Triple
T31645325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adi tala |
E807569
|
entity |
| Predicate | hasAksharaCount |
P155655
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Adi tala, hasAksharaCount, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAksharaCount Context triple: [Adi tala, hasAksharaCount, 8]
-
A.
hasMatraCount
chosen
Indicates that an entity (such as a word or syllable) is associated with a specific number of matras (metrical time units or beats).
-
B.
hasNumberOfVarnas
Indicates the relationship that specifies how many varnas (distinct categories or classes) are associated with a given entity.
-
C.
hasGranthaConsonants
Indicates that an entity includes or makes use of consonant characters from the Grantha script.
-
D.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
-
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.