Triple

T28395995
Position Surface form Disambiguated ID Type / Status
Subject Sridhar Vembu E719289 entity
Predicate associatedWith P37 FINISHED
Object Zoho Schools of Learning
Zoho Schools of Learning is an alternative education initiative by Zoho that offers industry-focused training to high school graduates as a pathway into software and business careers without requiring a traditional college degree.
 E1816595 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: Zoho Schools of Learning | Statement: [Sridhar Vembu, associatedWith, Zoho Schools of Learning]
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: Zoho Schools of Learning
Triple: [Sridhar Vembu, associatedWith, Zoho Schools of Learning]
Generated description
Zoho Schools of Learning is an alternative education initiative by Zoho that offers industry-focused training to high school graduates as a pathway into software and business careers without requiring a traditional college degree.

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_69eff6efd1b08190ae3cefd4f11388a2 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64cefa7f4819099cfebca23d70dae completed May 2, 2026, 7:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a16330be4bc8190aa1a0fda67a2f3a2 completed May 26, 2026, 11:55 p.m.
NEDg Description generation batch_6a1634256d74819090c95b262c5e6873 completed May 27, 2026, midnight
NED2 Entity disambiguation (via description) batch_6a1635bc8c488190bb284e3caa3c6641 completed May 27, 2026, 12:07 a.m.
Created at: April 28, 2026, 1:17 a.m.