Triple
T16542672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EAST Miami |
E401857
|
entity |
| Predicate | hasRooftopBar |
P27105
|
FINISHED |
| Object |
Sugar
Sugar is a stylish rooftop bar and lounge in Miami known for its panoramic city views and Asian-inspired cocktails.
|
E1219952
|
NE FINISHED |
How this triple was built (4 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: Sugar | Statement: [EAST Miami, hasRooftopBar, Sugar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sugar Context triple: [EAST Miami, hasRooftopBar, Sugar]
-
A.
Sugar
Sugar is a child-friendly, open-source learning platform and graphical interface designed to support education on low-cost laptops like those from the One Laptop per Child project.
-
B.
Sugar
"Sugar" is a 2014 pop song by American band Maroon 5, known for its catchy hook and a music video featuring surprise performances at real weddings.
-
C.
Sugar
Sugar is an American alternative rock band formed by Bob Mould in the early 1990s, known for its melodic yet heavy guitar sound and influential albums like "Copper Blue."
-
D.
Sugar
Sugar is a 1972 Broadway musical comedy with music by Jule Styne, adapted from the film "Some Like It Hot."
-
E.
Sugar
Sugar is the intelligent and ambitious Victorian-era prostitute who serves as the central protagonist in the TV adaptation of "The Crimson Petal and the White."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Sugar Triple: [EAST Miami, hasRooftopBar, Sugar]
Generated description
Sugar is a stylish rooftop bar and lounge in Miami known for its panoramic city views and Asian-inspired cocktails.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sugar Target entity description: Sugar is a stylish rooftop bar and lounge in Miami known for its panoramic city views and Asian-inspired cocktails.
-
A.
Sugar
Sugar is a child-friendly, open-source learning platform and graphical interface designed to support education on low-cost laptops like those from the One Laptop per Child project.
-
B.
Sugar
"Sugar" is a 2014 pop song by American band Maroon 5, known for its catchy hook and a music video featuring surprise performances at real weddings.
-
C.
Sugar
Sugar is an American alternative rock band formed by Bob Mould in the early 1990s, known for its melodic yet heavy guitar sound and influential albums like "Copper Blue."
-
D.
Sugar
Sugar is a 1972 Broadway musical comedy with music by Jule Styne, adapted from the film "Some Like It Hot."
-
E.
Sugar
Sugar is the intelligent and ambitious Victorian-era prostitute who serves as the central protagonist in the TV adaptation of "The Crimson Petal and the White."
- F. None of above. chosen
Provenance (5 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3455db6788190b929546050ea2488 |
completed | April 18, 2026, 8:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0067b0e5708190a286b8a316d6efd2 |
completed | May 10, 2026, 11:10 a.m. |
| NEDg | Description generation | batch_6a006895b8ac8190a8d078e6b9f5bb50 |
completed | May 10, 2026, 11:14 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00694da4a88190944ae4a70ac9f0c3 |
completed | May 10, 2026, 11:17 a.m. |
Created at: April 10, 2026, 5:15 a.m.