Triple
T23303953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EP2 |
E590380
|
entity |
| Predicate | hasMusicVideoFor |
P3287
|
FINISHED |
| Object | Water Me |
—
|
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: Water Me | Statement: [EP2, hasMusicVideoFor, Water Me]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Water Me Context triple: [EP2, hasMusicVideoFor, Water Me]
-
A.
Water Me
chosen
"Water Me" is a minimalist, emotionally charged R&B and electronic track by FKA twigs that showcases her ethereal vocals and experimental production style.
-
B.
Pour Me Water
"Pour Me Water" is a popular Afrobeats song by Nigerian singer Mr Eazi, known for its smooth, mid-tempo groove and romantic lyrics.
-
C.
Drink the Water
"Drink the Water" is a song by singer-songwriter Jack Johnson from his debut album *Brushfire Fairytales*.
-
D.
Bring Me Some Water
"Bring Me Some Water" is a 1988 blues-rock song by American singer-songwriter Melissa Etheridge that became one of her signature hits.
-
E.
Sweet Water
Sweet Water is a small rural town located in Marengo County in the state of Alabama, United States.
- 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_69e25d1c0ecc8190a355aa229f06d0e0 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19724de488190ac8eb89253c8dd53 |
completed | April 29, 2026, 5:29 a.m. |
Created at: April 17, 2026, 5:04 p.m.