Triple
T15435852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bored to Death |
E369759
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Olivia Thirlby |
E11657
|
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: Olivia Thirlby | Statement: [Bored to Death, castMember, Olivia Thirlby]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Olivia Thirlby Context triple: [Bored to Death, castMember, Olivia Thirlby]
-
A.
Olivia Thirlby
chosen
Olivia Thirlby is an American actress known for her roles in films such as "Juno," "Dredd," and various independent and mainstream productions.
-
B.
Emily Patterson
Emily Patterson is the daughter of American actress Téa Leoni.
-
C.
Cristin Milioti
Cristin Milioti is an American actress and singer known for her work in film, television, and theater, including roles in "How I Met Your Mother," "The Wolf of Wall Street," and "Palm Springs."
-
D.
Haley Bennett
Haley Bennett is an American actress and singer known for her versatile performances in films such as "The Girl on the Train," "The Magnificent Seven," and "Swallow."
-
E.
Eliza Scanlen
Eliza Scanlen is an Australian actress known for her roles in film and television, including prominent performances in projects like "Sharp Objects" and "Little Women."
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03edb3ec481908b26164d4470c9bc |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c3293dc819097f9e56963c333ee |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 3:21 a.m.