Triple
T2498347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laura Linney |
E52403
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Laura Leggett Linney |
E52403
|
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: Laura Leggett Linney | Statement: [Laura Linney, birthName, Laura Leggett Linney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Leggett Linney Context triple: [Laura Linney, birthName, Laura Leggett Linney]
-
A.
Laura Linney
chosen
Laura Linney is an American actress acclaimed for her versatile performances in film, television, and theater, with notable roles in works such as "You Can Count on Me," "The Truman Show," and the series "Ozark."
-
B.
Maura Tierney
Maura Tierney is an American actress best known for her roles on the television series "ER" and "NewsRadio," as well as in various film and stage productions.
-
C.
Anne McDonnell
Anne McDonnell was an American socialite best known as the first wife of industrialist Henry Ford II.
-
D.
Mary-Louise Parker
Mary-Louise Parker is an American actress best known for her roles in the television series "Weeds" and numerous acclaimed film and stage performances.
-
E.
Jill Clayburgh
Jill Clayburgh was an American actress acclaimed for her intelligent, nuanced performances in 1970s and 1980s films, including multiple Academy Award–nominated roles.
- 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_69ab4957b3a88190adf968ae0c1b931c |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1ae9040819091b3ca5b98659e99 |
completed | March 7, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af654aab208190adbc5fb6bd500106 |
completed | March 10, 2026, 12:26 a.m. |
Created at: March 6, 2026, 9:46 p.m.