Triple
T10692647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael York |
E252048
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Michael York |
E252048
|
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: Michael York | Statement: [Michael York, name, Michael York]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael York Context triple: [Michael York, name, Michael York]
-
A.
Michael York
chosen
Michael York is an English actor known for his roles in films such as "Cabaret," "Logan's Run," and the "Austin Powers" series.
-
B.
Tom Mison
Tom Mison is an English actor best known for playing Ichabod Crane in the supernatural drama television series "Sleepy Hollow."
-
C.
Dorian Harewood
Dorian Harewood is an American actor known for his work in film, television, and voice acting, including roles in projects such as Full Metal Jacket and the animated series Batman.
-
D.
Sam Waterston
Sam Waterston is an American actor known for his distinguished film, television, and stage career, including prominent roles in productions such as Law & Order and The Killing Fields.
-
E.
George Norton
George Norton was a British colonial-era lawyer and educator best known for establishing Presidency College in Madras, one of India’s earliest and most prestigious institutions of higher learning.
- 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_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd37cf408190a1912b3e0aa096a5 |
completed | April 9, 2026, 1:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e343ffc428819085599f251b7553e5 |
completed | April 18, 2026, 8:42 a.m. |
Created at: April 8, 2026, 9:11 p.m.