Triple

T8802584
Position Surface form Disambiguated ID Type / Status
Subject M. S. Viswanathan E209446 entity
Predicate composedForFilm P29643 FINISHED
Object Server Sundaram E743810 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: Server Sundaram | Statement: [M. S. Viswanathan, composedForFilm, Server Sundaram]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Server Sundaram
Context triple: [M. S. Viswanathan, composedForFilm, Server Sundaram]
  • A. Server Sundaram chosen
    Server Sundaram is a classic 1964 Tamil comedy film starring Nagesh, celebrated for its humorous yet poignant portrayal of a waiter’s life and social aspirations.
  • B. Simperium
    Simperium is a data synchronization and storage service designed to power real-time syncing across apps and devices, notably used as the backend for Simplenote.
  • C. Tymshare
    Tymshare was an influential American time-sharing and computer services company active in the 1960s–1980s that helped pioneer remote computing and software services for businesses.
  • D. Sundar
    Sundar is the given name of Sundar Pichai, the Indian-American CEO of Alphabet Inc. and Google.
  • E. Syntrillium Software
    Syntrillium Software was a software company best known for creating the audio editing program Cool Edit, which later evolved into Adobe Audition after Adobe acquired the firm.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca836320e48190b5cf585b90a322c4 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5fbb5b108190a9f889d40aa20521 completed March 31, 2026, 11:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf6f799f00819089159da177c816e9 completed April 3, 2026, 7:42 a.m.
Created at: March 30, 2026, 6:44 p.m.