Triple
T992103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ganesh Chaturthi |
E21413
|
entity |
| Predicate | immersionTerm |
P22838
|
FINISHED |
| Object | Visarjan |
—
|
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: Visarjan | Statement: [Ganesh Chaturthi, immersionTerm, Visarjan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: immersionTerm Context triple: [Ganesh Chaturthi, immersionTerm, Visarjan]
-
A.
keyTerm
Indicates that a term functions as a primary or central concept within a given context or information structure.
-
B.
submerged
Indicates that one entity is located beneath the surface of a liquid or other surrounding medium, typically fully covered by it.
-
C.
usedTerm
Indicates that one entity employed, referenced, or applied a particular term in some context.
-
D.
hasTerm
Indicates that an entity includes, is associated with, or is defined by a specific term or condition.
-
E.
termType
Indicates the classification or category of a term within a system, specifying what kind of term it is (e.g., type, role, or function) in relation to others.
- 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_69a493c476b48190b41fc5e793171cc6 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b4c25e5081909ff1ada6b8bf617a |
completed | March 1, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69a4b2adbde48190b07966d0c3179516 |
completed | March 1, 2026, 9:42 p.m. |
| PDg | Predicate description generation | batch_69a4b38630848190bd3898a4f42018ad |
completed | March 1, 2026, 9:45 p.m. |
Created at: March 1, 2026, 7:41 p.m.