Triple

T6469970
Position Surface form Disambiguated ID Type / Status
Subject East_Asian_Width E142322 entity
Predicate relatedTo P37 FINISHED
Object Unicode normalization E564765 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: Unicode normalization | Statement: [East_Asian_Width, relatedTo, Unicode normalization]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Unicode normalization
Context triple: [East_Asian_Width, relatedTo, Unicode normalization]
  • A. Unicode normalization chosen
    Unicode normalization is a set of standardized processes that convert equivalent Unicode text sequences into a consistent canonical form to ensure reliable comparison, searching, and processing of text across systems.
  • B. Unicode Technical Standard #35
    Unicode Technical Standard #35 is a Unicode Consortium specification that defines the Locale Data Markup Language (LDML) and related mechanisms for internationalization, including formatting of dates, times, numbers, and other locale-sensitive data.
  • C. Unicode Technical Standard #10
    Unicode Technical Standard #10 is the specification that defines the Unicode Collation Algorithm, providing a standardized method for comparing and sorting Unicode text across languages and platforms.
  • D. Unicode
    Unicode is a universal character encoding standard that assigns unique code points to virtually all written scripts, symbols, and emojis used in modern computing.
  • E. Unicode text processing algorithms
    Unicode text processing algorithms are standardized procedures that define how Unicode text is compared, sorted, segmented, normalized, and otherwise manipulated consistently across different systems and languages.
  • 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_69c008d3bf4c8190bcf798c5ba9d6fb3 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c06a2e896481908ed004e3b0a33121 completed March 22, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6539c93c481909bed35b68ce420d8 completed March 27, 2026, 9:53 a.m.
Created at: March 22, 2026, 4:50 p.m.