Triple

T25255744
Position Surface form Disambiguated ID Type / Status
Subject Kannada cinema E633167 entity
Predicate hasNotableLyricist P10455 FINISHED
Object Gopalakrishna Adiga
Gopalakrishna Adiga was a prominent modernist Kannada poet and writer whose work significantly influenced 20th-century Kannada literature and film lyrics.
 E1758125 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: Gopalakrishna Adiga | Statement: [Kannada cinema, hasNotableLyricist, Gopalakrishna Adiga]
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: Gopalakrishna Adiga
Triple: [Kannada cinema, hasNotableLyricist, Gopalakrishna Adiga]
Generated description
Gopalakrishna Adiga was a prominent modernist Kannada poet and writer whose work significantly influenced 20th-century Kannada literature and film lyrics.

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_69e75a922ad481908f4f1f884583cb42 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f4838d5a7881908b8bfd03cf6f51b6 completed May 1, 2026, 10:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1247cbc8808190b78d6f13de5cfe33 completed May 24, 2026, 12:35 a.m.
NEDg Description generation batch_6a1249b3e9888190b3bae29310007be4 completed May 24, 2026, 12:43 a.m.
NED2 Entity disambiguation (via description) batch_6a124a849c3c81908f1b3acdbaed65f8 completed May 24, 2026, 12:47 a.m.
Created at: April 21, 2026, 1:13 p.m.