Triple
T5570792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UTS #10 |
E146194
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object | UCA |
E146195
|
NE FINISHED |
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: UCA | Statement: [UTS #10, hasAbbreviation, UCA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UCA Context triple: [UTS #10, hasAbbreviation, UCA]
-
A.
UCA
UCA is a Jesuit-run Central American University in Managua, Nicaragua, known for its strong emphasis on social justice, human rights, and critical scholarship.
-
B.
UCA
chosen
UCA is the Unicode Collation Algorithm, a Unicode standard that defines a language-independent method for ordering and comparing Unicode text.
-
C.
UAK
UAK was the temporary currency code used for the Ukrainian karbovanets, a transitional currency of Ukraine in the early 1990s before the introduction of the hryvnia.
-
D.
UNA
UNA is the stock ticker symbol for Unilever, a major multinational consumer goods company known for its wide range of food, personal care, and household products.
-
E.
UNA
UNA is a public university located in Florence, Alabama, known for its regional academic programs and historic campus.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c008ffed108190a084602227af6157 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020502a288190af37f9ebb88fccae |
completed | March 22, 2026, 5:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0284bb71881908c0ac4ea2a302327 |
completed | March 22, 2026, 5:35 p.m. |
Created at: March 22, 2026, 3:37 p.m.