Triple
T22185519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UTS #35 |
E548284
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object | UTS 35 |
—
|
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: UTS 35 | Statement: [UTS #35, hasAbbreviation, UTS 35]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UTS 35 Context triple: [UTS #35, hasAbbreviation, UTS 35]
-
A.
UTS #35
chosen
UTS #35 is a Unicode Technical Standard that defines the Locale Data Markup Language (LDML) used for internationalization data in the Unicode CLDR project.
-
B.
UTS
UTS is a major Australian public research university in Sydney known for its industry-focused education and modern urban campus.
-
C.
UTS
UTS is a highly selective independent secondary school affiliated with the University of Toronto, known for its strong academic programs and gifted education.
-
D.
UTS
UTS is the station code used to identify Utsunomiya Station in Japan’s railway system.
-
E.
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.
- 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_69e11e3e0c7c8190b30d278845e2497e |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12aa823888190829368de6db4aa91 |
completed | April 28, 2026, 9:46 p.m. |
Created at: April 16, 2026, 8:35 p.m.