Triple
T9679762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Everyday Life |
E234249
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Daniel Green |
E816215
|
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: Daniel Green | Statement: [Everyday Life, producer, Daniel Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Green Context triple: [Everyday Life, producer, Daniel Green]
-
A.
Daniel Green
chosen
Daniel Green is a music producer known for his work on the track "Paradise."
-
B.
Richard Green
Richard Green was an American boxing referee best known for officiating major heavyweight bouts, including the 1980 title fight between Larry Holmes and Muhammad Ali.
-
C.
John Greenfield
John Greenfield was an individual significant enough in local or regional history that the city of Greenfield, California, was named in his honor.
-
D.
Steven J. Green
Steven J. Green is an American businessman, philanthropist, and former U.S. ambassador whose support for education and international affairs led to a major public policy school being named in his honor.
-
E.
Jeffrey Greenstein
Jeffrey Greenstein is a film producer known for his work on action and genre movies, including the war drama "The Outpost."
- 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_69ca84c99e34819092e5563a7106cfca |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9c9c586c8190abc0ab1771bf6c5c |
completed | April 1, 2026, 10:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1e3f844008190b73215136dae6fcd |
completed | April 5, 2026, 4:24 a.m. |
Created at: March 30, 2026, 8:16 p.m.