Triple
T12043085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | She Wants Me |
E286712
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Aaron Yoo |
E385694
|
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: Aaron Yoo | Statement: [She Wants Me, hasCastMember, Aaron Yoo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aaron Yoo Context triple: [She Wants Me, hasCastMember, Aaron Yoo]
-
A.
Aaron Yoo
chosen
Aaron Yoo is an American actor known for his supporting roles in films like "Disturbia," "21," and "Nick and Norah's Infinite Playlist," as well as various television appearances.
-
B.
Chris Yeh
Chris Yeh is an entrepreneur, investor, and author best known for co-authoring the business strategy book "Blitzscaling" with Reid Hoffman.
-
C.
Harry Yoon
Harry Yoon is a Korean-American film editor known for his work on acclaimed films such as "Minari" and "Detroit."
-
D.
Greg Yang
Greg Yang is a mathematician and AI researcher known for his work on the theoretical foundations of deep learning and his role at xAI.
-
E.
Bryan Oh
Bryan Oh is a television writer and producer best known for his work on series such as "Heroes" and "Chicago Fire."
- 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_69d6ab4780948190bdb9f7620c2ac27e |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9040d13108190bd1a969fa62aae5a |
completed | April 10, 2026, 2:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49da728ec819080c349fd8d0ed62c |
completed | May 1, 2026, 12:33 p.m. |
Created at: April 8, 2026, 9:47 p.m.