Triple
T19575857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarabhanga |
E489855
|
entity |
| Predicate | teachesByExample |
P21344
|
FINISHED |
| Object | leaving the body at will |
—
|
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: leaving the body at will | Statement: [Sarabhanga, teachesByExample, leaving the body at will]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teachesByExample Context triple: [Sarabhanga, teachesByExample, leaving the body at will]
-
A.
teachingExample
Indicates that one entity serves as an illustrative or instructional example used to teach or clarify something to another entity.
-
B.
baseExamples
Indicates that something serves as a fundamental or illustrative example for understanding or demonstrating another concept, item, or case.
-
C.
usedAsExampleIn
Indicates that one entity is cited or presented as an illustrative example within another entity, such as a text, discussion, or explanation.
-
D.
teachesAbout
chosen
Indicates that one entity provides instruction or information to another entity on a particular subject or topic.
-
E.
teachableFrom
Indicates that one entity can be taught or learned from another entity, capturing a directional teachability or learnability relationship between them.
- 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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e64024c5b08190bbff6df633857874 |
completed | April 20, 2026, 3:03 p.m. |
| PD | Predicate disambiguation | batch_69e514dbdb988190b55931a8138c73e7 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:42 p.m.