter
Indicates that one entity serves a teaching or instructional role toward another, conveying guidance, lessons, or moral instruction.
-
C.
hasEducationalAudience
Indicates that something is intended for or directed toward a specific educational audience or learner group.
-
D.
academicAudience
Indicates that something is intended for, directed toward, or primarily relevant to an academic or scholarly audience.
-
E.
typicalAudience
chosen
Indicates the group of people for whom something (such as a work, product, or resource) is primarily intended or most suitable.
- 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_69eecda481d08190aea69f2f7c745f56 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f61809c4808190ba4c81b0eb702eee |
completed | May 2, 2026, 3:28 p.m. |
| PD | Predicate disambiguation | batch_69f60b8dfa0c8190864e1a940024d0a0 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 27, 2026, 3:42 a.m.