Triple
T22279326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kāṇva recension |
E550688
|
entity |
| Predicate | textualFeatures |
P22618
|
FINISHED |
| Object | distinct arrangement of sections |
—
|
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: distinct arrangement of sections | Statement: [Kāṇva recension, textualFeatures, distinct arrangement of sections]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textualFeatures Context triple: [Kāṇva recension, textualFeatures, distinct arrangement of sections]
-
A.
textualCharacterization
Indicates that one entity provides a descriptive or narrative characterization of another entity, typically in textual form.
-
B.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
-
C.
linguisticFeature
Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
-
D.
textualFunction
Indicates a functional or structural role that a text segment serves within a larger document or discourse (e.g., title, caption, summary, instruction).
-
E.
textualStructure
chosen
Indicates how parts of a text are organized and related to each other within its overall structure.
- 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_69e11e44d538819097c6b8f333af3352 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f14eaa8cec819081c2ad031154ebe7 |
completed | April 29, 2026, 12:19 a.m. |
| PD | Predicate disambiguation | batch_69e72ff0363081909f794d19c8a64837 |
completed | April 21, 2026, 8:06 a.m. |
Created at: April 16, 2026, 8:40 p.m.