Triple
T16324616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Central Florida |
E396380
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Tavares |
E79684
|
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: Tavares | Statement: [North Central Florida, hasMajorCity, Tavares]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tavares Context triple: [North Central Florida, hasMajorCity, Tavares]
-
A.
Tavares
chosen
Tavares is a Portuguese surname borne by numerous notable individuals across fields such as business, sports, and the arts.
-
B.
Conley
Conley is a surname most prominently associated with NBA point guard Mike Conley Jr.
-
C.
Tolan
Tolan is a surname most notably associated with American television producer, writer, and director Peter Tolan.
-
D.
Toland
Toland is a surname most notably associated with Gregg Toland, the pioneering American cinematographer renowned for his innovative deep-focus techniques in films like "Citizen Kane."
-
E.
Dantley
Dantley is a surname most prominently associated with Adrian Dantley, a Hall of Fame American professional basketball player known for his prolific scoring in the NBA.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e296b8fe988190adee72b23246052f |
completed | April 17, 2026, 8:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00260ca9f08190aa95560fea482dd4 |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 5:06 a.m.