Triple
T13520232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyle Labine |
E322873
|
entity |
| Predicate | hasRelative |
P367
|
FINISHED |
| Object | Tyler Labine |
E80184
|
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: Tyler Labine | Statement: [Kyle Labine, hasRelative, Tyler Labine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tyler Labine Context triple: [Kyle Labine, hasRelative, Tyler Labine]
-
A.
Tyler Labine
chosen
Tyler Labine is a Canadian actor and comedian known for his roles in television series like "Reaper" and "Deadbeat" and films such as "Tucker & Dale vs. Evil."
-
B.
Kyle Labine
Kyle Labine is a Canadian actor known for his work in film and television and as the brother of fellow actor Tyler Labine.
-
C.
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.
-
D.
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.
-
E.
Dylan Walsh
Dylan Walsh is an American actor best known for his role as Dr. Sean McNamara on the television series "Nip/Tuck."
- 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_69d80766a21881909f21a1b7421d3b8a |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafa3df0c8190804174695587f0ea |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbac7a1ee88190951de590a4a07d6f |
completed | May 6, 2026, 9:02 p.m. |
Created at: April 9, 2026, 9:44 p.m.