Triple
T246904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telugu |
E5056
|
entity |
| Predicate | hasNotablePoet |
P4290
|
FINISHED |
| Object | Vemana |
E34331
|
NE 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: Vemana | Statement: [Telugu, hasNotablePoet, Vemana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vemana Context triple: [Telugu, hasNotablePoet, Vemana]
-
A.
Yerrapragada
chosen
Yerrapragada was a prominent medieval Telugu poet and scholar, renowned for his contributions to classical Telugu literature and refinement of earlier works.
-
B.
Sibi
Sibi is a historic town and district in the Balochistan region of Pakistan, known for its hot climate and traditional annual cattle and horse fair.
-
C.
Kalpeni
Kalpeni is a coral atoll and inhabited island in India’s Lakshadweep archipelago in the Arabian Sea, known for its lagoon, beaches, and coconut groves.
-
D.
Madura
Madura is an island off the northeastern coast of Java in Indonesia, known for its distinct Madurese culture and traditional bull races.
-
E.
Isha
Isha is the nightly Islamic prayer that is performed after dusk and marks the final of the five daily obligatory prayers in Islam.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a257c4bf688190a46ebbf411ab7473 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a260c592cc8190bc642fcd248a1f1b |
completed | Feb. 28, 2026, 3:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a38b8ca1f08190a13733c03b9161f9 |
completed | March 1, 2026, 12:42 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.