Triple
T5676526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How to Make It in America |
E125098
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Bryan Greenberg |
E482809
|
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: Bryan Greenberg | Statement: [How to Make It in America, starring, Bryan Greenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bryan Greenberg Context triple: [How to Make It in America, starring, Bryan Greenberg]
-
A.
Bryan Greenberg
chosen
Bryan Greenberg is an American actor and singer best known for his roles in television series like "One Tree Hill" and "How to Make It in America," as well as various romantic comedies.
-
B.
Matt Greenberg
Matt Greenberg is a screenwriter and film producer known for his work on various horror and thriller projects in American cinema.
-
C.
Michael Greenburg
Michael Greenburg is an American film and television producer best known for his work on projects such as the series "Stargate SG-1" and for his former marriage to actress Sharon Stone.
-
D.
Michael Greenberg
Michael Greenberg is a prominent American neuroscientist renowned for his pioneering work on activity-dependent gene expression in the brain.
-
E.
Josh Baskin
Josh Baskin is the young boy who magically becomes an adult overnight and navigates the adult world with childlike innocence in the film "Big."
- 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_69c008295c808190acfe78915e7d656a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c023728f488190a3622844d78caa13 |
completed | March 22, 2026, 5:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b0a7aff08190bca93ac0ab8a9be0 |
completed | March 23, 2026, 3:16 a.m. |
Created at: March 22, 2026, 3:43 p.m.