Triple
T14964986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marge Gunderson |
E373166
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Jerry Lundegaard |
E438118
|
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: Jerry Lundegaard | Statement: [Marge Gunderson, associatedWith, Jerry Lundegaard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jerry Lundegaard Context triple: [Marge Gunderson, associatedWith, Jerry Lundegaard]
-
A.
Jerry Lundegaard
chosen
Jerry Lundegaard is the financially desperate, bumbling car salesman whose botched crime scheme drives the darkly comic plot of the film "Fargo."
-
B.
Miles Straume
Miles Straume is a sarcastic, ghost-communicating medium and member of the freighter team on the television series "Lost."
-
C.
Jacob Stroud
Jacob Stroud was an early American settler and landowner who founded the community that became Stroudsburg, Pennsylvania.
-
D.
Jack Deerson
Jack Deerson is a cinematographer best known for his work on the 1971 road movie "Two-Lane Blacktop."
-
E.
Jack McFarland
Jack McFarland is a flamboyant, aspiring actor and Will Truman’s exuberant best friend on the sitcom "Will & Grace," known for his over-the-top personality and comedic antics.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6e2fdcc8190bffe603db3388736 |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8be122688190b20fe4450786158a |
completed | May 9, 2026, 1:20 a.m. |
Created at: April 10, 2026, 2:46 a.m.