Triple
T17095089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Holden Snyder |
E414825
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Jon Hensley |
E431760
|
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: Jon Hensley | Statement: [Holden Snyder, portrayedBy, Jon Hensley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jon Hensley Context triple: [Holden Snyder, portrayedBy, Jon Hensley]
-
A.
Jon Hensley
chosen
Jon Hensley is an American actor best known for his long-running role as Holden Snyder on the soap opera "As the World Turns."
-
B.
Bruce Hasselberg
Bruce Hasselberg is an American figure best known as the first husband of actress Loni Anderson.
-
C.
Ray Wise
Ray Wise is an American character actor known for his versatile roles in film and television, including memorable performances in "Twin Peaks," "RoboCop," and numerous other genre and dramatic works.
-
D.
Jeffery Wood
Jeffery Wood is an American actor best known for his role on the television sitcom "In the House."
-
E.
John Hensley
John Hensley is an American actor best known for his role as Matt McNamara on the television drama 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbfc9158819081689d3d594a1908 |
completed | April 18, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a015fbe1d4c81909ce7e4626516b51d |
completed | May 11, 2026, 4:49 a.m. |
Created at: April 10, 2026, 5:35 a.m.