Triple
T26126630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rahu |
E659121
|
entity |
| Predicate | remedialMeasures |
P83701
|
FINISHED |
| Object | mantra chanting |
—
|
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: mantra chanting | Statement: [Rahu, remedialMeasures, mantra chanting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: remedialMeasures Context triple: [Rahu, remedialMeasures, mantra chanting]
-
A.
remedy
Indicates that one entity serves to cure, alleviate, or counteract a problem, illness, or undesirable condition affecting another entity.
-
B.
controlMeasure
Indicates a relationship where one entity implements or applies a method, action, or mechanism to regulate, mitigate, or manage a risk, process, or condition associated with another entity.
-
C.
restorationMeasure
Indicates that an action or intervention is undertaken to repair, rehabilitate, or return something to a previous or improved state.
-
D.
hasRemedy
chosen
Indicates that one entity serves as a remedy, treatment, or corrective measure for a problem, condition, or undesirable state associated with another entity.
-
E.
protectionMeasures
Indicates actions or safeguards implemented to prevent harm, damage, or risk to someone or something.
- 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_69ee5bc2b2948190b458ad3f580af779 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f60b8eeeb48190a192fa8ba8cc20bf |
completed | May 2, 2026, 2:34 p.m. |
| PD | Predicate disambiguation | batch_69f5b0021da88190bdd4cf2698c23edf |
completed | May 2, 2026, 8:04 a.m. |
Created at: April 26, 2026, 8:12 p.m.