Triple
T6603976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gatorland |
E149064
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Orlando theme parks area |
E11265
|
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: Orlando theme parks area | Statement: [Gatorland, near, Orlando theme parks area]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orlando theme parks area Context triple: [Gatorland, near, Orlando theme parks area]
-
A.
Lake Buena Vista, Florida
Lake Buena Vista, Florida is a small city in Orange County best known as the municipal home of the Walt Disney World Resort and other Disney-related properties.
-
B.
Walt Disney World Resort
Walt Disney World Resort is a massive entertainment complex near Orlando known for its theme parks, resorts, and attractions operated by The Walt Disney Company.
-
C.
Orlando
chosen
Orlando is a major city in central Florida known for its theme parks, tourism industry, and entertainment attractions.
-
D.
Orlando
Orlando is a common Italian surname borne by numerous individuals, including notable political and cultural figures.
-
E.
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."
- 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_69c687eaa7508190bb58ce2aa02039b3 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6af11f0788190be010c6ee60e150c |
completed | March 27, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e43aaa8c8190b7fb3666e4209a75 |
completed | March 27, 2026, 8:10 p.m. |
Created at: March 27, 2026, 1:56 p.m.