Triple

T9213747
Position Surface form Disambiguated ID Type / Status
Subject Tod Nielsen E221190 entity
Predicate employer P7 FINISHED
Object Borland E39942 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: Borland | Statement: [Tod Nielsen, employer, Borland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Borland
Context triple: [Tod Nielsen, employer, Borland]
  • A. Borland chosen
    Borland was a prominent software company best known for its influential development tools and programming environments, particularly during the 1980s and 1990s.
  • B. Embarcadero
    Embarcadero is a historic waterfront district in San Francisco known for its piers, ferry terminal, and scenic promenade along the bay.
  • C. Embarcadero Technologies
    Embarcadero Technologies is a software company best known for developing database tools and the Delphi rapid application development environment for Windows and cross-platform applications.
  • D. Borland C++
    Borland C++ is an early, influential C and C++ integrated development environment and compiler for DOS and Windows that helped popularize C++ development on personal computers in the 1990s.
  • E. Turbo Pascal
    Turbo Pascal is a once-popular integrated development environment and compiler for the Pascal programming language, known for its fast compilation speed and influence on early PC software development.
  • 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_69ca83eae42c8190a0ea9e040710a277 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccda06bf80819094c6e74b4b6a31e4 completed April 1, 2026, 8:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1077d8a048190b4b45419fb0d7279 completed April 4, 2026, 12:43 p.m.
Created at: March 30, 2026, 7:27 p.m.