Triple
T22536372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My 1st Song |
E557164
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Aqua |
—
|
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: Aqua | Statement: [My 1st Song, writer, Aqua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aqua Context triple: [My 1st Song, writer, Aqua]
-
A.
Aqua
Aqua is a popular bottled drinking water brand owned by the multinational food and beverage company Danone, widely sold in various markets, especially in Asia.
-
B.
Aqua
Aqua is a NASA Earth-observing satellite focused on studying the planet’s water cycle and climate.
-
C.
Aqua
Aqua is the distinctive, glossy, and translucent graphical user interface introduced by Apple for macOS, known for its vibrant colors, smooth animations, and skeuomorphic design elements.
-
D.
Aqua
chosen
Aqua is a Danish-Norwegian pop group best known for their late-1990s Eurodance hits like "Barbie Girl."
-
E.
Aqua Marcia
Aqua Marcia was one of ancient Rome’s longest and most celebrated aqueducts, renowned for supplying the city with abundant, high-quality water.
- 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_69e11e57483c8190b0887c4f8ff26446 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15edbd1ec81908ef26b9e442d1921 |
completed | April 29, 2026, 1:29 a.m. |
Created at: April 16, 2026, 8:51 p.m.