Triple

T29593767
Position Surface form Disambiguated ID Type / Status
Subject Gautham Vasudev Menon E754237 entity
Predicate employer P7 FINISHED
Object Ondraga Entertainment
Ondraga Entertainment is an Indian film production company founded and headed by director Gautham Vasudev Menon, known for producing and promoting Tamil-language cinema.
E1874990 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: Ondraga Entertainment | Statement: [Gautham Vasudev Menon, employer, Ondraga Entertainment]
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: Ondraga Entertainment
Triple: [Gautham Vasudev Menon, employer, Ondraga Entertainment]
Generated description
Ondraga Entertainment is an Indian film production company founded and headed by director Gautham Vasudev Menon, known for producing and promoting Tamil-language cinema.

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_69f0ef836ac88190bd809dc58b5ec907 completed April 28, 2026, 5:33 p.m.
NER Named-entity recognition batch_69f66db5b6fc81908d5b3bdbb085a93d completed May 2, 2026, 9:33 p.m.
NED1 Entity disambiguation (via context triple) batch_6a262d7c583481908624f13987ea4337 completed June 8, 2026, 2:48 a.m.
NEDg Description generation batch_6a26320ad0ac8190872f8152afd4600b completed June 8, 2026, 3:07 a.m.
NED2 Entity disambiguation (via description) batch_6a26377a75008190b09b23268b3a0e14 completed June 8, 2026, 3:31 a.m.
Created at: April 28, 2026, 6:16 p.m.