Triple
T5708931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mia Farrow |
E125855
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mia Farrow |
E125855
|
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: Mia Farrow | Statement: [Mia Farrow, name, Mia Farrow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mia Farrow Context triple: [Mia Farrow, name, Mia Farrow]
-
A.
Mia Farrow
chosen
Mia Farrow is an American actress and humanitarian known for her roles in films like "Rosemary's Baby" and for her extensive advocacy work with UNICEF.
-
B.
Molly Elizabeth Brolin
Molly Elizabeth Brolin is an American film and television producer and assistant director, known for her behind-the-scenes work in the entertainment industry and as the daughter of actor James Brolin.
-
C.
Francesca Eastwood
Francesca Eastwood is an American actress, model, and television personality, and the daughter of filmmaker Clint Eastwood.
-
D.
Anna Ferzetti
Anna Ferzetti is an Italian actress known for her work in film and television, as well as for her presence in the contemporary Italian entertainment scene.
-
E.
Andie MacDowell
Andie MacDowell is an American actress and former fashion model best known for her roles in romantic comedies such as "Groundhog Day" and "Four Weddings and a Funeral."
- 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_69c0082d6fe48190b777fb383769e5c8 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0248ab6a88190be17bdc32c36e5cb |
completed | March 22, 2026, 5:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a6f5ac08190b5acbccea756d2de |
completed | March 22, 2026, 9:09 p.m. |
Created at: March 22, 2026, 3:46 p.m.