Triple
T18563527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unicode Technical Reports |
E453704
|
entity |
| Predicate | includesExample |
P1259
|
FINISHED |
| Object | Unicode Text Segmentation |
—
|
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 Text Segmentation | Statement: [Unicode Technical Reports, includesExample, Unicode Text Segmentation]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Unicode Text Segmentation Context triple: [Unicode Technical Reports, includesExample, Unicode Text Segmentation]
-
A.
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.
-
B.
VOCSegmentation
VOCSegmentation is a semantic segmentation dataset class in Torchvision that provides access to the PASCAL VOC image dataset with pixel-level object annotations.
-
C.
Unicode text processing algorithms
chosen
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.
Sentence_Break
Sentence_Break is a Unicode text segmentation property used to determine where sentence boundaries occur in a stream of characters.
- 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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53afc57448190abd90167d7e12a18 |
completed | April 19, 2026, 8:28 p.m. |
Created at: April 10, 2026, 11:42 a.m.