Triple
T5795807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unicode CLDR |
E128504
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object |
UTS #35
UTS #35 is a Unicode Technical Standard that defines the Locale Data Markup Language (LDML) used for internationalization data in the Unicode CLDR project.
|
E548284
|
NE FINISHED |
How this triple was built (4 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: UTS #35 | Statement: [Unicode CLDR, relatedTo, UTS #35]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UTS #35 Context triple: [Unicode CLDR, relatedTo, UTS #35]
-
A.
UTS #10
UTS #10 is the Unicode Collation Algorithm standard that defines how to consistently compare and sort Unicode text across different languages and platforms.
-
B.
UTS
UTS is a highly selective independent secondary school affiliated with the University of Toronto, known for its strong academic programs and gifted education.
-
C.
UTS
UTS is a major Australian public research university in Sydney known for its industry-focused education and modern urban campus.
-
D.
UTS Sport
UTS Sport is the sporting organisation representing the University of Technology Sydney, coordinating its student and representative sports programs and facilities.
-
E.
UTR #29
UTR #29 is a Unicode Technical Report that defines the standard rules and algorithms for text segmentation, such as determining grapheme clusters, words, and sentences in Unicode text.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: UTS #35 Triple: [Unicode CLDR, relatedTo, UTS #35]
Generated description
UTS #35 is a Unicode Technical Standard that defines the Locale Data Markup Language (LDML) used for internationalization data in the Unicode CLDR project.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: UTS #35 Target entity description: UTS #35 is a Unicode Technical Standard that defines the Locale Data Markup Language (LDML) used for internationalization data in the Unicode CLDR project.
-
A.
UTS #10
UTS #10 is the Unicode Collation Algorithm standard that defines how to consistently compare and sort Unicode text across different languages and platforms.
-
B.
UTS
UTS is a highly selective independent secondary school affiliated with the University of Toronto, known for its strong academic programs and gifted education.
-
C.
UTS
UTS is a major Australian public research university in Sydney known for its industry-focused education and modern urban campus.
-
D.
UTS Sport
UTS Sport is the sporting organisation representing the University of Technology Sydney, coordinating its student and representative sports programs and facilities.
-
E.
UTR #29
UTR #29 is a Unicode Technical Report that defines the standard rules and algorithms for text segmentation, such as determining grapheme clusters, words, and sentences in Unicode text.
- F. None of above. chosen
Provenance (5 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_69c00845ca68819081a2ce3ecca577f7 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02a9304b081909ea004902f4ca569 |
completed | March 22, 2026, 5:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0982ce0ac8190b9f12cedb66c5eb3 |
completed | March 23, 2026, 1:32 a.m. |
| NEDg | Description generation | batch_69c099b16d148190b442739fbe2802ad |
completed | March 23, 2026, 1:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c09a13a19c8190a04807755d16fb95 |
completed | March 23, 2026, 1:40 a.m. |
Created at: March 22, 2026, 3:51 p.m.