Triple
T10245289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Give Me Everything |
E240196
|
entity |
| Predicate | featuredArtist |
P997
|
FINISHED |
| Object | Nayer |
E763643
|
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: Nayer | Statement: [Give Me Everything, featuredArtist, Nayer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nayer Context triple: [Give Me Everything, featuredArtist, Nayer]
-
A.
Nayer
chosen
Nayer is a Cuban-American singer and songwriter best known for her collaborations on international pop and dance hits, including work with Pitbull and other Latin pop producers.
-
B.
Nafe
Nafe is an indigenous Oceanic language spoken in Vanuatu.
-
C.
Neyo
Ne-Yo is an American R&B singer, songwriter, and record producer known for hits like "So Sick" and "Closer" and for writing songs for numerous major artists.
-
D.
Nahan
Nahan is a small hill town and municipal council in Himachal Pradesh, India, known for its scenic surroundings, pleasant climate, and role as a regional administrative and commercial center.
-
E.
Nayel
Nayel is a masculine given name, notably borne by Egyptian-American professional show jumping rider Nayel Nassar.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d22be0208190b671a4e3f81d11b8 |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f79c20e08190928d061b3d4b4e47 |
completed | April 9, 2026, 12:49 a.m. |
Created at: April 6, 2026, 11:26 a.m.