Triple
T18088277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexican Moon |
E432898
|
entity |
| Predicate | featuresArtist |
P1952
|
FINISHED |
| Object | Harry Rushakoff |
—
|
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: Harry Rushakoff | Statement: [Mexican Moon, featuresArtist, Harry Rushakoff]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harry Rushakoff Context triple: [Mexican Moon, featuresArtist, Harry Rushakoff]
-
A.
Harry Rushakoff
chosen
Harry Rushakoff is an American rock drummer best known for his work with the alternative rock band Concrete Blonde.
-
B.
Harry Korshak
Harry Korshak is a film producer best known for his work on the biographical drama "Gable and Lombard."
-
C.
Ron Wasserman
Ron Wasserman is an American composer best known for his high-energy television theme and score work, particularly on shows like Mighty Morphin Power Rangers and other 1990s–2000s series.
-
D.
Daniel Rappaport
Daniel Rappaport is a film producer known for working on mainstream Hollywood comedies, including the movie "Office Christmas Party."
-
E.
Larry Grossman
Larry Grossman is an American composer best known for his work on Broadway and in musical theatre, as well as for writing songs for film and television.
- 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_69d8b907d05c819083cc3bd6021089e6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4dd16234c8190b547e893a829d6c5 |
completed | April 19, 2026, 1:48 p.m. |
Created at: April 10, 2026, 10:27 a.m.