Triple
T15527863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UVa |
E369128
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | UVa |
E369128
|
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: UVa | Statement: [UVa, abbreviation, UVa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UVa Context triple: [UVa, abbreviation, UVa]
-
A.
UVa
chosen
UVa is the commonly used abbreviation for the historic Spanish public institution the University of Valladolid.
-
B.
UVA
UVA is the three-letter IATA airport code assigned to Garner Field Airport in Uvalde, Texas.
-
C.
UVA
UVA is the University of Virginia, a major public research university in Charlottesville known for its strong academics and NCAA Division I athletic programs.
-
D.
UVM
UVM is a private Chilean university located in Viña del Mar, known for offering a wide range of undergraduate and graduate programs.
-
E.
UVM
UVM is a public research university in Burlington, Vermont, known for its strong programs in environmental studies, agriculture, and the liberal arts.
- 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_69d85a1794cc8190b0b428716296e63e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0414620588190958ffde651ccab5f |
completed | April 16, 2026, 1:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d598e6c8190870e9249197f5f53 |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 10, 2026, 4:05 a.m.