Triple
T963832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Florida |
E20791
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Gainesville |
E108579
|
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: Gainesville | Statement: [University of Florida, city, Gainesville]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gainesville Context triple: [University of Florida, city, Gainesville]
-
A.
Gainesville
Gainesville is a rapidly growing suburban community in Northern Virginia known for its residential developments, shopping centers, and proximity to Washington, D.C.
-
B.
Tallahassee
Tallahassee is a city in the Florida Panhandle known for its government institutions, universities, and rolling, forested hills.
-
C.
Tallahassee metropolitan area
The Tallahassee metropolitan area is a regional urban and economic hub in northern Florida centered on the state capital and its surrounding communities.
-
D.
Gainesville, Florida, United States
chosen
Gainesville, Florida, United States is a mid-sized North Florida city best known as the home of the University of Florida and a hub for education, research, and healthcare.
-
E.
Orlando
Orlando is a major city in central Florida known for its theme parks, tourism industry, and entertainment attractions.
- 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_69a493b21f2881908132dcf45dcd2f36 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b4303e5881909d101d11f9732c75 |
completed | March 1, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad013e770c8190a97a67d546da341a |
completed | March 8, 2026, 4:55 a.m. |
Created at: March 1, 2026, 7:40 p.m.