Triple
T9905107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Numbers on the Boards |
E184987
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Noah Goldstein |
E110937
|
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: Noah Goldstein | Statement: [Numbers on the Boards, writer, Noah Goldstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Noah Goldstein Context triple: [Numbers on the Boards, writer, Noah Goldstein]
-
A.
Noah Goldstein
chosen
Noah Goldstein is a Grammy-winning recording engineer and producer known for his extensive work with Kanye West and other major hip-hop and pop artists.
-
B.
Noah Goldstein
Noah Goldstein is an author known for his work related to violent crime themes.
-
C.
Josh Goldstein
Josh Goldstein is a screenwriter best known for co-writing the story for Disney’s adventure film "Jungle Cruise."
-
D.
Jacob Goldstein
Jacob Goldstein was the father of Australian suffragist and social reformer Vida Goldstein.
-
E.
Noah Levenstein
Noah Levenstein is the awkwardly earnest and well-meaning father character from the American Pie film series, best known for his cringeworthy yet heartfelt advice to his son.
- 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_69ca8296165881908ca4750701af1f29 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb4e641e881909dfba78fcb96c433 |
completed | April 2, 2026, 12:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20d9e132c8190a68f94e4cfd36c56 |
completed | April 5, 2026, 7:22 a.m. |
Created at: March 30, 2026, 8:40 p.m.