Triple
T25693183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pillai Lokacharya |
E644250
|
entity |
| Predicate | textFocus |
P82134
|
FINISHED |
| Object | exposition of the three rahasya mantras |
—
|
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: exposition of the three rahasya mantras | Statement: [Pillai Lokacharya, textFocus, exposition of the three rahasya mantras]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textFocus Context triple: [Pillai Lokacharya, textFocus, exposition of the three rahasya mantras]
-
A.
importFocus
Indicates that attention, priority, or emphasis is being brought into or concentrated on a particular entity or aspect.
-
B.
primaryTextualFocus
chosen
Indicates that one entity is the main subject or central topic emphasized within the text of another entity.
-
C.
trimFocus
Indicates that an entity’s attention or emphasis is narrowed or adjusted to concentrate more specifically on a particular target or subset of interest.
-
D.
focusShift
Indicates a change in attention or emphasis from one entity or topic to another.
-
E.
focusType
Indicates the specific kind or category of focus or attention that is being applied to or associated with an entity or interaction.
- 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_69e77e82c9bc8190893090b2f6c64f1d |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f5fbc2bcbc81909e16230a8ed01210 |
completed | May 2, 2026, 1:27 p.m. |
| PD | Predicate disambiguation | batch_69f4938262ac8190b41f922d0407d272 |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 21, 2026, 8:31 p.m.