Triple
T16153944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morrie Ryskind |
E391985
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Let 'Em Eat Cake |
E716942
|
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: Let 'Em Eat Cake | Statement: [Morrie Ryskind, notableWork, Let 'Em Eat Cake]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Let 'Em Eat Cake Context triple: [Morrie Ryskind, notableWork, Let 'Em Eat Cake]
-
A.
Let Them Eat Cake
chosen
Let Them Eat Cake is a British historical sitcom starring Dawn French and Jennifer Saunders, set in pre-revolutionary France and known for its sharp, irreverent humor.
-
B.
Cake & Eat It Too
Cake & Eat It Too is a track from the hip-hop album "Airtight's Revenge" by American rapper and producer Bilal.
-
C.
The Eat Up
The Eat Up is the debut EP by English actor and rapper Ed Skrein, showcasing his early work as a hip-hop artist.
-
D.
Lots of Candles, Plenty of Cake
Lots of Candles, Plenty of Cake is a reflective memoir-essay collection by Anna Quindlen that explores aging, family, friendship, and the pleasures and challenges of middle age.
-
E.
The Pie
The Pie is the spirited horse ridden by Velvet Brown in the classic novel and film "National Velvet."
- 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21e57e95c8190ae4ed641be974ce5 |
completed | April 17, 2026, 11:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff7ac6d1c8190a8553ceb5ec06119 |
completed | May 10, 2026, 3:12 a.m. |
Created at: April 10, 2026, 5:01 a.m.