Triple
T14218455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Jury |
E352421
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Jeffrey Nordling |
E595536
|
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: Jeffrey Nordling | Statement: [The Jury, stars, Jeffrey Nordling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffrey Nordling Context triple: [The Jury, stars, Jeffrey Nordling]
-
A.
Jeffrey Nordling
chosen
Jeffrey Nordling is an American actor known for his work in television dramas and films, often portraying complex professional and family-man characters.
-
B.
Daniel Nannskog
Daniel Nannskog is a retired Swedish striker best known for his prolific goal-scoring spell at Norwegian club Stabæk Fotball and later work as a football pundit.
-
C.
Greg Eklund
Greg Eklund is an American drummer best known for his work with the alternative rock band Everclear.
-
D.
Michael Eklund
Michael Eklund is a Canadian character actor known for his intense, often villainous roles in film and television thrillers.
-
E.
Christopher Akerlind
Christopher Akerlind is an American lighting designer renowned for his work in theatre, opera, and Broadway productions, including multiple award-winning designs.
- 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_69d8278a06e481908b5d6af0a8afe737 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de621258d4819085f358cd2cf109e4 |
completed | April 14, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd55008a5c8190b005a12df7ef2f75 |
completed | May 8, 2026, 3:14 a.m. |
Created at: April 10, 2026, 1:06 a.m.