Triple
T12321649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rema (EP) |
E293743
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object | Corny |
E292194
|
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: Corny | Statement: [Rema (EP), hasTrack, Corny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Corny Context triple: [Rema (EP), hasTrack, Corny]
-
A.
Corny
chosen
Corny is a song by Nigerian singer and rapper Rema, known for its mellow Afrobeats sound and romantic lyrics.
-
B.
Greasy
Greasy is a cartoon weasel character from the film "Who Framed Roger Rabbit," known for his slick appearance and membership in the Toon Patrol.
-
C.
Gross
Gross is a common German and Ashkenazi Jewish surname borne by numerous notable individuals across fields such as science, politics, and the arts.
-
D.
Molching
Molching is a fictional small German town near Munich that serves as the primary backdrop for Markus Zusak’s World War II novel "The Book Thief."
-
E.
Malarkey
Malarkey is a surname most notably associated with Donald Malarkey, a U.S. Army paratrooper of Easy Company whose World War II service was popularized in the book and miniseries "Band of Brothers."
- 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_69d6ab6ae0dc8190b1522a9c1c55c114 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f4c2b548190938fff9427f07dc7 |
completed | April 10, 2026, 6:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e8aa94881908e4c184062037ab5 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:53 p.m.