Triple
T18911608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Lakes neighborhood |
E462621
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Downtown Orlando |
—
|
NE NERFINISHED |
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: Downtown Orlando | Statement: [West Lakes neighborhood, near, Downtown Orlando]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Downtown Orlando Context triple: [West Lakes neighborhood, near, Downtown Orlando]
-
A.
downtown Orlando
chosen
Downtown Orlando is the central business and entertainment district of Orlando, Florida, known for its high-rise skyline, cultural venues, nightlife, and major sports and event facilities.
-
B.
International Drive, Orlando
International Drive in Orlando is a major tourist corridor known for its concentration of theme parks, attractions, hotels, restaurants, and shopping venues.
-
C.
Orlando
Orlando is a common Italian surname borne by numerous individuals, including notable political and cultural figures.
-
D.
Orlando
Orlando is the Italian literary counterpart of the medieval knight Roland, best known as the chivalric hero of epic poems such as "Orlando Furioso."
-
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 (2 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_69d8dcfd05bc819088903cca13cc2846 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c6238e288190b30311b5d80beafb |
completed | April 20, 2026, 6:22 a.m. |
Created at: April 10, 2026, 11:58 a.m.