Triple
T984649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Get Happy |
E21251
|
entity |
| Predicate | notablePerformer |
P17435
|
FINISHED |
| Object | Tony Bennett |
E60140
|
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: Tony Bennett | Statement: [Get Happy, notablePerformer, Tony Bennett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tony Bennett Context triple: [Get Happy, notablePerformer, Tony Bennett]
-
A.
Tony Bennett
chosen
Tony Bennett was an American traditional pop and jazz singer renowned for his smooth vocal style and timeless standards like "I Left My Heart in San Francisco."
-
B.
Mel Tormé
Mel Tormé was an American jazz singer, composer, and actor, celebrated for his smooth vocal style and known as "The Velvet Fog."
-
C.
Frank Sinatra
Frank Sinatra was an iconic American singer and actor renowned for his smooth baritone voice, classic pop and jazz recordings, and influential film roles.
-
D.
Sinatra
Sinatra is a lightweight Ruby web application framework known for its simple, DSL-based approach to building web services and APIs.
-
E.
Nat King Cole
Nat King Cole was an American jazz pianist and velvety-voiced pop singer who became one of the most influential and popular entertainers of the mid-20th century.
- 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_69a493c383dc8190a03257f22d4b4183 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b4959fe48190a78bd811cbc888ab |
completed | March 1, 2026, 9:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac66179f908190b3f0144d1ee91a1c |
completed | March 7, 2026, 5:53 p.m. |
Created at: March 1, 2026, 7:41 p.m.