Triple

T9532566
Position Surface form Disambiguated ID Type / Status
Subject VT100 E229930 entity
Predicate usesCharacterSet P7661 FINISHED
Object 7-bit ASCII E23922 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: 7-bit ASCII | Statement: [VT100, usesCharacterSet, 7-bit ASCII]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 7-bit ASCII
Context triple: [VT100, usesCharacterSet, 7-bit ASCII]
  • A. ASCII chosen
    ASCII is a widely used character encoding standard that represents text in computers and other devices using 7-bit numerical codes for letters, digits, punctuation, and control characters.
  • B. ISO 646
    ISO 646 is an international standard for 7-bit character encodings that defines a set of basic Latin characters and allows national variants, serving as a foundation for many early computer character sets.
  • C. UTF-7
    UTF-7 is an obsolete, 7-bit Unicode text encoding designed primarily for safe transmission of Unicode data over email systems that were not fully 8-bit clean.
  • D. ISO/IEC 2022
    ISO/IEC 2022 is an international standard that defines mechanisms for encoding and switching between multiple character sets within a single byte-oriented data stream.
  • E. ASCI
    ASCI is a prestigious U.S. honor society of physician-scientists dedicated to advancing clinical and translational research.
  • 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_69ca8479934c81908006d0e6e970ae05 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd98b5651881908241b040f123c6a8 completed April 1, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c4033c08190a71535b63d86f4df completed April 4, 2026, 5:37 p.m.
Created at: March 30, 2026, 8 p.m.