Triple
T3211516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dana Delany |
E67291
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Dana Delany |
E67291
|
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: Dana Delany | Statement: [Dana Delany, name, Dana Delany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dana Delany Context triple: [Dana Delany, name, Dana Delany]
-
A.
Dana Delany
chosen
Dana Delany is an American actress best known for her acclaimed work in television dramas such as "China Beach," for which she earned multiple Primetime Emmy Awards.
-
B.
Elizabeth Berkley
Elizabeth Berkley is an American actress best known for her roles in the TV series "Saved by the Bell" and the film "Showgirls."
-
C.
Téa Leoni
Téa Leoni is an American actress and producer best known for her leading roles in film and television, including the political drama series "Madam Secretary."
-
D.
Sharon Duncan-Brewster
Sharon Duncan-Brewster is a British actress known for her roles in film, television, and theatre, including a prominent appearance in the science fiction epic "Dune" (2021).
-
E.
Glenne Headly
Glenne Headly was an American actress known for her versatile film, television, and stage performances, including prominent roles in comedies and dramas from the 1980s onward.
- 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_69ad858ac36c81909962589cd277d6e2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaaba224c8190ad2f4e0ed1c2ca4a |
completed | March 8, 2026, 4:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28e864a1c8190b56ab9e80f72e48c |
completed | March 12, 2026, 9:59 a.m. |
Created at: March 8, 2026, 3:07 p.m.