Triple

T8608840
Position Surface form Disambiguated ID Type / Status
Subject GNOME Terminal E203869 entity
Predicate supportsProtocol P203 FINISHED
Object VT220 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: VT220 | Statement: [GNOME Terminal, supportsProtocol, VT220]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VT220
Context triple: [GNOME Terminal, supportsProtocol, VT220]
  • 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 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.
  • C. VDU
    VDU is the Lithuanian abbreviation for Vytautas Magnus University, a prominent public university in Kaunas, Lithuania.
  • D. VMS
    VMS is a multiuser, multitasking operating system originally created by Digital Equipment Corporation for its VAX minicomputers, known for its robustness, security features, and influence on later systems like Windows NT.
  • E. VMS
    VMS is a regional public transport association in the Chemnitz area of Germany that coordinates and manages integrated fares and services across multiple transit operators.
  • 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_69ca832c23e4819095a9f3eea4a21828 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc46ed77588190a872d22d9d1f7429 completed March 31, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69cea90dd93081908140ac0ce23be820 completed April 2, 2026, 5:36 p.m.
Created at: March 30, 2026, 6:25 p.m.