Triple
T12382645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Timeless |
E295780
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Napji |
E976741
|
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: Napji | Statement: [Timeless, producer, Napji]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Napji Context triple: [Timeless, producer, Napji]
-
A.
Napji
chosen
Napji is a music producer known for crafting beats and tracks, particularly within contemporary hip-hop and related genres.
-
B.
Mahwa
Mahwa is a town located in the Dausa district of the Indian state of Rajasthan.
-
C.
Napareuli
Napareuli is a Georgian wine appellation in the Kakheti region, known for producing high-quality wines, particularly from the Saperavi grape.
-
D.
Sijjin
Sijjin is an Islamic term referring to a record or register in which the deeds of the wicked are inscribed and a place associated with severe punishment in the Hereafter.
-
E.
Jinki
Jinki was a Japanese era name (nengō) of the Nara period, used during the reign of Empress Genshō.
- 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fbb3a2481908c2fcb5e6488eb3c |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f634760210819080bc0261aa059132 |
completed | May 2, 2026, 5:29 p.m. |
Created at: April 8, 2026, 9:54 p.m.