Triple
T19305262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bryan Greenberg |
E482809
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | The Perfect Score |
—
|
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: The Perfect Score | Statement: [Bryan Greenberg, notableWork, The Perfect Score]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Perfect Score Context triple: [Bryan Greenberg, notableWork, The Perfect Score]
-
A.
The Perfect Score
chosen
The Perfect Score is a 2004 teen heist comedy film about a group of high school students who plot to steal the answers to the SAT exam.
-
B.
Perfect Ten
Perfect Ten is a hip-hop album best known for its polished production and collaborations with prominent rap artists.
-
C.
The Score Takes Care of Itself
The Score Takes Care of Itself is a leadership and management book that distills legendary NFL coach Bill Walsh’s philosophy on building a winning culture through standards of performance.
-
D.
The Perfect 10
The Perfect 10 is an album by the artist Hello Friday, showcasing their polished pop sound and songwriting style.
-
E.
Perfect on Paper
Perfect on Paper is a romantic comedy film featuring actor Drew Fuller in a leading role.
- 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_69d8e8d04d5c8190baa816986f2b1d1e |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e604c744908190975c71a28acc96cc |
completed | April 20, 2026, 10:49 a.m. |
Created at: April 10, 2026, 1:31 p.m.