Triple
T19437541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Yates |
E486261
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Paul Sparks |
—
|
NE NERFINISHED |
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: Paul Sparks | Statement: [Tom Yates, portrayedBy, Paul Sparks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Sparks Context triple: [Tom Yates, portrayedBy, Paul Sparks]
-
A.
Paul Sparks
chosen
Paul Sparks is an American actor known for his work in film, television, and theater, including prominent roles in series like "Boardwalk Empire" and "House of Cards."
-
B.
Beth Riesgraf
Beth Riesgraf is an American actress best known for playing the quirky thief Parker on the television series "Leverage."
-
C.
Nicole Kassell
Nicole Kassell is an American film and television director and producer known for her work on acclaimed series such as Watchmen and The Leftovers.
-
D.
AnnaLynne McCord
AnnaLynne McCord is an American actress and activist best known for her roles in television series like "90210" and "Nip/Tuck" and in various horror and thriller films.
-
E.
Melissa Cobb
Melissa Cobb is an American film producer best known for her work on major animated features, including the Kung Fu Panda franchise.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633618c2881908f3d2a9cabb02289 |
completed | April 20, 2026, 2:08 p.m. |
Created at: April 10, 2026, 1:38 p.m.