Triple
T1174786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dreams |
E24996
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Dave Tozer |
E38574
|
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: Dave Tozer | Statement: [Dreams, producer, Dave Tozer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dave Tozer Context triple: [Dreams, producer, Dave Tozer]
-
A.
Dave Tozer
chosen
Dave Tozer is a Grammy-winning American record producer, songwriter, and musician best known for his extensive work with John Legend and contributions to contemporary R&B and soul music.
-
B.
John Toon
John Toon is a cinematographer known for his work on the film "Sunshine Cleaning."
-
C.
Mike Krieger
Mike Krieger is a Brazilian-American entrepreneur and software engineer best known as the co-founder and former CTO of the photo-sharing social media platform Instagram.
-
D.
Mike Gunton
Mike Gunton is a British television producer best known for his work on landmark BBC natural history documentaries.
-
E.
Don Zimmerman
Don Zimmerman is a film editor known for his work on major Hollywood movies, including the family adventure-comedy "Night at the Museum."
- 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_69a494082a7c819095004f423f294a64 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd0bed00819091d71983d787a030 |
completed | March 1, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad0146e1ec8190974e9d73acd6ca90 |
completed | March 8, 2026, 4:55 a.m. |
Created at: March 1, 2026, 7:45 p.m.