Triple
T22585104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unicode bidirectional algorithm |
E564766
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Unicode line breaking algorithm |
—
|
NE NERFINISHED |
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 line breaking algorithm | Statement: [Unicode bidirectional algorithm, relatedTo, Unicode line breaking algorithm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Unicode line breaking algorithm Context triple: [Unicode bidirectional algorithm, relatedTo, Unicode line breaking algorithm]
-
A.
Unicode Line Breaking Algorithm
chosen
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.
-
B.
Unicode bidirectional algorithm
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.
-
C.
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.
-
D.
Grapheme_Cluster_Break
Grapheme_Cluster_Break is a Unicode text segmentation property used to determine how sequences of code points form user-perceived characters (grapheme clusters) for operations like cursor movement and text selection.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245836014819091b91ed3074742a3 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1615c18f88190ad4f23639d15f337 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 2:45 p.m.