Triple

T37957156
Position Surface form Disambiguated ID Type / Status
Subject Zabaan Sambhalke E946897 entity
Predicate mainCharacter P1183 FINISHED
Object Mohandas B. Khandekar
Mohandas B. Khandekar is the bumbling yet well-meaning Hindi teacher protagonist of the Indian sitcom "Zabaan Sambhalke," known for his comedic attempts to manage a diverse class of adult students.
E2252977 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: Mohandas B. Khandekar | Statement: [Zabaan Sambhalke, mainCharacter, Mohandas B. Khandekar]
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: Mohandas B. Khandekar
Triple: [Zabaan Sambhalke, mainCharacter, Mohandas B. Khandekar]
Generated description
Mohandas B. Khandekar is the bumbling yet well-meaning Hindi teacher protagonist of the Indian sitcom "Zabaan Sambhalke," known for his comedic attempts to manage a diverse class of adult students.

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_69f76ef64cf08190ad3e1114b62aac67 completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbbdd422fc8190a8d69305850335af completed May 6, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41542a46348190948908471e652a9d completed June 28, 2026, 5:04 p.m.
NEDg Description generation batch_6a41550fcbb88190834e577210a7a540 completed June 28, 2026, 5:08 p.m.
NED2 Entity disambiguation (via description) batch_6a4155a3bd448190a5af1795e2e33070 completed June 28, 2026, 5:10 p.m.
Created at: May 3, 2026, 4:20 p.m.