Triple
T18016432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SqueezeNet |
E431007
|
entity |
| Predicate | implementedIn |
P2539
|
FINISHED |
| Object | Caffe |
—
|
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: Caffe | Statement: [SqueezeNet, implementedIn, Caffe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caffe Context triple: [SqueezeNet, implementedIn, Caffe]
-
A.
Caffe
chosen
Caffe is an open-source deep learning framework known for its speed and modular design, widely used in computer vision research and applications.
-
B.
Koffee
Koffee is a Jamaican reggae and dancehall singer, songwriter, and rapper known for her Grammy-winning EP "Rapture" and hit single "Toast."
-
C.
CAFE
CAFE is a U.S. regulatory program that sets mandatory fuel efficiency standards for cars and light trucks to reduce energy consumption and emissions.
-
D.
Cappachino
Cappachino is an alias of Cappadonna, an American rapper best known for his longtime affiliation with the Wu-Tang Clan.
-
E.
Café au Lait
Café au Lait is one of the short, conversational vignettes in Jim Jarmusch’s film "Coffee and Cigarettes," featuring characters chatting over coffee in a minimalist, black-and-white setting.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.