Triple
T9790216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Side Effects |
E237586
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Rooney Mara |
E63855
|
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: Rooney Mara | Statement: [Side Effects, starring, Rooney Mara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rooney Mara Context triple: [Side Effects, starring, Rooney Mara]
-
A.
Rooney Mara
chosen
Rooney Mara is an American actress known for her acclaimed performances in films such as "The Girl with the Dragon Tattoo" and "Carol."
-
B.
Elizabeth "Lizette" Rooney Mara
Elizabeth "Lizette" Rooney Mara is a member of the prominent Mara family associated with the ownership and management of the New York Giants NFL franchise.
-
C.
Gillian Murphy
Gillian Murphy is a renowned American ballet dancer and longtime principal with American Ballet Theatre, celebrated for her powerful technique and dramatic artistry.
-
D.
Carmen Ejogo
Carmen Ejogo is a British actress and singer known for her versatile film and television roles, including her acclaimed portrayal of Coretta Scott King in the historical drama "Selma."
-
E.
Carey Mulligan
Carey Mulligan is an acclaimed British actress known for her performances in films such as "An Education," "Drive," and "Promising Young Woman."
- 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_69ca84dc04488190b9c91193976c0960 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda215b3108190a897552e1dc91cc4 |
completed | April 1, 2026, 10:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eac78b1881908e77c3678f98fac9 |
completed | April 5, 2026, 4:53 a.m. |
Created at: March 30, 2026, 8:28 p.m.