Triple

T7935837
Position Surface form Disambiguated ID Type / Status
Subject IBM 3270-style terminal displays E184286 entity
Predicate contrastWith P278 FINISHED
Object DEC VT100 E229930 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: DEC VT100 | Statement: [IBM 3270-style terminal displays, contrastWith, DEC VT100]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: DEC VT100
Context triple: [IBM 3270-style terminal displays, contrastWith, DEC VT100]
  • A. VT100 terminal chosen
    The VT100 terminal is a widely influential video display terminal introduced by Digital Equipment Corporation in the late 1970s, known for popularizing ANSI escape codes and becoming a de facto standard for text-based computer interfaces.
  • B. Terminal
    Terminal is a 2018 neo-noir thriller film starring Margot Robbie, known for its stylized visuals and dark, twisting narrative.
  • C. Terminal
    Terminal is the built-in command-line interface application for macOS that allows users to interact with the operating system using text-based commands.
  • D. VDU
    VDU is the Lithuanian abbreviation for Vytautas Magnus University, a prominent public university in Kaunas, Lithuania.
  • E. Terminal 2D
    Terminal 2D is a passenger terminal at Paris Charles de Gaulle Airport, serving as one of the facilities handling flights and travelers at this major international hub.
  • 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_69ca8290c21c8190906a5ca6fe2b03c4 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3aec394081909a9569c02ac372af completed March 31, 2026, 3:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5c0791e48190af18299c22f6a804 completed March 31, 2026, 5:30 a.m.
Created at: March 30, 2026, 5:08 p.m.