Triple
T1216391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Florida |
E26115
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Kissimmee |
E26777
|
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: Kissimmee | Statement: [Central Florida, hasMajorCity, Kissimmee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kissimmee Context triple: [Central Florida, hasMajorCity, Kissimmee]
-
A.
Kissimmee, Florida
chosen
Kissimmee, Florida is a central Florida city in Osceola County known for its proximity to major Orlando-area theme parks and tourist attractions.
-
B.
Altamonte Springs
Altamonte Springs is a suburban city in the Orlando metropolitan area of Central Florida, known for its residential communities, shopping centers, and recreational amenities.
-
C.
Orlando
Orlando is a major city in central Florida known for its theme parks, tourism industry, and entertainment attractions.
-
D.
Lakeland, Florida
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.
-
E.
Lakeland
Lakeland is a residential neighborhood located within the city of College Park in Prince George's County, Maryland.
- 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_69a4948331fc8190b531ac9bec71c491 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be059c5c8190a200f09442c22334 |
completed | March 1, 2026, 10:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad158eca208190b763da8cb9af1dcd |
completed | March 8, 2026, 6:22 a.m. |
Created at: March 1, 2026, 7:46 p.m.