Triple

T7699651
Position Surface form Disambiguated ID Type / Status
Subject VisiCalc E174455 entity
Predicate influenced P9 FINISHED
Object Microsoft Multiplan E572856 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: Microsoft Multiplan | Statement: [VisiCalc, influenced, Microsoft Multiplan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Microsoft Multiplan
Context triple: [VisiCalc, influenced, Microsoft Multiplan]
  • A. Microsoft Multiplan chosen
    Microsoft Multiplan was an early spreadsheet program from Microsoft, released in the early 1980s as a competitor to VisiCalc and a precursor to Excel.
  • B. Lotus 1-2-3
    Lotus 1-2-3 is a pioneering spreadsheet software program for personal computers that became a dominant business application in the 1980s.
  • C. MS-DOS Executive
    MS-DOS Executive is the simple file management and program-launching shell that served as the primary user interface in early versions of Microsoft Windows, notably Windows 1.0.
  • D. Peter Norton Computing
    Peter Norton Computing was a software company best known for its Norton Utilities and other PC maintenance tools that helped popularize system optimization and data recovery for early personal computers.
  • E. Micros Systems
    Micros Systems was a leading provider of point-of-sale and hospitality management software and hardware solutions for restaurants, hotels, and retail businesses.
  • 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_69c6995a72cc8190998e56daa6f8e453 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c7026ba7b48190a18f1c1cbbf1b944 completed March 27, 2026, 10:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8acb3ac4481909acafe50b9507aaa completed March 29, 2026, 4:38 a.m.
Created at: March 27, 2026, 4:03 p.m.