Triple
T7035309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Vermont |
E163366
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
UVM
UVM is a public research university in Burlington, Vermont, known for its strong programs in environmental studies, agriculture, and the liberal arts.
|
E637212
|
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: UVM | Statement: [University of Vermont, abbreviation, UVM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UVM Context triple: [University of Vermont, abbreviation, UVM]
-
A.
UCT
UCT is a leading public research university in Cape Town, South Africa, renowned as one of Africa’s top higher education institutions.
-
B.
Uni
Uni is an Etruscan goddess, broadly equivalent to the Roman Juno and Greek Hera, associated with marriage, fertility, and protection of the state.
-
C.
UVA
UVA is the three-letter IATA airport code assigned to Garner Field Airport in Uvalde, Texas.
-
D.
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.
-
E.
UNI
UNI is a public university in Cedar Falls, Iowa, known for its strong teacher education programs and comprehensive undergraduate and graduate offerings.
- 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: UVM Triple: [University of Vermont, abbreviation, UVM]
Generated description
UVM is a public research university in Burlington, Vermont, known for its strong programs in environmental studies, agriculture, and the liberal arts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: UVM Target entity description: UVM is a public research university in Burlington, Vermont, known for its strong programs in environmental studies, agriculture, and the liberal arts.
-
A.
UCT
UCT is a leading public research university in Cape Town, South Africa, renowned as one of Africa’s top higher education institutions.
-
B.
Uni
Uni is an Etruscan goddess, broadly equivalent to the Roman Juno and Greek Hera, associated with marriage, fertility, and protection of the state.
-
C.
UVA
UVA is the three-letter IATA airport code assigned to Garner Field Airport in Uvalde, Texas.
-
D.
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.
-
E.
UNI
UNI is a public university in Cedar Falls, Iowa, known for its strong teacher education programs and comprehensive undergraduate and graduate offerings.
- 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_69c6885d691c81908cf7d31083113886 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e212e28c8190bf38ce9a25d2032e |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c775a211f88190afe5ed466abcac7a |
completed | March 28, 2026, 6:30 a.m. |
| NEDg | Description generation | batch_69c779c064548190bc17a399723f85e7 |
completed | March 28, 2026, 6:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c77a79e76c8190a42fe57ffc1dc23c |
completed | March 28, 2026, 6:51 a.m. |
Created at: March 27, 2026, 2:36 p.m.