Triple
T20964678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lava and Kusha |
E516335
|
entity |
| Predicate | learnedText |
P101902
|
FINISHED |
| Object | Ramayana composed by Valmiki |
—
|
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: Ramayana composed by Valmiki | Statement: [Lava and Kusha, learnedText, Ramayana composed by Valmiki]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learnedText Context triple: [Lava and Kusha, learnedText, Ramayana composed by Valmiki]
-
A.
learnedIn
Indicates that an entity acquired knowledge, skills, or information within a particular context, environment, or source.
-
B.
lessonsLearned
Indicates that certain insights, knowledge, or understanding have been gained from a prior experience, event, or process.
-
C.
primaryTextStudied
chosen
Indicates that a particular text is the main or principal material being studied by an entity.
-
D.
structureLearning
Indicates a process in which an agent infers or constructs the underlying structure or dependency relationships within a set of variables, data, or a model.
-
E.
learnsToRead
Indicates that an entity is in the process of acquiring the ability or skill to read.
- 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_69e0b4fde6c48190af1398e7e734629e |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fb71d644819087e00933fcc26217 |
completed | April 21, 2026, 4:22 a.m. |
| PD | Predicate disambiguation | batch_69e5dbe6976081908abd4e9c8734bae9 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 1:32 p.m.