Triple

T6668515
Position Surface form Disambiguated ID Type / Status
Subject Unicode 4.1 E151664 entity
Predicate refinesBidirectionalAlgorithm P25130 FINISHED
Object Unicode Bidirectional Algorithm E564766 NE FINISHED

How this triple was built (3 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 Bidirectional Algorithm | Statement: [Unicode 4.1, refinesBidirectionalAlgorithm, Unicode Bidirectional Algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Unicode Bidirectional Algorithm
Context triple: [Unicode 4.1, refinesBidirectionalAlgorithm, Unicode Bidirectional Algorithm]
  • A. Unicode bidirectional algorithm chosen
    The Unicode bidirectional algorithm is a core text-processing method that determines the correct display order of mixed left-to-right and right-to-left scripts, such as Latin and Arabic, in digital text.
  • B. 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.
  • C. Unicode Line Breaking Algorithm
    The Unicode Line Breaking Algorithm is a standard specification that defines how to determine valid line break opportunities in text encoded with Unicode, ensuring consistent and readable text layout across different systems and languages.
  • D. 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.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: refinesBidirectionalAlgorithm
Context triple: [Unicode 4.1, refinesBidirectionalAlgorithm, Unicode Bidirectional Algorithm]
  • A. relatedAlgorithm chosen
    Indicates that one algorithm has a meaningful connection or association with another algorithm, such as similarity, dependency, or complementary function.
  • B. refinesSupportFor
    Indicates that one entity improves, clarifies, or makes more precise the support or justification provided by another entity.
  • C. bidirectionalClass
    Indicates that a class participates in a bidirectional relationship, where each related class maintains a reference to the other.
  • D. supportsOptimizationAlgorithm
    Indicates that one entity is capable of running, integrating, or being compatible with a specified optimization algorithm.
  • E. alignmentWithMoreau
    Indicates a relationship where an entity’s views, actions, or characteristics are in agreement or ideological conformity with those associated with Moreau.
  • F. None of above.

Provenance (4 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_69c687f71fc081909dbd45d6377f6045 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6ce738fe88190a5557900efeec7ec completed March 27, 2026, 6:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6ef109f5c8190aa28b5d7aa192e6e completed March 27, 2026, 8:56 p.m.
PD Predicate disambiguation batch_69c6ad09974c81908784300ae218961f completed March 27, 2026, 4:15 p.m.
Created at: March 27, 2026, 2:02 p.m.