Triple
T16081062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth |
E390109
|
entity |
| Predicate | isMiddleNameOf |
P143
|
FINISHED |
| Object | Diane Elizabeth Dern |
E91776
|
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: Diane Elizabeth Dern | Statement: [Elizabeth, isMiddleNameOf, Diane Elizabeth Dern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diane Elizabeth Dern Context triple: [Elizabeth, isMiddleNameOf, Diane Elizabeth Dern]
-
A.
Diane Elizabeth Dern
chosen
Diane Elizabeth Dern is the daughter of acclaimed American actor Bruce Dern.
-
B.
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."
-
C.
Nancy Kyes
Nancy Kyes is an American actress best known for her roles in John Carpenter films, including the original Halloween and Assault on Precinct 13.
-
D.
Laura Davenport
Laura Davenport is the daughter of English actor Nigel Davenport.
-
E.
Diane Lester
Diane Lester is a key character in the financial thriller film "Money Monster," serving as a corporate communications chief entangled in the unfolding live-broadcast crisis.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1844a5c68819086a13c93a787b436 |
completed | April 17, 2026, 12:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00758380d08190bfe73d3e052c1f0a |
completed | May 10, 2026, 12:09 p.m. |
Created at: April 10, 2026, 4:57 a.m.