Triple
T31645186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nattai |
E807566
|
entity |
| Predicate | hasAvarohanaNoteCount |
P155831
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [Nattai, hasAvarohanaNoteCount, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAvarohanaNoteCount Context triple: [Nattai, hasAvarohanaNoteCount, 7]
-
A.
hasAvarohana
Indicates a descending sequence or movement from higher to lower elements within a structured pattern or scale.
-
B.
noteCountAvaroha
chosen
Indicates the number of notes involved in the descending (avarohana) movement of a musical pattern or scale.
-
C.
notableCount
Indicates the number of notable or distinguished items, entities, or instances associated with a given subject.
-
D.
hasNumberOfKandas
Indicates the relationship specifying how many kandas (sections or books) are associated with a given entity.
-
E.
hasNumberOfPrakaras
Indicates the relationship specifying how many prakaras (enclosure layers or surrounding structures) are associated with a given entity.
- 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_69f348d9ce58819093ea2da83cbeeec1 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a91e4f4c8190831089d81f5f0026 |
completed | May 3, 2026, 1:47 a.m. |
| PD | Predicate disambiguation | batch_69f6a757c6e081908e37631e5d8d246b |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 30, 2026, 10:50 p.m.