Triple

T4575439
Position Surface form Disambiguated ID Type / Status
Subject UTF-7 E123130 entity
Predicate replacedBy P101 FINISHED
Object UTF-32 E23921 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: UTF-32 | Statement: [UTF-7, replacedBy, UTF-32]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: UTF-32
Context triple: [UTF-7, replacedBy, UTF-32]
  • A. UTF-32 chosen
    UTF-32 is a fixed-length Unicode character encoding that represents each code point using 32 bits, providing simple indexing at the cost of higher memory usage.
  • B. UTF-8
    UTF-8 is a widely used variable-length character encoding standard for Unicode that efficiently represents text in most of the world's writing systems while maintaining backward compatibility with ASCII.
  • C. 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.
  • D. 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.
  • E. Unicode Character Database
    The Unicode Character Database is a comprehensive collection of machine-readable data files that define the properties, classifications, and behaviors of every character encoded in the Unicode Standard.
  • 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_69bd46466c7081909d07f36be2d08804 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd58dfe3508190b21836079e951a3c completed March 20, 2026, 2:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdd3e656a08190bb48d2ecae1eb798 completed March 20, 2026, 11:10 p.m.
Created at: March 20, 2026, 1:10 p.m.