Triple

T1884976
Position Surface form Disambiguated ID Type / Status
Subject Borland E39942 entity
Predicate notableProduct P1448 FINISHED
Object Delphi E10344 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: Delphi | Statement: [Borland, notableProduct, Delphi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Delphi
Context triple: [Borland, notableProduct, Delphi]
  • A. Delphi
    Delphi is an ancient Greek sanctuary and archaeological site famed for the Oracle of Apollo and its central role in classical Greek religion and culture.
  • B. Delphi (programming language) chosen
    Delphi is an object-oriented, rapid application development programming language and environment derived from Pascal, primarily used for building native Windows applications.
  • C. Embarcadero
    Embarcadero is a historic waterfront district in San Francisco known for its piers, ferry terminal, and scenic promenade along the bay.
  • D. 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.
  • E. Borland
    Borland was a prominent software company best known for its influential development tools and programming environments, particularly during the 1980s and 1990s.
  • 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_69a88633e4fc8190b7eb40463e048ec5 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb11eb2d0819088d67b1cfc772049 completed March 7, 2026, 5:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69addf61413081909c0e840590aaf631 completed March 8, 2026, 8:43 p.m.
Created at: March 4, 2026, 7:34 p.m.