Triple
T20388552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thinner |
E498022
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Lucinda Jenney |
—
|
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: Lucinda Jenney | Statement: [Thinner, stars, Lucinda Jenney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lucinda Jenney Context triple: [Thinner, stars, Lucinda Jenney]
-
A.
Lucinda Jenney
chosen
Lucinda Jenney is an American character actress known for her versatile supporting roles in films and television since the 1980s.
-
B.
Laurie Durning
Laurie Durning is an American filmmaker and costume designer best known for her long-term relationship and later marriage to Pink Floyd co-founder Roger Waters.
-
C.
Tyne Daly
Tyne Daly is an American actress acclaimed for her powerful performances in television dramas, film, and theater, including her iconic role in the series "Cagney & Lacey."
-
D.
Patty Considine
Patty Considine is an English actor, director, and screenwriter known for his intense, character-driven performances in films such as "Dead Man's Shoes," "In America," and "Hot Fuzz."
-
E.
Joely Richardson
Joely Richardson is an English actress known for her work in film and television, including roles in projects such as "Nip/Tuck" and various period dramas.
- 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6790d9e5881908bde7da9e5e541a0 |
completed | April 20, 2026, 7:05 p.m. |
Created at: April 16, 2026, 11:28 a.m.