Triple
T13718420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Born 2 Rap |
E328960
|
entity |
| Predicate | featuresArtist |
P1952
|
FINISHED |
| Object | Red Cafe |
E703380
|
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: Red Cafe | Statement: [Born 2 Rap, featuresArtist, Red Cafe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Red Cafe Context triple: [Born 2 Rap, featuresArtist, Red Cafe]
-
A.
Red Café
chosen
Red Café is a Brooklyn-born American rapper known for his mixtapes, club anthems, and collaborations within the East Coast hip-hop scene.
-
B.
The Cafe
The Cafe is a casual dining spot where people can relax, socialize, and enjoy beverages and light meals.
-
C.
Rocket Café
Rocket Café is a space-themed quick-service restaurant located in Discoveryland at Disneyland Paris, known for its futuristic decor and casual dining options.
-
D.
Café
Café is a renowned modernist painting by Brazilian artist Cândido Portinari that depicts coffee plantation workers and highlights the social realities of rural labor in Brazil.
-
E.
Tangierine Café
Tangierine Café is a quick-service restaurant in the Morocco Pavilion at EPCOT, known for serving Middle Eastern and North African-inspired dishes.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd439a121c81908cae964e7756274c |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d5a23bc8190942568658665bbb0 |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:55 p.m.