Triple
T10404029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Happy Perez |
E245217
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Coffee |
E49777
|
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: Coffee | Statement: [Happy Perez, notableWork, Coffee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coffee Context triple: [Happy Perez, notableWork, Coffee]
-
A.
Coffee
chosen
"Coffee" is a song by the American singer-songwriter Miguel, known for its smooth blend of R&B and sensual, atmospheric production.
-
B.
Coffee
Coffee is a popular brewed beverage made from roasted coffee beans, known for its stimulating caffeine content and rich, diverse flavors.
-
C.
Tea
Tea is a widely consumed beverage made by steeping processed leaves of the Camellia sinensis plant in hot water, known for its variety of flavors, caffeine content, and cultural significance worldwide.
-
D.
Caffe
Caffe is an open-source deep learning framework known for its speed and modular design, widely used in computer vision research and applications.
-
E.
Coffee Time
Coffee Time is a song featured in the "Coffee Time" sequence, likely themed around the rituals or atmosphere of drinking coffee.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9e5fb58819081d7d3e1dc625197 |
completed | April 7, 2026, 11:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7fbdb86788190bb1a8802f713fea2 |
completed | April 9, 2026, 7:19 p.m. |
Created at: April 6, 2026, 12:08 p.m.