Triple
T246902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telugu |
E5056
|
entity |
| Predicate | hasNotablePoet |
P4290
|
FINISHED |
| Object |
Yerrapragada
Yerrapragada was a prominent medieval Telugu poet and scholar, renowned for his contributions to classical Telugu literature and refinement of earlier works.
|
E34331
|
NE FINISHED |
How this triple was built (4 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: Yerrapragada | Statement: [Telugu, hasNotablePoet, Yerrapragada]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yerrapragada Context triple: [Telugu, hasNotablePoet, Yerrapragada]
-
A.
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.
-
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.
Baramati
Baramati is a town in the Pune district of Maharashtra, India, known as an agricultural and industrial hub with historical and political significance.
-
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.
Bauta
Bauta is a municipality in western Cuba known for its proximity to Havana and its mix of rural communities and small urban centers.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Yerrapragada Triple: [Telugu, hasNotablePoet, Yerrapragada]
Generated description
Yerrapragada was a prominent medieval Telugu poet and scholar, renowned for his contributions to classical Telugu literature and refinement of earlier works.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yerrapragada Target entity description: Yerrapragada was a prominent medieval Telugu poet and scholar, renowned for his contributions to classical Telugu literature and refinement of earlier works.
-
A.
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.
-
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.
Baramati
Baramati is a town in the Pune district of Maharashtra, India, known as an agricultural and industrial hub with historical and political significance.
-
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.
Bauta
Bauta is a municipality in western Cuba known for its proximity to Havana and its mix of rural communities and small urban centers.
- F. None of above. chosen
Provenance (5 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_69a389ab230c8190982eead1ef7b5c75 |
completed | March 1, 2026, 12:34 a.m. |
| NEDg | Description generation | batch_69a38a0114b481908c9363e926b4b3ae |
completed | March 1, 2026, 12:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a38a699a6081908c167ce9ad55a660 |
completed | March 1, 2026, 12:38 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.