Triple
T20624275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Curse of Blondie |
E506776
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Mark Saunders |
—
|
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: Mark Saunders | Statement: [The Curse of Blondie, producer, Mark Saunders]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Saunders Context triple: [The Curse of Blondie, producer, Mark Saunders]
-
A.
Mark Saunders
chosen
Mark Saunders is a British record producer and audio engineer known for his work with influential alternative and pop artists such as The Cure and Erasure.
-
B.
Adam Saunders
Adam Saunders is a film and television producer known for his work on various independent and studio-backed projects.
-
C.
Phil Saunders
Phil Saunders is an American entrepreneur best known as the founder of the truck stop and travel center chain TravelCenters of America.
-
D.
Jeremy Saunders
Jeremy Saunders is a recurring fictional character in Satyajit Ray’s Bengali science-fiction “Professor Shonku” stories, typically appearing as an English associate in the professor’s adventurous experiments.
-
E.
Wesley Saunders
Wesley Saunders is an American basketball player best known for starring as a versatile guard/forward for Harvard University, where he became one of the program’s top performers in the early 2010s.
- 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_69e0b4bc90988190ac360aaf645efc1d |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6abe3d76c8190a8a5cb52bb9bfc2b |
completed | April 20, 2026, 10:42 p.m. |
Created at: April 16, 2026, 11:42 a.m.