Triple
T5264378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Keery |
E118903
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Joseph David Keery |
E118903
|
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: Joseph David Keery | Statement: [Joe Keery, birthName, Joseph David Keery]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joseph David Keery Context triple: [Joe Keery, birthName, Joseph David Keery]
-
A.
Ethan Embry
Ethan Embry is an American actor known for his roles in 1990s films such as "Empire Records," "Can't Hardly Wait," and various television series.
-
B.
Joe Keery
chosen
Joe Keery is an American actor and musician best known for his role as Steve Harrington in the Netflix series "Stranger Things."
-
C.
Alan Ritchson
Alan Ritchson is an American actor, model, and filmmaker best known for roles in projects like "Reacher," "Blue Mountain State," and the "Teenage Mutant Ninja Turtles" films.
-
D.
Joel McKinnon Miller
Joel McKinnon Miller is an American character actor best known for playing the affable Detective Norm Scully on the television comedy series "Brooklyn Nine-Nine."
-
E.
Michael Kube-McDowell
Michael Kube-McDowell is an American science fiction author known for his novels, short stories, and contributions to major franchises such as Star Wars.
- 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_69bd446a42c88190b7ecbef006561d55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7bd4a9888190a79ef8e64c764f86 |
completed | March 20, 2026, 4:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf10d1207481909ea18248993b4b71 |
completed | March 21, 2026, 9:42 p.m. |
Created at: March 20, 2026, 1:51 p.m.