Triple
T22126157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glassheart |
E546794
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Sugar |
—
|
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: Sugar | Statement: [Glassheart, hasPart, Sugar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sugar Context triple: [Glassheart, hasPart, 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
chosen
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 a stylish rooftop bar and lounge in Miami known for its panoramic city views and Asian-inspired cocktails.
- 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_69e11e39bf348190b541bfa16a7b71e0 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12981ac008190aafb516f2a28fefc |
completed | April 28, 2026, 9:41 p.m. |
Created at: April 16, 2026, 8:31 p.m.