Triple
T10776786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ronan Farrow |
E254216
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Farrow |
E542583
|
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: Farrow | Statement: [Ronan Farrow, familyName, Farrow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Farrow Context triple: [Ronan Farrow, familyName, Farrow]
-
A.
Farrow
chosen
Farrow is the surname of American actress and humanitarian Mia Farrow, known for her work in film and activism.
-
B.
Farrah
Farrah is a feminine given name most notably associated with American reality television personality and author Farrah Abraham.
-
C.
Tisa Farrow
Tisa Farrow is an American actress known for her roles in 1970s and 1980s films, including several cult horror and exploitation movies.
-
D.
Farley
Farley is a rural-residential suburb in the Maitland region of New South Wales, Australia.
-
E.
Farley
Farley is a surname most notably associated with Jim Farley, an American business executive and CEO of Ford Motor Company.
- 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_69d6aa609f008190a294200aefcb7bd5 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7329d8c908190bddad40685133ea1 |
completed | April 9, 2026, 5:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de238ff88881908676d38dca041cb4 |
completed | April 14, 2026, 11:22 a.m. |
Created at: April 8, 2026, 9:16 p.m.