Triple
T7238396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Interstate 4 |
E155290
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Lakeland, Florida |
E155311
|
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: Lakeland, Florida | Statement: [Interstate 4, passesThrough, Lakeland, Florida]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lakeland, Florida Context triple: [Interstate 4, passesThrough, Lakeland, Florida]
-
A.
Lakeland, Florida
chosen
Lakeland, Florida is a mid-sized city in central Florida known for its numerous lakes, historic downtown, and long-standing ties to Major League Baseball.
-
B.
Lakeland
Lakeland is a residential neighborhood located within the city of College Park in Prince George's County, Maryland.
-
C.
Lauderdale Lakes, Florida
Lauderdale Lakes, Florida is a small suburban city in Broward County known for its diverse population and residential communities within the Miami metropolitan area.
-
D.
Kissimmee, Florida
Kissimmee, Florida is a central Florida city in Osceola County known for its proximity to major Orlando-area theme parks and tourist attractions.
-
E.
Sarasota, Florida
Sarasota, Florida is a Gulf Coast city known for its beaches, arts and cultural scene, and as a longtime hub for Major League Baseball spring training.
- 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_69c688143bfc81908d4176617735e601 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea368fb88190bd9e991e8b94dac6 |
completed | March 27, 2026, 8:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7db0fd90881908c6b84f1292f0b01 |
completed | March 28, 2026, 1:43 p.m. |
Created at: March 27, 2026, 2:55 p.m.