Triple
T19553540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paper Trail |
E489251
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Reefa |
—
|
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: Reefa | Statement: [Paper Trail, producer, Reefa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Reefa Context triple: [Paper Trail, producer, Reefa]
-
A.
Reefa
chosen
Reefa is a hip-hop and R&B record producer known for his work with prominent artists and contributions to contemporary urban music.
-
B.
Yamba
Yamba is a coastal town in northern New South Wales, Australia, known for its beaches, fishing, and laid-back holiday atmosphere.
-
C.
Majura
Majura is a district in the northeastern part of Canberra, Australia, known for its rural character, military training areas, and proximity to key transport infrastructure.
-
D.
Lameroo
Lameroo is a small rural town in South Australia's Murray Mallee region, serving as a local service and agricultural centre for the surrounding farming communities.
-
E.
Alwina
Alwina is the naive Tunisian shepherdess who becomes a sophisticated Parisian socialite in the 1935 French film "Princesse Tam-Tam."
- 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63d315c68819087402802d624a8c9 |
completed | April 20, 2026, 2:50 p.m. |
Created at: April 10, 2026, 1:41 p.m.