Triple
T20228949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Honeymoon |
E495464
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Dan Heath |
—
|
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: Dan Heath | Statement: [Honeymoon, producer, Dan Heath]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Heath Context triple: [Honeymoon, producer, Dan Heath]
-
A.
Dan Heath
chosen
Dan Heath is a music producer and composer known for his work on cinematic scores and collaborations with artists like Lana Del Rey.
-
B.
David Epstein
David Epstein is an American journalist and author best known for his books on the science of performance and human potential, including "The Sports Gene" and "Range."
-
C.
David Epstein
David Epstein is a mathematician known for his contributions to geometric topology and group theory, as well as for mentoring influential researchers in the field.
-
D.
Adam Grant
Adam Grant is an organizational psychologist, bestselling author, and Wharton professor known for his research on work, motivation, and generosity in professional life.
-
E.
David Gladwell
David Gladwell is a British film editor and director best known for his editing work on films such as Lindsay Anderson’s "If...." and "O Lucky Man!" and for his own experimental and documentary films.
- 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_69da626cff80819097b530718a7c98b6 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66fdb61b08190b850a9648ebfb720 |
completed | April 20, 2026, 6:26 p.m. |
Created at: April 11, 2026, 11:39 p.m.