Triple
T18884668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramanuja Jayanti |
E461927
|
entity |
| Predicate | featuresPractice |
P133670
|
FINISHED |
| Object | discourses on Vishishtadvaita philosophy |
—
|
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: discourses on Vishishtadvaita philosophy | Statement: [Ramanuja Jayanti, featuresPractice, discourses on Vishishtadvaita philosophy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresPractice Context triple: [Ramanuja Jayanti, featuresPractice, discourses on Vishishtadvaita philosophy]
-
A.
featuresSample
Indicates that an entity includes or presents a particular sample as one of its components or examples.
-
B.
featuresTechnique
Indicates that something incorporates or makes use of a particular technique as part of its content or execution.
-
C.
featuresIn
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
-
D.
featuresSuit
Indicates that one entity includes or presents a particular suit (e.g., clothing, armor, or outfit) as a notable component or attribute.
-
E.
featuresCross
Indicates that one feature or element intersects or passes across another in space or structure.
- 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c3d53e608190b8740c092b6e1523 |
completed | April 20, 2026, 6:12 a.m. |
| PD | Predicate disambiguation | batch_69e4a2e27e1481908a8da10b28f07875 |
completed | April 19, 2026, 9:39 a.m. |
| PDg | Predicate description generation | batch_69e4afa745c081908da20a51ebc147b4 |
completed | April 19, 2026, 10:34 a.m. |
Created at: April 10, 2026, 11:57 a.m.